code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import numpy
# List of input, output pairs
__snake_case :Optional[int] = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
__snake_case :Dict = (((515, 22, 13), 555), ((61, 35, 49), 150))
__snake_case :List[Any] = [2, 4, 1, 5]
__snake_c... | 60 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 60 | 1 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
__snake_case :List[str] = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', action... | 60 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _A ( __UpperCAmelCase ):
def _lowerCamelCase ( self : int):
'''simple docstring'''
return [
{"col_1": 3, "col_2": "a"},
... | 60 | 1 |
from manim import *
class _A ( __UpperCAmelCase ):
def _lowerCamelCase ( self : List[Any]):
'''simple docstring'''
__a = Rectangle(height=0.5 , width=0.5)
__a = Rectangle(height=0.25 , width=0.25)
__a = Rectang... | 60 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __snake_case ( _UpperCAmelCase ):
__a = []
embed.append(
... | 60 | 1 |
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__snake_case :Dict = get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.mod... | 60 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteS... | 60 | 1 |
import requests
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
__a = {'''Content-Type''': '''application/json'''}
__a = requests.post(_UpperCAmelCase , json={'''text''': message_body} , headers=_UpperCAmelCase )
if response.status_cod... | 60 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
__snake_case :Any = TypeVar('''KT''')
__snake_case :List[str] = TypeVar('''VT''')
class _A ( Generic[KT, VT] ):
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : KT | ... | 60 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case :List[str] = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''InstructBlipQFormer... | 60 |
__snake_case :str = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
# ... | 60 | 1 |
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
return round(float(moles / volume ) * nfactor )
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
return round(float((moles * 0.08_21 * tem... | 60 |
from __future__ import annotations
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
print(f'Vertex\tShortest Distance from vertex {src}' )
for i, d in enumerate(_UpperCAmelCase ):
print(f'{i}\t\t{d}' )
def __snake_case ( _UpperCAmelCase ... | 60 | 1 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class _A ( __UpperCAmelCase ):
UpperCamelCase__ : int = (DDIMParallelScheduler,)
UpperCamelCase__ : Optional[Any] = (('''eta''', 0.0), ('''num_infer... | 60 |
import os
import sys
import unittest
__snake_case :Union[str, Any] = 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_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, fi... | 60 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, to... | 60 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING... | 60 | 1 |
def __snake_case ( _UpperCAmelCase ):
__a = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def __snake_case ( _UpperCAmelCase ):
__a = [chr(i + 65 ) for i in r... | 60 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__snake_case :Tuple = logging.getLogger(__name__)
if __name__ == "__main__":
__snake... | 60 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case :Optional[int] = {
'''configuration_upernet''': ['''UperNetConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependen... | 60 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
__snake_case :List[str] = HfApi()
__snake_case :str = {}
# fmt: off
__snake_case :Optional[Any] = torch.tensor([
-0.7_5_1_5, -1.6_8_8_3, 0.2_4_2_0, 0.0_3_0_0, 0.6_3_4_7, 1.3_4_3_3, -1.1_7_4_3, -3.7... | 60 | 1 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils impo... | 60 |
from collections.abc import Generator
from math import sin
def __snake_case ( _UpperCAmelCase ):
if len(_UpperCAmelCase ) != 32:
raise ValueError('''Input must be of length 32''' )
__a = b''''''
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * ... | 60 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case :Dict = logging.get_logger(__name__)
__snake_case :Optional[Any] = {
'''tiiuae/falcon-40b''': '''https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json''',
'''tiiuae/falcon-7b''': '''https:/... | 60 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
__snake_case :Union[str, Any] = Path(__file__).resolve().parents[3] / '''src'''
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io #... | 60 | 1 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def __snake_case ( _UpperCAmelCase ):
if not is_accelerate_available():
return method
__a = version.parse(accelerate.__version__ ).base_... | 60 |
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase = False ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
__a = f'Expected string as input, found {type(_UpperCAmelCase )}'
raise ValueError(_UpperCAmelCase )
if not isinstance(... | 60 | 1 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def __snake_case ( _UpperCAmelCase ):
if "model" in orig_key:
__a = orig_key.replace('''model.''' , '''''' )
if "norm1" in orig_key:
__a = orig_key.replace('''no... | 60 |
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
__snake_case :List[str] = re.compile(r'''^(?P<major>\d+)''' r'''\.(?P<minor>\d+)''' r'''\.(?P<patch>\d+)$''')
@total_ordering
@dataclass
class _A :
