Prompt:
Implement the Python class `ReroNewspaperIssue` described below.

Class description:
Class representing an issue in RERO (Mets/Alto) data. All functions defined in this child class are specific to parsing RERO Mets/Alto format

Method signatures and docstrings:
- def _find_pages(self): Detects the Alto XML page files for a newspaper issue and initializes page objects.
- def _parse_content_parts(self, content_div) -> List[Dict[str, str]]: Given the div of a content item, this function parses the children and constructs the legacy `parts` component :param content_div: The div containing the content item :return: list[dict] of different parts for this content item (role, id, fileid, page)
- def _parse_content_item(self, item_div, counter: int): Parses a content item div and returns the dictionary representing it. :param item_div: Div of content item :param counter: Number of content items already added (needed to generate canonical id). :return: dict, of the resulting content item
- def _parse_content_items(self, mets_doc: BeautifulSoup): Extract content item elements from a Mets XML file. :param BeautifulSoup mets_doc: :return:
- def _parse_mets(self): Parses the Mets XML file of the newspaper issue.

Implement the Python class `ReroNewspaperIssue` described below.

Class description:
Class representing an issue in RERO (Mets/Alto) data. All functions defined in this child class are specific to parsing RERO Mets/Alto format

Method signatures and docstrings:
- def _find_pages(self): Detects the Alto XML page files for a newspaper issue and initializes page objects.
- def _parse_content_parts(self, content_div) -> List[Dict[str, str]]: Given the div of a content item, this function parses the children and constructs the legacy `parts` component :param content_div: The div containing the content item :return: list[dict] of different parts for this content item (role, id, fileid, page)
- def _parse_content_item(self, item_div, counter: int): Parses a content item div and returns the dictionary representing it. :param item_div: Div of content item :param counter: Number of content items already added (needed to generate canonical id). :return: dict, of the resulting content item
- def _parse_content_items(self, mets_doc: BeautifulSoup): Extract content item elements from a Mets XML file. :param BeautifulSoup mets_doc: :return:
- def _parse_mets(self): Parses the Mets XML file of the newspaper issue.

<|skeleton|>
class ReroNewspaperIssue:
    """Class representing an issue in RERO (Mets/Alto) data. All functions defined in this child class are specific to parsing RERO Mets/Alto format"""

    def _find_pages(self):
        """Detects the Alto XML page files for a newspaper issue and initializes page objects."""
        <|body_0|>

    def _parse_content_parts(self, content_div) -> List[Dict[str, str]]:
        """Given the div of a content item, this function parses the children and constructs the legacy `parts` component :param content_div: The div containing the content item :return: list[dict] of different parts for this content item (role, id, fileid, page)"""
        <|body_1|>

    def _parse_content_item(self, item_div, counter: int):
        """Parses a content item div and returns the dictionary representing it. :param item_div: Div of content item :param counter: Number of content items already added (needed to generate canonical id). :return: dict, of the resulting content item"""
        <|body_2|>

    def _parse_content_items(self, mets_doc: BeautifulSoup):
        """Extract content item elements from a Mets XML file. :param BeautifulSoup mets_doc: :return:"""
        <|body_3|>

    def _parse_mets(self):
        """Parses the Mets XML file of the newspaper issue."""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        alto_path = os.path.join(self.path, 'ALTO')
        if not os.path.exists(alto_path):
            logger.critical(f'Could not find pages for {self.id}')
        page_file_names = [file for file in os.listdir(alto_path) if not file.startswith('.') and '.xml' in file]
        page_numbers = []
        for fname in page_file_names:
            page_no = fname.split('.')[0]
            page_numbers.append(int(page_no))
        page_canonical_names = ['{}-p{}'.format(self.id, str(page_n).zfill(4)) for page_n in page_numbers]
        self.pages = []
        for filename, page_no, page_id in zip(page_file_names, page_numbers, page_canonical_names):
            try:
                self.pages.append(ReroNewspaperPage(page_id, page_no, filename, alto_path))
            except Exception as e:
                logger.error(f'Adding page {page_no} {page_id} {filename}', f'raised following exception: {e}')
                raise e
<|end_body_0|>

<|body_start_1|>
        parts = []
        for child in content_div.children:
            if isinstance(child, NavigableString):
                continue
            elif isinstance(child, Tag):
                type_attr = child.get('TYPE')
                comp_role = type_attr.lower() if type_attr else None
                areas = child.findAll('area')
                for area in areas:
                    comp_id = area.get('BEGIN')
                    comp_fileid = area.get('FILEID')
                    comp_page_no = int(comp_fileid.replace('ALTO', ''))
                    parts.append({'comp_role': comp_role, 'comp_id': comp_id, 'comp_fileid': comp_fileid, 'comp_page_no': comp_page_no})
        return parts
<|end_body_1|>

<|body_start_2|>
        div_type = item_div.get('TYPE').lower()
        if div_type == 'picture':
            div_type = CONTENTITEM_TYPE_IMAGE
        if div_type not in CONTENTITEM_TYPES:
            logger.warning(f'Found new content item type: {div_type}')
        metadata = {'id': '{}-i{}'.format(self.id, str(counter).zfill(4)), 'tp': div_type, 'pp': [], 't': item_div.get('LABEL')}
        content_item = {'m': metadata, 'l': {'id': item_div.get('ID'), 'parts': self._parse_content_parts(item_div)}}
        for p in content_item['l']['parts']:
            pge_no = p['comp_page_no']
            if pge_no not in content_item['m']['pp']:
                content_item['m']['pp'].append(pge_no)
        return content_item
<|end_body_2|>

<|body_start_3|>
        content_items = []
        divs = mets_doc.find('div', {'TYPE': 'CONTENT'}).findChildren('div', recursive=False)
        sorted_divs = sorted(divs, key=lambda x: x.get('ID').lower())
        found_types = set((x.get('TYPE') for x in sorted_divs))
        print(f'Found types {found_types} for content items')
        counter = 1
        for div in sorted_divs:
            content_items.append(self._parse_content_item(div, counter))
            counter += 1
        return content_items
<|end_body_3|>

<|body_start_4|>
        mets_doc = self.xml
        self.image_properties = parse_mets_amdsec(mets_doc, x_res='XphysScanResolution', y_res='YphysScanResolution')
        content_items = self._parse_content_items(mets_doc)
        self.issue_data = {'cdt': strftime('%Y-%m-%d %H:%M:%S'), 'i': content_items, 'id': self.id, 'ar': self.rights, 'pp': [p.id for p in self.pages]}
<|end_body_4|>

Solution:
class ReroNewspaperIssue:
    """Class representing an issue in RERO (Mets/Alto) data. All functions defined in this child class are specific to parsing RERO Mets/Alto format"""

    def _find_pages(self):
        """Detects the Alto XML page files for a newspaper issue and initializes page objects."""
        alto_path = os.path.join(self.path, 'ALTO')
        if not os.path.exists(alto_path):
            logger.critical(f'Could not find pages for {self.id}')
        page_file_names = [file for file in os.listdir(alto_path) if not file.startswith('.') and '.xml' in file]
        page_numbers = []
        for fname in page_file_names:
            page_no = fname.split('.')[0]
            page_numbers.append(int(page_no))
        page_canonical_names = ['{}-p{}'.format(self.id, str(page_n).zfill(4)) for page_n in page_numbers]
        self.pages = []
        for filename, page_no, page_id in zip(page_file_names, page_numbers, page_canonical_names):
            try:
                self.pages.append(ReroNewspaperPage(page_id, page_no, filename, alto_path))
            except Exception as e:
                logger.error(f'Adding page {page_no} {page_id} {filename}', f'raised following exception: {e}')
                raise e

    def _parse_content_parts(self, content_div) -> List[Dict[str, str]]:
        """Given the div of a content item, this function parses the children and constructs the legacy `parts` component :param content_div: The div containing the content item :return: list[dict] of different parts for this content item (role, id, fileid, page)"""
        parts = []
        for child in content_div.children:
            if isinstance(child, NavigableString):
                continue
            elif isinstance(child, Tag):
                type_attr = child.get('TYPE')
                comp_role = type_attr.lower() if type_attr else None
                areas = child.findAll('area')
                for area in areas:
                    comp_id = area.get('BEGIN')
                    comp_fileid = area.get('FILEID')
                    comp_page_no = int(comp_fileid.replace('ALTO', ''))
                    parts.append({'comp_role': comp_role, 'comp_id': comp_id, 'comp_fileid': comp_fileid, 'comp_page_no': comp_page_no})
        return parts

    def _parse_content_item(self, item_div, counter: int):
        """Parses a content item div and returns the dictionary representing it. :param item_div: Div of content item :param counter: Number of content items already added (needed to generate canonical id). :return: dict, of the resulting content item"""
        div_type = item_div.get('TYPE').lower()
        if div_type == 'picture':
            div_type = CONTENTITEM_TYPE_IMAGE
        if div_type not in CONTENTITEM_TYPES:
            logger.warning(f'Found new content item type: {div_type}')
        metadata = {'id': '{}-i{}'.format(self.id, str(counter).zfill(4)), 'tp': div_type, 'pp': [], 't': item_div.get('LABEL')}
        content_item = {'m': metadata, 'l': {'id': item_div.get('ID'), 'parts': self._parse_content_parts(item_div)}}
        for p in content_item['l']['parts']:
            pge_no = p['comp_page_no']
            if pge_no not in content_item['m']['pp']:
                content_item['m']['pp'].append(pge_no)
        return content_item

    def _parse_content_items(self, mets_doc: BeautifulSoup):
        """Extract content item elements from a Mets XML file. :param BeautifulSoup mets_doc: :return:"""
        content_items = []
        divs = mets_doc.find('div', {'TYPE': 'CONTENT'}).findChildren('div', recursive=False)
        sorted_divs = sorted(divs, key=lambda x: x.get('ID').lower())
        found_types = set((x.get('TYPE') for x in sorted_divs))
        print(f'Found types {found_types} for content items')
        counter = 1
        for div in sorted_divs:
            content_items.append(self._parse_content_item(div, counter))
            counter += 1
        return content_items

    def _parse_mets(self):
        """Parses the Mets XML file of the newspaper issue."""
        mets_doc = self.xml
        self.image_properties = parse_mets_amdsec(mets_doc, x_res='XphysScanResolution', y_res='YphysScanResolution')
        content_items = self._parse_content_items(mets_doc)
        self.issue_data = {'cdt': strftime('%Y-%m-%d %H:%M:%S'), 'i': content_items, 'id': self.id, 'ar': self.rights, 'pp': [p.id for p in self.pages]}


permuteUnique

Prompt:
Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def permuteUnique(self, nums: List[int]) -> List[List[int]]: 05/22/2022 16:32
- def permuteUnique(self, nums: List[int]) -> List[List[int]]: 05/22/2022 16:55 Not to recreate counter
- def permuteUnique(self, nums: List[int]) -> List[List[int]]: 05/22/2022 16:41 Cache previous result
- def permuteUnique(self, nums: List[int]) -> List[List[int]]: 05/22/2022 19:25 Backtrack

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def permuteUnique(self, nums: List[int]) -> List[List[int]]: 05/22/2022 16:32
- def permuteUnique(self, nums: List[int]) -> List[List[int]]: 05/22/2022 16:55 Not to recreate counter
- def permuteUnique(self, nums: List[int]) -> List[List[int]]: 05/22/2022 16:41 Cache previous result
- def permuteUnique(self, nums: List[int]) -> List[List[int]]: 05/22/2022 19:25 Backtrack

