code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
def __magic_name__ ( lowercase_ ) -> str:
'''simple docstring'''
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7]... | 606 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
fro... | 606 | 1 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torc... | 606 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils imp... | 606 | 1 |
import os
# Precomputes a list of the 100 first triangular numbers
__a : Optional[int] = [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def __magic_name__ ( ) -> Any:
'''simple docstring'''
UpperCamelCase = os.path.dirname(os.path.realpath(lowercas... | 606 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_uti... | 606 | 1 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_uti... | 606 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Model... | 606 | 1 |
def __magic_name__ ( lowercase_ = 1000 ) -> int:
'''simple docstring'''
UpperCamelCase = 3
UpperCamelCase = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
... | 606 |
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 : Tuple = logging.get_logger(__name__)
... | 606 | 1 |
from collections.abc import Callable
class __UpperCAmelCase :
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE = None ) -> None:
"""simple docstring"""
# Stores actual heap items.
... | 606 |
def __magic_name__ ( lowercase_ ) -> List[str]:
'''simple docstring'''
UpperCamelCase = []
UpperCamelCase = set({"(", "[", "{"} )
UpperCamelCase = set({")", "]", "}"} )
UpperCamelCase = {"{": "}", "[": "]", "(": ")... | 606 | 1 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncod... | 606 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
__a : Tuple = logging.getLogger(__name__)
def __magic_name__ ( ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase ... | 606 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler,... | 606 |
# Copyright 2023 The HuggingFace Inc. 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
#
# Unl... | 606 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a : Optional[Any] = {
"""configuration_clipseg""": [
"""CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPSegConfig""",
"""CLIPSegTextCon... | 606 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__a : Optional[int] = 1_0
def __magic_name__ ( lowercase_ , lowercase_ , lowercase_ , low... | 606 | 1 |
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallback,... | 606 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def __magic_name__ ( ) -> List[str]:
'''simple docstring'''
UpperCamelCase = ArgumentParser("Diffusers CLI tool" , usage="diffusers-cli <command> [<args>]" )
UpperCamelCase ... | 606 | 1 |
from itertools import permutations
def __magic_name__ ( lowercase_ ) -> bool:
'''simple docstring'''
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return F... | 606 |
from collections.abc import Iterable
from typing import Generic, TypeVar
__a : Optional[Any] = TypeVar("""_T""")
class __UpperCAmelCase ( Generic[_T] ):
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE = None ) ... | 606 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_imag... | 606 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __UpperCAmelCase ( snake_case__ , unittest.TestCase ):
"""simple ... | 606 | 1 |
def __magic_name__ ( lowercase_ ) -> int:
'''simple docstring'''
UpperCamelCase = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def __magic_name__ ( lowercase_ = 100 ) -> int:
'''... | 606 |
__a : Union[str, Any] = 6_5_5_2_1
def __magic_name__ ( lowercase_ ) -> int:
'''simple docstring'''
UpperCamelCase = 1
UpperCamelCase = 0
for plain_chr in plain_text:
UpperCamelCase = (a + ord(lowercase_ )) ... | 606 | 1 |
import warnings
from functools import wraps
from typing import Callable
def __magic_name__ ( lowercase_ ) -> Callable:
'''simple docstring'''
@wraps(lowercase_ )
def _inner_fn(*lowercase_ , **lowercase_ ):
warnings.warn(
... | 606 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingf... | 606 | 1 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoT... | 606 |
import warnings
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
cl... | 606 | 1 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
... | 606 |
def __magic_name__ ( lowercase_ ) -> tuple[int, int]:
'''simple docstring'''
try:
UpperCamelCase = float(lowercase_ )
except ValueError:
raise ValueError("Please enter a valid number" )
UpperCamelCase = decimal ... | 606 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
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 imp... | 606 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import ... | 606 | 1 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
__a : Any = logging.getLogger(__name__)
class __UpperCAmelCase ( snake_case__ ):
"""si... | 606 |
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_roberta import Robe... | 606 | 1 |
def __magic_name__ ( lowercase_ ) -> int:
'''simple docstring'''
UpperCamelCase = [0] * len(lowercase_ )
UpperCamelCase = []
UpperCamelCase = [1] * len(lowercase_ )
for values in graph.values():
for i in val... | 606 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a : Optional[Any] = {
"""configuration_distilbert""": [
"... | 606 | 1 |
def __magic_name__ ( lowercase_ , lowercase_ ) -> Any:
'''simple docstring'''
UpperCamelCase = (boundary[1] - boundary[0]) / steps
UpperCamelCase = boundary[0]
