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 |
|---|---|---|---|---|
def UpperCAmelCase_ (_lowerCAmelCase : Optional[int] , _lowerCAmelCase : int ):
return 1 if input_a == input_a else 0
def UpperCAmelCase_ ():
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , ... | 327 |
"""simple docstring"""
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
lowercase__ : str = datasets.logging.get_logger(__name__)
lowercase__ : List[Any] ... | 123 | 0 |
"""simple docstring"""
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pi... | 359 |
"""simple docstring"""
from typing import Dict
from .base import GenericTensor, Pipeline
class _UpperCAmelCase ( lowerCAmelCase__):
def _snake_case ( self : int , lowercase_ : Optional[Any]=None , lowercase_ : List[str]=None , lowercase_ : Optional[Any]=None... | 123 | 0 |
"""simple docstring"""
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 (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.ut... | 512 |
"""simple docstring"""
import numpy as np
from transformers import Pipeline
def __lowercase ( _a ):
snake_case_ : Any = np.max(_a , axis=-1 , keepdims=_a )
snake_case_ : Optional[int] = np.exp(outputs - maxes )
return shifted... | 123 | 0 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
_snake_case = 100
_snake_case = set(range(3, NUM_PRIMES, 2))
primes.add(2)
_snake_case = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_u... | 307 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandi... | 123 | 0 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def _lowerCAmelCase ( A__: Tuple , A__: Optional[Any] , A__: Optional[int] = 10**-10 ):
'''simple docstring'''
UpperCAmelCa... | 254 |
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available()... | 123 | 0 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common imp... | 303 |
"""simple docstring"""
import unittest
from transformers import MraConfig, 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, floats_tensor, ids_tensor, random_atte... | 123 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__A : Optional[Any] = {'''configuration_encoder_decoder''': ['''EncoderDecoderConfig''']}
try:... | 575 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import ja... | 123 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertToke... | 107 |
"""simple docstring"""
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 ..... | 123 | 0 |
import argparse
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, Distribu... | 385 |
"""simple docstring"""
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
... | 123 | 0 |
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=False ):
'''simple docstring'''
if isinstance(_a , _a ) and isinstance(_a , _a ):
__UpperCamelCase :List[Any] = len(set_a.intersection(_a ) ... | 167 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from ja... | 123 | 0 |
_SCREAMING_SNAKE_CASE = {
'''Pillow''': '''Pillow<10.0.0''',
'''accelerate''': '''accelerate>=0.20.3''',
'''av''': '''av==9.2.0''',
'''beautifulsoup4''': '''beautifulsoup4''',
'''black''': '''black~=23.1''',
'''codecarbon''': '''codecarbon==1.2.0''',
'''cookiecutter''': '''cookiecu... | 181 |
"""simple docstring"""
# 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 vocab first, and then a tiny model - so the outcome is truly tiny -
# all file... | 123 | 0 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipe... | 327 |
"""simple docstring"""
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
lowercase__ : Optional[int] = '''src/tr... | 123 | 0 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
__lowerCAmelCase : Dict =typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
__lowerCAmelCase : Dict =typing.Union[np.floataa,... | 359 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
lowercase__ : List[Any] = {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_AR... | 123 | 0 |
"""simple docstring"""
def _A (__a , __a , __a = 0 , __a = 0 ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = right or len(_a ) - 1
if left > right:
return -1
elif list_data[left] ... | 512 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
lowercase__ : Optional[Any] = {'''vocab_file''': '''vocab.txt'''... | 123 | 0 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'''kakaobrain/align-base''': '''https://huggingface.co/kak... | 307 |
"""simple docstring"""
from __future__ import annotations
def __lowercase ( _a , _a , _a , ):
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif electron_conc < 0:... | 123 | 0 |
import random
class lowercase :
'''simple docstring'''
@staticmethod
def snake_case_ ( _snake_case ) -> int:
"""simple docstring"""
UpperCAmelCase = [ord(lowercase_ ) for i in text]
UpperCA... | 254 |
"""simple docstring"""
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,
)
lowercase__ : str = pytest.mark.integration
@pytest.mark.pa... | 123 | 0 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __a ( ) -> Dict:
'''simple docstring'''
with offline(OfflineSimulationMode.CO... | 303 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : str = logging.get_logger(__name__)
lowercase__ : List[Any] = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_big... | 123 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
__A : Optional[Any] = {'''vocab_file''': '''vocab.txt''', '''tokenizer_fi... | 575 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __lowercase ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
... | 123 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImg... | 107 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_... | 123 | 0 |
from ...configuration_utils import PretrainedConfig
__lowerCamelCase : List[str] = {
'''google/tapas-base-finetuned-sqa''': (
'''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-wtq''': (
'''https://h... | 385 |
"""simple docstring"""
from math import sqrt
def __lowercase ( _a ):
assert isinstance(_a , _a ) and (
number >= 0
), "'number' must been an int and positive"