<|skeleton|>
class Solution:

    def permuteUnique(self, nums: List[int]) -> List[List[int]]:
        """05/22/2022 16:32"""
        <|body_0|>

    def permuteUnique(self, nums: List[int]) -> List[List[int]]:
        """05/22/2022 16:55 Not to recreate counter"""
        <|body_1|>

    def permuteUnique(self, nums: List[int]) -> List[List[int]]:
        """05/22/2022 16:41 Cache previous result"""
        <|body_2|>

    def permuteUnique(self, nums: List[int]) -> List[List[int]]:
        """05/22/2022 19:25 Backtrack"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        def perm(cnt):
            if len(cnt) == 1:
                k, v = cnt.popitem()
                return [[k] * v]
            ret = []
            for k in cnt:
                sub = perm({x: c if x != k else c - 1 for x, c in cnt.items() if (c if x != k else c - 1) > 0})
                ret.extend([[k, *y] for y in sub])
            return ret
        return perm(Counter(nums))
<|end_body_0|>

<|body_start_1|>
        def perm(cnt):
            if len(cnt) == 1:
                k = next(iter(cnt))
                return [[k] * cnt[k]]
            ret = []
            keys = list(cnt.keys())
            for k in keys:
                if cnt[k] == 0:
                    continue
                cnt[k] -= 1
                if cnt[k] == 0:
                    cnt.pop(k)
                sub = perm(cnt)
                cnt.setdefault(k, 0)
                cnt[k] += 1
                ret.extend([[k, *y] for y in sub])
            return ret
        return perm(Counter(nums))
<|end_body_1|>

<|body_start_2|>
        class HashableDict(dict):

            def __hash__(self):
                return hash((frozenset(self), frozenset(self.values())))

        @lru_cache(None)
        def perm(cnt: HashableDict):
            if len(cnt) == 1:
                k, v = cnt.popitem()
                return [[k] * v]
            ret = []
            for k in cnt:
                sub = perm(HashableDict({x: c if x != k else c - 1 for x, c in cnt.items() if (c if x != k else c - 1) > 0}))
                ret.extend([[k, *y] for y in sub])
            return ret
        return perm(HashableDict(Counter(nums)))
<|end_body_2|>

<|body_start_3|>
        nums.sort()

        def dfs(cur, remain):
            if remain == 0:
                return [cur]
            ret = []
            pv = None
            for i in range(len(nums)):
                if pv == nums[i]:
                    continue
                if remain & 1 << i == 0:
                    continue
                pv = nums[i]
                ret.extend(dfs(cur + [nums[i]], remain ^ 1 << i))
            return ret
        return dfs([], (1 << len(nums)) - 1)
<|end_body_3|>

Solution:
class Solution:
    def permuteUnique(self, nums: List[int]) -> List[List[int]]:
        """05/22/2022 16:32"""
        def perm(cnt):
            if len(cnt) == 1:
                k, v = cnt.popitem()
                return [[k] * v]
            ret = []
            for k in cnt:
                sub = perm({x: c if x != k else c - 1 for x, c in cnt.items() if (c if x != k else c - 1) > 0})
                ret.extend([[k, *y] for y in sub])
            return ret
        return perm(Counter(nums))

    def permuteUnique(self, nums: List[int]) -> List[List[int]]:
        """05/22/2022 16:55 Not to recreate counter"""
        def perm(cnt):
            if len(cnt) == 1:
                k = next(iter(cnt))
                return [[k] * cnt[k]]
            ret = []
            keys = list(cnt.keys())
            for k in keys:
                if cnt[k] == 0:
                    continue
                cnt[k] -= 1
                if cnt[k] == 0:
                    cnt.pop(k)
                sub = perm(cnt)
                cnt.setdefault(k, 0)
                cnt[k] += 1
                ret.extend([[k, *y] for y in sub])
            return ret
        return perm(Counter(nums))

    def permuteUnique(self, nums: List[int]) -> List[List[int]]:
        """05/22/2022 16:41 Cache previous result"""
        class HashableDict(dict):

            def __hash__(self):
                return hash((frozenset(self), frozenset(self.values())))

        @lru_cache(None)
        def perm(cnt: HashableDict):
            if len(cnt) == 1:
                k, v = cnt.popitem()
                return [[k] * v]
            ret = []
            for k in cnt:
                sub = perm(HashableDict({x: c if x != k else c - 1 for x, c in cnt.items() if (c if x != k else c - 1) > 0}))
                ret.extend([[k, *y] for y in sub])
            return ret
        return perm(HashableDict(Counter(nums)))

    def permuteUnique(self, nums: List[int]) -> List[List[int]]:
        """05/22/2022 19:25 Backtrack"""
        nums.sort()

        def dfs(cur, remain):
            if remain == 0:
                return [cur]
            ret = []
            pv = None
            for i in range(len(nums)):
                if pv == nums[i]:
                    continue
                if remain & 1 << i == 0:
                    continue
                pv = nums[i]
                ret.extend(dfs(cur + [nums[i]], remain ^ 1 << i))
            return ret
        return dfs([], (1 << len(nums)) - 1)


Prompt:
Implement the Python class `RunAddressFuzzersIntegrationTest` described below.

Class description:
Integration tests for build_fuzzers with an ASAN build.

Method signatures and docstrings:
- def test_new_bug_found(self): Tests run_fuzzers with a valid ASAN build.
- def test_old_bug_found(self, _): Tests run_fuzzers with a bug found in OSS-Fuzz before.
- def test_invalid_build(self): Tests run_fuzzers with an invalid ASAN build.

Implement the Python class `RunAddressFuzzersIntegrationTest` described below.

Class description:
Integration tests for build_fuzzers with an ASAN build.

Method signatures and docstrings:
- def test_new_bug_found(self): Tests run_fuzzers with a valid ASAN build.
- def test_old_bug_found(self, _): Tests run_fuzzers with a bug found in OSS-Fuzz before.
- def test_invalid_build(self): Tests run_fuzzers with an invalid ASAN build.

<|skeleton|>
class RunAddressFuzzersIntegrationTest:
    """Integration tests for build_fuzzers with an ASAN build."""

    def test_new_bug_found(self):
        """Tests run_fuzzers with a valid ASAN build."""
        <|body_0|>

    def test_old_bug_found(self, _):
        """Tests run_fuzzers with a bug found in OSS-Fuzz before."""
        <|body_1|>

    def test_invalid_build(self):
        """Tests run_fuzzers with an invalid ASAN build."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        with mock.patch('fuzz_target.FuzzTarget.is_reproducible', side_effect=[True, False]):
            with tempfile.TemporaryDirectory() as tmp_dir:
                workspace = os.path.join(tmp_dir, 'workspace')
                shutil.copytree(TEST_DATA_PATH, workspace)
                config = test_helpers.create_run_config(fuzz_seconds=FUZZ_SECONDS, workspace=workspace, oss_fuzz_project_name=EXAMPLE_PROJECT)
                result = run_fuzzers.run_fuzzers(config)
                self.assertEqual(result, run_fuzzers.RunFuzzersResult.BUG_FOUND)
<|end_body_0|>

<|body_start_1|>
        with tempfile.TemporaryDirectory() as tmp_dir:
            workspace = os.path.join(tmp_dir, 'workspace')
            shutil.copytree(TEST_DATA_PATH, workspace)
            config = test_helpers.create_run_config(fuzz_seconds=FUZZ_SECONDS, workspace=workspace, oss_fuzz_project_name=EXAMPLE_PROJECT)
            result = run_fuzzers.run_fuzzers(config)
            self.assertEqual(result, run_fuzzers.RunFuzzersResult.NO_BUG_FOUND)
<|end_body_1|>

<|body_start_2|>
        with tempfile.TemporaryDirectory() as tmp_dir:
            out_path = os.path.join(tmp_dir, 'build-out')
            os.mkdir(out_path)
            config = test_helpers.create_run_config(fuzz_seconds=FUZZ_SECONDS, workspace=tmp_dir, oss_fuzz_project_name=EXAMPLE_PROJECT)
            result = run_fuzzers.run_fuzzers(config)
        self.assertEqual(result, run_fuzzers.RunFuzzersResult.ERROR)
<|end_body_2|>

Solution:
class RunAddressFuzzersIntegrationTest:
    """Integration tests for build_fuzzers with an ASAN build."""

    def test_new_bug_found(self):
        """Tests run_fuzzers with a valid ASAN build."""
        with mock.patch('fuzz_target.FuzzTarget.is_reproducible', side_effect=[True, False]):
            with tempfile.TemporaryDirectory() as tmp_dir:
                workspace = os.path.join(tmp_dir, 'workspace')
                shutil.copytree(TEST_DATA_PATH, workspace)
                config = test_helpers.create_run_config(fuzz_seconds=FUZZ_SECONDS, workspace=workspace, oss_fuzz_project_name=EXAMPLE_PROJECT)
                result = run_fuzzers.run_fuzzers(config)
                self.assertEqual(result, run_fuzzers.RunFuzzersResult.BUG_FOUND)

    def test_old_bug_found(self, _):
        """Tests run_fuzzers with a bug found in OSS-Fuzz before."""
        with tempfile.TemporaryDirectory() as tmp_dir:
            workspace = os.path.join(tmp_dir, 'workspace')
            shutil.copytree(TEST_DATA_PATH, workspace)
            config = test_helpers.create_run_config(fuzz_seconds=FUZZ_SECONDS, workspace=workspace, oss_fuzz_project_name=EXAMPLE_PROJECT)
            result = run_fuzzers.run_fuzzers(config)
            self.assertEqual(result, run_fuzzers.RunFuzzersResult.NO_BUG_FOUND)

    def test_invalid_build(self):
        """Tests run_fuzzers with an invalid ASAN build."""
        with tempfile.TemporaryDirectory() as tmp_dir:
            out_path = os.path.join(tmp_dir, 'build-out')
            os.mkdir(out_path)
            config = test_helpers.create_run_config(fuzz_seconds=FUZZ_SECONDS, workspace=tmp_dir, oss_fuzz_project_name=EXAMPLE_PROJECT)
            result = run_fuzzers.run_fuzzers(config)
        self.assertEqual(result, run_fuzzers.RunFuzzersResult.ERROR)


run

Prompt:
Implement the Python class `AuditTranslationSuggestionsWithMissingContentIdJob` described below.

Class description:
Audits translation suggestions for missing content IDs.

Method signatures and docstrings:
- def _report_suggestions_with_missing_content_ids(suggestions: List[suggestion_models.GeneralSuggestionModel], exploration: exp_domain.Exploration) -> List[Dict[str, Union[str, List[Dict[str, str]]]]]: Audits translation suggestion models for missing content IDs. Reports the following for each exploration: - exploration ID - list of missing content IDs and corresponding state names. Args: suggestions: list(GeneralSuggestionModel). A list of translation suggestion models corresponding to the given exploration. exploration: Exploration. The corresponding exploration domain object. Returns: list(dict). Audit report result.
- def run(self) -> beam.PCollection[job_run_result.JobRunResult]: Returns a PCollection of audit job run results. Returns: PCollection. A PCollection of results.

Implement the Python class `AuditTranslationSuggestionsWithMissingContentIdJob` described below.

Class description:
Audits translation suggestions for missing content IDs.

Method signatures and docstrings:
- def _report_suggestions_with_missing_content_ids(suggestions: List[suggestion_models.GeneralSuggestionModel], exploration: exp_domain.Exploration) -> List[Dict[str, Union[str, List[Dict[str, str]]]]]: Audits translation suggestion models for missing content IDs. Reports the following for each exploration: - exploration ID - list of missing content IDs and corresponding state names. Args: suggestions: list(GeneralSuggestionModel). A list of translation suggestion models corresponding to the given exploration. exploration: Exploration. The corresponding exploration domain object. Returns: list(dict). Audit report result.
- def run(self) -> beam.PCollection[job_run_result.JobRunResult]: Returns a PCollection of audit job run results. Returns: PCollection. A PCollection of results.

