code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import torch
from torch import nn
class SCREAMING_SNAKE_CASE__ (nn.Module ):
def __init__( self : List[Any] , __lowerCamelCase : Tuple , __lowerCamelCase : str , __lowerCamelCase : List[Any] , __lowerCamelCase : List[Any] , ... | 615 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ : List[str] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxCon... | 615 | 1 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoin... | 615 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__magic_name__ : Any = {
"""configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_... | 615 | 1 |
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_util... | 615 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ : Optional[Any] = {
"""configuration_informer""": [
"""INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 615 | 1 |
from sklearn.metrics import recall_score
import datasets
__magic_name__ : Optional[Any] = """
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives a... | 615 |
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaT... | 615 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class SCREAMING_SNAKE_CASE__ (_a ):
@staticmethod
@abstractmethod
def A__ ( __lowerCamelCase : ArgumentParser ):
"""simple docstring"""
raise NotImplemented... | 615 |
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelFo... | 615 | 1 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError("""To use the rich extension, install rich with `pip install rich`""")
| 615 |
import numpy as np
def a_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
lowerCAmelCase__ = int(np.ceil((x_end - xa) / h ) )
lowerCAmelCase__ = np.zeros((n + 1,) )
lowerCAmelCase__ = ... | 615 | 1 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
__magic_name__ : Optional[int] = datasets.logging.get_logger(__name__)
__magic_name__ : Optional[int] = ""... | 615 |
from scipy.stats import pearsonr
import datasets
__magic_name__ : Union[str, Any] = """
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies ... | 615 | 1 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceC... | 615 |
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
__magic_name__ : List[str] = logging.get_logger(__name__) # pylint: disable=invalid-name
class ... | 615 | 1 |
def a_ ( __lowerCAmelCase ):
if len(__lowerCAmelCase ) <= 1:
return lst
lowerCAmelCase__ = 1
while i < len(__lowerCAmelCase ):
if lst[i - 1] <= lst[i]:
i += 1
else:
lowerCAmelCase__ , lowerCAmelCase__ = lst[i], lst[... | 615 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transform... | 615 | 1 |
import argparse
import os
import re
import packaging.version
__magic_name__ : int = """examples/"""
__magic_name__ : Any = {
"""examples""": (re.compile(R"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
"""init""":... | 615 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from t... | 615 | 1 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation imp... | 615 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__magic_name__ : Optional[int] = get_te... | 615 | 1 |
import unittest
from knapsack import knapsack as k
class SCREAMING_SNAKE_CASE__ (unittest.TestCase ):
def A__ ( self : List[Any] ):
"""simple docstring"""
lowerCAmelCase__ = 0
lowerCAmelCase__ = [0]
... | 615 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__magic_name__ : Any = TypeVar("""KEY""")
__magic_name__ : Dict = TypeVar("""VAL""")
@dataclass(frozen=_a , slots=_a )
class SCREAMI... | 615 | 1 |
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils i... | 615 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaToken... | 615 | 1 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__magic_name__ : Dict = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_copies # noqa: E40... | 615 |
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
__magic_name__ : List[Any] = """%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input... | 615 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ : List[Any] = logging.get_logger(__name__)
__magic_name__ : str = {
"""RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json""",
... | 615 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ : Optional[int] = {
"""configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
"""token... | 615 | 1 |
import math
from datetime import datetime, timedelta
def a_ ( __lowerCAmelCase ):
lowerCAmelCase__ = year % 19
lowerCAmelCase__ = year % 4
lowerCAmelCase__ = year % 7
lowerCAmelCase__ = math.floor(year / 1_00 )
lowerCAmelCa... | 615 |
import logging
from transformers.configuration_utils import PretrainedConfig
__magic_name__ : Tuple = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE__ (_a ):
lowercase_ : Tuple = "masked_bert"
def __init__( self : List[Any] , _... | 615 | 1 |
class SCREAMING_SNAKE_CASE__ :
def __init__( self : str ):
"""simple docstring"""
lowerCAmelCase__ = 0
lowerCAmelCase__ = 0
lowerCAmelCase__ = {}
def A__ ( self : List[str] , ... | 615 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl ... | 615 | 1 |