snake_case_ : List[str] = True
# 0 and 1 are none primes.
if number <= 1:
... | 123 | 0 |
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 Ba... | 167 |
"""simple docstring"""
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configurati... | 123 | 0 |
from math import sqrt
def _snake_case (_snake_case : List[str]) -> int:
_lowercase =0
for i in range(1 , int(sqrt(_a) + 1)):
if n % i == 0 and i != sqrt(_a):
total += i + n // i
elif i == sqrt(_a):
total += i
return total - n
... | 181 |
"""simple docstring"""
import argparse
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 ... | 123 | 0 |
import colorsys
from PIL import Image # type: ignore
def UpperCAmelCase_ (_lowerCAmelCase : str , _lowerCAmelCase : List[Any] , _lowerCAmelCase : str ):
__UpperCamelCase : Optional[int] = x
__UpperCamelCase : List[Any] = y
for step in ra... | 327 |
"""simple docstring"""
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
lowercase__ : str = datasets.logging.get_logger(__name__)
lowercase__ : List[Any] ... | 123 | 0 |
"""simple docstring"""
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
__lowerCAmelCase : str =datasets.logging.get_logger(__name__)
__lowerCAmelCase : List... | 359 |
"""simple docstring"""
from typing import Dict
from .base import GenericTensor, Pipeline
class _UpperCAmelCase ( lowerCAmelCase__):
def _snake_case ( self : int , lowercase_ : Optional[Any]=None , lowercase_ : List[str]=None , lowercase_ : Optional[Any]=None... | 123 | 0 |
"""simple docstring"""
from typing import Dict, Optional
import numpy as np
import datasets
UpperCAmelCase_ : Dict = '''
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth... | 512 |
"""simple docstring"""
import numpy as np
from transformers import Pipeline
def __lowercase ( _a ):
snake_case_ : Any = np.max(_a , axis=-1 , keepdims=_a )
snake_case_ : Optional[int] = np.exp(outputs - maxes )
return shifted... | 123 | 0 |
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Ima... | 307 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandi... | 123 | 0 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _lowerCAmelCase ( A__: int ):
'''simple docstring'''
if "img_encoder.pos_embed" in name:
UpperCAmelCase = ... | 254 |
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available()... | 123 | 0 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class ... | 303 |
"""simple docstring"""
import unittest
from transformers import MraConfig, 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, floats_tensor, ids_tensor, random_atte... | 123 | 0 |
"""simple docstring"""
import unittest
from transformers import MraConfig, 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, floats_tensor, ids_tensor, rand... | 575 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import ja... | 123 | 0 |
'''simple docstring'''
from maths.prime_check import is_prime
def _SCREAMING_SNAKE_CASE ( __snake_case : List[Any] ):
if not isinstance(_a , _a ):
_A = F'Input value of [number={number}] must be an integer'
raise TypeError(_a )
if is_prime(_a ... | 107 |
"""simple docstring"""
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 ..... | 123 | 0 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def A__ ( _a : Any ):
'''simple docstring'''
snake_case__ : Dict =[
'''decoder.version''',
'''decoder.output_p... | 385 |
"""simple docstring"""
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
... | 123 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
__lowercase = {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig''']}... | 167 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from ja... | 123 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE = {
'''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LlamaCo... | 181 |
"""simple docstring"""
# 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 vocab first, and then a tiny model - so the outcome is truly tiny -
# all file... | 123 | 0 |
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class SCREAMING_SNAKE_CASE__ ( lowerCAmelCase__ ):
"""s... | 327 |
"""simple docstring"""
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
lowercase__ : Optional[int] = '''src/tr... | 123 | 0 |
"""simple docstring"""
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class _A ( lowerCAmelCase__ ):
snake_case__ : Union[str, Any] = """M-CLIP"""
def __init__( self , __lowerCAmelCase=1024 , ... | 359 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
lowercase__ : List[Any] = {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_AR... | 123 | 0 |
"""simple docstring"""
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def _A (__a , __a = True , __a = math.inf , __a = -math.inf , __a = math.inf , __a = -math.inf , __a = False ... | 512 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
lowercase__ : Optional[Any] = {'''vocab_file''': '''vocab.txt'''... | 123 | 0 |
import functools