<|skeleton|>
class AuditTranslationSuggestionsWithMissingContentIdJob:
    """Audits translation suggestions for missing content IDs."""

    def _report_suggestions_with_missing_content_ids(suggestions: List[suggestion_models.GeneralSuggestionModel], exploration: exp_domain.Exploration) -> List[Dict[str, Union[str, List[Dict[str, str]]]]]:
        """Audits translation suggestion models for missing content IDs. Reports the following for each exploration: - exploration ID - list of missing content IDs and corresponding state names. Args: suggestions: list(GeneralSuggestionModel). A list of translation suggestion models corresponding to the given exploration. exploration: Exploration. The corresponding exploration domain object. Returns: list(dict). Audit report result."""
        <|body_0|>

    def run(self) -> beam.PCollection[job_run_result.JobRunResult]:
        """Returns a PCollection of audit job run results. Returns: PCollection. A PCollection of results."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        obsolete_content = []
        obsolete_translation_suggestion_error_report: List[Dict[str, Union[str, List[Dict[str, str]]]]] = []
        translatable_content_ids = exploration.get_translatable_content_ids()
        for suggestion in suggestions:
            suggestion_change = suggestion.change_cmd
            if not suggestion_change['content_id'] in translatable_content_ids:
                obsolete_content.append({'content_id': suggestion_change['content_id'], 'state_name': suggestion_change['state_name']})
        obsolete_translation_suggestion_error_report.append({'exp_id': exploration.id, 'obsolete_content': obsolete_content})
        return obsolete_translation_suggestion_error_report
<|end_body_0|>

<|body_start_1|>
        suggestion_dicts = _get_suggestion_dicts(self.pipeline)
        total_processed_suggestions_count_job_run_results = suggestion_dicts | 'Get suggestions' >> beam.FlatMap(lambda suggestions_dict: suggestions_dict['suggestions']) | 'Total processed suggestion count' >> job_result_transforms.CountObjectsToJobRunResult('TOTAL PROCESSED SUGGESTIONS COUNT')
        suggestion_results = suggestion_dicts | 'Report obsolete suggestions' >> beam.Map(lambda suggestions_dict: self._report_suggestions_with_missing_content_ids(suggestions_dict['suggestions'], suggestions_dict['exploration'])) | 'Flatten reports' >> beam.FlatMap(lambda x: x) | 'Filter out reports with no obsolete suggestions' >> beam.Filter(lambda report: len(report['obsolete_content']) > 0)
        job_run_results = suggestion_results | 'Report the obsolete suggestions' >> beam.Map(lambda result: job_run_result.JobRunResult.as_stdout(f'Results are - {result}'))
        obsolete_suggestions_count_job_run_results = suggestion_results | 'Flatten obsolete suggestions' >> beam.FlatMap(lambda report: report['obsolete_content']) | 'Report the obsolete suggestions count' >> job_result_transforms.CountObjectsToJobRunResult('OBSOLETE SUGGESTIONS COUNT')
        return (job_run_results, total_processed_suggestions_count_job_run_results, obsolete_suggestions_count_job_run_results) | 'Combine results' >> beam.Flatten()
<|end_body_1|>

Solution:
class AuditTranslationSuggestionsWithMissingContentIdJob:
    """Audits translation suggestions for missing content IDs."""

    def _report_suggestions_with_missing_content_ids(suggestions: List[suggestion_models.GeneralSuggestionModel], exploration: exp_domain.Exploration) -> List[Dict[str, Union[str, List[Dict[str, str]]]]]:
        """Audits translation suggestion models for missing content IDs. Reports the following for each exploration: - exploration ID - list of missing content IDs and corresponding state names. Args: suggestions: list(GeneralSuggestionModel). A list of translation suggestion models corresponding to the given exploration. exploration: Exploration. The corresponding exploration domain object. Returns: list(dict). Audit report result."""
        obsolete_content = []
        obsolete_translation_suggestion_error_report: List[Dict[str, Union[str, List[Dict[str, str]]]]] = []
        translatable_content_ids = exploration.get_translatable_content_ids()
        for suggestion in suggestions:
            suggestion_change = suggestion.change_cmd
            if not suggestion_change['content_id'] in translatable_content_ids:
                obsolete_content.append({'content_id': suggestion_change['content_id'], 'state_name': suggestion_change['state_name']})
        obsolete_translation_suggestion_error_report.append({'exp_id': exploration.id, 'obsolete_content': obsolete_content})
        return obsolete_translation_suggestion_error_report

    def run(self) -> beam.PCollection[job_run_result.JobRunResult]:
        """Returns a PCollection of audit job run results. Returns: PCollection. A PCollection of results."""
        suggestion_dicts = _get_suggestion_dicts(self.pipeline)
        total_processed_suggestions_count_job_run_results = suggestion_dicts | 'Get suggestions' >> beam.FlatMap(lambda suggestions_dict: suggestions_dict['suggestions']) | 'Total processed suggestion count' >> job_result_transforms.CountObjectsToJobRunResult('TOTAL PROCESSED SUGGESTIONS COUNT')
        suggestion_results = suggestion_dicts | 'Report obsolete suggestions' >> beam.Map(lambda suggestions_dict: self._report_suggestions_with_missing_content_ids(suggestions_dict['suggestions'], suggestions_dict['exploration'])) | 'Flatten reports' >> beam.FlatMap(lambda x: x) | 'Filter out reports with no obsolete suggestions' >> beam.Filter(lambda report: len(report['obsolete_content']) > 0)
        job_run_results = suggestion_results | 'Report the obsolete suggestions' >> beam.Map(lambda result: job_run_result.JobRunResult.as_stdout(f'Results are - {result}'))
        obsolete_suggestions_count_job_run_results = suggestion_results | 'Flatten obsolete suggestions' >> beam.FlatMap(lambda report: report['obsolete_content']) | 'Report the obsolete suggestions count' >> job_result_transforms.CountObjectsToJobRunResult('OBSOLETE SUGGESTIONS COUNT')
        return (job_run_results, total_processed_suggestions_count_job_run_results, obsolete_suggestions_count_job_run_results) | 'Combine results' >> beam.Flatten()


Prompt:
Implement the Python class `AssetLoader` described below.

Class description:
Base class for the Asset Loader thread and actually loads the assets from disk. Args: loader_queue: A reference to the asset manager's loader_queue which holds assets waiting to be loaded. Items are automatically sorted in reverse order by priority, then creation ID. loaded_queue: A reference to the asset manager's loaded_queue which holds assets that have just been loaded. Entries are Asset instances. exception_queue: Send a reference to self.machine.crash_queue. This way if the asset loader crashes, it will write the crash to that queue and cause an exception in the main thread. Otherwise it fails silently which is super annoying. :)

Method signatures and docstrings:
- def __init__(self, loader_queue, loaded_queue, exception_queue, thread_stopper): Initialise asset loader.
- def run(self): Run loop for the loader thread.

Implement the Python class `AssetLoader` described below.

Class description:
Base class for the Asset Loader thread and actually loads the assets from disk. Args: loader_queue: A reference to the asset manager's loader_queue which holds assets waiting to be loaded. Items are automatically sorted in reverse order by priority, then creation ID. loaded_queue: A reference to the asset manager's loaded_queue which holds assets that have just been loaded. Entries are Asset instances. exception_queue: Send a reference to self.machine.crash_queue. This way if the asset loader crashes, it will write the crash to that queue and cause an exception in the main thread. Otherwise it fails silently which is super annoying. :)

Method signatures and docstrings:
- def __init__(self, loader_queue, loaded_queue, exception_queue, thread_stopper): Initialise asset loader.
- def run(self): Run loop for the loader thread.

<|skeleton|>
class AssetLoader:
    """Base class for the Asset Loader thread and actually loads the assets from disk. Args: loader_queue: A reference to the asset manager's loader_queue which holds assets waiting to be loaded. Items are automatically sorted in reverse order by priority, then creation ID. loaded_queue: A reference to the asset manager's loaded_queue which holds assets that have just been loaded. Entries are Asset instances. exception_queue: Send a reference to self.machine.crash_queue. This way if the asset loader crashes, it will write the crash to that queue and cause an exception in the main thread. Otherwise it fails silently which is super annoying. :)"""

    def __init__(self, loader_queue, loaded_queue, exception_queue, thread_stopper):
        """Initialise asset loader."""
        <|body_0|>

    def run(self):
        """Run loop for the loader thread."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        threading.Thread.__init__(self)
        self.log = logging.getLogger('Asset Loader')
        self.loader_queue = loader_queue
        self.loaded_queue = loaded_queue
        self.exception_queue = exception_queue
        self.thread_stopper = thread_stopper
        self.name = 'asset_loader'
<|end_body_0|>

<|body_start_1|>
        try:
            while not self.thread_stopper.is_set():
                try:
                    asset = self.loader_queue.get(block=True, timeout=1)
                except Empty:
                    asset = None
                if asset:
                    with asset.lock:
                        if not asset.loaded:
                            try:
                                asset.do_load()
                            except Exception as e:
                                raise ConfigFileError("Error while loading {} asset file '{}'".format(asset.attribute, asset.file), 1, self.log.name, asset.name) from e
                            self.loaded_queue.put((asset, True))
                        else:
                            self.loaded_queue.put((asset, False))
            return
        except Exception:
            exc_type, exc_value, exc_traceback = sys.exc_info()
            lines = traceback.format_exception(exc_type, exc_value, exc_traceback)
            msg = ''.join((line for line in lines))
            self.exception_queue.put(msg)
            raise
<|end_body_1|>

Solution:
class AssetLoader:
    """Base class for the Asset Loader thread and actually loads the assets from disk. Args: loader_queue: A reference to the asset manager's loader_queue which holds assets waiting to be loaded. Items are automatically sorted in reverse order by priority, then creation ID. loaded_queue: A reference to the asset manager's loaded_queue which holds assets that have just been loaded. Entries are Asset instances. exception_queue: Send a reference to self.machine.crash_queue. This way if the asset loader crashes, it will write the crash to that queue and cause an exception in the main thread. Otherwise it fails silently which is super annoying. :)"""

    def __init__(self, loader_queue, loaded_queue, exception_queue, thread_stopper):
        """Initialise asset loader."""
        threading.Thread.__init__(self)
        self.log = logging.getLogger('Asset Loader')
        self.loader_queue = loader_queue
        self.loaded_queue = loaded_queue
        self.exception_queue = exception_queue
        self.thread_stopper = thread_stopper
        self.name = 'asset_loader'

    def run(self):
        """Run loop for the loader thread."""
        try:
            while not self.thread_stopper.is_set():
                try:
                    asset = self.loader_queue.get(block=True, timeout=1)
                except Empty:
                    asset = None
                if asset:
                    with asset.lock:
                        if not asset.loaded:
                            try:
                                asset.do_load()
                            except Exception as e:
                                raise ConfigFileError("Error while loading {} asset file '{}'".format(asset.attribute, asset.file), 1, self.log.name, asset.name) from e
                            self.loaded_queue.put((asset, True))
                        else:
                            self.loaded_queue.put((asset, False))
            return
        except Exception:
            exc_type, exc_value, exc_traceback = sys.exc_info()
            lines = traceback.format_exception(exc_type, exc_value, exc_traceback)
            msg = ''.join((line for line in lines))
            self.exception_queue.put(msg)
            raise


numSubarraysWithSum

Prompt:
Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def numSubarraysWithSum(self, nums: List[int], goal: int) -> int: 双指针 超时 :param nums: :param goal: :return:
- def numSubarraysWithSum(self, nums: List[int], goal: int) -> int: 双指针 :param nums: :param goal: :return:

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def numSubarraysWithSum(self, nums: List[int], goal: int) -> int: 双指针 超时 :param nums: :param goal: :return:
- def numSubarraysWithSum(self, nums: List[int], goal: int) -> int: 双指针 :param nums: :param goal: :return:

