code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : int = logging.get_logger(__name__)
a_ : List[str] = {
'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config... | 73 |
from __future__ import annotations
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = 2
SCREAMING_SNAKE_CASE = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_UpperCAmelCase)
if n > 1:
factors.... | 73 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokeni... | 73 |
import math
import os
import sys
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = ''
try:
with open(_UpperCAmelCase , 'rb') as binary_file:
SCREAMING_SNAKE_CASE = binary_file.read()
for dat in data:
SCREAMING_SNAKE_CASE ... | 73 | 1 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAdde... | 73 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIF... | 73 | 1 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ : Dict = logging.get_logger(__name__)
a_ : Tup... | 73 |
import heapq as hq
import math
from collections.abc import Iterator
class _snake_case :
def __init__( self , a) -> Optional[Any]:
SCREAMING_SNAKE_CASE = str(id_)
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAK... | 73 | 1 |
import re
def lowerCamelCase__ (_UpperCAmelCase):
if len(re.findall('[ATCG]' , _UpperCAmelCase)) != len(_UpperCAmelCase):
raise ValueError('Invalid Strand')
return dna.translate(dna.maketrans('ATCG' , 'TAGC'))
if __name__ == "__main__":
import doctest
doc... | 73 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ : Optional[Any] = {
'configuration_mask2former': [
'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Mask2FormerConfig',
],
}
try:... | 73 | 1 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _snake_case ( yaml.SafeLoader ):
def SCREAMING_SNAKE_CASE__ ( self , a) -> Dict:
SCREAMING_SNAKE_CASE = [self.constructed_objects[key_node] for ... | 73 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Dict = logging.get_logger(__name__)
a_ : Union[str, Any] = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-ho... | 73 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _snake_case ( A__ ):
@staticmethod
@abstractmethod
def SCREAMING_SNAKE_CASE__ ( a) -> Union[str, Any]:
raise NotImplementedError()
@abstractmethod
def SCREAMING_SNAKE_CASE_... | 73 |
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... | 73 | 1 |
class _snake_case :
def __init__( self , a) -> None:
SCREAMING_SNAKE_CASE = size
SCREAMING_SNAKE_CASE = [0] * size
SCREAMING_SNAKE_CASE = [0] * size
@staticmethod
def SCREAMING_SNAKE_CASE__ ( a) -> int:
... | 73 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a_ : int = {
'configuration_rag': ['RagConfig'],
'retrieval_rag': ['RagRetriever'],
'tokenization_rag': ['RagTokenizer'],
}
try:
if not is_to... | 73 | 1 |
import sys
import turtle
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ):
my_p... | 73 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = False):
if radian_mode:
return [magnitude * cos(_UpperCAmelCase), m... | 73 | 1 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_commo... | 73 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Optional[int] = logging.get_logger(__name__)
a_ : int = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
# See all Cvt models at https://hugg... | 73 | 1 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIF... | 73 |
def lowerCamelCase__ (_UpperCAmelCase = 10 , _UpperCAmelCase = 1000 , _UpperCAmelCase = True):
assert (
isinstance(_UpperCAmelCase , _UpperCAmelCase)
and isinstance(_UpperCAmelCase , _UpperCAmelCase)
and isinstance(_UpperCAmelCase , _U... | 73 | 1 |
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
print('\nThe shortest path matrix using Floyd Warshall algorithm\n')
for i in range(_UpperCAmelCase):
for j in range(_UpperCAmelCase):
if dist[i][j] != float('inf'):
print(int(dist[i][j]) , end='... | 73 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
fro... | 73 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a_ : List[str] = get_tests_dir('fix... | 73 |
from __future__ import annotations
a_ : str = []
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
for i in range(len(_UpperCAmelCase)):
if board[row][i] == 1:
return False
for i in range(len(_UpperCAmelCase)):
... | 73 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 73 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
... | 73 | 1 |
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
'The converted tokeni... | 73 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : List[str] = logging.get_logger(__name__)
a_ : Any = {
'microsoft/unispeech-large-1500h-cv': (
'https://huggingface.co/microsoft/unispeech-large-... | 73 | 1 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterM... | 73 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
a_ : Optional[Any] = re.compile(R'\b(a|an|the)\b', re.UNICODE)
a_ : List[str] = None
def lowerCamelCase__ ():
SCREAMING_SNAKE_CASE = argpa... | 73 | 1 |
import heapq as hq
import math
from collections.abc import Iterator
class _snake_case :
def __init__( self , a) -> Optional[Any]:
SCREAMING_SNAKE_CASE = str(id_)
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAK... | 73 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a_ : Dict = logging.get_logger(__name__)