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_... | 615 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ : Union[str, Any] = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltCLIPC... | 615 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ : Union[str, Any] = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltCLIPC... | 615 |
from __future__ import annotations
def a_ ( __lowerCAmelCase ):
if not nums:
return 0
lowerCAmelCase__ = nums[0]
lowerCAmelCase__ = 0
for num in nums[1:]:
lowerCAmelCase__ , lowerCAmelCase__ = (
max_excluding + num,
... | 615 | 1 |
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaT... | 615 |
from __future__ import annotations
def a_ ( __lowerCAmelCase , __lowerCAmelCase = None ):
lowerCAmelCase__ = word_bank or []
# create a table
lowerCAmelCase__ = len(__lowerCAmelCase ) + 1
lowerCAmelCase__ = []
for _ in range(__lowerCA... | 615 | 1 |
from collections import deque
class SCREAMING_SNAKE_CASE__ :
def __init__( self : int , __lowerCamelCase : str , __lowerCamelCase : int , __lowerCamelCase : int ):
"""simple docstring"""
lowerCAmelCase__ = ... | 615 |
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenize... | 615 | 1 |
from __future__ import annotations
from typing import TypedDict
class SCREAMING_SNAKE_CASE__ (_a ):
lowercase_ : str
lowercase_ : int
def a_ ( __lowerCAmelCase ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError(... | 615 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
__magic_name__ : List[Any] = logging.get_logger(__name__)
def a_ ( __lowerCAmelCase , __lowerCAmelC... | 615 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalD... | 615 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ : List[str] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxCon... | 615 | 1 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from t... | 615 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__magic_name__ : Any = {
"""configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_... | 615 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
__magic_name__ : Tuple = {
"""configuration_audio_spectrogram_transformer""": [
"""AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIV... | 615 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ : Optional[Any] = {
"""configuration_informer""": [
"""INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 615 | 1 |
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
__magic_name__ : Li... | 615 |
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaT... | 615 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimen... | 615 |
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelFo... | 615 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ : List[Any] = {
"""configuration_lxmert""": ["""LXMERT_PRETRAINED_CONFIG_ARCHIV... | 615 |
import numpy as np
def a_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
lowerCAmelCase__ = int(np.ceil((x_end - xa) / h ) )
lowerCAmelCase__ = np.zeros((n + 1,) )
lowerCAmelCase__ = ... | 615 | 1 |
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__magic_name__ : List[str] = logging.get_logger(__name__)
__magic_name__ : Tuple = {"""vocab_file""": """vocab.json"""}
__magic_n... | 615 |
from scipy.stats import pearsonr
import datasets
__magic_name__ : Union[str, Any] = """
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies ... | 615 | 1 |
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 615 |
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
__magic_name__ : List[str] = logging.get_logger(__name__) # pylint: disable=invalid-name
class ... | 615 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ : Optional[int] = logging.get_logger(__name__)
__magic_name__... | 615 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transform... | 615 | 1 |
from collections.abc import Callable
def a_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
lowerCAmelCase__ = a
lowerCAmelCase__ = b
if function(__lowerCAmelCase ) == 0: # one of the a or b is a root for the function
return a
elif f... | 615 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from t... | 615 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ : Tuple = {"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}
try:
if not is_torch_available():
... | 615 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__magic_name__ : Optional[int] = get_te... | 615 | 1 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common i... | 615 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__magic_name__ : Any = TypeVar("""KEY""")
__magic_name__ : Dict = TypeVar("""VAL""")
@dataclass(frozen=_a , slots=_a )
class SCREAMI... | 615 | 1 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class SCREAMING_SNAKE_CASE__ :
lowercase_ : int
lowercase_ : Node | None = None
lowerc... | 615 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaToken... | 615 | 1 |
import numpy as np
__magic_name__ : List[str] = [
["""a""", """b""", """c""", """d""", """e"""],
["""f""", """g""", """h""", """i""", """k"""],
["""l""", """m""", """n""", """o""", """p"""],