<|skeleton|>
class Solution:

    def numSubarraysWithSum(self, nums: List[int], goal: int) -> int:
        """双指针 超时 :param nums: :param goal: :return:"""
        <|body_0|>

    def numSubarraysWithSum(self, nums: List[int], goal: int) -> int:
        """双指针 :param nums: :param goal: :return:"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        count = 0
        for i in range(len(nums) - 1):
            for j in range(1, len(nums)):
                tmp = sum(nums[i:j + 1]) - goal
                if tmp < 0:
                    continue
                elif tmp == 0:
                    count += 1
                else:
                    break
        return count
<|end_body_0|>

<|body_start_1|>
        n = len(nums)
        ans = l1 = l2 = s1 = s2 = 0
        for r in range(n):
            s1 += nums[r]
            s2 += nums[r]
            while l1 <= r and s1 > goal:
                s1 -= nums[l1]
                l1 += 1
            while l2 <= r and s2 >= goal:
                s2 -= nums[l2]
                l2 += 1
            ans += l2 - l1
        return ans
<|end_body_1|>

Solution:
class Solution:
    def numSubarraysWithSum(self, nums: List[int], goal: int) -> int:
        """双指针 超时 :param nums: :param goal: :return:"""
        count = 0
        for i in range(len(nums) - 1):
            for j in range(1, len(nums)):
                tmp = sum(nums[i:j + 1]) - goal
                if tmp < 0:
                    continue
                elif tmp == 0:
                    count += 1
                else:
                    break
        return count

    def numSubarraysWithSum(self, nums: List[int], goal: int) -> int:
        """双指针 :param nums: :param goal: :return:"""
        n = len(nums)
        ans = l1 = l2 = s1 = s2 = 0
        for r in range(n):
            s1 += nums[r]
            s2 += nums[r]
            while l1 <= r and s1 > goal:
                s1 -= nums[l1]
                l1 += 1
            while l2 <= r and s2 >= goal:
                s2 -= nums[l2]
                l2 += 1
            ans += l2 - l1
        return ans


Prompt:
Implement the Python class `UsBaxAcqFunction` described below.

Class description:
Wrapper on BaxAcqFunction for uncertainty sampling acquisition, when we still want various BaxAcqFunction variables for visualizations.

Method signatures and docstrings:
- def set_params(self, params): Set self.params, the parameters for the AcqFunction.
- def __call__(self, x_list): Class is callable and returns acquisition function on x_list.

Implement the Python class `UsBaxAcqFunction` described below.

Class description:
Wrapper on BaxAcqFunction for uncertainty sampling acquisition, when we still want various BaxAcqFunction variables for visualizations.

Method signatures and docstrings:
- def set_params(self, params): Set self.params, the parameters for the AcqFunction.
- def __call__(self, x_list): Class is callable and returns acquisition function on x_list.

<|skeleton|>
class UsBaxAcqFunction:
    """Wrapper on BaxAcqFunction for uncertainty sampling acquisition, when we still want various BaxAcqFunction variables for visualizations."""

    def set_params(self, params):
        """Set self.params, the parameters for the AcqFunction."""
        <|body_0|>

    def __call__(self, x_list):
        """Class is callable and returns acquisition function on x_list."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super().set_params(params)
        params = dict_to_namespace(params)
        self.params.name = getattr(params, 'name', 'UsBaxAcqFunction')
<|end_body_0|>

<|body_start_1|>
        super().__call__(x_list)
        acq_list = self.acq_vars['std']
        return acq_list
<|end_body_1|>

Solution:
class UsBaxAcqFunction:
    """Wrapper on BaxAcqFunction for uncertainty sampling acquisition, when we still want various BaxAcqFunction variables for visualizations."""

    def set_params(self, params):
        """Set self.params, the parameters for the AcqFunction."""
        super().set_params(params)
        params = dict_to_namespace(params)
        self.params.name = getattr(params, 'name', 'UsBaxAcqFunction')

    def __call__(self, x_list):
        """Class is callable and returns acquisition function on x_list."""
        super().__call__(x_list)
        acq_list = self.acq_vars['std']
        return acq_list


Prompt:
Implement the Python class `PostUpgradeCmd` described below.

Class description:
Post Upgrade Setup Cmd.

Method signatures and docstrings:
- def __init__(self, config: str, services: str=None): Constructor.
- def process(self): Main processing function.
- def copy_config_files(self): Copy sample and unsafe attribute config files from /opt/seagate/cortx to /etc/cortx.
- def delete_config_files(self): delete config file which are installed by rpm

Implement the Python class `PostUpgradeCmd` described below.

Class description:
Post Upgrade Setup Cmd.

Method signatures and docstrings:
- def __init__(self, config: str, services: str=None): Constructor.
- def process(self): Main processing function.
- def copy_config_files(self): Copy sample and unsafe attribute config files from /opt/seagate/cortx to /etc/cortx.
- def delete_config_files(self): delete config file which are installed by rpm

<|skeleton|>
class PostUpgradeCmd:
    """Post Upgrade Setup Cmd."""

    def __init__(self, config: str, services: str=None):
        """Constructor."""
        <|body_0|>

    def process(self):
        """Main processing function."""
        <|body_1|>

    def copy_config_files(self):
        """Copy sample and unsafe attribute config files from /opt/seagate/cortx to /etc/cortx."""
        <|body_2|>

    def delete_config_files(self):
        """delete config file which are installed by rpm"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        try:
            super(PostUpgradeCmd, self).__init__(config, services)
        except Exception as e:
            raise e
<|end_body_0|>

<|body_start_1|>
        self.logger.info(f'Processing phase = {self.name}, config = {self.url}, service = {self.services}')
        try:
            self.logger.info('validations started')
            self.phase_prereqs_validate(self.name)
            self.logger.info('validations completed')
            self.logger.info('Copy .ample and unsafe attribute files started')
            self.copy_config_files()
            self.logger.info('Copy .ample and unsafe attribute files completed')
            self.logger.info('Delete config file started')
            self.delete_config_files()
            self.logger.info('Delete config file completed')
            self.logger.info('merge configs started')
            config_file_path = '/etc/cortx'
            merge_configs(config_file_path, self.s3_tmp_dir)
            self.logger.info('merge configs completed')
            self.logger.info('Remove sample.old files started')
            regex = '*.sample.old'
            self.DeleteFileOrDirWithRegex(self.s3_tmp_dir, regex)
            self.logger.info('Remove sample.old files completed')
            self.logger.info('config file validations started')
            self.validate_config_files(self.name)
            self.logger.info('config file validations completed')
        except Exception as e:
            raise S3PROVError(f'process: {self.name} failed with exception: {e}')
<|end_body_1|>

<|body_start_2|>
        config_files = [self.get_confkey('S3_CONFIG_SAMPLE_FILE'), self.get_confkey('S3_CONFIG_UNSAFE_ATTR_FILE'), self.get_confkey('S3_AUTHSERVER_CONFIG_SAMPLE_FILE'), self.get_confkey('S3_AUTHSERVER_CONFIG_UNSAFE_ATTR_FILE'), self.get_confkey('S3_KEYSTORE_CONFIG_SAMPLE_FILE'), self.get_confkey('S3_KEYSTORE_CONFIG_UNSAFE_ATTR_FILE'), self.get_confkey('S3_BGDELETE_CONFIG_SAMPLE_FILE'), self.get_confkey('S3_BGDELETE_CONFIG_UNSAFE_ATTR_FILE'), self.get_confkey('S3_CLUSTER_CONFIG_SAMPLE_FILE'), self.get_confkey('S3_CLUSTER_CONFIG_UNSAFE_ATTR_FILE')]
        for config_file in config_files:
            self.logger.info(f'Source config file: {config_file}')
            dest_config_file = config_file.replace('/opt/seagate/cortx', '/etc/cortx')
            self.logger.info(f'Dest config file: {dest_config_file}')
            os.makedirs(os.path.dirname(dest_config_file), exist_ok=True)
            shutil.move(config_file, dest_config_file)
            self.logger.info('Config file copied successfully to /etc/cortx')
<|end_body_2|>

<|body_start_3|>
        config_files = [self.get_confkey('S3_CONFIG_FILE'), self.get_confkey('S3_AUTHSERVER_CONFIG_FILE'), self.get_confkey('S3_KEYSTORE_CONFIG_FILE'), self.get_confkey('S3_BGDELETE_CONFIG_FILE'), self.get_confkey('S3_CLUSTER_CONFIG_FILE')]
        for config_file in config_files:
            self.DeleteFile(config_file)
            self.logger.info(f'Config file {config_file} deleted successfully')
<|end_body_3|>

Solution:
class PostUpgradeCmd:
    """Post Upgrade Setup Cmd."""

    def __init__(self, config: str, services: str=None):
        """Constructor."""
        try:
            super(PostUpgradeCmd, self).__init__(config, services)
        except Exception as e:
            raise e

    def process(self):
        """Main processing function."""
        self.logger.info(f'Processing phase = {self.name}, config = {self.url}, service = {self.services}')
        try:
            self.logger.info('validations started')
            self.phase_prereqs_validate(self.name)
            self.logger.info('validations completed')
            self.logger.info('Copy .ample and unsafe attribute files started')
            self.copy_config_files()
            self.logger.info('Copy .ample and unsafe attribute files completed')
            self.logger.info('Delete config file started')
            self.delete_config_files()
            self.logger.info('Delete config file completed')
            self.logger.info('merge configs started')
            config_file_path = '/etc/cortx'
            merge_configs(config_file_path, self.s3_tmp_dir)
            self.logger.info('merge configs completed')
            self.logger.info('Remove sample.old files started')
            regex = '*.sample.old'
            self.DeleteFileOrDirWithRegex(self.s3_tmp_dir, regex)
            self.logger.info('Remove sample.old files completed')
            self.logger.info('config file validations started')
            self.validate_config_files(self.name)
            self.logger.info('config file validations completed')
        except Exception as e:
            raise S3PROVError(f'process: {self.name} failed with exception: {e}')

    def copy_config_files(self):
        """Copy sample and unsafe attribute config files from /opt/seagate/cortx to /etc/cortx."""
        config_files = [self.get_confkey('S3_CONFIG_SAMPLE_FILE'), self.get_confkey('S3_CONFIG_UNSAFE_ATTR_FILE'), self.get_confkey('S3_AUTHSERVER_CONFIG_SAMPLE_FILE'), self.get_confkey('S3_AUTHSERVER_CONFIG_UNSAFE_ATTR_FILE'), self.get_confkey('S3_KEYSTORE_CONFIG_SAMPLE_FILE'), self.get_confkey('S3_KEYSTORE_CONFIG_UNSAFE_ATTR_FILE'), self.get_confkey('S3_BGDELETE_CONFIG_SAMPLE_FILE'), self.get_confkey('S3_BGDELETE_CONFIG_UNSAFE_ATTR_FILE'), self.get_confkey('S3_CLUSTER_CONFIG_SAMPLE_FILE'), self.get_confkey('S3_CLUSTER_CONFIG_UNSAFE_ATTR_FILE')]
        for config_file in config_files:
            self.logger.info(f'Source config file: {config_file}')
            dest_config_file = config_file.replace('/opt/seagate/cortx', '/etc/cortx')
            self.logger.info(f'Dest config file: {dest_config_file}')
            os.makedirs(os.path.dirname(dest_config_file), exist_ok=True)
            shutil.move(config_file, dest_config_file)
            self.logger.info('Config file copied successfully to /etc/cortx')

    def delete_config_files(self):
        """delete config file which are installed by rpm"""
        config_files = [self.get_confkey('S3_CONFIG_FILE'), self.get_confkey('S3_AUTHSERVER_CONFIG_FILE'), self.get_confkey('S3_KEYSTORE_CONFIG_FILE'), self.get_confkey('S3_BGDELETE_CONFIG_FILE'), self.get_confkey('S3_CLUSTER_CONFIG_FILE')]
        for config_file in config_files:
            self.DeleteFile(config_file)
            self.logger.info(f'Config file {config_file} deleted successfully')


Prompt:
Implement the Python class `TOS_THR` described below.