def lowerCAmelCase_ ( snake_case_,snake_case_ ):
# Validation
if not isinstance(_a,_a ) or not all(isinstance(_a,_a ) for day in days ):
raise ValueError("""The parameter days should be a list of integers""" )
if len(_a ) != 3 or not all(isinstanc... | 307 |
"""simple docstring"""
from __future__ import annotations
def __lowercase ( _a , _a , _a , ):
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif electron_conc < 0:... | 123 | 0 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def _lowerCAmelCase ( A__: Dict ):
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
... | 254 |
"""simple docstring"""
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,
)
lowercase__ : str = pytest.mark.integration
@pytest.mark.pa... | 123 | 0 |
import torch
from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool
if is_datasets_available():
from datasets import load_dataset
class A_ ( lowerCAmelCase__ ):
'''simp... | 303 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : str = logging.get_logger(__name__)
lowercase__ : List[Any] = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_big... | 123 | 0 |
"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__A : Optional[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_copies # noqa: E... | 575 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __lowercase ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
... | 123 | 0 |
'''simple docstring'''
from collections import defaultdict
from math import gcd
def _SCREAMING_SNAKE_CASE ( __snake_case : int = 1_5_0_0_0_0_0 ):
_A = defaultdict(_a )
_A = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_... | 107 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_... | 123 | 0 |
import unittest
import numpy as np
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():
impo... | 385 |
"""simple docstring"""
from math import sqrt
def __lowercase ( _a ):
assert isinstance(_a , _a ) and (
number >= 0
), "'number' must been an int and positive"
snake_case_ : List[str] = True
# 0 and 1 are none primes.
if number <= 1:
... | 123 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''shi-labs/dinat-mini-in1k-224''': '''https://huggin... | 167 |
"""simple docstring"""
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configurati... | 123 | 0 |
def _snake_case (_snake_case : Dict) -> List[str]:
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(_a))
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("doctest").testmod()
| 181 |
"""simple docstring"""
import argparse
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 ... | 123 | 0 |
from sklearn.metrics import matthews_corrcoef
import datasets
lowercase : int = '''
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true a... | 327 |
"""simple docstring"""
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
lowercase__ : str = datasets.logging.get_logger(__name__)
lowercase__ : List[Any] ... | 123 | 0 |
"""simple docstring"""
def UpperCAmelCase__ ( lowerCAmelCase__ :List[str] ) -> str:
'''simple docstring'''
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],... | 359 |
"""simple docstring"""
from typing import Dict
from .base import GenericTensor, Pipeline
class _UpperCAmelCase ( lowerCAmelCase__):
def _snake_case ( self : int , lowercase_ : Optional[Any]=None , lowercase_ : List[str]=None , lowercase_ : Optional[Any]=None... | 123 | 0 |
"""simple docstring"""
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
... | 512 |
"""simple docstring"""
import numpy as np
from transformers import Pipeline
def __lowercase ( _a ):
snake_case_ : Any = np.max(_a , axis=-1 , keepdims=_a )
snake_case_ : Optional[int] = np.exp(outputs - maxes )
return shifted... | 123 | 0 |
from pathlib import Path
import json
import tempfile
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
_snake_case = '''tiny-wmt19-en-ru'''
# Build
# borrowed from a test
_snake_case = [
''... | 307 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandi... | 123 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_... | 254 |
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available()... | 123 | 0 |
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
query_table,
)
from .np_formatter impo... | 303 |
"""simple docstring"""
import unittest
from transformers import MraConfig, 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, floats_tensor, ids_tensor, random_atte... | 123 | 0 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import ja... | 575 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import ja... | 123 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowercase_ ( metaclass=lowerCAmelCase__ ):
"""simple docstring"""
__lowerCAmelCase = ["""torch""", """torchsde"""]
def __init__( self : Any, *UpperCamelCase__ : Union[str, ... | 107 |
"""simple docstring"""
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 ..... | 123 | 0 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def A__ ( _a : List[Any] , _a : str , _a : int ):
