code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
SCREAMING_SNAKE_CASE_:Union[str, Any] = """Usage of script: script_name <size_of_canvas:int>"""
SCREAMING_SNAKE_CASE_:Union[str, Any] = [0] * 100 + [1] * 10
random.shuffle(choice)... | 662 |
from typing import Any
import numpy as np
def __UpperCamelCase ( _lowerCAmelCase ) -> bool:
"""simple docstring"""
return np.array_equal(_lowerCAmelCase , matrix.conjugate().T )
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> ... | 662 | 1 |
from math import asin, atan, cos, radians, sin, sqrt, tan
SCREAMING_SNAKE_CASE_:Dict = 6_378_137.0
SCREAMING_SNAKE_CASE_:List[str] = 6_356_752.314_245
SCREAMING_SNAKE_CASE_:Union[str, Any] = 6_378_137
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerC... | 662 |
from __future__ import annotations
import numpy as np
def __UpperCamelCase ( _lowerCAmelCase ) -> tuple[np.ndarray, np.ndarray]:
"""simple docstring"""
A , A : int = np.shape(_lowerCAmelCase )
if rows != columns:
A : Union[str, Any] = ... | 662 | 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 ... | 662 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STA... | 662 | 1 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from tra... | 662 |
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self, lowerCamelCase__ ):
# we need a list not a string, so do something to change the type
A : List[Any] = arr.split(""",""" )
def _lowerCAmelCase ( self ):
... | 662 | 1 |
import numpy as np
import qiskit
def __UpperCamelCase ( _lowerCAmelCase = 8 , _lowerCAmelCase = None ) -> str:
"""simple docstring"""
A : Tuple = np.random.default_rng(seed=_lowerCAmelCase )
# Roughly 25% of the qubits will contribute to the key.
# ... | 662 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_SNAKE_CASE_:List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_:List[Any] = {
"""google/bit-50""": ... | 662 | 1 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional... | 662 |
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTeste... | 662 | 1 |
def __UpperCamelCase ( _lowerCAmelCase ) -> str:
"""simple docstring"""
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 662 |
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,
)
fr... | 662 | 1 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE_:Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_:int = {
"""vocab_file""": """vocab.json""",
"""merges_fil... | 662 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResamplin... | 662 | 1 |
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 loggin... | 662 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
SCREAMING_SNAKE_CASE_:Optional[int] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def __init__( self, *... | 662 | 1 |
def __UpperCamelCase ( _lowerCAmelCase = 10**12 ) -> int:
"""simple docstring"""
A : Tuple = 1
A : Dict = 0
A : int = 1
A : Optional[int] = 1
while numerator <= 2 * min_total - 1:
prev_numerator += 2 * numerator
... | 662 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
... | 662 | 1 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
SCREAMING_SNAKE_CASE_:List[Any] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def __init__( self... | 662 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
SCREAMING_SNAKE_CASE_:int = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
SCREAMING_SNAKE_CASE_:Dict = _LazyModule(__name__, global... | 662 | 1 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_para... | 662 |
def __UpperCamelCase ( _lowerCAmelCase = 1000 ) -> int:
"""simple docstring"""
A , A : str = 1, 1
A : List[Any] = []
for i in range(1 , n + 1 ):
A : Optional[int] = prev_numerator + 2 * prev_denominator
A : Any =... | 662 | 1 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def __UpperCamelCase ( _lowerCAmelCase ) -> Any:
"""simple docstring"""
A : Optional[Any] = {}
A : Tuple = job["""started_at"""]
A : str = ... | 662 |
import re
def __UpperCamelCase ( _lowerCAmelCase ) -> str:
"""simple docstring"""
if len(re.findall("""[ATCG]""" , _lowerCAmelCase ) ) != len(_lowerCAmelCase ):
raise ValueError("""Invalid Strand""" )
return dna.translate(dna.maketrans(""... | 662 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_:List[Any] = {
"""configuration_git""": ["""GIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GitConfig""", """GitVisionConfig"""],
"""processing_git""": ["""GitProcessor"... | 662 |
from __future__ import annotations
SCREAMING_SNAKE_CASE_:Tuple = """#"""
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self ):
A : dict = {}
def _lowerCAmelCase ( self, lowerCamelCase__ ):
A : List[A... | 662 | 1 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
... | 662 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import Bn... | 662 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_:Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_:Optional[Any] = {"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNA... | 662 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
