code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {
'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
'processing_git': ['GitProcessor'],
}
try:
if not is_torch_avai... | 30 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, requir... | 30 | 1 |
def a ( snake_case__: str ):
'''simple docstring'''
lowercase_ = [0] * len(snake_case__ )
for i in range(1 , len(snake_case__ ) ):
# use last results for better performance - dynamic programming
lowercase_ = prefix_re... | 30 |
from __future__ import annotations
def a ( snake_case__: list[int] , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and... | 30 | 1 |
import argparse
import os
# New Code #
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... | 30 |
from __future__ import annotations
from collections.abc import MutableSequence
class lowercase__:
"""simple docstring"""
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : MutableSequence[float] ) -> ... | 30 | 1 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def a ( snake_case__: Optional[int] , snake_case__: Optional[int] , snake_case__: Union[str, Any] ):
'''simple docstring'''
lowercase_ ... | 30 |
import itertools
import math
def a ( snake_case__: int ):
'''simple docstring'''
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 multiple... | 30 | 1 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def a ( snake_case__: Dict[str, torch.Tensor] ):
'''simple docstring'''
lowercase_ = []
lowercase_ ... | 30 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvai... | 30 | 1 |
from math import factorial
def a ( snake_case__: int , snake_case__: int , snake_case__: float ):
'''simple docstring'''
if successes > trials:
raise ValueError('''successes must be lower or equal to trials''' )
if trials < 0 or suc... | 30 |
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.co... | 30 | 1 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def a ( snake_case__: Tuple ):
'''simple docstring'''
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class lowercase_... | 30 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class lowercase__( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : List[str] ) -> ... | 30 | 1 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
__a = pytest.mark.integration
@pytest.mark.parametrize('''path''' , ['''paw... | 30 |
def a ( snake_case__: list ):
'''simple docstring'''
if len(snake_case__ ) <= 1:
return [tuple(snake_case__ )]
lowercase_ = []
def generate(snake_case__: int , snake_case__: list ):
if k == 1:
res.... | 30 | 1 |
import os
def a ( snake_case__: str = "input.txt" ):
'''simple docstring'''
with open(os.path.join(os.path.dirname(snake_case__ ) , snake_case__ ) ) as input_file:
lowercase_ = [
[int(snake_case__ ) for element in l... | 30 |
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def a ( ):
'''s... | 30 | 1 |
import argparse
import json
import subprocess
def a ( snake_case__: Union[str, Any] , snake_case__: Any ):
'''simple docstring'''
lowercase_ = []
lowercase_ = (
F'''curl -H "Accept: application/vnd.github+json" -H "Authoriz... | 30 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 30 | 1 |
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transform... | 30 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__a = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}
try:
... | 30 | 1 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def a ( snake_case__: int , snake_case__: int , snake_case__: float = 1 / sqrt(2 ) ):
'''simple docstring'''
lowercase_ = tau * frequency / samplerat... | 30 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__a = {
'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
'proces... | 30 | 1 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import flo... | 30 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelO... | 30 | 1 |
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import... | 30 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import t... | 30 | 1 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__a = '\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mike ... | 30 |
import argparse
import os
import re
__a = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__a = re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern t... | 30 | 1 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowercase__( UpperCAmelCase , ... | 30 |
def a ( snake_case__: list , snake_case__: list , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
if index == number_of_items:
return 0
lowercase_ = 0
lowercase_ = ... | 30 | 1 |
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... | 30 |
import argparse
from collections import defaultdict
import yaml
__a = 'docs/source/en/_toctree.yml'
def a ( snake_case__: Dict ):
'''simple docstring'''
lowercase_ = defaultdict(snake_case__ )
for doc in model_doc:
counts[doc["... | 30 | 1 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import t... | 30 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
__a = logging.get_logger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :Union[str, Any] =... | 30 | 1 |
from math import pow, sqrt
def a ( *snake_case__: float ):
'''simple docstring'''
lowercase_ = len(snake_case__ ) > 0 and all(value > 0.0 for value in values )
return result
def a ( snake_case__: float , snake_case... | 30 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
__a = 'sshleifer/bart-tiny-random'
__a = 'pa... | 30 | 1 |
def a ( snake_case__: list , snake_case__: list , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
if index == number_of_items:
return 0
lowercase_ = 0
lowercase_ = ... | 30 |
def a ( snake_case__: int = 100 ):
'''simple docstring'''
lowercase_ = (n * (n + 1) // 2) ** 2