... | 60 | 1 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
__snake_case :Any = TypeVar('''KT''')
__snake_case :List[str] = TypeVar('''VT''')
class _A ( Generic[KT, VT] ):
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : KT | ... | 60 |
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('''3.8'''):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
__snake_case :int = ''''''
... | 60 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
__snake_case :str = {
'''configuration_speecht5''': [
'''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SPEECHT5_PRETRAINE... | 60 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__snake_case :Union[str, Any] = {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''V... | 60 | 1 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, D... | 60 |
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__snake_case :Dict = get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.mod... | 60 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case :str = {
'''configuration_mobilebert''': [
'''MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 60 |
from __future__ import annotations
__snake_case :Optional[Any] = []
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
for i in range(len(_UpperCAmelCase ) ):
if board[row][i] == 1:
return False
for i in range(len(_... | 60 | 1 |
from __future__ import annotations
import numpy as np
def __snake_case ( _UpperCAmelCase ):
__a , __a = np.shape(SCREAMING_SNAKE_CASE_ )
if rows != columns:
__a = (
'''\'table\' has to be of square shaped array but got a '''
f'{row... | 700 |
def __snake_case ( _UpperCAmelCase ):
__a = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def __snake_case ( _UpperCAmelCase ):
__a = [chr(i + 65 ) for i in r... | 60 | 0 |
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
while b:
__a , __a = b, a % b
return a
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
return a if b == 0 else euclidean_gcd_recursive(__A , a % b )
def __... | 701 |
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 .tokenization_barthez import B... | 60 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case :Union[str, Any] = logging.get_logger(__name__)
__snake_case :List[str] = {'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}
class _A ( snake_case__ ):
UpperCamelCase__ : ... | 702 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
__snake_case :Optional[int] = collections.namedtuple('''_Datasets''', ['''trai... | 60 | 0 |
import qiskit
def __snake_case ( _UpperCAmelCase = 2 ):
'''simple docstring'''
__a = qubits
# Using Aer's simulator
__a = qiskit.Aer.get_backend('''aer_simulator''' )
# Creating a Quantum Circuit acting on the q register
__a = qiskit.Qu... | 703 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 60 | 0 |
from __future__ import annotations
class _A :
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : str):
'''simple docstring'''
__a = text, pattern
__a = len(UpperCamelCase_), len(UpperCamelCase... | 704 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _A ( __UpperCAmelCase ):
def _lowerCamelCase ( self : int):
'''simple docstring'''
return [
{"col_1": 3, "col_2": "a"},
... | 60 | 0 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class _A :
UpperCamelCase__ : int = 42
UpperCamelCase__ : List[Any] = None
... | 705 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __snake_case ( _UpperCAmelCase ):
__a = []
embed.append(
... | 60 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_a... | 706 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteS... | 60 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case :List[str] = {
"configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}
try:
if not is_torch_available():
raise OptionalDepende... | 707 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
__snake_case :Any = TypeVar('''KT''')
__snake_case :List[str] = TypeVar('''VT''')
class _A ( Generic[KT, VT] ):
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : KT | ... | 60 | 0 |
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
... | 708 |
__snake_case :str = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
# ... | 60 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_re... | 709 |
from __future__ import annotations
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
print(f'Vertex\tShortest Distance from vertex {src}' )
for i, d in enumerate(_UpperCAmelCase ):
print(f'{i}\t\t{d}' )
def __snake_case ( _UpperCAmelCase ... | 60 | 0 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is... | 710 |
import os
import sys
import unittest
__snake_case :Union[str, Any] = 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_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, fi... | 60 | 0 |
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase = " " ):
__a = []
__a = 0
for index, char in enumerate(_lowerCAmelCase ):
if char == separator:
split_words.append(string[last_index:index] )
__a = index + 1
... | 711 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING... | 60 | 0 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase... | 712 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__snake_case :Tuple = logging.getLogger(__name__)
if __name__ == "__main__":
__snake... | 60 | 0 |
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
__snake_case :Optional[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
def __snake_case ( _... | 713 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