fro... | 662 | 1 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__v... | 662 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_:int = {
"""configuration_blenderbot""": [
"""BLENDERBOT_PRETRAINED_... | 662 | 1 |
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("""socket.socket""" )
@patch("""builtins.open""" )
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> Dict:
"""simple docstring"""
A : int = Mock()
... | 662 |
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> list[int]:
"""simple docstring"""
A : Optional[int] = int(_lowerCAmelCase )
# Initialize Result
A : int = []
# Traverse through all denomination
for denomination in reversed(... | 662 | 1 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
SCREAMING_SNAKE_CASE_:Optional[Any] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def __init__( self, ... | 662 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to ... | 662 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_util... | 662 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__v... | 662 | 1 |
from heapq import heappop, heappush
import numpy as np
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , ) -> tuple[float | int, list[tuple[int, int]]]:
"""simple docstring"""
A , A : Tuple = grid.shape
A : Li... | 662 |
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, BertTokenizer, BlipImageProce... | 662 | 1 |
from __future__ import annotations
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> list:
"""simple docstring"""
A : str = []
A , A : List[Any] = input_list[low:mid], input_list[mid : high + 1]
w... | 662 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@s... | 662 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, 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():
... | 662 |
from typing import Any
import numpy as np
def __UpperCamelCase ( _lowerCAmelCase ) -> bool:
"""simple docstring"""
return np.array_equal(_lowerCAmelCase , matrix.conjugate().T )
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> ... | 662 | 1 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
SCREAMING_SNAKE_CASE_:Dict = """\
"""
SCREAMING_SNAKE_CASE_:Dict = """
Perplexity (PPL) is one of the most common metrics for e... | 662 |
from __future__ import annotations
import numpy as np
def __UpperCamelCase ( _lowerCAmelCase ) -> tuple[np.ndarray, np.ndarray]:
"""simple docstring"""
A , A : int = np.shape(_lowerCAmelCase )
if rows != columns:
A : Union[str, Any] = ... | 662 | 1 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
SCREAMING_SNAKE_CASE_:List[Any] = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
SCREAMING_SNAKE_CASE_:int = typing.Union[np.floataa, int, float] # noqa: UP007
def ... | 662 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STA... | 662 | 1 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import Bn... | 662 |
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self, lowerCamelCase__ ):
# we need a list not a string, so do something to change the type
A : List[Any] = arr.split(""",""" )
def _lowerCAmelCase ( self ):
... | 662 | 1 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
imp... | 662 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_SNAKE_CASE_:List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_:List[Any] = {
"""google/bit-50""": ... | 662 | 1 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
SCREAMING_SNAKE_CASE_:Optional[int] = datasets.utils.logging.get_logger(__name__)
@dataclass
class SCREAMING_SNAK... | 662 |
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTeste... | 662 | 1 |
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, BertTokenizer, BlipImageProce... | 662 |
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,
)
fr... | 662 | 1 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_:Un... | 662 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResamplin... | 662 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_SNAKE_CASE_:List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_:List[Any] = {
"""google/bit-50""": ... | 662 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
SCREAMING_SNAKE_CASE_:Optional[int] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def __init__( self, *... | 662 | 1 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
SCREAMING_SNAKE_CASE_:Dict = """
import os
"""
SCREAMING_SNAKE_CASE_:Any = """
def foo():
import os
return False
"""
SCREAMING_SNAKE_CASE_:str = """
def foo():
def bar():
if True:
impo... | 662 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
... | 662 | 1 |
import warnings
from functools import wraps
from typing import Callable
def __UpperCamelCase ( _lowerCAmelCase ) -> Callable:
"""simple docstring"""
@wraps(_lowerCAmelCase )
def _inner_fn(*_lowerCAmelCase , **_lowerCAmelCase ):
warnings.warn(
... | 662 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
SCREAMING_SNAKE_CASE_:int = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