lowercase_ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f"{solution() = }")
| 30 | 1 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
__a = loggin... | 30 |
import logging
from transformers.configuration_utils import PretrainedConfig
__a = logging.getLogger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :Optional[int] = 'masked_bert'
def __init__( self : Optional[int] , ... | 30 | 1 |
def a ( snake_case__: int ):
'''simple docstring'''
lowercase_ = [0] * len(snake_case__ )
lowercase_ = []
lowercase_ = [1] * len(snake_case__ )
for values in graph.values():
for i in values:
indegree[i] +... | 30 |
import os
def a ( ):
'''simple docstring'''
lowercase_ = os.path.join(os.path.dirname(snake_case__ ) , '''num.txt''' )
with open(snake_case__ ) as file_hand:
return str(sum(int(snake_case__ ) for line in file_hand ) )[:10]
... | 30 | 1 |
def a ( snake_case__: int , snake_case__: list[int] , snake_case__: int ):
'''simple docstring'''
def count_of_possible_combinations(snake_case__: int ) -> int:
if target < 0:
return 0
if target == 0:
retu... | 30 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, requir... | 30 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvai... | 30 |
from __future__ import annotations
def a ( snake_case__: list[int] , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and... | 30 | 1 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class lowercase__:
"""simple docstring"""
def _lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE_ : List[Any] ) -> ... | 30 |
from __future__ import annotations
from collections.abc import MutableSequence
class lowercase__:
"""simple docstring"""
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : MutableSequence[float] ) -> ... | 30 | 1 |
from collections.abc import Callable
def a ( snake_case__: Callable[[float], float] , snake_case__: float , snake_case__: float ):
'''simple docstring'''
lowercase_ = a
lowercase_ = b
if function(snake_case__ ) == 0:... | 30 |
import itertools
import math
def a ( snake_case__: int ):
'''simple docstring'''
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 multiple... | 30 | 1 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if ... | 30 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvai... | 30 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils impor... | 30 |
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.co... | 30 | 1 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class lowercase__( nn.Module ):
"""simple docstring"""
a :int
a :jnp.dtype = jnp.floataa
def _lowercase ( self : List[Any] ) -> Union[str, Any]:
lowercase_ = nn.Con... | 30 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class lowercase__( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : List[str] ) -> ... | 30 | 1 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
__a = 'src/diffusers'
# Matches is_xxx_available()
__a = re.compile(r'is\_([a-z_]*)_available\(\)')
# Matches from... | 30 |
def a ( snake_case__: list ):
'''simple docstring'''
if len(snake_case__ ) <= 1:
return [tuple(snake_case__ )]
lowercase_ = []
def generate(snake_case__: int , snake_case__: list ):
if k == 1:
res.... | 30 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_C... | 30 |
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def a ( ):
'''s... | 30 | 1 |
def a ( snake_case__: int ):
'''simple docstring'''
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 30 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 30 | 1 |
def a ( snake_case__: list ):
'''simple docstring'''
def merge(snake_case__: list , snake_case__: list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
... | 30 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__a = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}
try:
... | 30 | 1 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuratio... | 30 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__a = {
'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
'proces... | 30 | 1 |
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_... | 30 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelO... | 30 | 1 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFComma... | 30 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import t... | 30 | 1 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagem... | 30 |
import argparse
import os
import re
__a = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__a = re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern t... | 30 | 1 |
def a ( snake_case__: int = 1_000 ):
'''simple docstring'''
lowercase_ = 2**power
lowercase_ = 0
while n:
lowercase_ , lowercase_ = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(inp... | 30 |
def a ( snake_case__: list , snake_case__: list , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
if index == number_of_items:
return 0
lowercase_ = 0
lowercase_ = ... | 30 | 1 |
def a ( snake_case__: list[int] , snake_case__: str ):
'''simple docstring'''
lowercase_ = int(snake_case__ )
# Initialize Result
lowercase_ = []
# Traverse through all denomination
for denomination in reversed(snake_case_... | 30 |
import argparse
from collections import defaultdict
import yaml
__a = 'docs/source/en/_toctree.yml'
def a ( snake_case__: Dict ):
'''simple docstring'''
lowercase_ = defaultdict(snake_case__ )
for doc in model_doc:
counts[doc["... | 30 | 1 |
def a ( snake_case__: list ):
'''simple docstring'''
if len(snake_case__ ) <= 1:
return [tuple(snake_case__ )]
lowercase_ = []
def generate(snake_case__: int , snake_case__: list ):
if k == 1:
res.... | 30 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