__snake_case :List[str] = HfApi()
__snake_case :str = {}
# fmt: off
__snake_case :Optional[Any] = torch.tensor([
-0.7_5_1_5, -1.6_8_8_3, 0.2_4_2_0, 0.0_3_0_0, 0.6_3_4_7, 1.3_4_3_3, -1.1_7_4_3, -3.7... | 60 | 0 |
'''simple docstring'''
import math
from collections.abc import Iterator
from itertools import takewhile
def __snake_case ( _UpperCAmelCase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives... | 714 |
from collections.abc import Generator
from math import sin
def __snake_case ( _UpperCAmelCase ):
if len(_UpperCAmelCase ) != 32:
raise ValueError('''Input must be of length 32''' )
__a = b''''''
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * ... | 60 | 0 |
'''simple docstring'''
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import Acce... | 715 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
__snake_case :Union[str, Any] = Path(__file__).resolve().parents[3] / '''src'''
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io #... | 60 | 0 |
import random
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = False ) -> Tuple:
__a = {i: [] for i in range(_lowerCamelCase )}
# if probability is greater or equal than 1, then generate a complete graph
if probability >= 1:
... | 716 |
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase = False ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
__a = f'Expected string as input, found {type(_UpperCAmelCase )}'
raise ValueError(_UpperCAmelCase )
if not isinstance(... | 60 | 0 |
from __future__ import annotations
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
__a = sorted(numsa + numsa )
__a = divmod(len(_lowerCamelCase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_nu... | 717 |
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
__snake_case :List[str] = re.compile(r'''^(?P<major>\d+)''' r'''\.(?P<minor>\d+)''' r'''\.(?P<patch>\d+)$''')
@total_ordering
@dataclass
class _A :
... | 60 | 0 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__snake... | 718 |
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('''3.8'''):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
__snake_case :int = ''''''
... | 60 | 0 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class _A ( _UpperCAmelCase ):
UpperCamelCase__ : Li... | 719 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__snake_case :Union[str, Any] = {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''V... | 60 | 0 |
'''simple docstring'''
def __snake_case ( _UpperCAmelCase ):
__a = len(_UpperCAmelCase )
__a = sum(_UpperCAmelCase )
__a = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1 , n + 1 ):
__a ... | 720 |
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__snake_case :Dict = get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.mod... | 60 | 0 |
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_weights_in_mobilenet_va,... | 721 |
from __future__ import annotations
__snake_case :Optional[Any] = []
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
for i in range(len(_UpperCAmelCase ) ):
if board[row][i] == 1:
return False
for i in range(len(_... | 60 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def __snake_case ( _UpperCAmelCase ):
__a = 384
... | 700 |
def __snake_case ( _UpperCAmelCase ):
__a = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def __snake_case ( _UpperCAmelCase ):
__a = [chr(i + 65 ) for i in r... | 60 | 0 |
__snake_case :Union[str, Any] = tuple[float, float, float]
__snake_case :List[str] = tuple[float, float, float]
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
__a = end_pointa[0] - end_pointa[0]
__a = end_pointa[1] - end_pointa[1]
__a = ... | 701 |
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 .tokenization_barthez import B... | 60 | 0 |
import os
__snake_case :Tuple = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def __snake_case ( _UpperCAmelCase ) -> Union[str, Any]:
__a = 0
__a = 0
while index < len(_UpperCAmelCase ) - 1:
__a... | 702 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
__snake_case :Optional[int] = collections.namedtuple('''_Datasets''', ['''trai... | 60 | 0 |
from ..utils import DummyObject, requires_backends
class _A ( metaclass=UpperCamelCase_ ):
UpperCamelCase__ : int = ['''torch''', '''torchsde''']
def __init__( self : List[str] , *__SCREAMING_SNAKE_CASE : List[str] , **__SCREAMING_SNAKE_CASE : str... | 703 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 60 | 0 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ... | 704 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _A ( __UpperCAmelCase ):
def _lowerCamelCase ( self : int):
'''simple docstring'''
return [
{"col_1": 3, "col_2": "a"},
... | 60 | 0 |
import itertools
import string
from collections.abc import Generator, Iterable
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
__a = iter(_UpperCAmelCase )
while True:
__a = tuple(itertools.islice(_UpperCAmelCase , _UpperCA... | 705 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __snake_case ( _UpperCAmelCase ):
__a = []
embed.append(
... | 60 | 0 |
'''simple docstring'''
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
... | 706 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteS... | 60 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResamplin... | 707 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
__snake_case :Any = TypeVar('''KT''')
__snake_case :List[str] = TypeVar('''VT''')
class _A ( Generic[KT, VT] ):