Class description:
Enumeration class for TOS_THR.

Method signatures and docstrings:
- def get(key, default=-1): Backport support for original codes.
- def _missing_(cls, value): Lookup function used when value is not found.

Implement the Python class `TOS_THR` described below.

Class description:
Enumeration class for TOS_THR.

Method signatures and docstrings:
- def get(key, default=-1): Backport support for original codes.
- def _missing_(cls, value): Lookup function used when value is not found.

<|skeleton|>
class TOS_THR:
    """Enumeration class for TOS_THR."""

    def get(key, default=-1):
        """Backport support for original codes."""
        <|body_0|>

    def _missing_(cls, value):
        """Lookup function used when value is not found."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if isinstance(key, int):
            return TOS_THR(key)
        if key not in TOS_THR._member_map_:
            extend_enum(TOS_THR, key, default)
        return TOS_THR[key]
<|end_body_0|>

<|body_start_1|>
        if not (isinstance(value, int) and 0 <= value <= 1):
            raise ValueError('%r is not a valid %s' % (value, cls.__name__))
        extend_enum(cls, 'Unassigned [%d]' % value, value)
        return cls(value)
        super()._missing_(value)
<|end_body_1|>

Solution:
class TOS_THR:
    """Enumeration class for TOS_THR."""

    def get(key, default=-1):
        """Backport support for original codes."""
        if isinstance(key, int):
            return TOS_THR(key)
        if key not in TOS_THR._member_map_:
            extend_enum(TOS_THR, key, default)
        return TOS_THR[key]

    def _missing_(cls, value):
        """Lookup function used when value is not found."""
        if not (isinstance(value, int) and 0 <= value <= 1):
            raise ValueError('%r is not a valid %s' % (value, cls.__name__))
        extend_enum(cls, 'Unassigned [%d]' % value, value)
        return cls(value)
        super()._missing_(value)


save

Prompt:
Implement the Python class `GenericModelForm` described below.

Class description:
This simple subclass of ModelForm fixes a couple of issues with django's ModelForm. - treat virtual fields like GenericForeignKey as normal fields, Django should already do that but it doesn't, - when setting a GenericForeignKey value, also set the object id and content type id fields, again Django could probably afford to do that.

Method signatures and docstrings:
- def __init__(self, *args, **kwargs): What ModelForm does, but also add virtual field values to self.initial.
- def _post_clean(self): What ModelForm does, but also set virtual field values from cleaned_data.
- def save(self, commit=True): What ModelForm does, but also set GFK.ct_field and GFK.fk_field if such a virtual field has a value. This should probably be done in the GFK field itself, but it's here for convenience until Django fixes that.

Implement the Python class `GenericModelForm` described below.

Class description:
This simple subclass of ModelForm fixes a couple of issues with django's ModelForm. - treat virtual fields like GenericForeignKey as normal fields, Django should already do that but it doesn't, - when setting a GenericForeignKey value, also set the object id and content type id fields, again Django could probably afford to do that.

Method signatures and docstrings:
- def __init__(self, *args, **kwargs): What ModelForm does, but also add virtual field values to self.initial.
- def _post_clean(self): What ModelForm does, but also set virtual field values from cleaned_data.
- def save(self, commit=True): What ModelForm does, but also set GFK.ct_field and GFK.fk_field if such a virtual field has a value. This should probably be done in the GFK field itself, but it's here for convenience until Django fixes that.

<|skeleton|>
class GenericModelForm:
    """This simple subclass of ModelForm fixes a couple of issues with django's ModelForm. - treat virtual fields like GenericForeignKey as normal fields, Django should already do that but it doesn't, - when setting a GenericForeignKey value, also set the object id and content type id fields, again Django could probably afford to do that."""

    def __init__(self, *args, **kwargs):
        """What ModelForm does, but also add virtual field values to self.initial."""
        <|body_0|>

    def _post_clean(self):
        """What ModelForm does, but also set virtual field values from cleaned_data."""
        <|body_1|>

    def save(self, commit=True):
        """What ModelForm does, but also set GFK.ct_field and GFK.fk_field if such a virtual field has a value. This should probably be done in the GFK field itself, but it's here for convenience until Django fixes that."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        super(GenericModelForm, self).__init__(*args, **kwargs)
        for field in self._meta.model._meta.virtual_fields:
            self.initial[field.name] = getattr(self.instance, field.name, None)
<|end_body_0|>

<|body_start_1|>
        super(GenericModelForm, self)._post_clean()
        for field in self._meta.model._meta.virtual_fields:
            value = self.cleaned_data.get(field.name, None)
            if value:
                setattr(self.instance, field.name, value)
<|end_body_1|>

<|body_start_2|>
        for field in self._meta.model._meta.virtual_fields:
            if isinstance(field, GenericForeignKey):
                value = self.cleaned_data.get(field.name, None)
                if not value:
                    continue
                setattr(self.instance, field.ct_field, ContentType.objects.get_for_model(value))
                setattr(self.instance, field.fk_field, value.pk)
        return super(GenericModelForm, self).save(commit)
<|end_body_2|>

Solution:
class GenericModelForm:
    """This simple subclass of ModelForm fixes a couple of issues with django's ModelForm. - treat virtual fields like GenericForeignKey as normal fields, Django should already do that but it doesn't, - when setting a GenericForeignKey value, also set the object id and content type id fields, again Django could probably afford to do that."""

    def __init__(self, *args, **kwargs):
        """What ModelForm does, but also add virtual field values to self.initial."""
        super(GenericModelForm, self).__init__(*args, **kwargs)
        for field in self._meta.model._meta.virtual_fields:
            self.initial[field.name] = getattr(self.instance, field.name, None)

    def _post_clean(self):
        """What ModelForm does, but also set virtual field values from cleaned_data."""
        super(GenericModelForm, self)._post_clean()
        for field in self._meta.model._meta.virtual_fields:
            value = self.cleaned_data.get(field.name, None)
            if value:
                setattr(self.instance, field.name, value)

    def save(self, commit=True):
        """What ModelForm does, but also set GFK.ct_field and GFK.fk_field if such a virtual field has a value. This should probably be done in the GFK field itself, but it's here for convenience until Django fixes that."""
        for field in self._meta.model._meta.virtual_fields:
            if isinstance(field, GenericForeignKey):
                value = self.cleaned_data.get(field.name, None)
                if not value:
                    continue
                setattr(self.instance, field.ct_field, ContentType.objects.get_for_model(value))
                setattr(self.instance, field.fk_field, value.pk)
        return super(GenericModelForm, self).save(commit)


execute

Prompt:
Implement the Python class `Comparar` described below.

Class description:
Clase representativa de la inferencia encargada de comparar el hallazgo recibido con la norma del parametro seleccionado :author: Michael Castillo Polo y Luis Miguel López Coleto :date: 10/06/2015

Method signatures and docstrings:
- def __init__(self, hallazgo, norma): Constructor de la clase :param hallazgo: Informacion recibida del exterior :param norma: Valor 'normal' del parametro :author: Michael Castillo Polo y Luis Miguel López Coleto :date: 10/06/2015
- def execute(self): Metodo encargado de llevar a cabo la comparacion del hallazgo con la norma :return: Resultado de la equiparacion [True o False], seguido de su respectiva explicacion :author: Manuel Alejandro Luque León :date 06/06/2020

Implement the Python class `Comparar` described below.

Class description:
Clase representativa de la inferencia encargada de comparar el hallazgo recibido con la norma del parametro seleccionado :author: Michael Castillo Polo y Luis Miguel López Coleto :date: 10/06/2015

Method signatures and docstrings:
- def __init__(self, hallazgo, norma): Constructor de la clase :param hallazgo: Informacion recibida del exterior :param norma: Valor 'normal' del parametro :author: Michael Castillo Polo y Luis Miguel López Coleto :date: 10/06/2015
- def execute(self): Metodo encargado de llevar a cabo la comparacion del hallazgo con la norma :return: Resultado de la equiparacion [True o False], seguido de su respectiva explicacion :author: Manuel Alejandro Luque León :date 06/06/2020

<|skeleton|>
class Comparar:
    """Clase representativa de la inferencia encargada de comparar el hallazgo recibido con la norma del parametro seleccionado :author: Michael Castillo Polo y Luis Miguel López Coleto :date: 10/06/2015"""

    def __init__(self, hallazgo, norma):
        """Constructor de la clase :param hallazgo: Informacion recibida del exterior :param norma: Valor 'normal' del parametro :author: Michael Castillo Polo y Luis Miguel López Coleto :date: 10/06/2015"""
        <|body_0|>

    def execute(self):
        """Metodo encargado de llevar a cabo la comparacion del hallazgo con la norma :return: Resultado de la equiparacion [True o False], seguido de su respectiva explicacion :author: Manuel Alejandro Luque León :date 06/06/2020"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        Inferencia.__init__(self)
        self.hallazgo = hallazgo
        self.norma = norma
        self.explicacion = u''
<|end_body_0|>

<|body_start_1|>
        diferencia = float(self.hallazgo.valor.replace(',', '.')) - float(self.norma.valor)
        if self.norma.tipo == '<=':
            if float(self.hallazgo.valor) <= float(self.norma.valor):
                return (True, abs(diferencia))
            else:
                return (False, abs(diferencia))
        elif self.norma.tipo == '>=':
            if float(self.hallazgo.valor) >= float(self.norma.valor):
                return (True, abs(diferencia))
            else:
                return (False, abs(diferencia))
<|end_body_1|>

Solution:
class Comparar:
    """Clase representativa de la inferencia encargada de comparar el hallazgo recibido con la norma del parametro seleccionado :author: Michael Castillo Polo y Luis Miguel López Coleto :date: 10/06/2015"""

    def __init__(self, hallazgo, norma):
        """Constructor de la clase :param hallazgo: Informacion recibida del exterior :param norma: Valor 'normal' del parametro :author: Michael Castillo Polo y Luis Miguel López Coleto :date: 10/06/2015"""
        Inferencia.__init__(self)
        self.hallazgo = hallazgo
        self.norma = norma
        self.explicacion = u''

    def execute(self):
        """Metodo encargado de llevar a cabo la comparacion del hallazgo con la norma :return: Resultado de la equiparacion [True o False], seguido de su respectiva explicacion :author: Manuel Alejandro Luque León :date 06/06/2020"""
        diferencia = float(self.hallazgo.valor.replace(',', '.')) - float(self.norma.valor)
        if self.norma.tipo == '<=':
            if float(self.hallazgo.valor) <= float(self.norma.valor):
                return (True, abs(diferencia))
            else:
                return (False, abs(diferencia))
        elif self.norma.tipo == '>=':
            if float(self.hallazgo.valor) >= float(self.norma.valor):
                return (True, abs(diferencia))
            else:
                return (False, abs(diferencia))


setUp

Prompt:
Implement the Python class `TestMopacMolPM7` described below.

Class description:
Contains unit tests for the Geometry class.

Method signatures and docstrings:
- def setUp(self): A function run before each unit test in this class.
- def test_generate_thermo_data(self): Test that generate_thermo_data() works correctly for MOPAC PM7
- def test_load_thermo_data(self): Test that generate_thermo_data() can load thermo from the previous MOPAC PM7 run. Check that it loaded, and the values are the same as above.

Implement the Python class `TestMopacMolPM7` described below.

Class description:
Contains unit tests for the Geometry class.

Method signatures and docstrings:
- def setUp(self): A function run before each unit test in this class.
- def test_generate_thermo_data(self): Test that generate_thermo_data() works correctly for MOPAC PM7
- def test_load_thermo_data(self): Test that generate_thermo_data() can load thermo from the previous MOPAC PM7 run. Check that it loaded, and the values are the same as above.

