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 |
|---|---|---|---|---|
'''simple docstring'''
from manim import *
class lowercase_ ( __snake_case ):
"""simple docstring"""
def lowerCAmelCase_ ( self : Optional[int] ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = Rectangle(height=0.5 , width=0.5 )... | 418 | import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
__SCREAMING_SNAKE_CASE : List[str] = Mapping[str, np.ndarray]
__SCREAMING_SNAKE_CASE : List[Any] = Mapping[st... | 670 | 0 |
from typing import Any
class lowercase :
"""simple docstring"""
def __init__( self : List[str] , __UpperCAmelCase : int ) -> int:
UpperCAmelCase_= data
UpperCAmelCase_= None
class lowercase :
"""simple docstring"""
def __init... | 593 | 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
if is_tf_available():
... | 670 | 0 |
import math
def UpperCAmelCase ( )-> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = input('''Enter message: ''' )
SCREAMING_SNAKE_CASE_ = int(input(f'''Enter key [2-{len(__lowercase ) - 1}]: ''' ) )
SCREAMING_SNAKE_CASE_ ... | 393 | def snake_case (__lowercase ) -> int:
'''simple docstring'''
if not grid or not grid[0]:
raise TypeError("The grid does not contain the appropriate information" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
_snake... | 670 | 0 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pyt... | 126 | import random
def snake_case (__lowercase , __lowercase ) -> tuple:
'''simple docstring'''
_snake_case ,_snake_case ,_snake_case : List[Any] = [], [], []
for element in data:
if element < pivot:
less.append(__lowercase )
... | 670 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/microsoft/trocr-base-han... | 163 | from math import pow, sqrt
def snake_case (*__lowercase ) -> bool:
'''simple docstring'''
_snake_case : str = len(__lowercase ) > 0 and all(value > 0.0 for value in values )
return result
def snake_case (__lowercase , __lowercase ) -> float | ValueError:
... | 670 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_lowerCAmelCase = {'tokenization_byt5': ['ByT5Tokenizer']}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
_lowerCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _i... | 569 | import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
def __init__( self , *lowercase_ , **lowercase_ ):... | 670 | 0 |
import operator
def lowerCamelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ = False , UpperCAmelCase__ = None ):
"""simple docstring"""
a_ = operator.lt if reverse else operator.gt
a_ = solution or []
if not arr:... | 483 | from __future__ import annotations
from typing import TypedDict
class lowercase_ ( __snake_case ):
_lowerCamelCase = 42
_lowerCamelCase = 42
def snake_case (__lowercase ) -> list[str]:
'''simple docstring'''
if not isinstance(__lowercase , __... | 670 | 0 |
'''simple docstring'''
from __future__ import annotations
def snake_case__ ( UpperCamelCase ,UpperCamelCase ) -> list[list[int]]:
_UpperCamelCase : list[list[int]] = []
_UpperCamelCase : list[int] = []
_UpperCamelCase : Optional[int] = 0
... | 683 | # NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet import ... | 670 | 0 |
"""simple docstring"""
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_to... | 636 | from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from ...test_pipeline_mixin import... | 670 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
lowerCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lower... | 462 | 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 import TokenizerTeste... | 670 | 0 |
"""simple docstring"""
def lowercase__() ->List[str]:
"""simple docstring"""
lowercase__ : Dict= 0
for i in range(1 , 1_001 ):
total += i**i
return str(__lowercase )[-10:]
if __name__ == "__main__":
pri... | 218 | 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 transformers.utils import I... | 670 | 0 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class lowercase_ ( unittest.Test... | 418 | from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__) # pylint: disable=invalid-name
def snake_case (... | 670 | 0 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, tor... | 593 | import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[Any] = loggin... | 670 | 0 |
from collections import defaultdict
def UpperCAmelCase ( UpperCAmelCase )-> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = True
for v in tree[start]:
if v not in visited:
ret += dfs(__... | 393 | import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase_ ( __snake_case ):
_lowerCamelCase = ['image_processor', 'tokenizer']
_lowerCamelCase = 'CLIPImageProcessor'
_lowerCamelCase = ('XLM... | 670 | 0 |
'''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 ( __snake_case , unittest.TestCase ):
_A : Optional[int... | 126 | from __future__ import annotations
def snake_case (__lowercase , __lowercase , __lowercase ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
... | 670 | 0 |
"""simple docstring"""
def __UpperCamelCase ( SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
if not grid or not grid[0]:
raise TypeError("The grid does not contain the appropriate information" )
for cell_n in range(1 , len(grid[0] ) ):
