code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import copy
import re
class snake_case_ :
__lowerCAmelCase = "hp"
__lowerCAmelCase = {}
__lowerCAmelCase = None
@classmethod
def snake_case_ ( cls , a_ , a_ ):
a_ :... | 237 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE_ = {
"""configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
"""tokenization_ctrl... | 237 | 1 |
from __future__ import annotations
from typing import Generic, TypeVar
lowercase : List[str] = TypeVar("""T""")
class A__ ( Generic[T] ):
"""simple docstring"""
def __init__( self , lowercase) -> None:
'''simple docstring'''
... | 710 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
lowercase : Any = logging.get_logger(__name__)
def A_ ( A__ , A__ ) -> List[Any]:
a__ : List[str]... | 392 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from t... | 24 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
__UpperCamelCase : Optional[Any] = tuple[int, int]
class a :
def __init__( self , _snake_case , _snake_case ):
"""simple docstri... | 4 | 0 |
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 snake_case__ (__UpperCAmelCase ):
"""simple d... | 717 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
__lowerCAmelCase : int = {'tokenization_tapex': ['TapexTokenizer']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
__lowerCAmelCase : Tuple = _LazyModule(__name__, globals()['__fi... | 662 | 0 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowerCamelCase__ ( _A):
"""simple docstring"""
a_... | 2 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr... | 596 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
SCREAMING_SNAKE_CASE_ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable... | 714 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.jso... | 116 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ : Tuple = {
'configuration_albert': ['A... | 278 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
... | 67 | 0 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
__lowerCamelCase = """examples/"""
__lowerCamelCase = {
"""examples""": (re.compile(r'''^check_min_version\(\"[^\"]+\"\)\s*$''', re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
"""init""... | 701 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAm... | 667 | 0 |
'''simple docstring'''
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowerCAmelCase_ ( __magic_name__ ):
__lowerCamelCas... | 18 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase_ ( __magic_name__ ):
__lowerCam... | 18 | 1 |
"""simple docstring"""
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_a = get_tests_dir("""fixtures/test_sen... | 716 |
"""simple docstring"""
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class _UpperCAmelCase(... | 78 | 0 |
"""simple docstring"""
from __future__ import annotations
def a ( __UpperCAmelCase : int | str ) -> bool:
__magic_name__: List[str] = str(__UpperCAmelCase )
return n == n[::-1]
def a ( __UpperCAmelCase : int = ... | 96 |
from __future__ import annotations
from collections.abc import MutableSequence
class _A :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if len(_SCREAMING_SNAKE_CASE ) != degree + 1:
raise V... | 511 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A : int = {"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", "... | 701 |
'''simple docstring'''
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class lowercase ( _lowerCa... | 187 | 0 |
from maths.prime_factors import prime_factors
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
__lowercase = F'Input value of [number={number}] must be an integer'
raise TypeError(lowercase__ )
if ... | 80 |
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 TokenizerTesterMi... | 600 | 0 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import Fla... | 30 | '''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=_UpperCAmelCase)
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = fiel... | 30 | 1 |
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class lowerCAmelCase_ :
lowerCamelCase_ = 42 # [batch_size x 3]
lowerCamelCase_ = 42 # [batch_size x 3]
lowerCamelCase_ = 42 # [batch_size x 3]
lowerCamelCase_ =... | 395 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/c... | 502 | 0 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since ... | 720 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : List[Any] = logging.get_logger(__name__)
snake_case_ : Dict = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
#... | 253 | 0 |
import os
def UpperCamelCase__ ( ):
'''simple docstring'''
__lowerCamelCase = os.path.dirname(os.path.realpath(_A ) )
__lowerCamelCase = os.path.join(_A , """triangle.txt""" )
with open(_A ) as f:
__lowerCamelCa... | 479 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_a : ... | 479 | 1 |
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
parser.add_argument("""--dump_path""", default=None, ty... | 427 |
'''simple docstring'''
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state... | 427 | 1 |
# using dfs for finding eulerian path traversal
def _lowerCAmelCase ( lowerCAmelCase_ :Optional[Any] , lowerCAmelCase_ :Dict , lowerCAmelCase_ :Union[str, Any] , lowerCAmelCase_ :Dict=None )->Optional[int]:
