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 collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
... | 127 |
'''simple docstring'''
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 transfor... | 127 | 1 |
'''simple docstring'''
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_ge... | 127 |
'''simple docstring'''
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class SCREAMING_SNAKE_CASE( nn.Module ):
"""simple docstring""... | 127 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase__: int = {
"configuration_bridgetower": [
"BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",... | 127 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Union[str, Any] ) -> Dict:
# Return True if there is node that has not iterated.
UpperCAmelCase : List[Any... | 127 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...tes... | 127 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__: int = logging.get_logger(__name__)
# TODO Update this
UpperCamelCase__: Any ... | 127 | 1 |
'''simple docstring'''
from __future__ import annotations
UpperCamelCase__: Optional[Any] = "#"
class SCREAMING_SNAKE_CASE:
"""simple docstring"""
def __init__( self : List[Any] ) -> None:
UpperCAmelCase : di... | 127 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__: List[Any] = logging.get_logger(__name__)
UpperCamelCase__: str = {
"unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmer... | 127 | 1 |
'''simple docstring'''
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
impor... | 127 |
'''simple docstring'''
import math
from numpy import inf
from scipy.integrate import quad
def snake_case_ ( _lowerCAmelCase : float ) -> float:
if num <= 0:
raise ValueError('''math domain error''' )
return quad(_lowerCAmelCase , 0 , _lo... | 127 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
Upp... | 127 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transforme... | 127 | 1 |
'''simple docstring'''
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__: Tuple = logging.get_logger(__name__)
UpperCamelCase__: Dict = {
"facebook/data2vec-base-960h": "https://huggingface.co/f... | 127 |
'''simple docstring'''
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ..... | 127 | 1 |
'''simple docstring'''
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def snake_case_ ( _lowerCAmelCase : Optional[int] ) -> Dict:
... | 127 |
'''simple docstring'''
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... | 127 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__: str = {
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_AR... | 127 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : str , _lowerCAmelCase : str ) -> int:
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
raise ValueError('''String lengths must match!''' )
UpperCAmelCase : List[str] ... | 127 | 1 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : list ) -> int:
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] ) ):
gri... | 127 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ ( _lowerCAmelCase : list[int] ) -> int:
if not nums:
return 0
UpperCAmelCase : Tuple = nums[0]
UpperCAmelCase : List[str] = 0
... | 127 | 1 |
'''simple docstring'''
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
UpperCamelCase__: Tuple = {
... | 127 |
'''simple docstring'''
import re
def snake_case_ ( _lowerCAmelCase : str ) -> str:
if len(re.findall('''[ATCG]''' , _lowerCAmelCase ) ) != len(_lowerCAmelCase ):
raise ValueError('''Invalid Strand''' )
return dna.translate(dna.make... | 127 | 1 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ ( _lowerCAmelCase : int = 4 ) -> list[list[int]]:
UpperCAmelCase : Any = abs(_lowerCAmelCase ) or 4
return [[1 + x + y * row_size for x in range(_lowerCAmelCase )] ... | 127 |
'''simple docstring'''
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
if is_torch... | 127 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__: List[Any] = logging.get_logger(__name__)
UpperCamelCase__: Optional[Any] = {
... | 127 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : str ) -> int:
if not head:
return True
# split the list to two parts
UpperCAmelCase , UpperCAmelCase : str = head.next, head
while fast and fast.next:
... | 127 | 1 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
UpperCamelCase__: Tuple = "examples/"
UpperCamelCase__: Any = {
"examples": (re.compile(r"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSIO... | 127 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Dict , _lowerCAmelCase : List[str] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[Any] ) -> Union[str, Any]:
if index == r:
... | 127 | 1 |
'''simple docstring'''
import warnings
from ..trainer import Trainer
from ..utils import logging
UpperCamelCase__: Union[str, Any] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE( A__ ):
"""simple docstring"""
def __ini... | 127 |
'''simple docstring'''
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
UpperCamelCase__: Any = datasets.logging.get_logger(__name__)