... | 163 | import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def snake_case (*__lowercase ) -> Dict:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ):
_snake_case : Dict = list(__lowercase )... | 670 | 0 |
import argparse
import collections
import os
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_table.py
_lowerCAmelCase = 'src/transformers'
_lowerCAmelCase ... | 569 | __SCREAMING_SNAKE_CASE : Union[str, Any] = {
'a': 'AAAAA',
'b': 'AAAAB',
'c': 'AAABA',
'd': 'AAABB',
'e': 'AABAA',
'f': 'AABAB',
'g': 'AABBA',
'h': 'AABBB',
'i': 'ABAAA',
'j': 'BBBAA',
'k': 'ABAAB',
'l': 'ABABA',
'm': 'ABABB',
'n': 'ABBAA',
'o'... | 670 | 0 |
import random
def lowerCamelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = False ):
"""simple docstring"""
a_ = {i: [] for i in range(__lowercase )}
# if probability is greater or equal than 1, then genera... | 483 | import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import rep... | 670 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : int = logging.get_logger(__name__)
_UpperCAmelCase : int = {
's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}
class UpperC... | 683 | from manim import *
class lowercase_ ( __snake_case ):
def UpperCamelCase ( self ):
_snake_case : Tuple = Rectangle(height=0.5 , width=0.5 )
_snake_case : List[str] = Rectangle(height=0.46 , width=0.46 ).se... | 670 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
A : int = {'tokenization_herbert': ['HerbertTokenizer']}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except Opti... | 636 | import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
_lower... | 670 | 0 |
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.... | 462 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/c... | 670 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __UpperCAmelCase( metaclass=__snake_case ):
"""simple docstring"""
__lowerCamelCase = ["flax"]
def __init__( self , *snake_case__ ,... | 218 | from cva import destroyAllWindows, imread, imshow, waitKey
def snake_case (__lowercase ) -> Tuple:
'''simple docstring'''
_snake_case ,_snake_case : int = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(__lowercase ):
... | 670 | 0 |
'''simple docstring'''
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_tokenizati... | 418 | import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
__SCREAMING_SNAKE_CASE : List[str] = Mapping[str, np.ndarray]
__SCREAMING_SNAKE_CASE : List[Any] = Mapping[st... | 670 | 0 |
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 lowercase ( unittest.TestCase):
"""simple do... | 593 | 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
if is_tf_available():
... | 670 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ = {
'configuration_roberta_prelayernorm': [
'ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Robe... | 393 | def snake_case (__lowercase ) -> int:
'''simple docstring'''
if not grid or not grid[0]:
raise TypeError("The grid does not contain the appropriate information" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
_snake... | 670 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
Dif... | 126 | import random
def snake_case (__lowercase , __lowercase ) -> tuple:
'''simple docstring'''
_snake_case ,_snake_case ,_snake_case : List[Any] = [], [], []
for element in data:
if element < pivot:
less.append(__lowercase )
... | 670 | 0 |
"""simple docstring"""
import os
from math import logaa
def __UpperCamelCase ( SCREAMING_SNAKE_CASE = "base_exp.txt" ) -> int:
"""simple docstring"""
__snake_case = 0
__snake_case = 0
for i, line in enumerate(open(os.path.join(os.path.dirn... | 163 | from math import pow, sqrt
def snake_case (*__lowercase ) -> bool:
'''simple docstring'''
_snake_case : str = len(__lowercase ) > 0 and all(value > 0.0 for value in values )
return result
def snake_case (__lowercase , __lowercase ) -> float | ValueError:
... | 670 | 0 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = OrderedDict(
[
... | 569 | import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
def __init__( self , *lowercase_ , **lowercase_ ):... | 670 | 0 |
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_subproc... | 483 | from __future__ import annotations
from typing import TypedDict
class lowercase_ ( __snake_case ):
_lowerCamelCase = 42
_lowerCamelCase = 42
def snake_case (__lowercase ) -> list[str]:
'''simple docstring'''
if not isinstance(__lowercase , __... | 670 | 0 |
'''simple docstring'''
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Versio... | 683 | # NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet import ... | 670 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = list(__lowercase )
__lowerCAmelCase ... | 636 | from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from ...test_pipeline_mixin import... | 670 | 0 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> Dict[str, torch.Tensor]:
'''simple docstring'''
__UpperCAmelCase : ... | 462 | 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 import TokenizerTeste... | 670 | 0 |
"""simple docstring"""
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto... | 218 | 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 transformers.utils import I... | 670 | 0 |