UpperCamelCase = boundary[1]
UpperCamelCase = make_poi... | 606 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
i... | 606 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a : Optional[Any] = logging.get_logger(__name__)
__a : Optional[Any] = {
"""Salesforce/blip-vqa-base""": """https://huggingface.co/Sale... | 606 |
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
... | 606 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPi... | 606 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merge... | 606 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=snake_case__ )
class __UpperCAmelCase ( snake_case__ ):
"""simple docstrin... | 606 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
fro... | 606 | 1 |
def __magic_name__ ( lowercase_ , lowercase_ ) -> Tuple:
'''simple docstring'''
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def __magic_name__ ( lowercase_ , lowercase_=0 ) -> Optional[Any]:
'''simple docstring'''... | 606 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils imp... | 606 | 1 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
... | 606 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_uti... | 606 | 1 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __magic_name__ ( lowercase_ , lowercase_ ) -> np.array:
'''simple docstring'''
UpperCamelCase = f'''{sampling_rate}'''
UpperCamelC... | 606 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Model... | 606 | 1 |
def __magic_name__ ( lowercase_ = 1000 ) -> int:
'''simple docstring'''
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 606 |
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 : Tuple = logging.get_logger(__name__)
... | 606 | 1 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterM... | 606 |
def __magic_name__ ( lowercase_ ) -> List[str]:
'''simple docstring'''
UpperCamelCase = []
UpperCamelCase = set({"(", "[", "{"} )
UpperCamelCase = set({")", "]", "}"} )
UpperCamelCase = {"{": "}", "[": "]", "(": ")... | 606 | 1 |
def __magic_name__ ( lowercase_ ) -> int:
'''simple docstring'''
UpperCamelCase = hex_num.strip()
if not hex_num:
raise ValueError("No value was passed to the function" )
UpperCamelCase = hex_num[0] == "-"
if is_negati... | 606 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
__a : Tuple = logging.getLogger(__name__)
def __magic_name__ ( ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase ... | 606 | 1 |
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore... | 606 |
# Copyright 2023 The HuggingFace Inc. 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
#
# Unl... | 606 | 1 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
i... | 606 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__a : Optional[int] = 1_0
def __magic_name__ ( lowercase_ , lowercase_ , lowercase_ , low... | 606 | 1 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_comm... | 606 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def __magic_name__ ( ) -> List[str]:
'''simple docstring'''
UpperCamelCase = ArgumentParser("Diffusers CLI tool" , usage="diffusers-cli <command> [<args>]" )
UpperCamelCase ... | 606 | 1 |
def __magic_name__ ( lowercase_ , lowercase_ ) -> float:
'''simple docstring'''
_validate_point(lowercase_ )
_validate_point(lowercase_ )
if len(lowercase_ ) != len(lowercase_ ):
raise ValueError("Both points must be in the same... | 606 |
from collections.abc import Iterable
from typing import Generic, TypeVar
__a : Optional[Any] = TypeVar("""_T""")
class __UpperCAmelCase ( Generic[_T] ):
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE = None ) ... | 606 | 1 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
__a : Tuple = logging.getLogger(__name__)
def __magic_name__ ( ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase ... | 606 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __UpperCAmelCase ( snake_case__ , unittest.TestCase ):
"""simple ... | 606 | 1 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def __magic_name__ ( ) -> List[str]:
'''simple docstring'''
UpperCamelCase = ArgumentParser("Diffusers CLI tool" , usage="diffusers-cli <command> [<args>]" )
UpperCamelCase ... | 606 |
__a : Union[str, Any] = 6_5_5_2_1
def __magic_name__ ( lowercase_ ) -> int:
'''simple docstring'''
UpperCamelCase = 1
UpperCamelCase = 0
for plain_chr in plain_text:
UpperCamelCase = (a + ord(lowercase_ )) ... | 606 | 1 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
re... | 606 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingf... | 606 | 1 |
__a : Union[str, Any] = 6_5_5_2_1
def __magic_name__ ( lowercase_ ) -> int:
'''simple docstring'''
UpperCamelCase = 1
UpperCamelCase = 0
for plain_chr in plain_text:
UpperCamelCase = (a + ord(lowercase_ )) ... | 606 |
import warnings
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
cl... | 606 | 1 |
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
__a : Dict = """\
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Mo... | 606 |
def __magic_name__ ( lowercase_ ) -> tuple[int, int]:
'''simple docstring'''
try:
UpperCamelCase = float(lowercase_ )
except ValueError:
raise ValueError("Please enter a valid number" )
UpperCamelCase = decimal ... | 606 | 1 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/res... | 606 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import ... | 606 | 1 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compu... | 606 |