SCREAMING_SNAKE_CASE_:Dict = _LazyModule(__name__, global... | 662 | 1 |
from ...processing_utils import ProcessorMixin
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__lowerCamelCase : int = "SpeechT5FeatureExtractor"
__lowerCamelCase : List[str] = "SpeechT5Tokenizer"
def __init__( s... | 662 |
def __UpperCamelCase ( _lowerCAmelCase = 1000 ) -> int:
"""simple docstring"""
A , A : str = 1, 1
A : List[Any] = []
for i in range(1 , n + 1 ):
A : Optional[int] = prev_numerator + 2 * prev_denominator
A : Any =... | 662 | 1 |
import re
from filelock import FileLock
try:
import nltk
SCREAMING_SNAKE_CASE_:Optional[Any] = True
except (ImportError, ModuleNotFoundError):
SCREAMING_SNAKE_CASE_:int = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quiet=True)
... | 662 |
import re
def __UpperCamelCase ( _lowerCAmelCase ) -> str:
"""simple docstring"""
if len(re.findall("""[ATCG]""" , _lowerCAmelCase ) ) != len(_lowerCAmelCase ):
raise ValueError("""Invalid Strand""" )
return dna.translate(dna.maketrans(""... | 662 | 1 |
import warnings
from .generation import TFGenerationMixin
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
warnings.warn(
"Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
"be remo... | 662 |
from __future__ import annotations
SCREAMING_SNAKE_CASE_:Tuple = """#"""
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self ):
A : dict = {}
def _lowerCAmelCase ( self, lowerCamelCase__ ):
A : List[A... | 662 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, ... | 662 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import Bn... | 662 | 1 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
fro... | 662 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
fro... | 662 | 1 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...t... | 662 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_:int = {
"""configuration_blenderbot""": [
"""BLENDERBOT_PRETRAINED_... | 662 | 1 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __UpperCamelCase ( ) -> tuple[list[int], int]:
"""simple docstring"""
A : List[str] = [randint(-1000 , 1000 ) for i in range(10 )]... | 662 |
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> list[int]:
"""simple docstring"""
A : Optional[int] = int(_lowerCAmelCase )
# Initialize Result
A : int = []
# Traverse through all denomination
for denomination in reversed(... | 662 | 1 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
SCREAMING_SNAKE_CASE_:List[Any] = object()
# For specifying empty leaf dict `{}`
SCREAMING_SNAKE_CASE_:Optional[Any] = object()
d... | 662 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to ... | 662 | 1 |
import argparse
import os
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_task_guides.py
SCREAMING_SNAKE_CASE_:int = """src/transformers"""
SCREAMING_SNAKE_CASE_:Union[str,... | 662 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__v... | 662 | 1 |
from __future__ import annotations
from collections import deque
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self, lowerCamelCase__ ):
A : list[dict] = []
self.adlist.append(
{"""value""": """""", """next_states""": [],... | 662 |
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, BertTokenizer, BlipImageProce... | 662 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_:Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_:int = {
"""EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json""",
# See all... | 662 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@s... | 662 | 1 |
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...t... | 662 |
from typing import Any
import numpy as np
def __UpperCamelCase ( _lowerCAmelCase ) -> bool:
"""simple docstring"""
return np.array_equal(_lowerCAmelCase , matrix.conjugate().T )
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> ... | 662 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_... | 662 |
from __future__ import annotations
import numpy as np
def __UpperCamelCase ( _lowerCAmelCase ) -> tuple[np.ndarray, np.ndarray]:
"""simple docstring"""
A , A : int = np.shape(_lowerCAmelCase )
if rows != columns:
A : Union[str, Any] = ... | 662 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
SCREAMING_SNAKE_CASE_:List[Any] = logging.get_logger(__name__)
SCREAMI... | 662 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STA... | 662 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
SCREAMING_SNAKE_CASE_:List[str] = logging.get_logger(__name__)
SCREAMI... | 662 |
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self, lowerCamelCase__ ):
# we need a list not a string, so do something to change the type
A : List[Any] = arr.split(""",""" )
def _lowerCAmelCase ( self ):
... | 662 | 1 |
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetr... | 662 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_SNAKE_CASE_:List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_:List[Any] = {