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : KT | ... | 60 | 0 |
from collections.abc import Sequence
from queue import Queue
class _A :
def __init__( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Dict=None , __SCREAMING... | 708 |
__snake_case :str = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
# ... | 60 | 0 |
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():... | 709 |
from __future__ import annotations
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
print(f'Vertex\tShortest Distance from vertex {src}' )
for i, d in enumerate(_UpperCAmelCase ):
print(f'{i}\t\t{d}' )
def __snake_case ( _UpperCAmelCase ... | 60 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig''',
'''CLIPSegVision... | 710 |
import os
import sys
import unittest
__snake_case :Union[str, Any] = 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_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, fi... | 60 | 0 |
from __future__ import annotations
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
if len(_snake_case ) < k or k < 0:
raise ValueError('''Invalid Input''' )
__a = __a = sum(array[:k] )
for i in range(len(_snake_case ) - k ... | 711 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING... | 60 | 0 |
import numpy as np
def __snake_case ( _UpperCAmelCase ):
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 712 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__snake_case :Tuple = logging.getLogger(__name__)
if __name__ == "__main__":
__snake... | 60 | 0 |
def __snake_case ( _UpperCAmelCase ):
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(__UpperCamelCase , __UpperCamelCase ):
raise TypeError('''Input value must be a \'int\' type''' )
return bin(__UpperCamelCase ... | 713 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
__snake_case :List[str] = HfApi()
__snake_case :str = {}
# fmt: off
__snake_case :Optional[Any] = torch.tensor([
-0.7_5_1_5, -1.6_8_8_3, 0.2_4_2_0, 0.0_3_0_0, 0.6_3_4_7, 1.3_4_3_3, -1.1_7_4_3, -3.7... | 60 | 0 |
'''simple docstring'''
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class _A ( UpperCAmelCase__ ,UpperCAmelCase__ ):
UpperCamelCase__ ... | 714 |
from collections.abc import Generator
from math import sin
def __snake_case ( _UpperCAmelCase ):
if len(_UpperCAmelCase ) != 32:
raise ValueError('''Input must be of length 32''' )
__a = b''''''
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * ... | 60 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inpu... | 715 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
__snake_case :Union[str, Any] = Path(__file__).resolve().parents[3] / '''src'''
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io #... | 60 | 0 |
from __future__ import annotations
class _A :
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : int):
'''simple docstring'''
__a = order
# a_{0} ... a_{k}
__a = [1.0] + [0.0] * order
# b_{0} ... b_{k}
... | 716 |
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase = False ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
__a = f'Expected string as input, found {type(_UpperCAmelCase )}'
raise ValueError(_UpperCAmelCase )
if not isinstance(... | 60 | 0 |
def __snake_case ( _UpperCAmelCase = "The quick brown fox jumps over the lazy dog" , ):
__a = set()
# Replace all the whitespace in our sentence
__a = input_str.replace(''' ''' , '''''' )
for alpha in input_str:
if "a" <= alpha.lower() <= "z":
... | 717 |
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
__snake_case :List[str] = re.compile(r'''^(?P<major>\d+)''' r'''\.(?P<minor>\d+)''' r'''\.(?P<patch>\d+)$''')
@total_ordering
@dataclass
class _A :
... | 60 | 0 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__snake_case :Optional[int] = TypeVar('''KEY''')
__snake_case :str = TypeVar('''VAL''')
@dataclass(frozen=UpperCAmelCase__ ,slots=UpperCAmelCase__ )
class _A ( Generic[... | 718 |
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('''3.8'''):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
__snake_case :int = ''''''
... | 60 | 0 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_t... | 719 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__snake_case :Union[str, Any] = {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''V... | 60 | 0 |
'''simple docstring'''
from __future__ import annotations
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
__a = get_failure_array(UpperCamelCase__ )
# 2) Step through text searching for pattern
__a = 0, 0 # index into text, pattern
while ... | 720 |
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__snake_case :Dict = get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.mod... | 60 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case :Tuple = logging.get_logger(__name__)
class _A ( __UpperCAmelCase ):
UpperCamelCase__ : Optional[int] = '''timm_backbone'''
def __init__( self : Any , __SCREAMIN... | 721 |
from __future__ import annotations
__snake_case :Optional[Any] = []
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
for i in range(len(_UpperCAmelCase ) ):
if board[row][i] == 1:
return False
for i in range(len(_... | 60 | 0 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTe... | 700 |
def __snake_case ( _UpperCAmelCase ):
__a = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def __snake_case ( _UpperCAmelCase ):
__a = [chr(i + 65 ) for i in r... | 60 | 0 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and ... | 701 |
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 .tokenization_barthez import B... | 60 | 0 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