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_roberta import Robe... | 606 | 1 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class __UpperCAmelCase ( snake_case__ ):
"""simple docstring"""
def __init__(... | 606 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a : Optional[Any] = {
"""configuration_distilbert""": [
"... | 606 | 1 |
def __magic_name__ ( lowercase_ = 1000000 ) -> int:
'''simple docstring'''
UpperCamelCase = limit + 1
UpperCamelCase = [0] * limit
for first_term in range(1 , lowercase_ ):
for n in range(lowercase_ , lowercase_ ... | 606 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
i... | 606 | 1 |
from abc import ABC, abstractmethod
from typing import List, Optional
class __UpperCAmelCase ( snake_case__ ):
"""simple docstring"""
def __init__( self ) -> Any:
"""simple docstring"""
# test for the a... | 606 |
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
... | 606 | 1 |
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_s... | 606 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merge... | 606 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
__a : Optional[int] = logging.get_logger(__name__)
... | 606 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
fro... | 606 | 1 |
from __future__ import annotations
def __magic_name__ ( lowercase_ ) -> list[int]:
'''simple docstring'''
UpperCamelCase = 2
UpperCamelCase = []
while i * i <= n:
if n % i:
i += 1
else:
... | 606 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils imp... | 606 | 1 |
def __magic_name__ ( lowercase_ ) -> int:
'''simple docstring'''
UpperCamelCase = len(lowercase_ )
UpperCamelCase = len(matrix[0] )
UpperCamelCase = min(lowercase_ , lowercase_ )
for row in range(lowercase_ )... | 606 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_uti... | 606 | 1 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a : Tuple = logging.get_logger(__name__)
__a : Dict = {
"""huggingface/time-series-transformer-tourism-monthly""": (
"""https://huggin... | 606 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Model... | 606 | 1 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
__a : List[Any] = logging.get_logger(__name__)
def __magic_name__ ( lowercase_ ) -> List[Any]:
'''simple ... | 606 |
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 : Tuple = logging.get_logger(__name__)
... | 606 | 1 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __magic_name__ ( lowercase_ = "laptop" ) -> DataFrame:
'''simple docstring'''
UpperCamelCase = f'''https://www.amazon.in/laptop/s?k={product}'''
... | 606 |
def __magic_name__ ( lowercase_ ) -> List[str]:
'''simple docstring'''
UpperCamelCase = []
UpperCamelCase = set({"(", "[", "{"} )
UpperCamelCase = set({")", "]", "}"} )
UpperCamelCase = {"{": "}", "[": "]", "(": ")... | 606 | 1 |
from collections.abc import Iterable
from typing import Generic, TypeVar
__a : Optional[Any] = TypeVar("""_T""")
class __UpperCAmelCase ( Generic[_T] ):
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE = None ) ... | 606 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
__a : Tuple = logging.getLogger(__name__)
def __magic_name__ ( ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase ... | 606 | 1 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __UpperCAmelCase ( ... | 606 |
# Copyright 2023 The HuggingFace Inc. 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
#
# Unl... | 606 | 1 |
def __magic_name__ ( lowercase_ ) -> tuple[int, int]:
'''simple docstring'''
try:
UpperCamelCase = float(lowercase_ )
except ValueError:
raise ValueError("Please enter a valid number" )
UpperCamelCase = decimal ... | 606 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__a : Optional[int] = 1_0
def __magic_name__ ( lowercase_ , lowercase_ , lowercase_ , low... | 606 | 1 |
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
__a : List[str] = logging.get_logger(__name__)
class __UpperCAmelCase :
"""simple docstring"""
lowercase ... | 606 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def __magic_name__ ( ) -> List[str]:
'''simple docstring'''
UpperCamelCase = ArgumentParser("Diffusers CLI tool" , usage="diffusers-cli <command> [<args>]" )
UpperCamelCase ... | 606 | 1 |
def __magic_name__ ( lowercase_ ) -> float:
'''simple docstring'''
if edge <= 0 or not isinstance(lowercase_ , lowercase_ ):
raise ValueError("Length must be a positive." )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def ... | 606 |
from collections.abc import Iterable
from typing import Generic, TypeVar
__a : Optional[Any] = TypeVar("""_T""")
class __UpperCAmelCase ( Generic[_T] ):
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE = None ) ... | 606 | 1 |
from __future__ import annotations
import math
class __UpperCAmelCase :
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
UpperCamelCase = size
... | 606 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __UpperCAmelCase ( snake_case__ , unittest.TestCase ):
"""simple ... | 606 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __UpperCAmelCase ( snake_case__ ):
"""simple docstring"""
lowercase = ["""image_processor""", """tokenizer"""]
... | 606 |
__a : Union[str, Any] = 6_5_5_2_1
def __magic_name__ ( lowercase_ ) -> int:
'''simple docstring'''
UpperCamelCase = 1
UpperCamelCase = 0
for plain_chr in plain_text:
UpperCamelCase = (a + ord(lowercase_ )) ... | 606 | 1 |