'''simple docstring'''
from manim import *
class lowercase_ ( __snake_case ):
"""simple docstring"""
def lowerCAmelCase_ ( self : Any ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = Rectangle(height=0.5 , width=0.5 )
_SC... | 418 | from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__) # pylint: disable=invalid-name
def snake_case (... | 670 | 0 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .toke... | 593 | import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[Any] = loggin... | 670 | 0 |
def UpperCAmelCase ( UpperCAmelCase ,UpperCAmelCase )-> Union[str, Any]:
'''simple docstring'''
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def UpperCAmelCase ( UpperCAmelCase ,UpperCAmelCase=0 )-> Optional[Any]:
'''simple ... | 393 | import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase_ ( __snake_case ):
_lowerCamelCase = ['image_processor', 'tokenizer']
_lowerCamelCase = 'CLIPImageProcessor'
_lowerCamelCase = ('XLM... | 670 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 0 , _lowerCAmelCase = 0 )-> int:
__UpperCAmelCase = right or len(__lowercase ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif... | 126 | from __future__ import annotations
def snake_case (__lowercase , __lowercase , __lowercase ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
... | 670 | 0 |
"""simple docstring"""
from __future__ import annotations
def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> float:
"""simple docstring"""
if days_between_payments <= 0:
raise ValueError("days_between_paymen... | 163 | import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def snake_case (*__lowercase ) -> Dict:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ):
_snake_case : Dict = list(__lowercase )... | 670 | 0 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class _UpperCAmelCase ( __snake_case ):
a = '''M-CLIP'''
def __init__( self , a__=1024 , a__=768 , **a__ ):
A_ : Union[str, Any] = tran... | 569 | __SCREAMING_SNAKE_CASE : Union[str, Any] = {
'a': 'AAAAA',
'b': 'AAAAB',
'c': 'AAABA',
'd': 'AAABB',
'e': 'AABAA',
'f': 'AABAB',
'g': 'AABBA',
'h': 'AABBB',
'i': 'ABAAA',
'j': 'BBBAA',
'k': 'ABAAB',
'l': 'ABABA',
'm': 'ABABB',
'n': 'ABBAA',
'o'... | 670 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipI... | 483 | import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import rep... | 670 | 0 |
from math import ceil
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: List[str] = list(range(0 , _UpperCAmelCase ) )
SCREAMING_SNAKE_CASE_: Optional[int] = [item for sublist in list(device_map.val... | 671 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
... | 671 | 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 transformers.utils import... | 671 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ... | 671 | 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
lowerCAmelCase : str = logging.get_logger(__name__)
lowerCAmelCase : Optional[Any] ... | 671 |
from itertools import count
def A_ ( _UpperCAmelCase = 50 ):
SCREAMING_SNAKE_CASE_: Union[str, Any] = [1] * min_block_length
for n in count(_UpperCAmelCase ):
fill_count_functions.append(1 )
for block_length in range(_UpperCAmelC... | 671 | 1 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class __lowercase :
"""simple docstring"""
_UpperCAmelCase : int
_UpperCAmelCase : Node | None = N... | 671 |
def A_ ( _UpperCAmelCase ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError("only integers accepted as input" )
else:
SCREAMING_SNAKE_CASE_: List[Any] = str(abs(_UpperCAmelCase ) )
... | 671 | 1 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : int = logging.get_logger(__name__)
lowerCAmelCase : Dict = {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggingf... | 671 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __lowercase :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : Any):
SCREAMING_SNAKE_CASE_: Any = data
SCREAMING_SN... | 671 | 1 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase__ ... | 671 |
from collections import defaultdict
from math import ceil, sqrt
def A_ ( _UpperCAmelCase = 1_00_00_00 , _UpperCAmelCase = 10 ):
SCREAMING_SNAKE_CASE_: defaultdict = defaultdict(_UpperCAmelCase )
for outer_width in range(3 , (t_limit // 4)... | 671 | 1 |
import argparse
import datetime
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: List[Any] = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wednesday",
"4": "Thursday",
"5": "Friday",
"6": "Satu... | 671 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase : str = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_xlm""": [""... | 671 | 1 |
from math import sqrt
def A_ ( _UpperCAmelCase = 1_00_00_00 ):
SCREAMING_SNAKE_CASE_: int = 0
SCREAMING_SNAKE_CASE_: int = 0
SCREAMING_SNAKE_CASE_: int
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_sh... | 671 |
lowerCAmelCase : List[str] = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
def A_ ( _Upp... | 671 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
Stab... | 671 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase__ ... | 671 | 1 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ... | 671 |