class _snake_case ( A__ ):
def __init__( self , *a , **a) -> None:
warnings.warn(
'The clas... | 73 | 1 |
import sys
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = len(_UpperCAmelCase)
SCREAMING_SNAKE_CASE = [[0 for x in range(_UpperCAmelCase)] for x in range(_UpperCAmelCase)]
SCREAMING_SNAKE_CASE = [[0 for x in range(_UpperCAmelCase)] for x in range(_U... | 73 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _snake_case ( unittest.TestCase , A__ ):
def SCREAMING_SNAKE_CASE__ ( self) -> List[str]:
SCREAMING_SNAKE_CASE = load_tool('text-classification')
... | 73 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a_ : Union[str, Any] = logging.get_logger(__name__)
a_ : List[str] ... | 73 |
import sys
import turtle
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ):
my_p... | 73 | 1 |
from __future__ import annotations
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
if days_between_payments <= 0:
raise ValueError('days_between_payments must be > 0')
if daily_interest_rate < 0:
raise ValueError('daily_interest_rate ... | 73 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerat... | 73 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Dict = logging.get_logger(__name__)
a_ : List[str] = {
'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
'studio-ousia/luke-large': ... | 73 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a_ : List[str] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:... | 73 | 1 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = False):
if radian_mode:
return [magnitude * cos(_UpperCAmelCase), m... | 73 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
fr... | 73 | 1 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class _snake_case ( A__ ):
_lowercase : Dict = (DDPMParallelScheduler,)
def SCREAMING_SNAKE_CASE__ ( self , **a) -> List[Any]:
SCREAMING_... | 73 |
from __future__ import annotations
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = 2
SCREAMING_SNAKE_CASE = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_UpperCAmelCase)
if n > 1:
factors.... | 73 | 1 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
fr... | 73 |
import math
import os
import sys
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = ''
try:
with open(_UpperCAmelCase , 'rb') as binary_file:
SCREAMING_SNAKE_CASE = binary_file.read()
for dat in data:
SCREAMING_SNAKE_CASE ... | 73 | 1 |
# using dfs for finding eulerian path traversal
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=None):
SCREAMING_SNAKE_CASE = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
SC... | 73 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIF... | 73 | 1 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCamelCase__ (_UpperCAmelCase):
# A local function to see if a dot lands in the circle.
def is_in_circle(_UpperCAmelCase , _UpperCAmelCase) -> bool:
S... | 73 |
import heapq as hq
import math
from collections.abc import Iterator
class _snake_case :
def __init__( self , a) -> Optional[Any]:
SCREAMING_SNAKE_CASE = str(id_)
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAK... | 73 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Optional[int] = logging.get_logger(__name__)
a_ : int = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
# See all Cvt models at https://hugg... | 73 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ : Optional[Any] = {
'configuration_mask2former': [
'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Mask2FormerConfig',
],
}
try:... | 73 | 1 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
# Initialise PyTorc... | 73 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Dict = logging.get_logger(__name__)
a_ : Union[str, Any] = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-ho... | 73 | 1 |
def lowerCamelCase__ (_UpperCAmelCase):
if any(not isinstance(_UpperCAmelCase , _UpperCAmelCase) or x < 0 for x in sequence):
raise TypeError('Sequence must be list of non-negative integers')
for _ in range(len(_UpperCAmelCase)):
for i, (rod_upper, rod_lower) in enumerate(zip(... | 73 |
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... | 73 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer
a_ : ... | 73 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a_ : int = {
'configuration_rag': ['RagConfig'],
'retrieval_rag': ['RagRetriever'],
'tokenization_rag': ['RagTokenizer'],
}
try:
if not is_to... | 73 | 1 |
import json
import sys
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
with open(_UpperCAmelCase , encoding='utf-8') as f:
SCREAMING_SNAKE_CASE = json.load(_UpperCAmelCase)
SCREAMING_SNAKE_CASE = ['<details>', '<summary>Show updated benchmar... | 73 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = False):
if radian_mode:
return [magnitude * cos(_UpperCAmelCase), m... | 73 | 1 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functiona... | 73 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Optional[int] = logging.get_logger(__name__)
a_ : int = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
# See all Cvt models at https://hugg... | 73 | 1 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
a_ : Optional[Any] = re.compile(R'\b(a|an|the)\b', re.UNICODE)