UpperCamelCase__: Union[str, Any] = "\\n@inproceedings{bleur... | 127 | 1 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Dict , _lowerCAmelCase : List[str] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[Any] ) -> Union[str, Any]:
if index == r:
... | 127 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class SCREAMING_SNAKE_CASE( unittest.TestCase ):
"""simple docstring"""
def A ( self : Tuple ) -> Optional[Any]:... | 127 | 1 |
'''simple docstring'''
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
UpperCamelCase__: Dict = "\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machi... | 127 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__: str = logging.get_lo... | 127 | 1 |
'''simple docstring'''
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVis... | 127 |
'''simple docstring'''
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class SCREAMING_SNAKE_CASE:
"""simple docstring"""
def __init__( self : List[str] , __snake_case : Any ) -> Lis... | 127 | 1 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : int = 1000 ) -> int:
UpperCAmelCase : List[str] = -1
UpperCAmelCase : int = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2... | 127 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : int ) -> list:
UpperCAmelCase : Union[str, Any] = int(_lowerCAmelCase )
if n_element < 1:
UpperCAmelCase : int = ValueError('''a should be a positive num... | 127 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__: List[str] = {
"configuration_trajectory_transformer": [
"TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHI... | 127 |
'''simple docstring'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
UpperCamelCase__: str = "\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchm... | 127 | 1 |
'''simple docstring'''
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
impor... | 127 |
'''simple docstring'''
from __future__ import annotations
UpperCamelCase__: Tuple = 1.60_21E-19 # units = C
def snake_case_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float , ) -> tuple[str, float]:
if (conductivity,... | 127 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def snake_case_ ( _lowerCAmelCase : Dict ) -> List[str]:
# vision encoder
if "img_encoder.pos_e... | 127 |
'''simple docstring'''
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 transfor... | 127 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class SCREAMING_... | 127 |
'''simple docstring'''
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class SCREAMING_SNAKE_CASE( nn.Module ):
"""simple docstring""... | 127 | 1 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : int = 1000000 ) -> int:
UpperCAmelCase : Union[str, Any] = limit + 1
UpperCAmelCase : str = [0] * limit
for first_term in range(1 , _lowerCAmelCase ):
... | 127 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Union[str, Any] ) -> Dict:
# Return True if there is node that has not iterated.
UpperCAmelCase : List[Any... | 127 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
Wav... | 127 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__: int = logging.get_logger(__name__)
# TODO Update this
UpperCamelCase__: Any ... | 127 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
... | 127 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__: List[Any] = logging.get_logger(__name__)
UpperCamelCase__: str = {
"unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmer... | 127 | 1 |
'''simple docstring'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
UpperCamelCase__: str = "\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchm... | 127 |
'''simple docstring'''
import math
from numpy import inf
from scipy.integrate import quad
def snake_case_ ( _lowerCAmelCase : float ) -> float:
if num <= 0:
raise ValueError('''math domain error''' )
return quad(_lowerCAmelCase , 0 , _lo... | 127 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenizatio... | 127 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transforme... | 127 | 1 |
'''simple docstring'''
from collections import defaultdict
from math import ceil, sqrt
def snake_case_ ( _lowerCAmelCase : int = 1000000 , _lowerCAmelCase : int = 10 ) -> int:
UpperCAmelCase : defaultdict = defaultdict(_lowerCAmelCase )
... | 127 |
'''simple docstring'''
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ..... | 127 | 1 |
'''simple docstring'''
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__: Optional[Any] = logging.get_logger(__name__)
UpperCamelCase__: List[Any] = {
"microsoft... | 127 |
'''simple docstring'''
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... | 127 | 1 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : list ) -> list:
if len(_lowerCAmelCase ) <= 1:
return [tuple(_lowerCAmelCase )]
UpperCAmelCase : Dict = []
def generate(_lowerCAmelCase : int , _lower... | 127 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : str , _lowerCAmelCase : str ) -> int:
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
raise ValueError('''String lengths must match!''' )
UpperCAmelCase : List[str] ... | 127 | 1 |
'''simple docstring'''
from string import ascii_uppercase