"""google/bit-50""": ... | 662 | 1 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.m... | 662 |
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTeste... | 662 | 1 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, id... | 662 |
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,
)
fr... | 662 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtr... | 662 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResamplin... | 662 | 1 |
import copy
import re
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
__lowerCamelCase : Any = "hp"
__lowerCamelCase : Dict = {}
__lowerCamelCase : Optional[Any] = None
@classmethod
def _lowerCAmelCase ( cls, lowerCamelC... | 662 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
SCREAMING_SNAKE_CASE_:Optional[int] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def __init__( self, *... | 662 | 1 |
import math
import flax.linen as nn
import jax.numpy as jnp
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 1 , _lowerCAmelCase = 1 , _lowerCAmelCase = 1.0e4 , _lowerCAmelCase = False , _lowerCAmelCase = 1.0 , ) -> jnp.ndarray:
"""simple docstring"""
... | 662 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
... | 662 | 1 |
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(0 ) == 0 )
def __UpperCamelCase ( ) -> None:
"""simple docstring"""
assert and_gate(0 , 0 ) ==... | 662 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
SCREAMING_SNAKE_CASE_:int = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
SCREAMING_SNAKE_CASE_:Dict = _LazyModule(__name__, global... | 662 | 1 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipe... | 662 |
def __UpperCamelCase ( _lowerCAmelCase = 1000 ) -> int:
"""simple docstring"""
A , A : str = 1, 1
A : List[Any] = []
for i in range(1 , n + 1 ):
A : Optional[int] = prev_numerator + 2 * prev_denominator
A : Any =... | 662 | 1 |
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def __lt__( self, lowerCamelCase__ ):
... | 662 |
import re
def __UpperCamelCase ( _lowerCAmelCase ) -> str:
"""simple docstring"""
if len(re.findall("""[ATCG]""" , _lowerCAmelCase ) ) != len(_lowerCAmelCase ):
raise ValueError("""Invalid Strand""" )
return dna.translate(dna.maketrans(""... | 662 | 1 |
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self, lowerCamelCase__, lowerCamelCase__=None, lowerCamelCase__=None ):
A : str = data
A : Union[str, Any] = previous
A : Dict = next_node
def __str__... | 662 |
from __future__ import annotations
SCREAMING_SNAKE_CASE_:Tuple = """#"""
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self ):
A : dict = {}
def _lowerCAmelCase ( self, lowerCamelCase__ ):
A : List[A... | 662 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_:Any = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""]... | 662 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import Bn... | 662 | 1 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __UpperCamelCase ( _lowerCAmelCase ) -> str:
"""simple docstring"""
return "".join(sorted(_lowerCAmelCase ) )
def __UpperCamelCase ( _lowerCAmelC... | 662 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
fro... | 662 | 1 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTest... | 662 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_:int = {
"""configuration_blenderbot""": [
"""BLENDERBOT_PRETRAINED_... | 662 | 1 |
import math
import sys
def __UpperCamelCase ( _lowerCAmelCase ) -> int:
"""simple docstring"""
if number != int(_lowerCAmelCase ):
raise ValueError("""the value of input must be a natural number""" )
if number < 0:
raise ValueError("""the va... | 662 |
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> list[int]:
"""simple docstring"""
A : Optional[int] = int(_lowerCAmelCase )
# Initialize Result
A : int = []
# Traverse through all denomination
for denomination in reversed(... | 662 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_:List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_:int = {
"""edbeeching/decision-transformer-gym-hopper-medium""": (
"""https://huggingface.co/edbeeching/decision-transformer-gy... | 662 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to ... | 662 | 1 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
SCREAMING_SNAKE_CASE_:str = datasets.utils.logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( folder_based_builder.FolderBasedBuilderConfig ... | 662 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__v... | 662 | 1 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.t... | 662 |
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, BertTokenizer, BlipImageProce... | 662 | 1 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
imp... | 662 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@s... | 662 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_:int = {
"""configuration_timesformer""": ["""TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TimesformerConfig"""],
}
try:
if not is_torch_available():
... | 662 |
from typing import Any
import numpy as np