a_ : List[str] = None
def lowerCamelCase__ ():
SCREAMING_SNAKE_CASE = argpa... | 73 |
def lowerCamelCase__ (_UpperCAmelCase = 10 , _UpperCAmelCase = 1000 , _UpperCAmelCase = True):
assert (
isinstance(_UpperCAmelCase , _UpperCAmelCase)
and isinstance(_UpperCAmelCase , _UpperCAmelCase)
and isinstance(_UpperCAmelCase , _U... | 73 | 1 |
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_... | 73 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
fro... | 73 | 1 |
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
a_ ... | 73 |
from __future__ import annotations
a_ : str = []
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
for i in range(len(_UpperCAmelCase)):
if board[row][i] == 1:
return False
for i in range(len(_UpperCAmelCase)):
... | 73 | 1 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
fro... | 73 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
... | 73 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a_ : List[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else... | 73 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : List[str] = logging.get_logger(__name__)
a_ : Any = {
'microsoft/unispeech-large-1500h-cv': (
'https://huggingface.co/microsoft/unispeech-large-... | 73 | 1 |
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
... | 73 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
a_ : Optional[Any] = re.compile(R'\b(a|an|the)\b', re.UNICODE)
a_ : List[str] = None
def lowerCamelCase__ ():
SCREAMING_SNAKE_CASE = argpa... | 73 | 1 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class _snake_case ( unittest.TestCase ):
_lowercase : Optional[int] = insp... | 73 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a_ : Dict = logging.get_logger(__name__)
class _snake_case ( A__ ):
def __init__( self , *a , **a) -> None:
warnings.warn(
'The clas... | 73 | 1 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def lowerCamelCase__ (_UpperCAmelCase):
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output , args.config ... | 73 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _snake_case ( unittest.TestCase , A__ ):
def SCREAMING_SNAKE_CASE__ ( self) -> List[str]:
SCREAMING_SNAKE_CASE = load_tool('text-classification')
... | 73 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
"""google/vivit-b-16x2-kinetics400""": (
"""https://huggingface.co/google/vivit-... | 0 |
import sys
import turtle
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ):
my_p... | 73 | 0 |
from math import pow, sqrt
def _A ( *_lowercase ) -> bool:
"""simple docstring"""
__UpperCamelCase = len(_lowercase ) > 0 and all(value > 0.0 for value in values )
return result
def _A ( _lowercase , _lowercase ) -> float | ValueError:
... | 1 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerat... | 73 | 0 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""vocab_file""": """vocab.json""",
"""tokenizer_config... | 2 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a_ : List[str] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:... | 73 | 0 |
'''simple docstring'''
def A_( A : int):
if not isinstance(A , A):
UpperCamelCase = f'''Input value of [number={number}] must be an integer'''
raise TypeError(A)
if number < 0:
return False
UpperCamelCase = number... | 3 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
fr... | 73 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__UpperCamelCase : Union[str, Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable... | 4 |
from __future__ import annotations
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = 2
SCREAMING_SNAKE_CASE = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_UpperCAmelCase)
if n > 1:
factors.... | 73 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
_lowercase = TypeVar("""T""")
_lowercase = TypeVar("""U""")
class UpperCAmelCase_ ( Generic[T, U] ):
'''simple docstring'''
... | 5 |
import math
import os
import sys
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = ''
try:
with open(_UpperCAmelCase , 'rb') as binary_file:
SCREAMING_SNAKE_CASE = binary_file.read()
for dat in data:
SCREAMING_SNAKE_CASE ... | 73 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepie... | 6 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIF... | 73 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DD... | 7 |
import heapq as hq
import math
from collections.abc import Iterator
class _snake_case :
def __init__( self , a) -> Optional[Any]:
SCREAMING_SNAKE_CASE = str(id_)
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAK... | 73 | 0 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from ... | 8 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ : Optional[Any] = {
'configuration_mask2former': [
'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Mask2FormerConfig',
],
}
try:... | 73 | 0 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , __Upper... | 9 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Dict = logging.get_logger(__name__)
a_ : Union[str, Any] = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-ho... | 73 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"microsoft/swinv2-tiny-patch4-window8-256": (
"https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