'''simple docstring'''
snake_case_ ... | 283 |
def _lowerCAmelCase ( lowerCAmelCase_ :List[str] , lowerCAmelCase_ :int )->Optional[int]:
'''simple docstring'''
snake_case_ = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def _lowe... | 283 | 1 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
UpperCamelCase_ = re.compile(r"\b(a|an|the)\b", re.UNICODE)
UpperCamelCase_ = None
def A ( ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase_... | 561 |
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
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = ... | 561 | 1 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUM... | 105 |
'''simple docstring'''
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase ( lowercase_ ):
'''simple docstring'''
__snake_case = (IPNDMScheduler,)
__snake_case = (('num_in... | 215 | 0 |
"""simple docstring"""
def lowercase ( a__ : Optional[int] ) -> int:
assert (
isinstance(UpperCamelCase__ , UpperCamelCase__ ) and number_of_steps > 0
), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
if number_of_steps == 1:
retu... | 703 | """simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokeniz... | 342 | 0 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
fr... | 103 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transfo... | 392 | 0 |
import unittest
from knapsack import knapsack as k
class __lowerCAmelCase ( unittest.TestCase ):
def A__ ( self ) -> List[str]:
'''simple docstring'''
_lowercase =0
_lowercase =[0]
_lowercase ... | 380 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging ... | 380 | 1 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torc... | 504 |
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... | 504 | 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 import BertTokenizer
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelC... | 297 |
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_image_inputs
if is_tor... | 297 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable... | 282 |
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.d... | 282 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
__a = logging.get_logger(__name__)
class A__ ( UpperCamelCase ):
"""simple docstring"""
def __init__( self : List[str] , *lowerCAmelCa... | 720 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__a = {
'configuration_swiftformer': [
'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SwiftFormerConfig',
'SwiftF... | 257 | 0 |
def UpperCamelCase_ ( lowerCAmelCase__ = 50_00_00_00 ):
"""simple docstring"""
_lowerCAmelCase : int = set()
_lowerCAmelCase : int = int((limit - 24) ** (1 / 2) )
_lowerCAmelCase : Union[str, Any] = set(range(3 , prime_square_limit + 1 ... | 424 | import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_V... | 424 | 1 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__lowerCAmelCase : Tuple = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_copies # noqa: E402
# This is the ref... | 662 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCAmelCase : Any = {
'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
'tokenization_roc_bert': ['RoCBertTok... | 662 | 1 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDi... | 35 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a_ :Tuple = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
a_ :Optional[int] = _LazyMod... | 35 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion imp... | 701 | '''simple docstring'''
_lowerCamelCase : Union[str, Any] = """0.18.2"""
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_avail... | 512 | 0 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_re... | 72 |
'''simple docstring'''
import sys
_UpperCAmelCase : Dict = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''6... | 72 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase = {
'''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}
try:
if no... | 22 |
"""simple docstring"""
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't ... | 22 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"""tanreinama/GPTSAN-2.8B-spout_is_uniform""": (
"""https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_i... | 88 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _snake_case ( ):
"""simple docstring"""
_lowerCamelCase : Any = HfArgumentParser(__snake_case )
_lowerCamelCase : int = parser.pa... | 88 | 1 |
'''simple docstring'''
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class a ( __magic_name__ ):
@require_torch
def __snake_case ( self : int )... | 555 |
'''simple docstring'''
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
UpperCAmelCase = "src/transformers"
# This is... | 555 | 1 |
"""simple docstring"""
import logging
from transformers.configuration_utils import PretrainedConfig
a_ = logging.getLogger(__name__)
class UpperCAmelCase_ ( snake_case ):
UpperCamelCase ="masked_bert"
def __init__( self , UpperCamelCase_=3_05_2... | 76 |
def _a ( __UpperCamelCase : int ):
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
raise ValueError('''check_bouncy() accepts only integer arguments''' )
lowerCAmelCase__ : List[str] = str(__UpperCamelCase )
lowerCAmelCase__ : List[Any] ... | 233 | 0 |
from __future__ import annotations