__snake_case :Optional[Any] = logging.get_logger(__name__)
_... | 702 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
__snake_case :Optional[int] = collections.namedtuple('''_Datasets''', ['''trai... | 60 | 0 |
import math
from datetime import datetime, timedelta
def __snake_case ( _UpperCAmelCase ):
'''simple docstring'''
__a = year % 19
__a = year % 4
__a = year % 7
__a = math.floor(year / 100 )
__a = math.floor((13 + 8 * leap_... | 703 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 60 | 0 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__snake_case :Any = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__snake_case :Tuple = [file for file in filepaths if file... | 704 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _A ( __UpperCAmelCase ):
def _lowerCamelCase ( self : int):
'''simple docstring'''
return [
{"col_1": 3, "col_2": "a"},
... | 60 | 0 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__snake_case :List[str] = logging.get_logger(__name__... | 705 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __snake_case ( _UpperCAmelCase ):
__a = []
embed.append(
... | 60 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _A ( metaclass=__UpperCAmelCase ):
UpperCamelCase__ : int = ["torch", "transformers", "onnx"]
def __init__( self : int , *__SCREAMING_SNAKE_CASE : Any , **__SCREAMING_SN... | 706 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteS... | 60 | 0 |
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
__snake_case :str = parse(importlib.metadata.version('''torch'''))
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ... | 707 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
__snake_case :Any = TypeVar('''KT''')
__snake_case :List[str] = TypeVar('''VT''')
class _A ( Generic[KT, VT] ):
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : KT | ... | 60 | 0 |
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,
)
__snake_case :List[str] = {
'''configuration_xlm_roberta''': [
... | 708 |
__snake_case :str = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
# ... | 60 | 0 |
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
... | 709 |
from __future__ import annotations
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
print(f'Vertex\tShortest Distance from vertex {src}' )
for i, d in enumerate(_UpperCAmelCase ):
print(f'{i}\t\t{d}' )
def __snake_case ( _UpperCAmelCase ... | 60 | 0 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from... | 710 |
import os
import sys
import unittest
__snake_case :Union[str, Any] = 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_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, fi... | 60 | 0 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
__snake_case :List[Any] ... | 711 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING... | 60 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__snake_case :Dict = logging.get_logger(__name__)
__snake_case :List... | 712 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__snake_case :Tuple = logging.getLogger(__name__)
if __name__ == "__main__":
__snake... | 60 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__snake_case :Tuple = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case :List[Any]... | 713 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
__snake_case :List[str] = HfApi()
__snake_case :str = {}
# fmt: off
__snake_case :Optional[Any] = torch.tensor([
-0.7_5_1_5, -1.6_8_8_3, 0.2_4_2_0, 0.0_3_0_0, 0.6_3_4_7, 1.3_4_3_3, -1.1_7_4_3, -3.7... | 60 | 0 |
'''simple docstring'''
class _A :
def __init__( self : int):
'''simple docstring'''
__a = ''''''
__a = ''''''
__a = []
def _lowerCamelCase ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : ... | 714 |
from collections.abc import Generator
from math import sin
def __snake_case ( _UpperCAmelCase ):
if len(_UpperCAmelCase ) != 32:
raise ValueError('''Input must be of length 32''' )
__a = b''''''
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * ... | 60 | 0 |
'''simple docstring'''
from __future__ import annotations
import requests
__snake_case :List[Any] = set(
'''approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked c... | 715 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
__snake_case :Union[str, Any] = Path(__file__).resolve().parents[3] / '''src'''
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io #... | 60 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case :Tuple = {'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP''... | 716 |
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase = False ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
__a = f'Expected string as input, found {type(_UpperCAmelCase )}'
raise ValueError(_UpperCAmelCase )
if not isinstance(... | 60 | 0 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case :Dict = logging.get_logger(__name__)
__snake_case :Dict = {
'''microsoft/xprophetnet-large-wiki100-cased''': (
'''https://huggingface.co/microsoft/xpro... | 717 |
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
__snake_case :List[str] = re.compile(r'''^(?P<major>\d+)''' r'''\.(?P<minor>\d+)''' r'''\.(?P<patch>\d+)$''')
@total_ordering
@dataclass
class _A :
... | 60 | 0 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common i... | 718 |