),... | 10 |
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... | 73 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/reso... | 11 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a_ : int = {
'configuration_rag': ['RagConfig'],
'retrieval_rag': ['RagRetriever'],
'tokenization_rag': ['RagTokenizer'],
}
try:
if not is_to... | 73 | 0 |
lowerCamelCase__ : dict[tuple[int, int, int], int] = {}
def UpperCamelCase ( lowercase_ , lowercase_ , lowercase_ ) -> int:
'''simple docstring'''
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,... | 12 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = False):
if radian_mode:
return [magnitude * cos(_UpperCAmelCase), m... | 73 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : Optional[int] = {
"""configuration_blip_2""": [
"""BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Blip2Config""",
"""Blip2QFor... | 13 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Optional[int] = logging.get_logger(__name__)
a_ : int = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
# See all Cvt models at https://hugg... | 73 | 0 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
a__ = logging.get_logger(__name__)
... | 14 |
def lowerCamelCase__ (_UpperCAmelCase = 10 , _UpperCAmelCase = 1000 , _UpperCAmelCase = True):
assert (
isinstance(_UpperCAmelCase , _UpperCAmelCase)
and isinstance(_UpperCAmelCase , _UpperCAmelCase)
and isinstance(_UpperCAmelCase , _U... | 73 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : List[str] = {
'configuration_trajectory_transformer': [
'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TrajectoryTransformerConfig',
... | 15 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
fro... | 73 | 0 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from dataset... | 16 |
from __future__ import annotations
a_ : str = []
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
for i in range(len(_UpperCAmelCase)):
if board[row][i] == 1:
return False
for i in range(len(_UpperCAmelCase)):
... | 73 | 0 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowerCamelCase_ ( _lowercase ):
_lowercase : Dict = '''M-CLIP'''
def __init__( self : List[str] , __A : str=1024 , __A : Optional[Any]=76... | 17 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
... | 73 | 0 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config... | 18 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : List[str] = logging.get_logger(__name__)
a_ : Any = {
'microsoft/unispeech-large-1500h-cv': (
'https://huggingface.co/microsoft/unispeech-large-... | 73 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"""microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main... | 19 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
a_ : Optional[Any] = re.compile(R'\b(a|an|the)\b', re.UNICODE)
a_ : List[str] = None
def lowerCamelCase__ ():
SCREAMING_SNAKE_CASE = argpa... | 73 | 0 |
from __future__ import annotations
import queue
class lowercase_ :
def __init__( self , lowercase_) -> Dict:
a__ =data
a__ =None
a__ =None
def _lowercase( ):
print('\n********Press N to stop entering at any point o... | 20 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a_ : Dict = logging.get_logger(__name__)
class _snake_case ( A__ ):
def __init__( self , *a , **a) -> None:
warnings.warn(
'The clas... | 73 | 0 |
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
@requ... | 21 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _snake_case ( unittest.TestCase , A__ ):
def SCREAMING_SNAKE_CASE__ ( self) -> List[str]:
SCREAMING_SNAKE_CASE = load_tool('text-classification')
... | 73 | 0 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class A ( _a ):
lowe... | 22 |
import sys
import turtle
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ):
my_p... | 73 | 0 |
import string
def _snake_case (__lowercase):
for key in range(len(string.ascii_uppercase)):
UpperCamelCase_ = ''
for symbol in message:
if symbol in string.ascii_uppercase:
UpperCamelCase_ = string.ascii_uppercase.find(__lowercase)... | 23 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerat... | 73 | 0 |
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
UpperCAmelCase_ : str = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _UpperCamelCase (_lowerCamelCase : dict[int, list[int]] , _lowerCamelCase :... | 24 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a_ : List[str] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:... | 73 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_clipseg': [
'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPSegConfig',
'CLIPSegTextConfig',
'CLIPSegVisionConfig',
],
'proc... | 25 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
fr... | 73 | 0 |
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _A ( __lowercase ):
def lowercase__ ( self : Any ) -> str:
"""simple docstring"""
return [
... | 26 |
from __future__ import annotations
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = 2
SCREAMING_SNAKE_CASE = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_UpperCAmelCase)
if n > 1:
factors.... | 73 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils impor... | 27 |
import math
import os
import sys