__lowerCAmelCase : Optional[int] = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__lowerCAmelCase : Optional[Any] = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def a_ (_lowerCAmelCase : list[float] ... | 718 | from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase ( __snake_case ):
__lowerCamelCase = ['image_processor', 'tokenizer']
__lowerCamelCase = 'AutoImageProcessor'
__lowerCamelCase = 'AutoT... | 164 | 0 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class _A :
"""simple docstring"""
pass | 564 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMix... | 504 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase :Optional[int] = {
'configuration_mask2former': [
'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Mask2FormerConfig',
],... | 707 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def __snake_case ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Dict:
# Initia... | 346 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,... | 675 |
'''simple docstring'''
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
a_ : Any = [
"""Prosecutor: \"No videos were used in the crash investigation\" German paper... | 675 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCAmelCase = {"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}
try:
if not is_vision_available(... | 589 |
def _lowercase ( a__ : int , a__ : int ) -> Dict:
"""simple docstring"""
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(a__ , int(b / 2 ) ) * actual_power(a__ , int(b / 2 ) )
else:
return a * actual_power(a__ , int(b / 2... | 589 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
_snake_case = TypeVar("""T""")
_snake_case = TypeVar("""U""")
class lowerCAmelCase ( Generic[T, U] ):
def __init__( self :int , _lowercas... | 655 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCAmelCase ( ... | 655 | 1 |
def __magic_name__ ( lowercase = 100 ) -> int:
"""simple docstring"""
lowercase_ : int = n * (n + 1) * (2 * n + 1) / 6
lowercase_ : List[Any] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_... | 436 |
from ...configuration_utils import PretrainedConfig
UpperCAmelCase_ = {
"""google/tapas-base-finetuned-sqa""": (
"""https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"""
),
"""google/tapas-base-finetuned-wtq""": (
"""https://hug... | 436 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
UpperCAmelCase__ : Optional[str] = field(
default='''codeparrot/codeparrot''' , metadata={'''help''': '''Mod... | 664 |
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
__magic_name__ : str ={
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Ima... | 664 | 1 |
'''simple docstring'''
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_... | 703 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
"uclanlp/visualbert-... | 557 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def ... | 696 |
import numpy as np
a_ = [
['''a''', '''b''', '''c''', '''d''', '''e'''],
['''f''', '''g''', '''h''', '''i''', '''k'''],
['''l''', '''m''', '''n''', '''o''', '''p'''],
['''q''', '''r''', '''s''', '''t''', '''u'''],
['''v''', '''w''', '''x''', '''y''', '''z'''],
]
class lowercase__ ... | 339 | 0 |
"""simple docstring"""
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
... | 614 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 1_00 , ) -> float:
"""simple docstring... | 614 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a__: int = {
'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
'tokenization_m2... | 190 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ , unittest.TestCase ):
__SCREAMING... | 190 | 1 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
__lowerCamelCase = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow('', '|', '|'),
dataro... | 328 |
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common impor... | 328 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPImag... | 659 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModel... | 659 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor... | 138 | SCREAMING_SNAKE_CASE : List[Any] = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .features import A... | 138 | 1 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
def snake_case_ ( A_ : int, A_ : int ):
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
... | 83 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffuse... | 243 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor... | 171 |
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def __lowerCamelCase ( ... | 171 | 1 |
# Copyright 2022 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 app... | 326 |
def A_ ( lowercase_ , lowercase_ , lowercase_ ) -> float:
return round(float(moles / volume ) * nfactor )
def A_ ( lowercase_ , lowercase_ , lowercase_ ) -> float:
return round(float((moles * 0.08_21 * temperature) / (volume) ) )
def A_ ... | 326 | 1 |
"""simple docstring"""
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
E... | 714 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase__ =... | 549 | 0 |
import math
def A_ ( A__ ) -> str:
a__ : Any = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(lowercase_ )
def A_ ( A__ = 1 / 1_2345 ) -> str:
a__ : Dict = ... | 302 |
'''simple docstring'''