["""q""", """r""", """s""", """t""", """u"""],
["""v""", """w""", """x""", """y... | 615 |
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
__magic_name__ : List[Any] = """%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input... | 615 | 1 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class SCREAMING_SNAKE_CASE__ :
@property
def A__ ... | 615 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ : Optional[int] = {
"""configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
"""token... | 615 | 1 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow... | 615 |
import logging
from transformers.configuration_utils import PretrainedConfig
__magic_name__ : Tuple = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE__ (_a ):
lowercase_ : Tuple = "masked_bert"
def __init__( self : List[Any] , _... | 615 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class SCREAMING_SNAKE_CASE__ (_a ):
lowercase_ :... | 615 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl ... | 615 | 1 |
import string
def a_ ( __lowerCAmelCase ):
for key in range(len(string.ascii_uppercase ) ):
lowerCAmelCase__ = ''''''
for symbol in message:
if symbol in string.ascii_uppercase:
lowerCAmelCase__ = string.ascii_uppercase.find(__lowerCAm... | 615 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ : Union[str, Any] = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltCLIPC... | 615 | 1 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class SCREAMING_SNAKE_CASE__ (ctypes.Structure ):
# _fields is a specific attr expected by ctypes
lowercase_ :... | 615 |
from __future__ import annotations
def a_ ( __lowerCAmelCase ):
if not nums:
return 0
lowerCAmelCase__ = nums[0]
lowerCAmelCase__ = 0
for num in nums[1:]:
lowerCAmelCase__ , lowerCAmelCase__ = (
max_excluding + num,
... | 615 | 1 |
from __future__ import annotations
def a_ ( __lowerCAmelCase , __lowerCAmelCase ):
lowerCAmelCase__ = []
lowerCAmelCase__ = []
lowerCAmelCase__ = 0
lowerCAmelCase__ = sum(__lowerCAmelCase )
create_state_space_tree(__lowerC... | 615 |
from __future__ import annotations
def a_ ( __lowerCAmelCase , __lowerCAmelCase = None ):
lowerCAmelCase__ = word_bank or []
# create a table
lowerCAmelCase__ = len(__lowerCAmelCase ) + 1
lowerCAmelCase__ = []
for _ in range(__lowerCA... | 615 | 1 |
from torch import nn
class SCREAMING_SNAKE_CASE__ (nn.Module ):
def __init__( self : List[str] , __lowerCamelCase : int , __lowerCamelCase : Any ):
"""simple docstring"""
super().__init__()
lowerCAmelCase__ = ... | 615 |
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenize... | 615 | 1 |
import numpy as np
def a_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
lowerCAmelCase__ = int(np.ceil((x_end - xa) / h ) )
lowerCAmelCase__ = np.zeros((n + 1,) )
lowerCAmelCase__ = ... | 615 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
__magic_name__ : List[Any] = logging.get_logger(__name__)
def a_ ( __lowerCAmelCase , __lowerCAmelC... | 615 | 1 |
def a_ ( ):
for n in range(1 , 1_00_00_00 ):
yield n * (n + 1) // 2
def a_ ( __lowerCAmelCase ):
lowerCAmelCase__ = 1
lowerCAmelCase__ = 2
while i * i <= n:
lowerCAmelCase__ = 0
while n % i == 0:
n //= i
... | 615 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ : List[str] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxCon... | 615 | 1 |
from __future__ import annotations
from collections.abc import Iterator
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Any , __lowerCamelCase : int ):
"""simple docstring"""
lowerCAmelCase__ = value
lowerCAmelCase... | 615 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__magic_name__ : Any = {
"""configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_... | 615 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__magic_name__ : List[Any] = {
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCH... | 615 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ : Optional[Any] = {
"""configuration_informer""": [
"""INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 615 | 1 |
def a_ ( __lowerCAmelCase ):
lowerCAmelCase__ = [0 for i in range(len(__lowerCAmelCase ) )]
# initialize interval's left pointer and right pointer
lowerCAmelCase__ , lowerCAmelCase__ = 0, 0
for i in range(1 , len(__lowerCAmelCase ) )... | 615 |
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaT... | 615 | 1 |
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids... | 615 |
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelFo... | 615 | 1 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
__magic_name__ : Any = {
"""config""": [
"""EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
"""OnnxConfig""",
"""OnnxConfigWithPast""",
"""OnnxSeq2SeqConfigWithPast""",
"""PatchingSpec"""... | 615 |
import numpy as np
def a_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
lowerCAmelCase__ = int(np.ceil((x_end - xa) / h ) )
lowerCAmelCase__ = np.zeros((n + 1,) )
lowerCAmelCase__ = ... | 615 | 1 |
from __future__ import annotations
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Dict , __lowerCamelCase : int ):