from __future__ import annotations
from math import ceil, floor, sqrt
def A_ ( _UpperCAmelCase = 2_00_00_00 ):
SCREAMING_SNAKE_CASE_: list[int] = [0]
SCREAMING_SNAKE_CASE_: int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ... | 671 | 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
from .... | 671 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase : Optional[int] = {
"""configuration_longformer""": [
"""LONGFORMER_PRETRAINED_CONFIG_ARC... | 671 | 1 |
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_ ( _UpperCAmelCase , _Upper... | 671 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
lowerCAmelCase : Optional[int] = [
# (stable-diffusion, HF Diffusers)
("""time_embed.0.weight""", """time_embedding.li... | 671 | 1 |
import os
def A_ ( ):
SCREAMING_SNAKE_CASE_: List[str] = os.path.dirname(os.path.realpath(_UpperCAmelCase ) )
SCREAMING_SNAKE_CASE_: str = os.path.join(_UpperCAmelCase , "triangle.txt" )
with open(_UpperCAmelCase ) a... | 671 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : int = logging.get_logger(__name__)
lowerCAmelCase : Dict = {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggingf... | 671 | 1 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: str = arg... | 671 |
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampli... | 671 | 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 .tokenization_camembert imp... | 671 |
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
lowerCAmelCase : Optional[int] = logging.get_logger(__... | 671 | 1 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, 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... | 671 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
... | 671 | 1 |
class __lowercase :
"""simple docstring"""
def __init__( self : Any , lowerCAmelCase__ : str = "" , lowerCAmelCase__ : bool = False):
# Mapping from the first character of the prefix of the node
SCREAMING_SNAKE_CASE_: dict[str, RadixNode] = ... | 671 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
lowerCAmelCase : List[Any] = WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def A_ ( _UpperCAmelCase ... | 671 | 1 |
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Union[str, Any] = set()
# edges = list of graph's edges
SCREAMING_SNAKE_CASE_: List[str] = get_edges(_UpperCAmelCase )
# While there are still elements in edges list, take an arbitrary edge... | 671 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Dis... | 671 | 1 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
lowerCAmelCase : Optional[int] ... | 671 |
from math import asin, atan, cos, radians, sin, sqrt, tan
lowerCAmelCase : Union[str, Any] = 637_8137.0
lowerCAmelCase : int = 635_6752.31_4245
lowerCAmelCase : Union[str, Any] = 6378137
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _Up... | 671 | 1 |
import math
def A_ ( _UpperCAmelCase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
... | 671 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
# Initialise PyTorch... | 671 | 1 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWit... | 671 |
import math
def A_ ( _UpperCAmelCase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
... | 671 | 1 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, 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 ModelTesterMixin, ids_tensor, ra... | 671 |
import re
def A_ ( _UpperCAmelCase ):
return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]" , str_ )]
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: int = split_input(str_ )
return "".join(
... | 671 | 1 |
import functools
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: int = len(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_: Optional[int] = len(_UpperCAmelCase )
@functools.cache
def min_distance(_UpperC... | 671 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
... | 671 | 1 |
from itertools import count
def A_ ( _UpperCAmelCase = 50 ):
SCREAMING_SNAKE_CASE_: Union[str, Any] = [1] * min_block_length
for n in count(_UpperCAmelCase ):
fill_count_functions.append(1 )
for block_length in range(_UpperCAmelC... | 671 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ... | 671 | 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 .tokenization_xlnet import ... | 671 |
from itertools import count
def A_ ( _UpperCAmelCase = 50 ):
SCREAMING_SNAKE_CASE_: Union[str, Any] = [1] * min_block_length
for n in count(_UpperCAmelCase ):
fill_count_functions.append(1 )
for block_length in range(_UpperCAmelC... | 671 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
lowerCAme... | 671 |
def A_ ( _UpperCAmelCase ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError("only integers accepted as input" )
else:
SCREAMING_SNAKE_CASE_: List[Any] = str(abs(_UpperCAmelCase ) )
... | 671 | 1 |
import random
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Union[str, Any] = a[left_index]
SCREAMING_SNAKE_CASE_: Any = left_index + 1
for j in range(left_index + 1 , _Upp... | 671 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __lowercase :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : Any):
SCREAMING_SNAKE_CASE_: Any = data
SCREAMING_SN... | 671 | 1 |