__a = logging.get_logger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :Union[str, Any] =... | 30 | 1 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
... | 30 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
__a = 'sshleifer/bart-tiny-random'
__a = 'pa... | 30 | 1 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class lowercase__( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : List[str] ) -> ... | 30 |
def a ( snake_case__: int = 100 ):
'''simple docstring'''
lowercase_ = (n * (n + 1) // 2) ** 2
lowercase_ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f"{solution() = }")
| 30 | 1 |
import sys
__a = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856403098711121722383113... | 30 |
import logging
from transformers.configuration_utils import PretrainedConfig
__a = logging.getLogger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :Optional[int] = 'masked_bert'
def __init__( self : Optional[int] , ... | 30 | 1 |
import mpmath # for roots of unity
import numpy as np
class lowercase__:
"""simple docstring"""
def __init__( self : Dict , SCREAMING_SNAKE_CASE_ : str=None , SCREAMING_SNAKE_CASE_ : Tuple=None ) -> List[Any]:
# Input as list
low... | 30 |
import os
def a ( ):
'''simple docstring'''
lowercase_ = os.path.join(os.path.dirname(snake_case__ ) , '''num.txt''' )
with open(snake_case__ ) as file_hand:
return str(sum(int(snake_case__ ) for line in file_hand ) )[:10]
... | 30 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers
... | 30 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, requir... | 30 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'BAAI/AltCLIP': 'https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json',
# See all AltCLIP models at... | 30 |
from __future__ import annotations
def a ( snake_case__: list[int] , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and... | 30 | 1 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class lowercase__( UpperCAmelCase )... | 30 |
from __future__ import annotations
from collections.abc import MutableSequence
class lowercase__:
"""simple docstring"""
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : MutableSequence[float] ) -> ... | 30 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__a = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}
try:
... | 30 |
import itertools
import math
def a ( snake_case__: int ):
'''simple docstring'''
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 multiple... | 30 | 1 |
def a ( snake_case__: int = 3 , snake_case__: int = 7 , snake_case__: int = 1_000_000 ):
'''simple docstring'''
lowercase_ = 0
lowercase_ = 1
for current_denominator in range(1 , limit + 1 ):
lowercase_ ... | 30 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvai... | 30 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a = {
'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
'configuration_data2vec_text': [
'D... | 30 |
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.co... | 30 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AutoformerConfig',
],
}
try:
... | 30 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class lowercase__( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : List[str] ) -> ... | 30 | 1 |
def a ( snake_case__: float , snake_case__: float , snake_case__: float , snake_case__: float , snake_case__: float , ):
'''simple docstring'''
lowercase_ = [redshift, radiation_density, matter_density, dark_energy]
if ... | 30 |
def a ( snake_case__: list ):
'''simple docstring'''
if len(snake_case__ ) <= 1:
return [tuple(snake_case__ )]
lowercase_ = []
def generate(snake_case__: int , snake_case__: list ):
if k == 1:
res.... | 30 | 1 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'facebook/data2vec-base-960h': 'https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json',
# See all Data2VecAudio mode... | 30 |
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def a ( ):
'''s... | 30 | 1 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
... | 30 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 30 | 1 |
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 30 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__a = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}
try:
... | 30 | 1 |
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
__a = get_logger(__name__)
__a = r'\n Args:\n input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n ... | 30 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__a = {
'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
'proces... | 30 | 1 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelO... | 30 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelO... | 30 | 1 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.ut... | 30 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import t... | 30 | 1 |
def a ( snake_case__: int ):
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
raise TypeError('''Input value must be an \'int\' type''' )
lowercase_ = 0
while number:
position += 1
number >>= 1
... | 30 |
import argparse
import os
import re
__a = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__a = re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern t... | 30 | 1 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
__a = np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(),... | 30 |
def a ( snake_case__: list , snake_case__: list , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
if index == number_of_items:
return 0
lowercase_ = 0
lowercase_ = ... | 30 | 1 |
import string
import numpy
def a ( snake_case__: int , snake_case__: int ):
'''simple docstring'''
return b if a == 0 else greatest_common_divisor(b % a , snake_case__ )
class lowercase__:
"""simple docstring"""
a :Tuple ... | 30 |
import argparse