import argparse
import os
import re
__lowerCAmelCase = "src/diffusers"
# Pattern that looks at the indentation in a line.
__lowerCAmelCase = re.compile(R"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
__lowerCAmelCase = ... | 536 | 0 |
from __future__ import annotations
from typing import Any
def __A(lowerCAmelCase ) -> None:
"""simple docstring"""
create_state_space_tree(lowerCAmelCase , [] , 0 )
def __A(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) -> None:
"""simple docstring""... | 202 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def __A(lowerCAmelCase ) -> List[str]:
"""simple docstring"""
if "model" in orig_key:
_UpperCamelCase = orig_key.replace("""model.""" , """""" )
if "norm1" in orig_key:
_UpperCamelCase ... | 202 | 1 |
def __magic_name__ ( __a : str , __a : str ):
'''simple docstring'''
UpperCamelCase__ = len(__a )
UpperCamelCase__ = []
for i in range(len(__a ) - pat_len + 1 ):
UpperCamelCase__ = True
... | 513 |
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
lowerCamelCase_ = logging.get_logger(__name__)
class __A( __lowerCamelCase ):
"""simple docstring"""
def __init__(self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_... | 513 | 1 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_... | 189 |
class a :
def __init__( self : Any , SCREAMING_SNAKE_CASE_ : str = "" , SCREAMING_SNAKE_CASE_ : bool = False ):
# Mapping from the first character of the prefix of the node
__lowerCamelCase: dict[str, RadixNode] = {}
# A node wi... | 189 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
lowerCamelCase_ = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.j... | 418 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase_ = logging.get_logger(... | 418 | 1 |
from itertools import permutations
def UpperCAmelCase__ ( lowerCamelCase ):
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
lowercase :Optional[int] = [7, 11, 13, 17]
for i, test in enumera... | 453 |
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase ):
return int((input_a, input_a).count(0 ) != 0 )
def UpperCAmelCase__ ( ):
assert nand_gate(0, 0 ) == 1
assert nand_gate(0, 1 ) == 1
assert nand_gate(1, 0 ) == 1
assert nand_gate(1, 1 ) == 0
if __nam... | 453 | 1 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
lowerCamelCa... | 474 |
'''simple docstring'''
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class _UpperCamelCase ( A , unittest.TestCase ):
'''simple docstring'''
lowerCAm... | 474 | 1 |
from collections.abc import Generator
def _a ( ) -> Generator[int, None, None]:
lowerCamelCase_ , lowerCamelCase_ : int = 0, 1
while True:
lowerCamelCase_ , lowerCamelCase_ : Any = b, a + b
yield b
def _a ( lowerCamelCase... | 144 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
# See all GPTNeoX models at https:... | 144 | 1 |
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,
)
_A : Any = {"""configuration_xglm""": ["""XGLM_PRETRAINED_C... | 100 |
'''simple docstring'''
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 GenerationT... | 41 | 0 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
_lowercase = 2048
_lowercase = 4096
_lowercase = 42
_lowercase = os.environ.pop("""PROCESS_TRAIN""", """false""")
_lowercase = {"""null""": 0, """short""": 1, """long""": 2, """yes""": 3, """... | 431 |
# Function to print upper half of diamond (pyramid)
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : int ) -> Dict:
for i in range(0 , UpperCAmelCase_ ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(''' ''' , end... | 431 | 1 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class lowercase_ ( lowercase , unittest.TestCase ):
'''sim... | 117 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 117 | 1 |
import torch
from transformers import AutoModel
class UpperCAmelCase__ ( torch.nn.Module ):
"""simple docstring"""
def __init__( self: List[str] , __lowerCAmelCase: Any="sayef/fsner-bert-base-uncased" ) -> Dict:
'''simple docstring'''
super(__lowerCAmelCase ,... | 286 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a_ = {"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}
try:
if not is_... | 286 | 1 |
"""simple docstring"""
from __future__ import annotations
from scipy.special import comb # type: ignore
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Tuple , UpperCAmelCase_ : list[tuple[float, float]] ) -> int:... | 580 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, rand... | 580 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase = 10**9 ) -> int:
'''simple docstring'''
lowercase_ = 1
lowercase_ = 2
lowercase_ = 0
lowercase_ = 0
lowercase_ = 0
while perimeter <= max_perimeter:... | 100 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : List[Any] = logging.get_logger(__name__)
UpperCAmelCase : Optional[Any] = {
"alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resol... | 100 | 1 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, se... | 55 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
... | 249 | 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 logging
lowercase_ = logging.get_logger(__name__)
lowercase_ =... | 703 |
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
lowercase... | 230 | 0 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, se... | 463 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_... | 59 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDepend... | 713 |
def UpperCAmelCase__ ( lowerCamelCase_ : int ):
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise ValueError('Input must be an integer' )
if input_num <= 0:
raise ValueError('Input must be positive' )
return sum(
d... | 577 | 0 |
def _a ( SCREAMING_SNAKE_CASE__ : list , SCREAMING_SNAKE_CASE__ : list , SCREAMING_SNAKE_CASE__ : int ) -> int:
'''simple docstring'''
if len(SCREAMING_SNAKE_CASE__ ) != len(SCREAMING_SNAKE_CASE__ ):
raise ValueError("... | 663 |
def _a ( SCREAMING_SNAKE_CASE__ : List[Any]=2_81_23 ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
... | 663 | 1 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
UpperCAmelCase = logging.get_logger(__name__)
class A_ ( __lowerCamelCase ):
'''simple docstring'''
def __init__( self , *snake_case , **snake_case ):
warnings.warn(
... | 565 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
Upp... | 565 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=_lowerCAmelCase )
class a (_lowerCAmelCase ):
"""simple docstring"""
__UpperCAmelCase : str = ... | 81 |
from __future__ import annotations
def lowerCAmelCase_ ( __lowerCamelCase ):
if not nums:
return 0
__snake_case : Any = nums[0]
__snake_case : str = 0
for num in nums[1:]:
__snake_case , __snake_c... | 81 | 1 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
@require_torch
def ... | 707 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffu... | 167 | 0 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def SCREAMING_SNAKE_CASE_ ( __A : Any ) -> List[str]:
"""simple docstring"""... | 570 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
snake_case__ : Optional[int] = (DDPMParallelScheduler,)
def SCREAMING_SNAKE_CASE ( self : Optional... | 570 | 1 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
lowerCAmelCase__ = True
except (ImportError, ModuleNotFoundError):
lowerCAmelCase__ = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
def _A ... | 718 |
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
... | 624 | 0 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __a ( A... | 649 |
'''simple docstring'''
def __a ( A__ , A__ ) -> Optional[int]:
_enforce_args(A__ , A__ )
if n == 0:
return 0
lowerCAmelCase = float("-inf" )
for i in range(1 , n + 1 ):
lowerCAmelCase = max(
... | 649 | 1 |
def lowerCAmelCase ( lowerCAmelCase_ = 1_000_000 ):
lowerCAmelCase_ : str = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , _lowerCamelCase ):
phi[j] -= phi[j] // i
ret... | 717 |
from manim import *
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
def lowercase_ ( self ) -> Tuple:
lowerCAmelCase_ : Dict = Rectangle(height=0.5 , width=0.5 )
lowerCAmelCase_ : T... | 619 | 0 |
"""simple docstring"""
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
_SCREAMING_SNAKE_CASE : Tuple = logging.get_logger... | 549 |
"""simple docstring"""
def lowerCamelCase__ ( _lowerCamelCase : list[list[int | float]] ) -> int:
lowerCamelCase_ = len(_lowerCamelCase )
lowerCamelCase_ = len(matrix[0] )
lowerCamelCase_ = min(_lowerCamelCase , _lowerCa... | 549 | 1 |
def UpperCamelCase ( lowercase_ ) -> int:
'''simple docstring'''
assert (
isinstance(lowercase_ , lowercase_ ) and number_of_steps > 0
), F'number_of_steps needs to be positive integer, your input {number_of_steps}'
if number_of_steps == 1:
return 1
lowercase__ ... | 495 |