<|skeleton|>
class TestMopacMolPM7:
    """Contains unit tests for the Geometry class."""

    def setUp(self):
        """A function run before each unit test in this class."""
        <|body_0|>

    def test_generate_thermo_data(self):
        """Test that generate_thermo_data() works correctly for MOPAC PM7"""
        <|body_1|>

    def test_load_thermo_data(self):
        """Test that generate_thermo_data() can load thermo from the previous MOPAC PM7 run. Check that it loaded, and the values are the same as above."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        rmgpy_path = os.path.normpath(os.path.join(get_path(), '..'))
        qm = QMCalculator(software='mopac', method='pm7', fileStore=os.path.join(rmgpy_path, 'testing', 'qm', 'QMfiles'), scratchDirectory=os.path.join(rmgpy_path, 'testing', 'qm', 'QMscratch'))
        if not os.path.exists(qm.settings.fileStore):
            os.makedirs(qm.settings.fileStore)
        mol1 = Molecule().from_smiles('C1=CC=C2C=CC=CC2=C1')
        self.qmmol1 = MopacMolPM7(mol1, qm.settings)
<|end_body_0|>

<|body_start_1|>
        for directory in (self.qmmol1.settings.fileStore, self.qmmol1.settings.scratchDirectory):
            shutil.rmtree(directory, ignore_errors=True)
        self.qmmol1.generate_thermo_data()
        result = self.qmmol1.qm_data
        self.assertTrue(self.qmmol1.verify_output_file())
        self.assertTrue(self.qmmol1.thermo.comment.startswith('QM MopacMolPM7 calculation'))
        self.assertEqual(result.numberOfAtoms, 18)
        self.assertIsInstance(result.atomicNumbers, np.ndarray)
        if result.molecularMass.units == 'amu':
            self.assertAlmostEqual(result.molecularMass.value, 128.173, 2)
        self.assertAlmostEqual(self.qmmol1.thermo.H298.value_si, 166168.9863, 0)
        self.assertAlmostEqual(self.qmmol1.thermo.S298.value_si, 336.3330406, 1)
<|end_body_1|>

<|body_start_2|>
        self.qmmol1.generate_thermo_data()
        result = self.qmmol1.qm_data
        self.assertTrue(self.qmmol1.thermo.comment.startswith('QM MopacMolPM7 calculation'))
        self.assertEqual(result.numberOfAtoms, 18)
        self.assertIsInstance(result.atomicNumbers, np.ndarray)
        if result.molecularMass.units == 'amu':
            self.assertAlmostEqual(result.molecularMass.value, 128.173, 2)
        self.assertAlmostEqual(self.qmmol1.thermo.H298.value_si, 166168.8571, 0)
        self.assertAlmostEqual(self.qmmol1.thermo.S298.value_si, 336.3330406, 1)
<|end_body_2|>

Solution:
class TestMopacMolPM7:
    """Contains unit tests for the Geometry class."""

    def setUp(self):
        """A function run before each unit test in this class."""
        rmgpy_path = os.path.normpath(os.path.join(get_path(), '..'))
        qm = QMCalculator(software='mopac', method='pm7', fileStore=os.path.join(rmgpy_path, 'testing', 'qm', 'QMfiles'), scratchDirectory=os.path.join(rmgpy_path, 'testing', 'qm', 'QMscratch'))
        if not os.path.exists(qm.settings.fileStore):
            os.makedirs(qm.settings.fileStore)
        mol1 = Molecule().from_smiles('C1=CC=C2C=CC=CC2=C1')
        self.qmmol1 = MopacMolPM7(mol1, qm.settings)

    def test_generate_thermo_data(self):
        """Test that generate_thermo_data() works correctly for MOPAC PM7"""
        for directory in (self.qmmol1.settings.fileStore, self.qmmol1.settings.scratchDirectory):
            shutil.rmtree(directory, ignore_errors=True)
        self.qmmol1.generate_thermo_data()
        result = self.qmmol1.qm_data
        self.assertTrue(self.qmmol1.verify_output_file())
        self.assertTrue(self.qmmol1.thermo.comment.startswith('QM MopacMolPM7 calculation'))
        self.assertEqual(result.numberOfAtoms, 18)
        self.assertIsInstance(result.atomicNumbers, np.ndarray)
        if result.molecularMass.units == 'amu':
            self.assertAlmostEqual(result.molecularMass.value, 128.173, 2)
        self.assertAlmostEqual(self.qmmol1.thermo.H298.value_si, 166168.9863, 0)
        self.assertAlmostEqual(self.qmmol1.thermo.S298.value_si, 336.3330406, 1)

    def test_load_thermo_data(self):
        """Test that generate_thermo_data() can load thermo from the previous MOPAC PM7 run. Check that it loaded, and the values are the same as above."""
        self.qmmol1.generate_thermo_data()
        result = self.qmmol1.qm_data
        self.assertTrue(self.qmmol1.thermo.comment.startswith('QM MopacMolPM7 calculation'))
        self.assertEqual(result.numberOfAtoms, 18)
        self.assertIsInstance(result.atomicNumbers, np.ndarray)
        if result.molecularMass.units == 'amu':
            self.assertAlmostEqual(result.molecularMass.value, 128.173, 2)
        self.assertAlmostEqual(self.qmmol1.thermo.H298.value_si, 166168.8571, 0)
        self.assertAlmostEqual(self.qmmol1.thermo.S298.value_si, 336.3330406, 1)


forward

Prompt:
Implement the Python class `LayerNorm` described below.

Class description:
Module that perform normalization of the input x. Attributes: a_2 (nn.Parameter): learnable parameter used to project the normalization of the batch. b_2 (nn.Parameter): learnable parameter corresponding to the bias of the projection on the normalization. eps (float): epsilon float to use when dividing by the std.

Method signatures and docstrings:
- def __init__(self, features: int, eps: float=1e-06) -> None: Initialize the layer normalization module. Args: features (int): dimension of the features / model to normalize. eps (float): epsilon float to use when dividing by the std.
- def forward(self, x: Tensor) -> Tensor: Perform the normalization on `x`. Args: x (Tensor): input tensor of shape `(batch, seq_len, size)`. Returns: Tensor: output normalized tensor of the same shape.

Implement the Python class `LayerNorm` described below.

Class description:
Module that perform normalization of the input x. Attributes: a_2 (nn.Parameter): learnable parameter used to project the normalization of the batch. b_2 (nn.Parameter): learnable parameter corresponding to the bias of the projection on the normalization. eps (float): epsilon float to use when dividing by the std.

Method signatures and docstrings:
- def __init__(self, features: int, eps: float=1e-06) -> None: Initialize the layer normalization module. Args: features (int): dimension of the features / model to normalize. eps (float): epsilon float to use when dividing by the std.
- def forward(self, x: Tensor) -> Tensor: Perform the normalization on `x`. Args: x (Tensor): input tensor of shape `(batch, seq_len, size)`. Returns: Tensor: output normalized tensor of the same shape.

<|skeleton|>
class LayerNorm:
    """Module that perform normalization of the input x. Attributes: a_2 (nn.Parameter): learnable parameter used to project the normalization of the batch. b_2 (nn.Parameter): learnable parameter corresponding to the bias of the projection on the normalization. eps (float): epsilon float to use when dividing by the std."""

    def __init__(self, features: int, eps: float=1e-06) -> None:
        """Initialize the layer normalization module. Args: features (int): dimension of the features / model to normalize. eps (float): epsilon float to use when dividing by the std."""
        <|body_0|>

    def forward(self, x: Tensor) -> Tensor:
        """Perform the normalization on `x`. Args: x (Tensor): input tensor of shape `(batch, seq_len, size)`. Returns: Tensor: output normalized tensor of the same shape."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(LayerNorm, self).__init__()
        self.a_2 = nn.Parameter(torch.ones(features))
        self.b_2 = nn.Parameter(torch.zeros(features))
        self.eps = eps
<|end_body_0|>

<|body_start_1|>
        mean = x.mean(-1, keepdim=True)
        std = x.std(-1, keepdim=True)
        return self.a_2 * (x - mean) / (std + self.eps) + self.b_2
<|end_body_1|>

Solution:
class LayerNorm:
    """Module that perform normalization of the input x. Attributes: a_2 (nn.Parameter): learnable parameter used to project the normalization of the batch. b_2 (nn.Parameter): learnable parameter corresponding to the bias of the projection on the normalization. eps (float): epsilon float to use when dividing by the std."""

    def __init__(self, features: int, eps: float=1e-06) -> None:
        """Initialize the layer normalization module. Args: features (int): dimension of the features / model to normalize. eps (float): epsilon float to use when dividing by the std."""
        super(LayerNorm, self).__init__()
        self.a_2 = nn.Parameter(torch.ones(features))
        self.b_2 = nn.Parameter(torch.zeros(features))
        self.eps = eps

    def forward(self, x: Tensor) -> Tensor:
        """Perform the normalization on `x`. Args: x (Tensor): input tensor of shape `(batch, seq_len, size)`. Returns: Tensor: output normalized tensor of the same shape."""
        mean = x.mean(-1, keepdim=True)
        std = x.std(-1, keepdim=True)
        return self.a_2 * (x - mean) / (std + self.eps) + self.b_2


Prompt:
Implement the Python class `AndoverSemiCustomFilter` described below.

Class description:
Implement the AndoverSemiCustomFilter class.

Method signatures and docstrings:
- def __init__(self, wl_centre, fwhm, peak_tx, type, ref_index): set the normalised transmission values, and the corresponding fwhm coefficients based on info from: https://www.andovercorp.com/technical/bandpass-filters-fundamentals/filters-types/ :param wl_centre: centre wavelength [m] :param fwhm: full width half max. wavelength [m] :param peak_tx: transmission at centre, as a fraction. :param type: type of semi-custom filters, according to Andover, basically the number of cavities. :param ref_index
- def _get_semicustom_tx(self): use the info on the filters profiles from https://www.andovercorp.com/technical/bandpass-filters-fundamentals/filters-types/ to calculate the rough tx profile. :return: tx
- def get_interp_profile(self): interpolate the few points provided by the manufacturer onto a finer wavelength grid.

Implement the Python class `AndoverSemiCustomFilter` described below.

Class description:
Implement the AndoverSemiCustomFilter class.

Method signatures and docstrings:
- def __init__(self, wl_centre, fwhm, peak_tx, type, ref_index): set the normalised transmission values, and the corresponding fwhm coefficients based on info from: https://www.andovercorp.com/technical/bandpass-filters-fundamentals/filters-types/ :param wl_centre: centre wavelength [m] :param fwhm: full width half max. wavelength [m] :param peak_tx: transmission at centre, as a fraction. :param type: type of semi-custom filters, according to Andover, basically the number of cavities. :param ref_index
- def _get_semicustom_tx(self): use the info on the filters profiles from https://www.andovercorp.com/technical/bandpass-filters-fundamentals/filters-types/ to calculate the rough tx profile. :return: tx
- def get_interp_profile(self): interpolate the few points provided by the manufacturer onto a finer wavelength grid.