"""simple docstring"""
lowerCAmelCase__ = order
# a_{0} ... a_{k}
lowerCAmelCase__ ... | 615 |
from scipy.stats import pearsonr
import datasets
__magic_name__ : Union[str, Any] = """
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies ... | 615 | 1 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
__magic_name__ : List[Any] = logging.get_logger(__name__)
def a_ ( __lowerCAmelCase , __lowerCAmelC... | 615 |
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
__magic_name__ : List[str] = logging.get_logger(__name__) # pylint: disable=invalid-name
class ... | 615 | 1 |
import copy
import re
class SCREAMING_SNAKE_CASE__ :
lowercase_ : Optional[Any] = "hp"
lowercase_ : Any = {}
lowercase_ : Dict = None
@classmethod
def A__ ( cls : Tuple , __lowerCamelCase : Tuple , ... | 615 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transform... | 615 | 1 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def a_ ( __lowerCAmelCase ):
lowerCAmelCase__ = [
'''decoder.version''',
'''decoder.output_projection.weight''',
'''_f... | 615 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from t... | 615 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokeniza... | 615 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__magic_name__ : Optional[int] = get_te... | 615 | 1 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__magic_name__ : Tuple = get_tests_dir("""fix... | 615 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__magic_name__ : Any = TypeVar("""KEY""")
__magic_name__ : Dict = TypeVar("""VAL""")
@dataclass(frozen=_a , slots=_a )
class SCREAMI... | 615 | 1 |
from __future__ import annotations
def a_ ( __lowerCAmelCase ):
lowerCAmelCase__ = 2
lowerCAmelCase__ = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__lowerCAmelCase )
if n > 1:
factors.append(__lo... | 615 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaToken... | 615 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__magic_name__ : List[Any] = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
__magic_name__ : Optional[Any] ... | 615 |
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
__magic_name__ : List[Any] = """%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input... | 615 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.toke... | 615 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ : Optional[int] = {
"""configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
"""token... | 615 | 1 |
from __future__ import annotations
def a_ ( __lowerCAmelCase , __lowerCAmelCase ):
lowerCAmelCase__ , lowerCAmelCase__ = set(__lowerCAmelCase ), [start]
while stack:
lowerCAmelCase__ = stack.pop()
explored.add(__lowerCAmelCase )
# ... | 615 |
import logging
from transformers.configuration_utils import PretrainedConfig
__magic_name__ : Tuple = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE__ (_a ):
lowercase_ : Tuple = "masked_bert"
def __init__( self : List[Any] , _... | 615 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless req... | 615 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl ... | 615 | 1 |
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ : Optional[Any] = logging.get_logger(__name__)
... | 615 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ : Union[str, Any] = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltCLIPC... | 615 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcesso... | 615 |
from __future__ import annotations
def a_ ( __lowerCAmelCase ):
if not nums:
return 0
lowerCAmelCase__ = nums[0]
lowerCAmelCase__ = 0
for num in nums[1:]:
lowerCAmelCase__ , lowerCAmelCase__ = (
max_excluding + num,
... | 615 | 1 |
from __future__ import annotations
def a_ ( __lowerCAmelCase , __lowerCAmelCase ):
lowerCAmelCase__ = []
create_all_state(1 , __lowerCAmelCase , __lowerCAmelCase , [] , __lowerCAmelCase )
return result
def a_ ( __lowerCAmelCase , __lowerCAmelCase , ... | 615 |
from __future__ import annotations
def a_ ( __lowerCAmelCase , __lowerCAmelCase = None ):
lowerCAmelCase__ = word_bank or []
# create a table
lowerCAmelCase__ = len(__lowerCAmelCase ) + 1
lowerCAmelCase__ = []
for _ in range(__lowerCA... | 615 | 1 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
__magic_name__ : Any = get_logger(__name__)
class SCREAMING_SNAKE_CASE__ (enum.Enum ):
lowercase_ : Tuple = "all_checks"
... | 615 |
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenize... | 615 | 1 |
from __future__ import annotations
from collections import Counter
from random import random
class SCREAMING_SNAKE_CASE__ :
def __init__( self : int ):
"""simple docstring"""
lowerCAmelCase__ = {}
def A__ ( self : ... | 615 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
__magic_name__ : List[Any] = logging.get_logger(__name__)
def a_ ( __lowerCAmelCase , __lowerCAmelC... | 615 | 1 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl ... | 615 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ : List[str] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxCon... | 615 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless req... | 615 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__magic_name__ : Any = {