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - ... | 495 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ : Dict = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''... | 105 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _a ( datasets.BeamBasedBuilder ):
"""simple docstring"""
... | 23 | 0 |
"""simple docstring"""
from itertools import permutations
def a_ ( _lowerCAmelCase : tuple ):
'''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:
... | 645 | """simple docstring"""
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.num... | 645 | 1 |
import json
import sys
def UpperCamelCase_( snake_case__: str , snake_case__: List[Any] ) -> Dict:
with open(snake_case__ , encoding='utf-8' ) as f:
UpperCAmelCase__ = json.load(snake_case__ )
UpperCAmelCase__ = ['<details>', '<summ... | 146 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
_UpperCamelCase = (
'''4S 3H 2C 7S 5H''',
'''9D 8H 2C 6S 7H''',
'''2D 6D 9D TH 7D''',
'''TC 8C 2S JH 6C''',
'''JH 8S TH AH QH''',
'''TS KS 5S 9S A... | 146 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_t... | 716 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : list ):
if len(UpperCAmelCase_ ) <= 1:
return lst
A__ = 1
while i < len(UpperCAmelCase_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
A__ ... | 500 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAv... | 624 |
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 transformers import HfArgumentParser, Tr... | 39 | 0 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
lowercase : Tuple = [
# tf -> hf
("/", "."),
("layer_", "layers."),
("kernel", "weight"),
("b... | 703 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipeline... | 542 | 0 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
a_ : str = ... | 194 |
from typing import Any
class A__ :
def __init__( self , lowerCamelCase ) -> Dict:
"""simple docstring"""
__magic_name__ : List[str] = data
__magic_name__ : List[Any] = None
de... | 154 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class __UpperCamelCase ( lowercase__ ):
def __init__( self :Any ,_UpperCamelCase :Union[str, Any] ,_UpperCamelCase :Any ):
snake_case_ : Opt... | 267 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
__A : Tuple = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 267 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : List[Any] = {
"facebook/timesformer": "https://huggingface.co/facebook/timesformer/resolve/main/config.json",
}
class ... | 63 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:... | 420 | 0 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import requ... | 715 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPIN... | 11 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase__ = {'configuration_encoder_decoder': ['EncoderDecoderConfig']}
try:
... | 75 |
'''simple docstring'''
import numpy
# List of input, output pairs
lowercase : Any = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
lowercase : str = (((5_15, 22, 13), 5_55), ((61,... | 634 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
class A( UpperCame... | 712 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase : Dict = {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARC... | 651 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a : Tuple = logging.get_logger(__name__)
a : List[str] = {
'''microsoft/focalnet-tiny''': '... | 639 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
a : Dict = '''2.13.1'''
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('''3.7'''):
raise ... | 639 | 1 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
SCREAMING_SNAKE_CASE__ : Union[str, ... | 636 | import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_S... | 636 | 1 |
'''simple docstring'''
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def lowerCamelCase_ ( __UpperCamelCase : Sequence[float] , __UpperCamelCase : int , __Up... | 292 |
'''simple docstring'''
from statistics import mean, stdev
def lowerCamelCase_ ( __UpperCamelCase : list , __UpperCamelCase : int = 3 ) -> list:
"""simple docstring"""
_A = min(__UpperCamelCase )
_A = max(__UpperCamelCase... | 292 | 1 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> float:
'''simple docstring'''
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
... | 593 |
'''simple docstring'''
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... | 593 | 1 |
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