from __future__ import annotations
from cmath import sqrt
def __magic_name__ ( lowercase_ , lowercase_ , lowercase_ ) -> tuple[complex, complex]:
'''simple docstring'''
if a == 0:
raise ValueError("Coefficient 'a' must not be zero." )
... | 606 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingf... | 606 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a : Union[str, Any] = logging.get_logger(__name__)
__a : Union[str, Any] = {
"""asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-1... | 606 |
import warnings
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
cl... | 606 | 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 BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__a : Tuple = logging.get_l... | 606 |
def __magic_name__ ( lowercase_ ) -> tuple[int, int]:
'''simple docstring'''
try:
UpperCamelCase = float(lowercase_ )
except ValueError:
raise ValueError("Please enter a valid number" )
UpperCamelCase = decimal ... | 606 | 1 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class __UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ) -> Union[str, Any]:
"... | 606 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import ... | 606 | 1 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import ... | 606 |
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_roberta import Robe... | 606 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
__a : Tuple = logging.get_logger(__name__)
... | 606 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a : Optional[Any] = {
"""configuration_distilbert""": [
"... | 606 | 1 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files" , [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.json"],
... | 606 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
i... | 606 | 1 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__a : Tuple = 0
__a : List[Any] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
... | 606 |
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
... | 606 | 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 : Tuple = logging.get_logger(__name__)
... | 606 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merge... | 606 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from tra... | 606 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
fro... | 606 | 1 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__a : Dict = logging.get_logger(__name__)
__a : List[str] ... | 606 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils imp... | 606 | 1 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils imp... | 606 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_uti... | 606 | 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 r... | 606 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Model... | 606 | 1 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
__a : List[str] = """2.13.1"""
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.pars... | 606 |
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 : Tuple = logging.get_logger(__name__)
... | 606 | 1 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __magic_name__ ( lowercase_ ) -> int:
'''simple docstring'''
UpperCamelCase = os.path.join(args.tf_model_dir , "para... | 606 |
def __magic_name__ ( lowercase_ ) -> List[str]:
'''simple docstring'''
UpperCamelCase = []
UpperCamelCase = set({"(", "[", "{"} )
UpperCamelCase = set({")", "]", "}"} )
UpperCamelCase = {"{": "}", "[": "]", "(": ")... | 606 | 1 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __UpperCAmelCase ... | 606 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
__a : Tuple = logging.getLogger(__name__)
def __magic_name__ ( ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase ... | 606 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPa... | 606 |
# Copyright 2023 The HuggingFace Inc. 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
#
# Unl... | 606 | 1 |
def __magic_name__ ( lowercase_ ) -> str:
'''simple docstring'''
if not all(char in "01" for char in bin_string ):
raise ValueError("Non-binary value was passed to the function" )
if not bin_string:
raise ValueError("Empty string was ... | 606 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__a : Optional[int] = 1_0
def __magic_name__ ( lowercase_ , lowercase_ , lowercase_ , low... | 606 | 1 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
__a : Union[str, Any] = logging.get_logger(__name__)
class __UpperCAmelCase ( snake_case__ ):
"""simple docstring"""
def __init__... | 606 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def __magic_name__ ( ) -> List[str]:
'''simple docstring'''
UpperCamelCase = ArgumentParser("Diffusers CLI tool" , usage="diffusers-cli <command> [<args>]" )
UpperCamelCase ... | 606 | 1 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTeste... | 606 |
from collections.abc import Iterable
from typing import Generic, TypeVar
__a : Optional[Any] = TypeVar("""_T""")
class __UpperCAmelCase ( Generic[_T] ):
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE = None ) ... | 606 | 1 |
from __future__ import annotations
from collections.abc import Iterator
class __UpperCAmelCase :
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE ) -> None:
"""simple docstring"""
UpperCamel... | 606 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __UpperCAmelCase ( snake_case__ , unittest.TestCase ):
"""simple ... | 606 | 1 |