UpperCamelCase__: List[str] = {str(ord(c) - 55): c for c in ascii_uppercase}
def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int ) -> str:
if isinstance(_lowerCAmelCase , _lowerC... | 127 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ ( _lowerCAmelCase : list[int] ) -> int:
if not nums:
return 0
UpperCAmelCase : Tuple = nums[0]
UpperCAmelCase : List[str] = 0
... | 127 | 1 |
'''simple docstring'''
import os
def snake_case_ ( _lowerCAmelCase : str = "input.txt" ) -> int:
with open(os.path.join(os.path.dirname(_lowerCAmelCase ) , _lowerCAmelCase ) ) as input_file:
UpperCAmelCase : List[str] = [
... | 127 |
'''simple docstring'''
import re
def snake_case_ ( _lowerCAmelCase : str ) -> str:
if len(re.findall('''[ATCG]''' , _lowerCAmelCase ) ) != len(_lowerCAmelCase ):
raise ValueError('''Invalid Strand''' )
return dna.translate(dna.make... | 127 | 1 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datase... | 127 |
'''simple docstring'''
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
if is_torch... | 127 | 1 |
'''simple docstring'''
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,
)
UpperCamelCase__: Optional... | 127 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : str ) -> int:
if not head:
return True
# split the list to two parts
UpperCAmelCase , UpperCAmelCase : str = head.next, head
while fast and fast.next:
... | 127 | 1 |
'''simple docstring'''
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def ... | 127 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Dict , _lowerCAmelCase : List[str] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[Any] ) -> Union[str, Any]:
if index == r:
... | 127 | 1 |
'''simple docstring'''
from heapq import heappop, heappush
import numpy as np
def snake_case_ ( _lowerCAmelCase : np.ndarray , _lowerCAmelCase : tuple[int, int] , _lowerCAmelCase : tuple[int, int] , _lowerCAmelCase : bool , ) -> tuple[float | int, list[tuple[int,... | 127 |
'''simple docstring'''
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
UpperCamelCase__: Any = datasets.logging.get_logger(__name__)
UpperCamelCase__: Union[str, Any] = "\\n@inproceedings{bleur... | 127 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import gcd
def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int = 2 , _lowerCAmelCase : int = 1 , _lowerCAmelCase : int = 3 , ) -> int | None:
# A value less than 2 can cause a... | 127 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class SCREAMING_SNAKE_CASE( unittest.TestCase ):
"""simple docstring"""
def A ( self : Tuple ) -> Optional[Any]:... | 127 | 1 |
'''simple docstring'''
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
UpperCamelCase__: Any = "src/transformers"
# Th... | 127 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__: str = logging.get_lo... | 127 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE( A__ ):
"""simple docstring"""
lowerCamelCase__ = ["""image_processor""... | 127 |
'''simple docstring'''
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class SCREAMING_SNAKE_CASE:
"""simple docstring"""
def __init__( self : List[str] , __snake_case : Any ) -> Lis... | 127 | 1 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS... | 127 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : int ) -> list:
UpperCAmelCase : Union[str, Any] = int(_lowerCAmelCase )
if n_element < 1:
UpperCAmelCase : int = ValueError('''a should be a positive num... | 127 | 1 |
'''simple docstring'''
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 t... | 127 |
'''simple docstring'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
UpperCamelCase__: str = "\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchm... | 127 | 1 |
'''simple docstring'''
import json
import sys
def snake_case_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict ) -> int:
with open(_lowerCAmelCase , encoding='''utf-8''' ) as f:
UpperCAmelCase : int = json.load(_lowerCAme... | 127 |
'''simple docstring'''
from __future__ import annotations
UpperCamelCase__: Tuple = 1.60_21E-19 # units = C
def snake_case_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float , ) -> tuple[str, float]:
if (conductivity,... | 127 | 1 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase__: List[str] = logging.get_logger(__name__)
# TODO: upload to AWS
UpperCamelCase__: Tuple = {
"yjernite/retribert-base-uncased": (
... | 127 |
'''simple docstring'''
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 transfor... | 127 | 1 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : Tuple , _lowerCAmelCase : List[str] ) -> Optional[int]:
if height >= 1:
move_tower(height - 1 , _lowerCAmelCase , _lowerCAmelCase , _low... | 127 |
'''simple docstring'''
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class SCREAMING_SNAKE_CASE( nn.Module ):
"""simple docstring""... | 127 | 1 |
'''simple docstring'''
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def snake_case_ ( _lowerCAmelCase : Sequence[float] , _lowerCAmelCase : int , _lowerCAmelCase :... | 127 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Union[str, Any] ) -> Dict:
# Return True if there is node that has not iterated.
UpperCAmelCase : List[Any... | 127 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase__: str = logging.get_logger(__name__)
UpperCamelCase__: D... | 127 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__: int = logging.get_logger(__name__)
# TODO Update this
UpperCamelCase__: Any ... | 127 | 1 |
'''simple docstring'''
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,... | 127 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__: List[Any] = logging.get_logger(__name__)
UpperCamelCase__: str = {
"unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmer... | 127 | 1 |
'''simple docstring'''
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_... | 127 |
'''simple docstring'''
import math
from numpy import inf
from scipy.integrate import quad
def snake_case_ ( _lowerCAmelCase : float ) -> float:
if num <= 0:
raise ValueError('''math domain error''' )
return quad(_lowerCAmelCase , 0 , _lo... | 127 | 1 |
'''simple docstring'''
from __future__ import annotations
class SCREAMING_SNAKE_CASE:
"""simple docstring"""
def __init__( self : Dict , __snake_case : int = 0 ) -> Optional[Any]:
UpperCAmelCase : int = ... | 127 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transforme... | 127 | 1 |
'''simple docstring'''
import re
import string
import numpy as np
import datasets
UpperCamelCase__: List[str] = "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"
... | 127 |
'''simple docstring'''
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ..... | 127 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def snake_case_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float ) -> dict[str, float]:
if (resistance, reactance, impedance).count(0 ) != 1:... | 127 |
'''simple docstring'''
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... | 127 | 1 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvaila... | 127 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : str , _lowerCAmelCase : str ) -> int:
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
raise ValueError('''String lengths must match!''' )
UpperCAmelCase : List[str] ... | 127 | 1 |
'''simple docstring'''
from PIL import Image
def snake_case_ ( _lowerCAmelCase : Image ) -> Image:
UpperCAmelCase , UpperCAmelCase : Optional[int] = image.size
UpperCAmelCase : int = 0
UpperCAmelCase : D... | 127 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ ( _lowerCAmelCase : list[int] ) -> int:
if not nums:
return 0
UpperCAmelCase : Tuple = nums[0]
UpperCAmelCase : List[str] = 0
... | 127 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
... | 127 |
'''simple docstring'''
import re
def snake_case_ ( _lowerCAmelCase : str ) -> str:
if len(re.findall('''[ATCG]''' , _lowerCAmelCase ) ) != len(_lowerCAmelCase ):
raise ValueError('''Invalid Strand''' )
return dna.translate(dna.make... | 127 | 1 |
'''simple docstring'''
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
UpperCamelCase__: List[str] = 0b1... | 127 |
'''simple docstring'''
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
if is_torch... | 127 | 1 |
'''simple docstring'''
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
if is_torch... | 127 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : str ) -> int:
if not head:
return True
# split the list to two parts
UpperCAmelCase , UpperCAmelCase : str = head.next, head
while fast and fast.next:
... | 127 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
... | 127 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Dict , _lowerCAmelCase : List[str] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[Any] ) -> Union[str, Any]:
if index == r:
... | 127 | 1 |
'''simple docstring'''
import sys
def snake_case_ ( _lowerCAmelCase : List[Any] ) -> List[str]:
UpperCAmelCase : Any = len(_lowerCAmelCase )
UpperCAmelCase : List[Any] = [[0 for x in range(_lowerCAmelCase )] for x in ra... | 127 |
'''simple docstring'''
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
UpperCamelCase__: Any = datasets.logging.get_logger(__name__)
UpperCamelCase__: Union[str, Any] = "\\n@inproceedings{bleur... | 127 | 1 |
'''simple docstring'''
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
UpperCamelCase__: Union[str, Any... | 127 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class SCREAMING_SNAKE_CASE( unittest.TestCase ):
"""simple docstring"""
def A ( self : Tuple ) -> Optional[Any]:... | 127 | 1 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : str ) -> list[int]:
UpperCAmelCase : Optional[Any] = [0 for i in range(len(_lowerCAmelCase ) )]
# initialize interval's left pointer and right pointer
UpperCAmelCase , ... | 127 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__: str = logging.get_lo... | 127 | 1 |
'''simple docstring'''
import numpy as np
def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : List[str] , _lowerCAmelCase : Union[str, Any] ) -> Union[str, Any]:
UpperCAmelCase : Li... | 127 |
'''simple docstring'''
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class SCREAMING_SNAKE_CASE:
"""simple docstring"""
def __init__( self : List[str] , __snake_case : Any ) -> Lis... | 127 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase__: str = logging.get_l... | 127 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : int ) -> list:
UpperCAmelCase : Union[str, Any] = int(_lowerCAmelCase )
if n_element < 1:
UpperCAmelCase : int = ValueError('''a should be a positive num... | 127 | 1 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class SCREAMING_SNAKE_CASE:
"""simple docstring"""
def __init__( self : Optional[int] , __snake_case : list[tuple[float, float]] ... | 127 |
'''simple docstring'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
UpperCamelCase__: str = "\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchm... | 127 | 1 |
'''simple docstring'''
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class SCREAMING_SNAKE_CASE( tf.k... | 127 |
'''simple docstring'''
from __future__ import annotations
UpperCamelCase__: Tuple = 1.60_21E-19 # units = C
def snake_case_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float , ) -> tuple[str, float]:
if (conductivity,... | 127 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__: List[Any] = logging.get_logger(__name__)
UpperCamelCase__: str = {
"unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmer... | 127 |
'''simple docstring'''
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 transfor... | 127 | 1 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ ( _lowerCAmelCase : list[int] ) -> int:
if not nums:
return 0
UpperCAmelCase : Tuple = nums[0]
UpperCAmelCase : List[str] = 0
... | 127 |
'''simple docstring'''
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class SCREAMING_SNAKE_CASE( nn.Module ):
"""simple docstring""... | 127 | 1 |
'''simple docstring'''
# 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.or... | 127 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Union[str, Any] ) -> Dict:
# Return True if there is node that has not iterated.
UpperCAmelCase : List[Any... | 127 | 1 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : int ) -> bool:
UpperCAmelCase : Union[str, Any] = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
... | 127 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__: int = logging.get_logger(__name__)
# TODO Update this
UpperCamelCase__: Any ... | 127 | 1 |
'''simple docstring'''
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_M... | 127 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__: List[Any] = logging.get_logger(__name__)
UpperCamelCase__: str = {
"unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmer... | 127 | 1 |
'''simple docstring'''
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
... | 127 |
'''simple docstring'''
import math
from numpy import inf
from scipy.integrate import quad
def snake_case_ ( _lowerCAmelCase : float ) -> float:
if num <= 0:
raise ValueError('''math domain error''' )
return quad(_lowerCAmelCase , 0 , _lo... | 127 | 1 |
'''simple docstring'''
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transfor... | 127 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transforme... | 127 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__: Optional[int] = logging.get_logger(__name__)
UpperCamelCase__: Any = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/mar... | 127 |
'''simple docstring'''
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ..... | 127 | 1 |
'''simple docstring'''
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def snake_case_ ( _lowerCAmelCase : Dict , _lowerCAmelCase : Dict , _lowerCAmelCase : str ) -> Optional[int]:
Upp... | 127 |
'''simple docstring'''
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... | 127 | 1 |
'''simple docstring'''
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
UpperCamelCase__: str = "<<<<<<< This should probably be modified beca... | 127 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : str , _lowerCAmelCase : str ) -> int:
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
raise ValueError('''String lengths must match!''' )
UpperCAmelCase : List[str] ... | 127 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import _LazyModule
UpperCamelCase__: Dict = {"tokenization_tapex": ["TapexTokenizer"]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
Uppe... | 127 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ ( _lowerCAmelCase : list[int] ) -> int:
if not nums:
return 0
UpperCAmelCase : Tuple = nums[0]
UpperCAmelCase : List[str] = 0
... | 127 | 1 |
'''simple docstring'''
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
UpperCamelCase__: Tuple = "src/diffusers"
# Matches is_xxx_available()
Uppe... | 127 |
'''simple docstring'''
import re
def snake_case_ ( _lowerCAmelCase : str ) -> str:
if len(re.findall('''[ATCG]''' , _lowerCAmelCase ) ) != len(_lowerCAmelCase ):
raise ValueError('''Invalid Strand''' )
return dna.translate(dna.make... | 127 | 1 |
'''simple docstring'''
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 transfor... | 127 |
'''simple docstring'''
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
if is_torch... | 127 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=A__ )
class SCREAMING_SNAKE_CASE( A__ ):
"""s... | 127 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : str ) -> int:
if not head:
return True
# split the list to two parts
UpperCAmelCase , UpperCAmelCase : str = head.next, head
while fast and fast.next:
... | 127 | 1 |
'''simple docstring'''
import re
def snake_case_ ( _lowerCAmelCase : str ) -> bool:
UpperCAmelCase : Optional[int] = re.compile(R'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(_lowerCAmelCase , _lowerCAmelCase ):
... | 127 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Dict , _lowerCAmelCase : List[str] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[Any] ) -> Union[str, Any]:
if index == r:
... | 127 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_com... | 127 |
'''simple docstring'''
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
UpperCamelCase__: Any = datasets.logging.get_logger(__name__)
UpperCamelCase__: Union[str, Any] = "\\n@inproceedings{bleur... | 127 | 1 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ ( _lowerCAmelCase : str ) -> list[int]:
return [ord(_lowerCAmelCase ) - 96 for elem in plain]
def snake_case_ ( _lowerCAmelCase : list[int] ) -> str:
return "".join(c... | 127 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class SCREAMING_SNAKE_CASE( unittest.TestCase ):
"""simple docstring"""
def A ( self : Tuple ) -> Optional[Any]:... | 127 | 1 |
'''simple docstring'''
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
UpperCamelCase__: int = object()
# For specifying empty leaf dict `{}`
Upper... | 127 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__: str = logging.get_lo... | 127 | 1 |
'''simple docstring'''