"""configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_... | 615 | 1 |
import torch
from transformers import AutoModel
class SCREAMING_SNAKE_CASE__ (torch.nn.Module ):
def __init__( self : Dict , __lowerCamelCase : int="sayef/fsner-bert-base-uncased" ):
"""simple docstring"""
super(__lowerCamelCase , ... | 615 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ : Optional[Any] = {
"""configuration_informer""": [
"""INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 615 | 1 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def a_ ( __lowerCAmelCase ):
lowerCAmelCase__ = prime_factors(__lowerCAmelCase )
if is_square_free(__lowerCAmelCase ):
return -1 if len(__lowerCAmelCase ) % 2 else 1
... | 615 |
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaT... | 615 | 1 |
import logging
from transformers.configuration_utils import PretrainedConfig
__magic_name__ : Tuple = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE__ (_a ):
lowercase_ : Tuple = "masked_bert"
def __init__( self : List[Any] , _... | 615 |
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelFo... | 615 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ : List[str] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxCon... | 615 |
import numpy as np
def a_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
lowerCAmelCase__ = int(np.ceil((x_end - xa) / h ) )
lowerCAmelCase__ = np.zeros((n + 1,) )
lowerCAmelCase__ = ... | 615 | 1 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
__magic_name__ : int = pytest.mark.integration
@pytest.mark.parametrize('''pat... | 615 |
from scipy.stats import pearsonr
import datasets
__magic_name__ : Union[str, Any] = """
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies ... | 615 | 1 |
def a_ ( __lowerCAmelCase ):
lowerCAmelCase__ = []
if len(__lowerCAmelCase ) == 1:
return [nums.copy()]
for _ in range(len(__lowerCAmelCase ) ):
lowerCAmelCase__ = nums.pop(0 )
lowerCAmelCase__ = permute(__lowerCAmelCase ... | 615 |
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
__magic_name__ : List[str] = logging.get_logger(__name__) # pylint: disable=invalid-name
class ... | 615 | 1 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code... | 615 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transform... | 615 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ : Optional[int] = logging.get_logger(__name__)
__magic_name__ : List[Any] = {
"""asapp/sew-tiny-100k""": """https://huggingface.co/asapp/se... | 615 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from t... | 615 | 1 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def a_ ( __lowerCAmelCase ):
return np.dot(__lowerCAmelCase , __lowerCAmelCase )
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Union[str, Any] ... | 615 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__magic_name__ : Optional[int] = get_te... | 615 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ : Tuple = {"""configuration_plbart""": ["""PLBART_PRETRAINED_CONFIG_ARCHI... | 615 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__magic_name__ : Any = TypeVar("""KEY""")
__magic_name__ : Dict = TypeVar("""VAL""")
@dataclass(frozen=_a , slots=_a )
class SCREAMI... | 615 | 1 |
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
__magic_name__ : Tuple = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ (_a ):
def __init__( self : List[str] , *__lo... | 615 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaToken... | 615 | 1 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
__magic_name__ : Li... | 615 |
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
__magic_name__ : List[Any] = """%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input... | 615 | 1 |
from __future__ import annotations
def a_ ( __lowerCAmelCase , __lowerCAmelCase = None ):
lowerCAmelCase__ = word_bank or []
# create a table
lowerCAmelCase__ = len(__lowerCAmelCase ) + 1
lowerCAmelCase__ = []
for _ in range(__lowerCA... | 615 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ : Optional[int] = {
"""configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
"""token... | 615 | 1 |
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Optional[Any] ):
"""simple docstring"""
lowerCAmelCase__ = {} # Mapping from char to TrieNode
lowerCAmelCase__ = False
def A__ ( self : Union[str, Any] ... | 615 |
import logging
from transformers.configuration_utils import PretrainedConfig
__magic_name__ : Tuple = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE__ (_a ):
lowercase_ : Tuple = "masked_bert"
def __init__( self : List[Any] , _... | 615 | 1 |
import csv
import tweepy
# Twitter API credentials
__magic_name__ : str = """"""
__magic_name__ : Union[str, Any] = """"""
__magic_name__ : List[Any] = """"""
__magic_name__ : Dict = """"""
def a_ ( __lowerCAmelCase ):
# authorize twitte... | 615 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl ... | 615 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__magic_name__ : Union[str, Any] = {
"""configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