def __UpperCamelCase ( _lowerCAmelCase ) -> bool:
"""simple docstring"""
return np.array_equal(_lowerCAmelCase , matrix.conjugate().T )
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> ... | 662 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_:List[str] = {
"""configuration_table_transformer""": [
"""TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TableTransformerConfig""",
"... | 662 |
from __future__ import annotations
import numpy as np
def __UpperCamelCase ( _lowerCAmelCase ) -> tuple[np.ndarray, np.ndarray]:
"""simple docstring"""
A , A : int = np.shape(_lowerCAmelCase )
if rows != columns:
A : Union[str, Any] = ... | 662 | 1 |
from statistics import mean
import numpy as np
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> list:
"""simple docstring"""
A : str = 0
# Number of processes finished
A : Union[str, Any] = 0
#... | 662 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STA... | 662 | 1 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def __UpperCamelCase ( _lowerCAmelCase ) -> str:
"""simple docstring"""
monkeypatch.setattr("""datasets.utils.deprecation_utils._emitted_deprecation_warnings""" , set() )
... | 662 |
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self, lowerCamelCase__ ):
# we need a list not a string, so do something to change the type
A : List[Any] = arr.split(""",""" )
def _lowerCAmelCase ( self ):
... | 662 | 1 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE_:Union[str, Any] = logging.get_logger(__name__)
... | 662 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_SNAKE_CASE_:List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_:List[Any] = {
"""google/bit-50""": ... | 662 | 1 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE__ ( metaclass=SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__lowerCamelCase : Any = ["torch", "transformers", "onnx"]
def __init__( self, *lowerCamelCase__, **lowerCam... | 662 |
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTeste... | 662 | 1 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
A : Optional[Any] = tau * frequency / sample... | 662 |
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,
)
fr... | 662 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE_:List[str] = {
"""configuration_clip""": [
... | 662 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResamplin... | 662 | 1 |
# 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 ... | 662 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
SCREAMING_SNAKE_CASE_:Optional[int] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def __init__( self, *... | 662 | 1 |
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> bool:
"""simple docstring"""
A : Optional[Any] = len(_lowerCAmelCase )
A : Optional[Any] = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum... | 662 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
... | 662 | 1 |
import itertools
import math
def __UpperCamelCase ( _lowerCAmelCase ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even number... | 662 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
SCREAMING_SNAKE_CASE_:int = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
SCREAMING_SNAKE_CASE_:Dict = _LazyModule(__name__, global... | 662 | 1 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from... | 662 |
def __UpperCamelCase ( _lowerCAmelCase = 1000 ) -> int:
"""simple docstring"""
A , A : str = 1, 1
A : List[Any] = []
for i in range(1 , n + 1 ):
A : Optional[int] = prev_numerator + 2 * prev_denominator
A : Any =... | 662 | 1 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from... | 662 |
import re
def __UpperCamelCase ( _lowerCAmelCase ) -> str:
"""simple docstring"""
if len(re.findall("""[ATCG]""" , _lowerCAmelCase ) ) != len(_lowerCAmelCase ):
raise ValueError("""Invalid Strand""" )
return dna.translate(dna.maketrans(""... | 662 | 1 |
from __future__ import annotations
import time
import numpy as np
SCREAMING_SNAKE_CASE_:Union[str, Any] = [8, 5, 9, 7]
SCREAMING_SNAKE_CASE_:Any = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
SCREAMING_SNAKE_CASE_:Optional[int] = [
[3, 2, 1, 4],
... | 662 |
from __future__ import annotations
SCREAMING_SNAKE_CASE_:Tuple = """#"""
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self ):
A : dict = {}
def _lowerCAmelCase ( self, lowerCamelCase__ ):
A : List[A... | 662 | 1 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@requ... | 662 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import Bn... | 662 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResamplin... | 662 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
fro... | 662 | 1 |
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> list[int]:
"""simple docstring"""
A : Optional[int] = int(_lowerCAmelCase )
# Initialize Result
A : int = []
# Traverse through all denomination
for denomination in reversed(... | 662 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_:int = {
"""configuration_blenderbot""": [