UpperCamelCase__: List[str] = [0, 2, 4, 6, 8]
UpperCamelCase__: Optional[Any] = [1, 3, 5, 7, 9]
def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : list[int] , _lowerCAmelCase : int ... | 127 |
'''simple docstring'''
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class SCREAMING_SNAKE_CASE:
"""simple docstring"""
def __init__( self : List[str] , __snake_case : Any ) -> Lis... | 127 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
UpperCamelCase__: Unio... | 127 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : int ) -> list:
UpperCAmelCase : Union[str, Any] = int(_lowerCAmelCase )
if n_element < 1:
UpperCAmelCase : int = ValueError('''a should be a positive num... | 127 | 1 |
'''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info... | 127 |
'''simple docstring'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
UpperCamelCase__: str = "\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchm... | 127 | 1 |
'''simple docstring'''
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
UpperCamelCase__: Optional[Any] = [
# (stable-diffusion, HF Diffuse... | 127 |
'''simple docstring'''
from __future__ import annotations
UpperCamelCase__: Tuple = 1.60_21E-19 # units = C
def snake_case_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float , ) -> tuple[str, float]:
if (conductivity,... | 127 | 1 |
'''simple docstring'''
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... | 127 |
'''simple docstring'''
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 transfor... | 127 | 1 |
'''simple docstring'''
from __future__ import annotations
UpperCamelCase__: Tuple = 1.60_21E-19 # units = C
def snake_case_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float , ) -> tuple[str, float]:
if (conductivity,... | 127 |
'''simple docstring'''
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class SCREAMING_SNAKE_CASE( nn.Module ):
"""simple docstring""... | 127 | 1 |
'''simple docstring'''
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin,... | 127 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Union[str, Any] ) -> Dict:
# Return True if there is node that has not iterated.
UpperCAmelCase : List[Any... | 127 | 1 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subpr... | 127 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__: int = logging.get_logger(__name__)
# TODO Update this
UpperCamelCase__: Any ... | 127 | 1 |
'''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def snake_case_ ( _lowerCAmelCase : int ) -> Dict:
# A local function to see if a dot lands in the circle.
def is_in_... | 127 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__: List[Any] = logging.get_logger(__name__)
UpperCamelCase__: str = {
"unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmer... | 127 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class SCREAMIN... | 127 |
'''simple docstring'''
import math
from numpy import inf
from scipy.integrate import quad
def snake_case_ ( _lowerCAmelCase : float ) -> float:
if num <= 0:
raise ValueError('''math domain error''' )
return quad(_lowerCAmelCase , 0 , _lo... | 127 | 1 |
'''simple docstring'''
import math
def snake_case_ ( _lowerCAmelCase : int ) -> bool:
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 ... | 127 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transforme... | 127 | 1 |
'''simple docstring'''
import argparse
import os
import re
UpperCamelCase__: Optional[Any] = "src/transformers"
# Pattern that looks at the indentation in a line.
UpperCamelCase__: Union[str, Any] = re.compile(r"^(\s*)\S")
# Pattern that matches `"key":" and ... | 127 |
'''simple docstring'''
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ..... | 127 | 1 |
'''simple docstring'''
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_rea... | 127 |
'''simple docstring'''
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... | 127 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_... | 127 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : str , _lowerCAmelCase : str ) -> int:
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
raise ValueError('''String lengths must match!''' )
UpperCAmelCase : List[str] ... | 127 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import req... | 127 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ ( _lowerCAmelCase : list[int] ) -> int:
if not nums:
return 0
UpperCAmelCase : Tuple = nums[0]
UpperCAmelCase : List[str] = 0
... | 127 | 1 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : Dict , _lowerCAmelCase : Any ) -> Any:
UpperCAmelCase : List[str] = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1... | 127 |
'''simple docstring'''
import re
def snake_case_ ( _lowerCAmelCase : str ) -> str:
if len(re.findall('''[ATCG]''' , _lowerCAmelCase ) ) != len(_lowerCAmelCase ):
raise ValueError('''Invalid Strand''' )
return dna.translate(dna.make... | 127 | 1 |
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