from collections import defaultdict
import yaml
__a = 'docs/source/en/_toctree.yml'
def a ( snake_case__: Dict ):
'''simple docstring'''
lowercase_ = defaultdict(snake_case__ )
for doc in model_doc:
counts[doc["... | 30 | 1 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataL... | 30 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
__a = logging.get_logger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :Union[str, Any] =... | 30 | 1 |
import os
def a ( ):
'''simple docstring'''
lowercase_ = os.path.join(os.path.dirname(snake_case__ ) , '''num.txt''' )
with open(snake_case__ ) as file_hand:
return str(sum(int(snake_case__ ) for line in file_hand ) )[:10]
... | 30 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
__a = 'sshleifer/bart-tiny-random'
__a = 'pa... | 30 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transfor... | 30 |
def a ( snake_case__: int = 100 ):
'''simple docstring'''
lowercase_ = (n * (n + 1) // 2) ** 2
lowercase_ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f"{solution() = }")
| 30 | 1 |
from __future__ import annotations
from collections.abc import MutableSequence
class lowercase__:
"""simple docstring"""
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : MutableSequence[float] ) -> ... | 30 |
import logging
from transformers.configuration_utils import PretrainedConfig
__a = logging.getLogger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :Optional[int] = 'masked_bert'
def __init__( self : Optional[int] , ... | 30 | 1 |
def a ( snake_case__: int , snake_case__: int ):
'''simple docstring'''
return 1 if input_a == input_a else 0
def a ( ):
'''simple docstring'''
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
... | 30 |
import os
def a ( ):
'''simple docstring'''
lowercase_ = os.path.join(os.path.dirname(snake_case__ ) , '''num.txt''' )
with open(snake_case__ ) as file_hand:
return str(sum(int(snake_case__ ) for line in file_hand ) )[:10]
... | 30 | 1 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as ... | 30 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, requir... | 30 | 1 |
import argparse
import os
import re
__a = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__a = re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern t... | 30 |
from __future__ import annotations
def a ( snake_case__: list[int] , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and... | 30 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece... | 30 |
from __future__ import annotations
from collections.abc import MutableSequence
class lowercase__:
"""simple docstring"""
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : MutableSequence[float] ) -> ... | 30 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__a = {
'configuration_layoutlmv3': [
'LAYOUTLMV3_PRETRAINED_CONFIG_ARC... | 30 |
import itertools
import math
def a ( snake_case__: int ):
'''simple docstring'''
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 multiple... | 30 | 1 |
from ....utils import logging
__a = logging.get_logger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
def __init__( self : Any , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : str=None , SCREAM... | 30 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvai... | 30 | 1 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class lowercase__:
"""simple docstring"""
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Any ) -> Any:
lowercase_ = data
... | 30 |
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.co... | 30 | 1 |
from ..utils import DummyObject, requires_backends
class lowercase__( metaclass=UpperCAmelCase ):
"""simple docstring"""
a :str = ['flax']
def __init__( self : int , *SCREAMING_SNAKE_CASE_ : Tuple , **SCREAMING_SNAKE_CASE_ : List[str] ... | 30 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class lowercase__( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : List[str] ) -> ... | 30 | 1 |
def a ( snake_case__: list[int] , snake_case__: list[int] ):
'''simple docstring'''
lowercase_ = len(snake_case__ )
print('''The following activities are selected:''' )
# The first activity is always selected
lowercase_ = 0
... | 30 |
def a ( snake_case__: list ):
'''simple docstring'''
if len(snake_case__ ) <= 1:
return [tuple(snake_case__ )]
lowercase_ = []
def generate(snake_case__: int , snake_case__: list ):
if k == 1:
res.... | 30 | 1 |
import math
def a ( snake_case__: int ):
'''simple docstring'''
lowercase_ = [True] * n
lowercase_ = False
lowercase_ = False
lowercase_ = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
... | 30 |
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def a ( ):
'''s... | 30 | 1 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json'... | 30 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 30 | 1 |
def a ( snake_case__: list ):
'''simple docstring'''
for i in range(len(snake_case__ ) - 1 , 0 , -1 ):
lowercase_ = False
for j in range(snake_case__ , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
... | 30 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__a = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}
try:
... | 30 | 1 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowercase__( unittest.TestCase ):
... | 30 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__a = {
'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
'proces... | 30 | 1 |
def a ( snake_case__: int = 100 ):
'''simple docstring'''
lowercase_ = (n * (n + 1) // 2) ** 2
lowercase_ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f"{solution() = }")
| 30 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelO... | 30 | 1 |
import inspect
import unittest
from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...... | 30 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import t... | 30 | 1 |