'''simple docstring'''
snake_case_... | 385 |
"""simple docstring"""
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
... | 123 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_available,
is_torch... | 167 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from ja... | 123 | 0 |
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialF... | 181 |
"""simple docstring"""
# 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 vocab first, and then a tiny model - so the outcome is truly tiny -
# all file... | 123 | 0 |
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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase... | 327 |
"""simple docstring"""
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
lowercase__ : Optional[int] = '''src/tr... | 123 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowerCAmelCase : Dict ={'''configuration_unispeech''': ['''UNISPEECH_PRETRAINE... | 359 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
lowercase__ : List[Any] = {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_AR... | 123 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
UpperCAmelCase_ : Optional[int] = {'''configuration_dpt''': ['''DPT_PRETRAINE... | 512 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
lowercase__ : Optional[Any] = {'''vocab_file''': '''vocab.txt'''... | 123 | 0 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class lowercase ( lowerCAmelCase__ ):
def __init__( self , _a , _a , _a ) -> Dict:
_A : Tuple = dataset
_A... | 307 |
"""simple docstring"""
from __future__ import annotations
def __lowercase ( _a , _a , _a , ):
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif electron_conc < 0:... | 123 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}
class low... | 254 |
"""simple docstring"""
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,
)
lowercase__ : str = pytest.mark.integration
@pytest.mark.pa... | 123 | 0 |
import unittest
import numpy as np
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
if is_torch_available():
import torch
if is_vision_availabl... | 124 |
import requests
def lowerCAmelCase_ ( _snake_case : str , _snake_case : str ) -> None:
'''simple docstring'''
__magic_name__ : Any = {"Content-Type": "application/json"}
__magic_name__ : Optional[int] = requests.post(_snake_case , json={"te... | 124 | 1 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
snake_case : Any = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow("", "|", "|"),
datarow=Da... | 124 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(snake_case ) , 'Tatoeba dir... | 124 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTe... | 124 |
import math
def lowerCAmelCase_ ( _snake_case : int ) -> int:
'''simple docstring'''
if not isinstance(_snake_case , _snake_case ):
__magic_name__ : str = F'''Input value of [number={number}] must be an integer'''
raise TypeError(_snake_case )
if number ... | 124 | 1 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__vers... | 124 |
from collections import defaultdict
from math import ceil, sqrt
def lowerCAmelCase_ ( _snake_case : int = 1000000 , _snake_case : int = 10 ) -> int:
'''simple docstring'''
__magic_name__ : defaultdict = defaultdict(_snake_case )
for outer_width in range(3 ... | 124 | 1 |
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_process... | 124 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
snake_case : Optional[int] = ... | 124 | 1 |
snake_case : List[str] = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def lowerCAmelCase_ ( _snake_case : bytes ) -> bytes:
'''simple docstring'''
if not isinstance(_snake_case , _snake_case ):
__magic_name__ : int = F'''... | 124 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
snake_case : int = "\\n@misc{chen2021evaluating,\n title={Evaluating Large Language... | 124 | 1 |
from typing import Any
def lowerCAmelCase_ ( _snake_case : list ) -> list[Any]:
'''simple docstring'''
if not input_list:
return []
__magic_name__ : Union[str, Any] = [input_list.count(_snake_case ) for value in input_list]
__magic_name__ : Union[s... | 124 |
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
snake_case : Optional[int] = get_logger(__name__)
snake_case : Union[str, Any] = R"\n Args:\n input_ids (`jnp.ndarray` ... | 124 | 1 |
import math
import qiskit
def lowerCAmelCase_ ( _snake_case : int = 1 , _snake_case : int = 1 , _snake_case : int = 1 ) -> qiskit.result.counts.Counts:
'''simple docstring'''
if (
isinstance(_snake_case , _snake_case )
or isinstance(_snake_case , _snake_case... | 124 |
from typing import Dict
from .base import GenericTensor, Pipeline
class _snake_case ( snake_case ):
def SCREAMING_SNAKE_CASE ( self , _a=None , _a=None , _a=None , **_a ):
if tokenize_kwargs is None:
__magic_name__ : Tuple = {}
if tr... | 124 | 1 |