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = ''
try:
with open(_UpperCAmelCase , 'rb') as binary_file:
SCREAMING_SNAKE_CASE = binary_file.read()
for dat in data:
SCREAMING_SNAKE_CASE ... | 73 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowercase__( __UpperCamelCase: int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = 2
SCREAMING_SNAKE_CASE : Tuple = []
whi... | 28 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIF... | 73 | 0 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ..... | 29 |
import heapq as hq
import math
from collections.abc import Iterator
class _snake_case :
def __init__( self , a) -> Optional[Any]:
SCREAMING_SNAKE_CASE = str(id_)
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAK... | 73 | 0 |
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.ro... | 30 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ : Optional[Any] = {
'configuration_mask2former': [
'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Mask2FormerConfig',
],
}
try:... | 73 | 0 |
def UpperCAmelCase_ ( __UpperCAmelCase : int ) -> int:
assert isinstance(__UpperCAmelCase , __UpperCAmelCase ), f"The input value of [n={number}] is not an integer"
if number == 1:
return 2
elif number < 1:
SCREAMING_SNAKE_CASE_ = ... | 31 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Dict = logging.get_logger(__name__)
a_ : Union[str, Any] = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-ho... | 73 | 0 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator,... | 32 |
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... | 73 | 0 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
if len(__lowerCAmelCase ) < k or k < 0:
raise ValueError('''Invalid Input''' )
snake_case__ = snake_case__ = sum(array[:k] )
for i in range... | 33 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a_ : int = {
'configuration_rag': ['RagConfig'],
'retrieval_rag': ['RagRetriever'],
'tokenization_rag': ['RagTokenizer'],
}
try:
if not is_to... | 73 | 0 |
"""simple docstring"""
def __snake_case ( _lowercase ):
"""simple docstring"""
UpperCamelCase = int(_lowercase )
if n_element < 1:
UpperCamelCase = ValueError('''a should be a positive number''' )
raise my_error
UpperCamelC... | 34 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = False):
if radian_mode:
return [magnitude * cos(_UpperCAmelCase), m... | 73 | 0 |
import fire
from utils import calculate_rouge, save_json
def a ( A__ , A__ , A__=None , **A__ ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = [x.strip() for x in open(A__ ).readlines()]
SCREAMING_SNAKE... | 35 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Optional[int] = logging.get_logger(__name__)
a_ : int = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
# See all Cvt models at https://hugg... | 73 | 0 |
def lowercase ( __A : int ) -> int:
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
snake_case : Tuple = 1
snake_case : Union[str, Any] = 1
while repunit:
snake_case : Tuple = (10 * r... | 36 |
def lowerCamelCase__ (_UpperCAmelCase = 10 , _UpperCAmelCase = 1000 , _UpperCAmelCase = True):
assert (
isinstance(_UpperCAmelCase , _UpperCAmelCase)
and isinstance(_UpperCAmelCase , _UpperCAmelCase)
and isinstance(_UpperCAmelCase , _U... | 73 | 0 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import c... | 37 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
fro... | 73 | 0 |
'''simple docstring'''
import os
def UpperCamelCase__ ( ) -> Union[str, Any]:
'''simple docstring'''
with open(os.path.dirname(__magic_name__ ) + """/grid.txt""" ) as f:
snake_case__ : List[str] = [] # noqa: E741
for _ in range(20 ):
l.append([... | 38 |
from __future__ import annotations
a_ : str = []
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
for i in range(len(_UpperCAmelCase)):
if board[row][i] == 1:
return False
for i in range(len(_UpperCAmelCase)):
... | 73 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {'''vocab_file''':... | 39 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
... | 73 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''facebook/nllb-moe-54B''': '''https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json''',
}
class lowerCAmelCase_ ( ... | 40 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : List[str] = logging.get_logger(__name__)
a_ : Any = {
'microsoft/unispeech-large-1500h-cv': (
'https://huggingface.co/microsoft/unispeech-large-... | 73 | 0 |
'''simple docstring'''
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def _A ( A__ , A__ ):
"""simple docstring"""
__lowercase = F"{sampling_rate}"
__lowercase = '''1'''
__lowercase = ... | 41 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
a_ : Optional[Any] = re.compile(R'\b(a|an|the)\b', re.UNICODE)
a_ : List[str] = None
def lowerCamelCase__ ():
SCREAMING_SNAKE_CASE = argpa... | 73 | 0 |
'''simple docstring'''
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
A_ = 500_000
A_ , A_ = os.path.split(__file__)
A_ = os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))
@get_dura... | 42 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a_ : Dict = logging.get_logger(__name__)
class _snake_case ( A__ ):
def __init__( self , *a , **a) -> None:
warnings.warn(
'The clas... | 73 | 0 |