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class __UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( se... | 606 |
__a : Union[str, Any] = 6_5_5_2_1
def __magic_name__ ( lowercase_ ) -> int:
'''simple docstring'''
UpperCamelCase = 1
UpperCamelCase = 0
for plain_chr in plain_text:
UpperCamelCase = (a + ord(lowercase_ )) ... | 606 | 1 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_... | 606 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingf... | 606 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenizati... | 606 |
import warnings
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
cl... | 606 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | 606 |
def __magic_name__ ( lowercase_ ) -> tuple[int, int]:
'''simple docstring'''
try:
UpperCamelCase = float(lowercase_ )
except ValueError:
raise ValueError("Please enter a valid number" )
UpperCamelCase = decimal ... | 606 | 1 |
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
__a : int = [
"""kernels/rwkv/wkv_cuda.cu""",
"""kernels/rwkv/wkv_op.cpp""",
"""kernels/deformable_detr/ms_deform_attn.h""",
"""kernels/deformable_detr/cuda/ms_defor... | 606 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import ... | 606 | 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, prepare_image_inputs
if is_torch_available... | 606 |
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_roberta import Robe... | 606 | 1 |
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __magic_name__ ( lowercase_ , lowercase_ , lowercase_ ) ->... | 606 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a : Optional[Any] = {
"""configuration_distilbert""": [
"... | 606 | 1 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceC... | 606 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
i... | 606 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__a : Union[str, Any] = logging.get_logger(__name__)
__a : str = ... | 606 |
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
... | 606 | 1 |
from typing import Any
import numpy as np
def __magic_name__ ( lowercase_ ) -> bool:
'''simple docstring'''
return np.array_equal(lowercase_ , matrix.conjugate().T )
def __magic_name__ ( lowercase_ , lowercase_ ) -> Any:
'''simpl... | 606 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merge... | 606 | 1 |
__a : Optional[int] = {"""a""": ["""c""", """b"""], """b""": ["""d""", """e"""], """c""": [], """d""": [], """e""": []}
__a : int = ["""a""", """b""", """c""", """d""", """e"""]
def __magic_name__ ( lowercase_ , lowercase_ , lowercase_ ) -> List[str]:
... | 606 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
fro... | 606 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from tra... | 606 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils imp... | 606 | 1 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemantic... | 606 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_uti... | 606 | 1 |
from __future__ import annotations
import math
def __magic_name__ ( lowercase_ ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# N... | 606 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Model... | 606 | 1 |
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __UpperCAmelCase ( unittest.... | 606 |
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 : Tuple = logging.get_logger(__name__)
... | 606 | 1 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class ... | 606 |
def __magic_name__ ( lowercase_ ) -> List[str]:
'''simple docstring'''
UpperCamelCase = []
UpperCamelCase = set({"(", "[", "{"} )
UpperCamelCase = set({")", "]", "}"} )
UpperCamelCase = {"{": "}", "[": "]", "(": ")... | 606 | 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 (
Diffusio... | 606 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
__a : Tuple = logging.getLogger(__name__)
def __magic_name__ ( ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase ... | 606 | 1 |
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_sof... | 606 |
# Copyright 2023 The HuggingFace Inc. 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
#
# Unl... | 606 | 1 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_... | 606 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__a : Optional[int] = 1_0
def __magic_name__ ( lowercase_ , lowercase_ , lowercase_ , low... | 606 | 1 |
from math import sqrt
def __magic_name__ ( lowercase_ ) -> bool:
'''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 num... | 606 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def __magic_name__ ( ) -> List[str]:
'''simple docstring'''
UpperCamelCase = ArgumentParser("Diffusers CLI tool" , usage="diffusers-cli <command> [<args>]" )
UpperCamelCase ... | 606 | 1 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class __UpperCAmelCase ( snake_case__ ... | 606 |
from collections.abc import Iterable
from typing import Generic, TypeVar
__a : Optional[Any] = TypeVar("""_T""")
class __UpperCAmelCase ( Generic[_T] ):
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE = None ) ... | 606 | 1 |
from functools import lru_cache
def __magic_name__ ( lowercase_ ) -> set:
'''simple docstring'''
UpperCamelCase = 2
UpperCamelCase = set()
while i * i <= n:
if n % i:
i += 1
else:
... | 606 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __UpperCAmelCase ( snake_case__ , unittest.TestCase ):
"""simple ... | 606 | 1 |
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