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils impor... | 124 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
... | 124 | 1 |
from __future__ import annotations
from typing import Any
class _snake_case ( snake_case ):
pass
class _snake_case :
def __init__( self , _a ):
__magic_name__ : Any = data
__magic_name__ : Node | None = None
def __it... | 124 |
def lowerCAmelCase_ ( _snake_case : str , _snake_case : str ) -> float:
'''simple docstring'''
def get_matched_characters(_snake_case : str , _snake_case : str ) -> str:
__magic_name__ : str = []
__magic_name__ : Optional[Any] = min(len... | 124 | 1 |
from __future__ import annotations
import unittest
from transformers import 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
from ...test_pipeline... | 124 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_te... | 124 | 1 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class _snake_case ( snake_case ):
def SCREAMING_SNAKE_CASE ( self , _a ):
return 0.0
def lowerCAmelCase_ ( _snake_cas... | 124 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipe... | 124 | 1 |
from __future__ import annotations
def lowerCAmelCase_ ( _snake_case : list[float] ) -> float:
'''simple docstring'''
__magic_name__ : Optional[int] = 0.00
__magic_name__ : Dict = 0
for resistor in resistors:
if resistor <= 0:
__magic_name... | 124 |
import os
import numpy
import onnx
def lowerCAmelCase_ ( _snake_case : Tuple , _snake_case : int ) -> List[str]:
'''simple docstring'''
__magic_name__ : Dict = a.name
__magic_name__ : Optional[Any] = b.name
__magic_name__ : Opti... | 124 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case : str = logging.get_logger(__name__)
snake_case : Optional[Any] = {
"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
# See all BioGPT models at h... | 124 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinis... | 124 | 1 |
def lowerCAmelCase_ ( _snake_case : int , _snake_case : int ) -> int:
'''simple docstring'''
return int(input_a == input_a == 0 )
def lowerCAmelCase_ ( ) -> None:
'''simple docstring'''
print("Truth Table of NOR Gate:" )
print("| Input 1 | ... | 124 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( _snake_case : int , _snake_case : Union[str, Any] ) ... | 124 | 1 |
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join ... | 124 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler... | 124 | 1 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxT... | 124 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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 required by applicabl... | 124 | 1 |
from math import factorial, pi
def lowerCAmelCase_ ( _snake_case : float , _snake_case : int = 30 ) -> float:
'''simple docstring'''
if not isinstance(_snake_case , (int, float) ):
raise ValueError("maclaurin_sin() requires either an int or float for theta" )
if not is... | 124 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__vers... | 124 | 1 |
from pathlib import Path
import fire
from tqdm import tqdm
def lowerCAmelCase_ ( _snake_case : Optional[Any]="ro" , _snake_case : Union[str, Any]="en" , _snake_case : Any="wmt16" , _snake_case : Union[str, Any]=None ) -> None:
'''simple docstring'''
try:
import dat... | 124 |
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 PILImageResampling
f... | 124 | 1 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class _snake_case ( snake_case ):
# to overwrite at feature extractactor specific tests
UpperCamel... | 124 |
import os
import sys
import unittest
snake_case : List[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, find_... | 124 | 1 |
def lowerCAmelCase_ ( _snake_case : int = 10**9 ) -> int:
'''simple docstring'''
__magic_name__ : Any = 1
__magic_name__ : Optional[Any] = 2
__magic_name__ : Dict = 0
__magic_name__ : Optional[int] = 0
__magic_nam... | 124 |
from __future__ import annotations
from typing import Any
class _snake_case ( snake_case ):
pass
class _snake_case :
def __init__( self , _a ):
__magic_name__ : Any = data
__magic_name__ : Node | None = None
def __it... | 124 | 1 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, 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 Con... | 124 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxT... | 124 | 1 |
def lowerCAmelCase_ ( _snake_case : int , _snake_case : int ) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0 ) != 0 )
def lowerCAmelCase_ ( ) -> None:
'''simple docstring'''
assert nand_gate(0 , 0 ) == 1
assert nand_gate... | 124 |
import requests
def lowerCAmelCase_ ( _snake_case : str , _snake_case : str ) -> None:
'''simple docstring'''
__magic_name__ : Any = {"Content-Type": "application/json"}
__magic_name__ : Optional[int] = requests.post(_snake_case , json={"te... | 124 | 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
snake_case : Union[str, Any] = logging.get_logger(__name__)