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
lowercase__ = str(bin(SCREAMING_SNAKE_CASE ) )[2:] # remove the leading "0b"
lowercase__ = str(... | 43 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _snake_case ( unittest.TestCase , A__ ):
def SCREAMING_SNAKE_CASE__ ( self) -> List[str]:
SCREAMING_SNAKE_CASE = load_tool('text-classification')
... | 73 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
UpperCAmelCase_ : Optional[Any] = '\\n\n'
UpperCAmelCase_ : Optional[int] = '\nPerplex... | 44 |
import sys
import turtle
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase):
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ):
my_p... | 73 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta impo... | 45 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerat... | 73 | 0 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> float:
'''simple docstring'''
def get_matched_characters(_lowerCamelCase , _lowerCamelCase ) -> str:
_lowerCamelCase : Tuple = []
_lowerCamelCase : List... | 46 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a_ : List[str] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:... | 73 | 0 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ ... | 47 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
fr... | 73 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A ... | 48 |
from __future__ import annotations
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = 2
SCREAMING_SNAKE_CASE = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(_UpperCAmelCase)
if n > 1:
factors.... | 73 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class _UpperCAmelCase ( _lowerCAmelCase ):
a__ : Optional[Any] = "bert-generation"
def __init__( self : Any , _lowercase : List[Any]=5_03_58 , _lowercase : ... | 49 |
import math
import os
import sys
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = ''
try:
with open(_UpperCAmelCase , 'rb') as binary_file:
SCREAMING_SNAKE_CASE = binary_file.read()
for dat in data:
SCREAMING_SNAKE_CASE ... | 73 | 0 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : int ):
lowerCamelCase__ = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def A__ ( __lowerCAmelCase : int = 5000 ):
lowerCamelCase__ = [(i * (3 * i - 1)) // 2 for i in rang... | 50 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIF... | 73 | 0 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __snake_case ( self : Dict ):
UpperCAmelCase = [
... | 51 |
import heapq as hq
import math
from collections.abc import Iterator
class _snake_case :
def __init__( self , a) -> Optional[Any]:
SCREAMING_SNAKE_CASE = str(id_)
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAK... | 73 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_co... | 52 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ : Optional[Any] = {
'configuration_mask2former': [
'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Mask2FormerConfig',
],
}
try:... | 73 | 0 |
import math
class _UpperCAmelCase :
"""simple docstring"""
def lowercase ( self : Union[str, Any] , lowerCAmelCase_ : list[list[float]] , lowerCAmelCase_ : list[int] ) -> int:
__lowerCAmelCase = 0.0
__lowerCA... | 53 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Dict = logging.get_logger(__name__)
a_ : Union[str, Any] = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-transformer-gym-ho... | 73 | 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... | 54 |
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... | 73 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :int = {
'configuration_mobilebert': [
'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 55 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a_ : int = {
'configuration_rag': ['RagConfig'],
'retrieval_rag': ['RagRetriever'],
'tokenization_rag': ['RagTokenizer'],
}
try:
if not is_to... | 73 | 0 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def _a (lowercase__ : Tuple , lowercase__ : bool = True , lowercase__ : float = math.inf , lowercase__ : float = -math.inf , lowercase__ : ... | 56 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = False):
if radian_mode:
return [magnitude * cos(_UpperCAmelCase), m... | 73 | 0 |
import unittest
from typing import Dict, List, Optional, Union
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 ImageProcessingSavingTestMixi... | 57 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Optional[int] = logging.get_logger(__name__)
a_ : int = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
# See all Cvt models at https://hugg... | 73 | 0 |
"""simple docstring"""
import collections
import os
import re
from pathlib import Path
__lowerCAmelCase : str = '''src/transformers'''
# Matches is_xxx_available()
__lowerCAmelCase : Dict = re.compile(R'''is\_([a-z_]*)_available()'''... | 58 |
def lowerCamelCase__ (_UpperCAmelCase = 10 , _UpperCAmelCase = 1000 , _UpperCAmelCase = True):
assert (
isinstance(_UpperCAmelCase , _UpperCAmelCase)
and isinstance(_UpperCAmelCase , _UpperCAmelCase)
and isinstance(_UpperCAmelCase , _U... | 73 | 0 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
i... | 59 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
fro... | 73 | 0 |
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