<|skeleton|>
class AndoverSemiCustomFilter:

    def __init__(self, wl_centre, fwhm, peak_tx, type, ref_index):
        """set the normalised transmission values, and the corresponding fwhm coefficients based on info from: https://www.andovercorp.com/technical/bandpass-filters-fundamentals/filters-types/ :param wl_centre: centre wavelength [m] :param fwhm: full width half max. wavelength [m] :param peak_tx: transmission at centre, as a fraction. :param type: type of semi-custom filters, according to Andover, basically the number of cavities. :param ref_index"""
        <|body_0|>

    def _get_semicustom_tx(self):
        """use the info on the filters profiles from https://www.andovercorp.com/technical/bandpass-filters-fundamentals/filters-types/ to calculate the rough tx profile. :return: tx"""
        <|body_1|>

    def get_interp_profile(self):
        """interpolate the few points provided by the manufacturer onto a finer wavelength grid."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.wl_centre = wl_centre
        self.fwhm = fwhm
        self.peak_tx = peak_tx
        self.type = type
        self._tx_values = np.array([1e-05, 0.0001, 0.001, 0.01, 0.1, 0.5, 0.9]) * self.peak_tx
        self.fwhm_coeffs = {2: np.array([45, 15, 6.3, 3.5, 2, 1, 0.5]), 3: np.array([15, 5.4, 3.2, 2.2, 1.5, 1, 0.65]), 4: np.array([12, 4.25, 2.25, 1.8, 1.3, 1, 0.8])}
        wl, tx = self._get_semicustom_tx()
        super().__init__(wl, tx, ref_index)
<|end_body_0|>

<|body_start_1|>
        tx = np.concatenate([self._tx_values, [1 * self.peak_tx], self._tx_values[::-1]])
        wl = np.zeros_like(tx)
        for idx in range(len(self._tx_values)):
            half_width = self.fwhm_coeffs[self.type][idx] * self.fwhm / 2
            wl[idx] = self.wl_centre - half_width
            wl[-(idx + 1)] = self.wl_centre + half_width
        wl[len(self._tx_values)] = self.wl_centre
        return (wl, tx)
<|end_body_1|>

<|body_start_2|>
        no_fwhm = 10
        wl_lo = self.wl_centre - no_fwhm * self.fwhm
        wl_hi = self.wl_centre + no_fwhm * self.fwhm
        interp_wavelength_axis = np.linspace(wl_lo, wl_hi, 100)
        f = scipy.interpolate.InterpolatedUnivariateSpline(self.wls, self.tx, k=1, ext='zeros')
        interp_transmission = f(interp_wavelength_axis)
        return (interp_wavelength_axis, interp_transmission)
<|end_body_2|>

Solution:
class AndoverSemiCustomFilter:
    def __init__(self, wl_centre, fwhm, peak_tx, type, ref_index):
        """set the normalised transmission values, and the corresponding fwhm coefficients based on info from: https://www.andovercorp.com/technical/bandpass-filters-fundamentals/filters-types/ :param wl_centre: centre wavelength [m] :param fwhm: full width half max. wavelength [m] :param peak_tx: transmission at centre, as a fraction. :param type: type of semi-custom filters, according to Andover, basically the number of cavities. :param ref_index"""
        self.wl_centre = wl_centre
        self.fwhm = fwhm
        self.peak_tx = peak_tx
        self.type = type
        self._tx_values = np.array([1e-05, 0.0001, 0.001, 0.01, 0.1, 0.5, 0.9]) * self.peak_tx
        self.fwhm_coeffs = {2: np.array([45, 15, 6.3, 3.5, 2, 1, 0.5]), 3: np.array([15, 5.4, 3.2, 2.2, 1.5, 1, 0.65]), 4: np.array([12, 4.25, 2.25, 1.8, 1.3, 1, 0.8])}
        wl, tx = self._get_semicustom_tx()
        super().__init__(wl, tx, ref_index)

    def _get_semicustom_tx(self):
        """use the info on the filters profiles from https://www.andovercorp.com/technical/bandpass-filters-fundamentals/filters-types/ to calculate the rough tx profile. :return: tx"""
        tx = np.concatenate([self._tx_values, [1 * self.peak_tx], self._tx_values[::-1]])
        wl = np.zeros_like(tx)
        for idx in range(len(self._tx_values)):
            half_width = self.fwhm_coeffs[self.type][idx] * self.fwhm / 2
            wl[idx] = self.wl_centre - half_width
            wl[-(idx + 1)] = self.wl_centre + half_width
        wl[len(self._tx_values)] = self.wl_centre
        return (wl, tx)

    def get_interp_profile(self):
        """interpolate the few points provided by the manufacturer onto a finer wavelength grid."""
        no_fwhm = 10
        wl_lo = self.wl_centre - no_fwhm * self.fwhm
        wl_hi = self.wl_centre + no_fwhm * self.fwhm
        interp_wavelength_axis = np.linspace(wl_lo, wl_hi, 100)
        f = scipy.interpolate.InterpolatedUnivariateSpline(self.wls, self.tx, k=1, ext='zeros')
        interp_transmission = f(interp_wavelength_axis)
        return (interp_wavelength_axis, interp_transmission)


handle

Prompt:
Implement the Python class `UserGetInteractorImpl` described below.

Class description:
UserGetInteractorImpl

Method signatures and docstrings:
- def handle(self, id: int=0) -> UserOutputData | list[UserOutputData] | None: handle
- def __find_users(self) -> list[UserOutputData] | None: __find_users
- def __find_user_by_id(self, id: int) -> UserOutputData | None: __find_user_by_id

Implement the Python class `UserGetInteractorImpl` described below.

Class description:
UserGetInteractorImpl

Method signatures and docstrings:
- def handle(self, id: int=0) -> UserOutputData | list[UserOutputData] | None: handle
- def __find_users(self) -> list[UserOutputData] | None: __find_users
- def __find_user_by_id(self, id: int) -> UserOutputData | None: __find_user_by_id

<|skeleton|>
class UserGetInteractorImpl:
    """UserGetInteractorImpl"""

    def handle(self, id: int=0) -> UserOutputData | list[UserOutputData] | None:
        """handle"""
        <|body_0|>

    def __find_users(self) -> list[UserOutputData] | None:
        """__find_users"""
        <|body_1|>

    def __find_user_by_id(self, id: int) -> UserOutputData | None:
        """__find_user_by_id"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        response: UserOutputData | list[UserOutputData] | None
        if id:
            response = self.__find_user_by_id(id)
        else:
            response = self.__find_users()
        return response
<|end_body_0|>

<|body_start_1|>
        data = self.repository.find_users()
        response = []
        for d in data:
            response.append(parse_obj_as(UserOutputData, d))
        return response
<|end_body_1|>

<|body_start_2|>
        user = UserBase(id=id)
        data = self.repository.find_user_by_id(user.id)
        return parse_obj_as(UserOutputData, data)
<|end_body_2|>

Solution:
class UserGetInteractorImpl:
    """UserGetInteractorImpl"""

    def handle(self, id: int=0) -> UserOutputData | list[UserOutputData] | None:
        """handle"""
        response: UserOutputData | list[UserOutputData] | None
        if id:
            response = self.__find_user_by_id(id)
        else:
            response = self.__find_users()
        return response

    def __find_users(self) -> list[UserOutputData] | None:
        """__find_users"""
        data = self.repository.find_users()
        response = []
        for d in data:
            response.append(parse_obj_as(UserOutputData, d))
        return response

    def __find_user_by_id(self, id: int) -> UserOutputData | None:
        """__find_user_by_id"""
        user = UserBase(id=id)
        data = self.repository.find_user_by_id(user.id)
        return parse_obj_as(UserOutputData, data)


evaluate

Prompt:
Implement the Python class `Evaluator` described below.

Class description:
evaluate model and calculate metrics

Method signatures and docstrings:
- def __init__(self, model, perturb_generator, data_name='Data', folder_path='results/Evaluation/'): Args: model: object of Model class perturb_generator: object of PerturbationGenerator data_name: string folder_path: string, determines path where Evaluator is stored
- def reset(self): Delete old evaluator object (and its storage) and replaces it with a newly initialized one. Delete also logits in model. Careful: No save or backup is happening, so be sure nothing is lost.
- def evaluate(self, data, test_metric, perturb_type, epsilon, from_cache=True, to_cache=True, save_to_file=False, bins_calibration=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], **params): evaluate metrics Args: data: tf.data.Dataset object of (x_data, y_labels), prepared with batch_size, shuffle etc. test_metric: metric from utilsevaluation.measures perturb_type: string, in case dataset should be perturbed epsilon: int, level of perturbation from_cache: if False the logits and labels are calculated via the Tensorflow graph, if True the logits are restored from cache to_cache: bool, if True the logits and labels are stored to cache save_to_file: bool, if True the logits and labels are saved to file bins_calibration: list of floats, limits of calibration bins
- def save(self, folder_path=None): Method to save evaluation storage
- def load(self, folder_path=None): Method to load evaluation storage

Implement the Python class `Evaluator` described below.

Class description:
evaluate model and calculate metrics

Method signatures and docstrings:
- def __init__(self, model, perturb_generator, data_name='Data', folder_path='results/Evaluation/'): Args: model: object of Model class perturb_generator: object of PerturbationGenerator data_name: string folder_path: string, determines path where Evaluator is stored
- def reset(self): Delete old evaluator object (and its storage) and replaces it with a newly initialized one. Delete also logits in model. Careful: No save or backup is happening, so be sure nothing is lost.
- def evaluate(self, data, test_metric, perturb_type, epsilon, from_cache=True, to_cache=True, save_to_file=False, bins_calibration=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], **params): evaluate metrics Args: data: tf.data.Dataset object of (x_data, y_labels), prepared with batch_size, shuffle etc. test_metric: metric from utilsevaluation.measures perturb_type: string, in case dataset should be perturbed epsilon: int, level of perturbation from_cache: if False the logits and labels are calculated via the Tensorflow graph, if True the logits are restored from cache to_cache: bool, if True the logits and labels are stored to cache save_to_file: bool, if True the logits and labels are saved to file bins_calibration: list of floats, limits of calibration bins
- def save(self, folder_path=None): Method to save evaluation storage
- def load(self, folder_path=None): Method to load evaluation storage

<|skeleton|>
class Evaluator:
    """evaluate model and calculate metrics"""

    def __init__(self, model, perturb_generator, data_name='Data', folder_path='results/Evaluation/'):
        """Args: model: object of Model class perturb_generator: object of PerturbationGenerator data_name: string folder_path: string, determines path where Evaluator is stored"""
        <|body_0|>

    def reset(self):
        """Delete old evaluator object (and its storage) and replaces it with a newly initialized one. Delete also logits in model. Careful: No save or backup is happening, so be sure nothing is lost."""
        <|body_1|>

    def evaluate(self, data, test_metric, perturb_type, epsilon, from_cache=True, to_cache=True, save_to_file=False, bins_calibration=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], **params):
        """evaluate metrics Args: data: tf.data.Dataset object of (x_data, y_labels), prepared with batch_size, shuffle etc. test_metric: metric from utilsevaluation.measures perturb_type: string, in case dataset should be perturbed epsilon: int, level of perturbation from_cache: if False the logits and labels are calculated via the Tensorflow graph, if True the logits are restored from cache to_cache: bool, if True the logits and labels are stored to cache save_to_file: bool, if True the logits and labels are saved to file bins_calibration: list of floats, limits of calibration bins"""
        <|body_2|>

    def save(self, folder_path=None):
        """Method to save evaluation storage"""
        <|body_3|>

    def load(self, folder_path=None):
        """Method to load evaluation storage"""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        self.evaluator = Metrics_Evaluator()
        self.model = model
        self.perturb_generator = perturb_generator
        self.folder_path = os.path.join(folder_path, data_name, os.path.split(os.path.split(self.model.save_path)[0])[1])
<|end_body_0|>

<|body_start_1|>
        self.evaluator = Metrics_Evaluator()
        self.model.logits_test = {}
        self.model.labels_test = {}
<|end_body_1|>

<|body_start_2|>
        logits, labels = self.model.logits(data, self.perturb_generator, perturb_type, epsilon, from_cache=from_cache, to_cache=to_cache, save_to_file=save_to_file)
        test_values = test_metric(logits, labels, bins_calibration=bins_calibration)
        perturb_type = perturb_type.replace('/', '_')
        self.evaluator.add(test_metric.__name__, perturb_type, epsilon, test_values)
<|end_body_2|>

<|body_start_3|>
        if folder_path is None:
            folder_path = self.folder_path
        self.evaluator.save(folder_path)
<|end_body_3|>