from itertools import product
def a ( snake_case__: int , snake_case__: int ):
'''simple docstring'''
lowercase_ = sides_number
lowercase_ = max_face_number * dice_number
lowercase_ = [0] * (max_total + 1)
lowercas... | 30 |
import argparse
import os
import re
__a = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__a = re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern t... | 30 | 1 |
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_m... | 30 |
def a ( snake_case__: list , snake_case__: list , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
if index == number_of_items:
return 0
lowercase_ = 0
lowercase_ = ... | 30 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,... | 30 |
import argparse
from collections import defaultdict
import yaml
__a = 'docs/source/en/_toctree.yml'
def a ( snake_case__: Dict ):
'''simple docstring'''
lowercase_ = defaultdict(snake_case__ )
for doc in model_doc:
counts[doc["... | 30 | 1 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script f... | 30 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
__a = logging.get_logger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :Union[str, Any] =... | 30 | 1 |
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
__a = logging.get_logger(__name__)
__a = 'T5Config'
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
... | 30 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
__a = 'sshleifer/bart-tiny-random'
__a = 'pa... | 30 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}
class lowercase__( UpperCAmelCase ):
"""simple... | 30 |
def a ( snake_case__: int = 100 ):
'''simple docstring'''
lowercase_ = (n * (n + 1) // 2) ** 2
lowercase_ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f"{solution() = }")
| 30 | 1 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class lowercase__( unittest.TestCase ):
"""simple docstring"""
def _l... | 30 |
import logging
from transformers.configuration_utils import PretrainedConfig
__a = logging.getLogger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
a :Optional[int] = 'masked_bert'
def __init__( self : Optional[int] , ... | 30 | 1 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_chann... | 30 |
import os
def a ( ):
'''simple docstring'''
lowercase_ = os.path.join(os.path.dirname(snake_case__ ) , '''num.txt''' )
with open(snake_case__ ) as file_hand:
return str(sum(int(snake_case__ ) for line in file_hand ) )[:10]
... | 30 | 1 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, requir... | 30 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, requir... | 30 | 1 |
class lowercase__: # Public class to implement a graph
"""simple docstring"""
def __init__( self : Dict , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : list[list[bool]] ) -> None:
lowe... | 30 |
from __future__ import annotations
def a ( snake_case__: list[int] , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and... | 30 | 1 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a ( snake_case__: str ):
'''simple docstring'''
return "".join(sorted(snake_case__ ) )
def a ( snake_case__: str ):
'''si... | 30 |
from __future__ import annotations
from collections.abc import MutableSequence
class lowercase__:
"""simple docstring"""
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : MutableSequence[float] ) -> ... | 30 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__a = {
'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'Per... | 30 |
import itertools
import math
def a ( snake_case__: int ):
'''simple docstring'''
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 multiple... | 30 | 1 |
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_dat... | 30 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvai... | 30 | 1 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Tuple ... | 30 |
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.co... | 30 | 1 |
import math
from collections.abc import Iterator
from itertools import takewhile
def a ( snake_case__: int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
... | 30 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class lowercase__( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : List[str] ) -> ... | 30 | 1 |
import csv
import tweepy
# Twitter API credentials
__a = ''
__a = ''
__a = ''
__a = ''
def a ( snake_case__: str ):
'''simple docstring'''
# authorize twitter, initialize tweepy
lowercase_ = tweepy.OAuthHandler(snak... | 30 |
def a ( snake_case__: list ):
'''simple docstring'''
if len(snake_case__ ) <= 1:
return [tuple(snake_case__ )]
lowercase_ = []
def generate(snake_case__: int , snake_case__: list ):
if k == 1:
res.... | 30 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__a = {
'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
'proces... | 30 |
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def a ( ):
'''s... | 30 | 1 |
import functools
def a ( snake_case__: list[int] , snake_case__: list[int] ):
'''simple docstring'''
# Validation
if not isinstance(snake_case__ , snake_case__ ) or not all(isinstance(snake_case__ , snake_case__ ) for day in days ... | 30 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 30 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteSchedul... | 30 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__a = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}
try:
... | 30 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__a = logging.get_logger(__name__)
__a = {
'... | 30 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__a = {
'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
'proces... | 30 | 1 |
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