snake_case : Any = ... | 124 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(snake_case ) , 'Tatoeba dir... | 124 | 1 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class _snake_case ( snake_case ):
UpperCamelCase__ = (CMStochasticIterativeScheduler,)
UpperCamelCase__ = 10
def SCREAMING_SNAKE_CASE (... | 124 |
import math
def lowerCAmelCase_ ( _snake_case : int ) -> int:
'''simple docstring'''
if not isinstance(_snake_case , _snake_case ):
__magic_name__ : str = F'''Input value of [number={number}] must be an integer'''
raise TypeError(_snake_case )
if number ... | 124 | 1 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
snake_case : Union[str, Any] = "\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author=... | 124 |
from collections import defaultdict
from math import ceil, sqrt
def lowerCAmelCase_ ( _snake_case : int = 1000000 , _snake_case : int = 10 ) -> int:
'''simple docstring'''
__magic_name__ : defaultdict = defaultdict(_snake_case )
for outer_width in range(3 ... | 124 | 1 |
from __future__ import annotations
from typing import Generic, TypeVar
snake_case : List[str] = TypeVar("T")
class _snake_case ( Generic[T] ):
def __init__( self , _a ):
__magic_name__ : Any = data
__magic_name__ : Optional[int] ... | 124 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
snake_case : Optional[int] = ... | 124 | 1 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ... | 124 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
snake_case : int = "\\n@misc{chen2021evaluating,\n title={Evaluating Large Language... | 124 | 1 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case : Dict = logging.get_logger(__name__)
snake_case : Optional[Any] = {
"facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/conf... | 124 |
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
snake_case : Optional[int] = get_logger(__name__)
snake_case : Union[str, Any] = R"\n Args:\n input_ids (`jnp.ndarray` ... | 124 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
C... | 124 |
from typing import Dict
from .base import GenericTensor, Pipeline
class _snake_case ( snake_case ):
def SCREAMING_SNAKE_CASE ( self , _a=None , _a=None , _a=None , **_a ):
if tokenize_kwargs is None:
__magic_name__ : Tuple = {}
if tr... | 124 | 1 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(snake_case ) , 'Tatoeba dir... | 124 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
... | 124 | 1 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class _snake_case ( snake_case , unittest.Te... | 124 |
def lowerCAmelCase_ ( _snake_case : str , _snake_case : str ) -> float:
'''simple docstring'''
def get_matched_characters(_snake_case : str , _snake_case : str ) -> str:
__magic_name__ : str = []
__magic_name__ : Optional[Any] = min(len... | 124 | 1 |
from jiwer import compute_measures
import datasets
snake_case : Dict = "\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation meas... | 124 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_te... | 124 | 1 |
def lowerCAmelCase_ ( _snake_case : int , _snake_case : Union[str, Any] , _snake_case : int , _snake_case : int ) -> List[Any]:
'''simple docstring'''
__magic_name__ : List[Any] = [False] * len(_snake_case )
__magic_name__ : List[Any] = [... | 124 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipe... | 124 | 1 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def lowerCAmelCase_ ( _snake_case : str , _snake_case : str = "cpu" , _snake_case : Union[str, None] = None ) -> None:
'''simple docstring'''
__magic_name__ : List[str] = torch.load(... | 124 |
import os
import numpy
import onnx
def lowerCAmelCase_ ( _snake_case : Tuple , _snake_case : int ) -> List[str]:
'''simple docstring'''
__magic_name__ : Dict = a.name
__magic_name__ : Optional[Any] = b.name
__magic_name__ : Opti... | 124 | 1 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class _snake_case ( unittest.TestCase ):
def SCREAMING_S... | 124 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinis... | 124 | 1 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
snake_case : Dict = HfApi()
snake_case : int = {}
# fmt: off
snake_case : Any = torch.tensor([
-0.75_15, -1.68_83, 0.24_20, 0.03_00, 0.63_47, 1.34_33, -1.17_43, -3.74_... | 124 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( _snake_case : int , _snake_case : Union[str, Any] ) ... | 124 | 1 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils... | 124 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler... | 124 | 1 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
snake_case : Optional[int] = logging.getLogger(__n... | 124 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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 required by applicabl... | 124 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils i... | 124 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__vers... | 124 | 1 |
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