... | 615 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ : Union[str, Any] = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltCLIPC... | 615 | 1 |
import sys
__magic_name__ : Any = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""668... | 615 |
from __future__ import annotations
def a_ ( __lowerCAmelCase ):
if not nums:
return 0
lowerCAmelCase__ = nums[0]
lowerCAmelCase__ = 0
for num in nums[1:]:
lowerCAmelCase__ , lowerCAmelCase__ = (
max_excluding + num,
... | 615 | 1 |
def a_ ( __lowerCAmelCase , __lowerCAmelCase ):
if len(__lowerCAmelCase ) != len(__lowerCAmelCase ):
raise ValueError('''String lengths must match!''' )
lowerCAmelCase__ = 0
for chara, chara in zip(__lowerCAmelCase , __lowerCAmelCase ):
if chara ... | 615 |
from __future__ import annotations
def a_ ( __lowerCAmelCase , __lowerCAmelCase = None ):
lowerCAmelCase__ = word_bank or []
# create a table
lowerCAmelCase__ = len(__lowerCAmelCase ) + 1
lowerCAmelCase__ = []
for _ in range(__lowerCA... | 615 | 1 |
def a_ ( __lowerCAmelCase ):
try:
lowerCAmelCase__ = float(__lowerCAmelCase )
except ValueError:
raise ValueError('''Please enter a valid number''' )
lowerCAmelCase__ = decimal - int(__lowerCAmelCase )
if fractional_part == 0:
return i... | 615 |
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenize... | 615 | 1 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, ta... | 615 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
__magic_name__ : List[Any] = logging.get_logger(__name__)
def a_ ( __lowerCAmelCase , __lowerCAmelC... | 615 | 1 |
from maths.prime_check import is_prime
def a_ ( __lowerCAmelCase ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
lowerCAmelCase__ = F"""Input value of [number={number}] must be an integer"""
raise TypeError(__lowerCAmelCase )
if is_prime(__lowerCAm... | 615 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ : List[str] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxCon... | 615 | 1 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__magic_name__ : Optional[int] = get_te... | 615 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__magic_name__ : Any = {
"""configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_... | 615 | 1 |
import socket
def a_ ( ):
lowerCAmelCase__ = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
lowerCAmelCase__ = socket.gethostname()
lowerCAmelCase__ = 1_23_12
sock.connect((host, port) )
sock.send(B'''Hello server!''' )
w... | 615 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ : Optional[Any] = {
"""configuration_informer""": [
"""INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 615 | 1 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__magic_name__ : Optional[int] = logging.getLogger()
@unittest.skip("T... | 615 |
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaT... | 615 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ : Any = logging.get_logger(__name__)
__magic_name__ : Union... | 615 |
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelFo... | 615 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_... | 615 |
import numpy as np
def a_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
lowerCAmelCase__ = int(np.ceil((x_end - xa) / h ) )
lowerCAmelCase__ = np.zeros((n + 1,) )
lowerCAmelCase__ = ... | 615 | 1 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__magic_name__ : Any = logging.get_logger(__name__)
__magic_name__ : Optional[Any] = {"""... | 615 |
from scipy.stats import pearsonr
import datasets
__magic_name__ : Union[str, Any] = """
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies ... | 615 | 1 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE__ (_a ):
lowercase_ : int = (DDPMParallelScheduler,)
def A__ ( self : Optional[int] , **__lowerCamelCase... | 615 |
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
__magic_name__ : List[str] = logging.get_logger(__name__) # pylint: disable=invalid-name
class ... | 615 | 1 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def a_ ( __lowerCAmelCase ):
lowerCAmelCase__ = [
'''encoder.version''',
'''decoder.version''',
'''model.encoder.version''',
'... | 615 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transform... | 615 | 1 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def a_ ( __lowerCAmelCase ):
for param in module.parameters():
lowerCAmelCase__ = False
def a_ ( ):
lowerCAmelCase__ = '''cuda''' if torch.cuda.is_available() else '''cpu'... | 615 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from t... | 615 | 1 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from tra... | 615 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__magic_name__ : Optional[int] = get_te... | 615 | 1 |
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelFo... | 615 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__magic_name__ : Any = TypeVar("""KEY""")
__magic_name__ : Dict = TypeVar("""VAL""")
@dataclass(frozen=_a , slots=_a )
class SCREAMI... | 615 | 1 |
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