lowerCAmelCase : Optional[Any] = frozenset(
[
"""prompt""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
]
)
lowerCAmelCase... | 671 |
from collections import defaultdict
from math import ceil, sqrt
def A_ ( _UpperCAmelCase = 1_00_00_00 , _UpperCAmelCase = 10 ):
SCREAMING_SNAKE_CASE_: defaultdict = defaultdict(_UpperCAmelCase )
for outer_width in range(3 , (t_limit // 4)... | 671 | 1 |
from collections import defaultdict
from math import ceil, sqrt
def A_ ( _UpperCAmelCase = 1_00_00_00 , _UpperCAmelCase = 10 ):
SCREAMING_SNAKE_CASE_: defaultdict = defaultdict(_UpperCAmelCase )
for outer_width in range(3 , (t_limit // 4)... | 671 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase : str = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_xlm""": [""... | 671 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
lowerCAmelCase : Dict = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pa... | 671 |
lowerCAmelCase : List[str] = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
def A_ ( _Upp... | 671 | 1 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
# Initialise PyTorch... | 671 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase__ ... | 671 | 1 |
class __lowercase :
"""simple docstring"""
def __init__( self : int , lowerCAmelCase__ : Optional[int]):
SCREAMING_SNAKE_CASE_: Optional[int] = val
SCREAMING_SNAKE_CASE_: List[Any] = None
SCREAMING_SNAKE_CASE_: List[An... | 671 |
from __future__ import annotations
from math import ceil, floor, sqrt
def A_ ( _UpperCAmelCase = 2_00_00_00 ):
SCREAMING_SNAKE_CASE_: list[int] = [0]
SCREAMING_SNAKE_CASE_: int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ... | 671 | 1 |
lowerCAmelCase : List[str] = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
def A_ ( _Upp... | 671 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase : Optional[int] = {
"""configuration_longformer""": [
"""LONGFORMER_PRETRAINED_CONFIG_ARC... | 671 | 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():
import tor... | 671 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
lowerCAmelCase : Optional[int] = [
# (stable-diffusion, HF Diffusers)
("""time_embed.0.weight""", """time_embedding.li... | 671 | 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,
)
lowerCAmelCase : Optional[Any] = {"""configuration_mbart""": ... | 671 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : int = logging.get_logger(__name__)
lowerCAmelCase : Dict = {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggingf... | 671 | 1 |
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
lowerCAmelCase : List[str] = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3,... | 671 |
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampli... | 671 | 1 |
from ..utils import DummyObject, requires_backends
class __lowercase ( metaclass=UpperCAmelCase_ ):
"""simple docstring"""
_UpperCAmelCase : Tuple = ['''onnx''']
def __init__( self : List[str] , *lowerCAmelCase__ : Optional[int] , **lowerCAmelCase... | 671 |
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
lowerCAmelCase : Optional[int] = logging.get_logger(__... | 671 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase : Union[str, Any] = {
"""configuration_efficientformer""": [
"""EFFICIENTFORMER_PRETRAINED_CO... | 671 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
... | 671 | 1 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
lowerCAmelCase : List[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_copies # noqa: E402
# This is ... | 671 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
lowerCAmelCase : List[Any] = WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def A_ ( _UpperCAmelCase ... | 671 | 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 required by applica... | 671 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Dis... | 671 | 1 |
import collections
import os
import re
from pathlib import Path
lowerCAmelCase : Union[str, Any] = """src/transformers"""
# Matches is_xxx_available()
lowerCAmelCase : Optional[int] = re.compile(R"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
lowerCAmelCas... | 671 |
from math import asin, atan, cos, radians, sin, sqrt, tan
lowerCAmelCase : Union[str, Any] = 637_8137.0
lowerCAmelCase : int = 635_6752.31_4245
lowerCAmelCase : Union[str, Any] = 6378137
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _Up... | 671 | 1 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_... | 671 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
# Initialise PyTorch... | 671 | 1 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: List[Any] = AutoConfig.from_pretrained(_UpperCAmelCase ... | 671 |
import math
def A_ ( _UpperCAmelCase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
... | 671 | 1 |
from __future__ import annotations
from math import ceil, floor, sqrt
def A_ ( _UpperCAmelCase = 2_00_00_00 ):
SCREAMING_SNAKE_CASE_: list[int] = [0]
SCREAMING_SNAKE_CASE_: int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ... | 671 |
import re
def A_ ( _UpperCAmelCase ):
return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]" , str_ )]
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: int = split_input(str_ )