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('''3.8'''):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
__snake_case :int = ''''''
... | 60 | 0 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _A ( __UpperCAmelCase ):
UpperCamelCase__ : str = (IPNDMScheduler,)
UpperCamelCase__ : Union[str, Any] = (('''num_inference_steps'''... | 719 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__snake_case :Union[str, Any] = {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''V... | 60 | 0 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPP... | 720 |
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__snake_case :Dict = get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.mod... | 60 | 0 |
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
__snake_case :Union[str, Any] = logging.getLogger(__name__)
__snake_case :str = 50 # max wi... | 721 |
from __future__ import annotations
__snake_case :Optional[Any] = []
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
for i in range(len(_UpperCAmelCase ) ):
if board[row][i] == 1:
return False
for i in range(len(_... | 60 | 0 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
# Init... | 700 |
def __snake_case ( _UpperCAmelCase ):
__a = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def __snake_case ( _UpperCAmelCase ):
__a = [chr(i + 65 ) for i in r... | 60 | 0 |
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configur... | 701 |
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 .tokenization_barthez import B... | 60 | 0 |
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ) -> str:
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__a = str(bin(SCREAMING_SNAKE_CASE_ ) )[2:] # remove the leading "0b"
__a = str(bin(S... | 702 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
__snake_case :Optional[int] = collections.namedtuple('''_Datasets''', ['''trai... | 60 | 0 |
from collections import defaultdict
from math import ceil, sqrt
def __snake_case ( _UpperCAmelCase = 1000000 , _UpperCAmelCase = 10 ):
'''simple docstring'''
__a = defaultdict(__lowercase )
for outer_width in range(3 , (t_limit // 4) + 2 ):
... | 703 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 60 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_r... | 704 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _A ( __UpperCAmelCase ):
def _lowerCamelCase ( self : int):
'''simple docstring'''
return [
{"col_1": 3, "col_2": "a"},
... | 60 | 0 |
import sys
__snake_case :Optional[int] = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896... | 705 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __snake_case ( _UpperCAmelCase ):
__a = []
embed.append(
... | 60 | 0 |
'''simple docstring'''
from math import sqrt
def __snake_case ( _UpperCAmelCase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not prime... | 706 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteS... | 60 | 0 |
class _A :
def __init__( self : Optional[Any] , __SCREAMING_SNAKE_CASE : int):
'''simple docstring'''
__a = arr.split(''',''')
def _lowerCamelCase ( self : str):
'''simple docstring'''
__a = [int(s... | 707 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
__snake_case :Any = TypeVar('''KT''')
__snake_case :List[str] = TypeVar('''VT''')
class _A ( Generic[KT, VT] ):
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : KT | ... | 60 | 0 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 708 |
__snake_case :str = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
# ... | 60 | 0 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
__snake_case :str = {
'gwf-440k': {
'u... | 709 |
from __future__ import annotations
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
print(f'Vertex\tShortest Distance from vertex {src}' )
for i, d in enumerate(_UpperCAmelCase ):
print(f'{i}\t\t{d}' )
def __snake_case ( _UpperCAmelCase ... | 60 | 0 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class _A ( unittest.TestCase ):
def _lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
debug_launcher(test_s... | 710 |
import os
import sys
import unittest
__snake_case :Union[str, Any] = 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_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, fi... | 60 | 0 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
req... | 711 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING... | 60 | 0 |
def __snake_case ( _UpperCAmelCase ):
if not nums: # Makes sure that the list is not empty
raise ValueError('''List is empty''' )
__a = sum(lowerCamelCase_ ) / len(lowerCamelCase_ ) # Calculate the average
return sum(abs(x - average ) for x in n... | 712 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__snake_case :Tuple = logging.getLogger(__name__)
if __name__ == "__main__":
__snake... | 60 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import I... | 713 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
__snake_case :List[str] = HfApi()
__snake_case :str = {}
# fmt: off
__snake_case :Optional[Any] = torch.tensor([
-0.7_5_1_5, -1.6_8_8_3, 0.2_4_2_0, 0.0_3_0_0, 0.6_3_4_7, 1.3_4_3_3, -1.1_7_4_3, -3.7... | 60 | 0 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
f... | 714 |
from collections.abc import Generator
from math import sin
def __snake_case ( _UpperCAmelCase ):
if len(_UpperCAmelCase ) != 32:
raise ValueError('''Input must be of length 32''' )
__a = b''''''
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * ... | 60 | 0 |
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