<|body_start_4|>
        if folder_path is None:
            folder_path = self.folder_path
        self.evaluator.load(folder_path)
<|end_body_4|>

Solution:
class Evaluator:
    """evaluate model and calculate metrics"""

    def __init__(self, model, perturb_generator, data_name='Data', folder_path='results/Evaluation/'):
        """Args: model: object of Model class perturb_generator: object of PerturbationGenerator data_name: string folder_path: string, determines path where Evaluator is stored"""
        self.evaluator = Metrics_Evaluator()
        self.model = model
        self.perturb_generator = perturb_generator
        self.folder_path = os.path.join(folder_path, data_name, os.path.split(os.path.split(self.model.save_path)[0])[1])

    def reset(self):
        """Delete old evaluator object (and its storage) and replaces it with a newly initialized one. Delete also logits in model. Careful: No save or backup is happening, so be sure nothing is lost."""
        self.evaluator = Metrics_Evaluator()
        self.model.logits_test = {}
        self.model.labels_test = {}

    def evaluate(self, data, test_metric, perturb_type, epsilon, from_cache=True, to_cache=True, save_to_file=False, bins_calibration=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], **params):
        """evaluate metrics Args: data: tf.data.Dataset object of (x_data, y_labels), prepared with batch_size, shuffle etc. test_metric: metric from utilsevaluation.measures perturb_type: string, in case dataset should be perturbed epsilon: int, level of perturbation from_cache: if False the logits and labels are calculated via the Tensorflow graph, if True the logits are restored from cache to_cache: bool, if True the logits and labels are stored to cache save_to_file: bool, if True the logits and labels are saved to file bins_calibration: list of floats, limits of calibration bins"""
        logits, labels = self.model.logits(data, self.perturb_generator, perturb_type, epsilon, from_cache=from_cache, to_cache=to_cache, save_to_file=save_to_file)
        test_values = test_metric(logits, labels, bins_calibration=bins_calibration)
        perturb_type = perturb_type.replace('/', '_')
        self.evaluator.add(test_metric.__name__, perturb_type, epsilon, test_values)

    def save(self, folder_path=None):
        """Method to save evaluation storage"""
        if folder_path is None:
            folder_path = self.folder_path
        self.evaluator.save(folder_path)

    def load(self, folder_path=None):
        """Method to load evaluation storage"""
        if folder_path is None:
            folder_path = self.folder_path
        self.evaluator.load(folder_path)


hammingDistance

Prompt:
Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def hammingDistance(self, x, y): :type x: int :type y: int :rtype: int 移位实现计数 我们可以不断地检查 s 的最低位，如果最低位为 1，那么令计数器加一 然后我们令 s 整体右移一位这样 s 的最低位将被舍去，原本的次低位就变成了新的最低位。 我们重复这个过程直到 s=0 为止。这样计数器中就累计了 s 的二进制表示中 1 的数量。 时间击败95.89%，内存击败43.84%
- def hammingDistance1(self, x, y): :type x: int :type y: int :rtype: int Brian Kernighan 算法 在方法二中，对于 s=(10001100)2的情况，我们需要循环右移 8 次才能得到答案。 而实际上如果我们可以跳过两个 1 之间的 0，直接对 1 进行计数，那么就只需要循环 3 次即可。 我们可以使用 Brian Kernighan 算法进行优化， 具体地，该算法可以被描述为这样一个结论：记 f(x) 表示 x 和 x−1 进行与运算所得的结果（即 f(x)=x & (x−1)） 那么 f(x) 恰为 x 删去其二进制表示中最右侧的 1 的结果。 基于该算法，当我们计算出 s=x⊕y，只需要不断让 s=f(s)，直到 s=0 即可。 这样每循环一次，s 都会删去其二进制表示中最右侧的 1，最终循环的次数即为 s 的二进制表示中 1 的数量。 时间击败87.51%，内存击败8.22%

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def hammingDistance(self, x, y): :type x: int :type y: int :rtype: int 移位实现计数 我们可以不断地检查 s 的最低位，如果最低位为 1，那么令计数器加一 然后我们令 s 整体右移一位这样 s 的最低位将被舍去，原本的次低位就变成了新的最低位。 我们重复这个过程直到 s=0 为止。这样计数器中就累计了 s 的二进制表示中 1 的数量。 时间击败95.89%，内存击败43.84%
- def hammingDistance1(self, x, y): :type x: int :type y: int :rtype: int Brian Kernighan 算法 在方法二中，对于 s=(10001100)2的情况，我们需要循环右移 8 次才能得到答案。 而实际上如果我们可以跳过两个 1 之间的 0，直接对 1 进行计数，那么就只需要循环 3 次即可。 我们可以使用 Brian Kernighan 算法进行优化， 具体地，该算法可以被描述为这样一个结论：记 f(x) 表示 x 和 x−1 进行与运算所得的结果（即 f(x)=x & (x−1)） 那么 f(x) 恰为 x 删去其二进制表示中最右侧的 1 的结果。 基于该算法，当我们计算出 s=x⊕y，只需要不断让 s=f(s)，直到 s=0 即可。 这样每循环一次，s 都会删去其二进制表示中最右侧的 1，最终循环的次数即为 s 的二进制表示中 1 的数量。 时间击败87.51%，内存击败8.22%

<|skeleton|>
class Solution:

    def hammingDistance(self, x, y):
        """:type x: int :type y: int :rtype: int 移位实现计数 我们可以不断地检查 s 的最低位，如果最低位为 1，那么令计数器加一 然后我们令 s 整体右移一位这样 s 的最低位将被舍去，原本的次低位就变成了新的最低位。 我们重复这个过程直到 s=0 为止。这样计数器中就累计了 s 的二进制表示中 1 的数量。 时间击败95.89%，内存击败43.84%"""
        <|body_0|>

    def hammingDistance1(self, x, y):
        """:type x: int :type y: int :rtype: int Brian Kernighan 算法 在方法二中，对于 s=(10001100)2的情况，我们需要循环右移 8 次才能得到答案。 而实际上如果我们可以跳过两个 1 之间的 0，直接对 1 进行计数，那么就只需要循环 3 次即可。 我们可以使用 Brian Kernighan 算法进行优化， 具体地，该算法可以被描述为这样一个结论：记 f(x) 表示 x 和 x−1 进行与运算所得的结果（即 f(x)=x & (x−1)） 那么 f(x) 恰为 x 删去其二进制表示中最右侧的 1 的结果。 基于该算法，当我们计算出 s=x⊕y，只需要不断让 s=f(s)，直到 s=0 即可。 这样每循环一次，s 都会删去其二进制表示中最右侧的 1，最终循环的次数即为 s 的二进制表示中 1 的数量。 时间击败87.51%，内存击败8.22%"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        n, result = (x ^ y, 0)
        while n:
            result += 1 if n & 1 else 0
            n = n >> 1
        return result
<|end_body_0|>

<|body_start_1|>
        n, result = (x ^ y, 0)
        while n:
            n = n & n - 1
            result += 1
        return result
<|end_body_1|>

Solution:
class Solution:
    def hammingDistance(self, x, y):
        """:type x: int :type y: int :rtype: int 移位实现计数 我们可以不断地检查 s 的最低位，如果最低位为 1，那么令计数器加一 然后我们令 s 整体右移一位这样 s 的最低位将被舍去，原本的次低位就变成了新的最低位。 我们重复这个过程直到 s=0 为止。这样计数器中就累计了 s 的二进制表示中 1 的数量。 时间击败95.89%，内存击败43.84%"""
        n, result = (x ^ y, 0)
        while n:
            result += 1 if n & 1 else 0
            n = n >> 1
        return result

    def hammingDistance1(self, x, y):
        """:type x: int :type y: int :rtype: int Brian Kernighan 算法 在方法二中，对于 s=(10001100)2的情况，我们需要循环右移 8 次才能得到答案。 而实际上如果我们可以跳过两个 1 之间的 0，直接对 1 进行计数，那么就只需要循环 3 次即可。 我们可以使用 Brian Kernighan 算法进行优化， 具体地，该算法可以被描述为这样一个结论：记 f(x) 表示 x 和 x−1 进行与运算所得的结果（即 f(x)=x & (x−1)） 那么 f(x) 恰为 x 删去其二进制表示中最右侧的 1 的结果。 基于该算法，当我们计算出 s=x⊕y，只需要不断让 s=f(s)，直到 s=0 即可。 这样每循环一次，s 都会删去其二进制表示中最右侧的 1，最终循环的次数即为 s 的二进制表示中 1 的数量。 时间击败87.51%，内存击败8.22%"""
        n, result = (x ^ y, 0)
        while n:
            n = n & n - 1
            result += 1
        return result


Prompt:
Implement the Python class `transform` described below.

Class description:
Class for defining empirical normal score transform based on data. Attributes: z: ordered and processed data cdf: cumulative distribution function y: inverse cdf

Method signatures and docstrings:
- def __init__(self, z): Constructs empirical normal score transform for a given data. Arguments: z: array containing measurements
- def direct(self, z): Returns normal score transformed data
- def back(self, y): Inverse normal score transform

Implement the Python class `transform` described below.

Class description:
Class for defining empirical normal score transform based on data. Attributes: z: ordered and processed data cdf: cumulative distribution function y: inverse cdf

Method signatures and docstrings:
- def __init__(self, z): Constructs empirical normal score transform for a given data. Arguments: z: array containing measurements
- def direct(self, z): Returns normal score transformed data
- def back(self, y): Inverse normal score transform

<|skeleton|>
class transform:
    """Class for defining empirical normal score transform based on data. Attributes: z: ordered and processed data cdf: cumulative distribution function y: inverse cdf"""

    def __init__(self, z):
        """Constructs empirical normal score transform for a given data. Arguments: z: array containing measurements"""
        <|body_0|>

    def direct(self, z):
        """Returns normal score transformed data"""
        <|body_1|>

    def back(self, y):
        """Inverse normal score transform"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        ordered_data = np.sort(z)
        extension = (ordered_data[1] - ordered_data[0]) / 2
        ordered_data = np.insert(ordered_data, 0, ordered_data[0] - extension)
        ordered_data = np.append(ordered_data, ordered_data[-1] + extension)
        n = ordered_data.shape[0]
        F = np.arange(n + 1) / n
        ordered, i = np.unique(ordered_data, return_index=True)
        F = F[i]
        self.z = ordered
        self.cdf = F
        self.y = norm.ppf(F)
<|end_body_0|>

<|body_start_1|>
        u = np.interp(z, self.z, self.cdf)
        y = norm.ppf(u)
        return y
<|end_body_1|>

<|body_start_2|>
        u = norm.cdf(y)
        z = np.interp(u, self.cdf, self.z)
        return z
<|end_body_2|>

Solution:
class transform:
    """Class for defining empirical normal score transform based on data. Attributes: z: ordered and processed data cdf: cumulative distribution function y: inverse cdf"""

    def __init__(self, z):
        """Constructs empirical normal score transform for a given data. Arguments: z: array containing measurements"""
        ordered_data = np.sort(z)
        extension = (ordered_data[1] - ordered_data[0]) / 2
        ordered_data = np.insert(ordered_data, 0, ordered_data[0] - extension)
        ordered_data = np.append(ordered_data, ordered_data[-1] + extension)
        n = ordered_data.shape[0]
        F = np.arange(n + 1) / n
        ordered, i = np.unique(ordered_data, return_index=True)
        F = F[i]
        self.z = ordered
        self.cdf = F
        self.y = norm.ppf(F)

    def direct(self, z):
        """Returns normal score transformed data"""
        u = np.interp(z, self.z, self.cdf)
        y = norm.ppf(u)
        return y

    def back(self, y):
        """Inverse normal score transform"""
        u = norm.cdf(y)
        z = np.interp(u, self.cdf, self.z)
        return z