return "".join(
... | 671 | 1 |
from __future__ import annotations
lowerCAmelCase : Any = tuple[int, int, int]
lowerCAmelCase : str = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
lowerCAmelCase : Union[str, Any] = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
# ------... | 671 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
... | 671 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase : Union[str, Any] = {
"""configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig"""],
}
try:
... | 671 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ... | 671 | 1 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
lowerCAmelCase : str = None
try:
import msvcrt
except ImportError:
lowerCAmelCase : Optional[int] = None
try:
import fcntl
except ImportError:
lowerCAmelCa... | 671 |
from itertools import count
def A_ ( _UpperCAmelCase = 50 ):
SCREAMING_SNAKE_CASE_: Union[str, Any] = [1] * min_block_length
for n in count(_UpperCAmelCase ):
fill_count_functions.append(1 )
for block_length in range(_UpperCAmelC... | 671 | 1 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
lowerCAmelCase : Tuple = logging.getLogger()
@unittest.skip('''Temporarily disable the doc test... | 671 |
def A_ ( _UpperCAmelCase ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError("only integers accepted as input" )
else:
SCREAMING_SNAKE_CASE_: List[Any] = str(abs(_UpperCAmelCase ) )
... | 671 | 1 |
from datetime import datetime as dt
import os
from github import Github
lowerCAmelCase : int = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""feature request""",
"""new model""",
"""wip""",
]
def A_ ( ):
SCREAMING_SNAK... | 671 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __lowercase :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : Any):
SCREAMING_SNAKE_CASE_: Any = data
SCREAMING_SN... | 671 | 1 |
from collections.abc import Callable
import numpy as np
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Tuple = int(np.ceil((x_end - xa) / step_size ... | 671 |
from collections import defaultdict
from math import ceil, sqrt
def A_ ( _UpperCAmelCase = 1_00_00_00 , _UpperCAmelCase = 10 ):
SCREAMING_SNAKE_CASE_: defaultdict = defaultdict(_UpperCAmelCase )
for outer_width in range(3 , (t_limit // 4)... | 671 | 1 |
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
return [sentence[i : i + ngram_size] for i in range(len(_UpperCAmelCase ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 671 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase : str = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_xlm""": [""... | 671 | 1 |
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_c... | 671 |
lowerCAmelCase : List[str] = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
def A_ ( _Upp... | 671 | 1 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class __lowercase :
"""simple docstring"""
_UpperCAmelCase : Optional[Union[str, Path]] = None
_UpperCAmelCase : bool = False
_UpperCAm... | 671 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase__ ... | 671 | 1 |
def A_ ( _UpperCAmelCase ):
try:
SCREAMING_SNAKE_CASE_: int = float(_UpperCAmelCase )
except ValueError:
raise ValueError("Please enter a valid number" )
SCREAMING_SNAKE_CASE_: str = decimal - int(_UpperCAmelCase ... | 671 |
from __future__ import annotations
from math import ceil, floor, sqrt
def A_ ( _UpperCAmelCase = 2_00_00_00 ):
SCREAMING_SNAKE_CASE_: list[int] = [0]
SCREAMING_SNAKE_CASE_: int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ... | 671 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase : Optional[Any] = {"""configuration_unispeech""": ["""UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP""", """UniSpe... | 671 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase : Optional[int] = {
"""configuration_longformer""": [
"""LONGFORMER_PRETRAINED_CONFIG_ARC... | 671 | 1 |
# 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
#
# Unless required by ap... | 671 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
lowerCAmelCase : Optional[int] = [
# (stable-diffusion, HF Diffusers)
("""time_embed.0.weight""", """time_embedding.li... | 671 | 1 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, 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_mask
from ... | 671 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : int = logging.get_logger(__name__)
lowerCAmelCase : Dict = {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggingf... | 671 | 1 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
... | 671 |
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampli... | 671 | 1 |
def A_ ( ):
return 1
def A_ ( _UpperCAmelCase ):
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def A_ ( _UpperCAmelCase ):
return 0 if x < 0 else five_pence(x - 5 ) + two_pence(_UpperCAmelCase )
def A_ ... | 671 |
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
lowerCAmelCase : Optional[int] = logging.get_logger(__... | 671 | 1 |
def A_ ( _UpperCAmelCase ):
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError("'str' ... | 671 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
... | 671 | 1 |
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