"""BLENDERBOT_PRETRAINED_... | 662 | 1 |
SCREAMING_SNAKE_CASE_:str = """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_available,
is_k_diffusion_version,
is_librosa_availabl... | 662 |
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> list[int]:
"""simple docstring"""
A : Optional[int] = int(_lowerCAmelCase )
# Initialize Result
A : int = []
# Traverse through all denomination
for denomination in reversed(... | 662 | 1 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning thi... | 662 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to ... | 662 | 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
SCREAMING_SNAKE_CASE_:Dict = logging.getLogger()
@unittest.skip("Temporarily disable the doc tests."... | 662 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__v... | 662 | 1 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
SCREAMING_SNAKE_CASE_:Any = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def __init__( self, *lo... | 662 |
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, BertTokenizer, BlipImageProce... | 662 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_:Tuple = {
"""configuration_distilbert""": [
"""DISTILBERT_PRETRAINE... | 662 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@s... | 662 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_SNAKE_CASE_:Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_:Union[str, Any] = {
"""shi-labs... | 662 |
from typing import Any
import numpy as np
def __UpperCamelCase ( _lowerCAmelCase ) -> bool:
"""simple docstring"""
return np.array_equal(_lowerCAmelCase , matrix.conjugate().T )
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> ... | 662 | 1 |
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:List[str] = logg... | 662 |
from __future__ import annotations
import numpy as np
def __UpperCamelCase ( _lowerCAmelCase ) -> tuple[np.ndarray, np.ndarray]:
"""simple docstring"""
A , A : int = np.shape(_lowerCAmelCase )
if rows != columns:
A : Union[str, Any] = ... | 662 | 1 |
import operator
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase = False , _lowerCAmelCase = None ) -> list:
"""simple docstring"""
A : Optional[int] = operator.lt if reverse else operator.gt
A : Dict = solution or []
if not arr:
... | 662 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STA... | 662 | 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_big_bird imp... | 662 |
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self, lowerCamelCase__ ):
# we need a list not a string, so do something to change the type
A : List[Any] = arr.split(""",""" )
def _lowerCAmelCase ( self ):
... | 662 | 1 |
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_remote_code
from ...fea... | 662 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_SNAKE_CASE_:List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_:List[Any] = {
"""google/bit-50""": ... | 662 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_:int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_:Union[str, Any] = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class SCREAMING_SNAKE_CASE__ ( ... | 662 |
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTeste... | 662 | 1 |
from __future__ import annotations
from collections.abc import Iterator
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self, lowerCamelCase__ ):
A : Any = value
A : Node | None = None
A : Node | None = None
... | 662 |
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,
)
fr... | 662 | 1 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = "Speech2TextFeatureExtractor"
__lowerCamelCase ... | 662 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResamplin... | 662 | 1 |
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> List[Any]:
""... | 662 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
SCREAMING_SNAKE_CASE_:Optional[int] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def __init__( self, *... | 662 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE_:Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_:Tuple = """▁"""
SCREA... | 662 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
... | 662 | 1 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
... | 662 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
SCREAMING_SNAKE_CASE_:int = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
SCREAMING_SNAKE_CASE_:Dict = _LazyModule(__name__, global... | 662 | 1 |
from __future__ import annotations
import numpy as np
def __UpperCamelCase ( _lowerCAmelCase ) -> tuple[np.ndarray, np.ndarray]:
"""simple docstring"""
A , A : int = np.shape(_lowerCAmelCase )
if rows != columns:
A : Union[str, Any] = ... | 662 |
def __UpperCamelCase ( _lowerCAmelCase = 1000 ) -> int:
"""simple docstring"""
A , A : str = 1, 1
A : List[Any] = []
for i in range(1 , n + 1 ):
A : Optional[int] = prev_numerator + 2 * prev_denominator
A : Any =... | 662 | 1 |
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