code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_util... | 88 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_co... | 149 | 0 |
import random
def a ( SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : List[Any] ):
"""simple docstring"""
UpperCamelCase : Optional[int] = a[left_index]
... | 367 |
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
__UpperCAmelCase : int = logging.get_logger(__name__)
class ... | 315 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : List[Any] = {
"configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}
try:
if not is_tor... | 144 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class lowerCAmelCase__ ( unittest.TestCase ):
def _snake_case ( self ):
"""simple docstring"""
lowercase_ : List[str] ... | 93 | 0 |
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
UpperCAmelCase_ = """\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models fo... | 371 |
def lowerCamelCase__ ( UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[int] ) -> Tuple:
'''simple docstring'''
_snake_case = [0 for i in range(r + 1 )]
# nc0 = 1
_snake_case = 1
for i in range(1 , ... | 295 | 0 |
import heapq
import sys
import numpy as np
__a = tuple[int, int]
class lowercase__:
"""simple docstring"""
def __init__( self : Any ) -> List[str]:
lowercase_ = []
lowercase_ = set()
def _lowercase ( self : i... | 30 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
lowercase : Optional[Any] = TypeVar('T')
class lowerCamelCase__ ( Generic[T]):
'''simple docstring'''
_A = 42 # Cache store ... | 232 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class SCREAMING_SNAKE_CASE__ :
lowercase__ = 42
lowercase__ = None
lowercase__ = None
UpperCAmelCase :... | 350 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
... | 313 | 0 |
from math import pi
def __A ( __lowerCAmelCase , __lowerCAmelCase )-> float:
"""simple docstring"""
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 39 |
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available
... | 21 | 0 |
"""simple docstring"""
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
lowerCam... | 215 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ ... | 215 | 1 |
'''simple docstring'''
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
lowerCamelCase_ = {
'''tiny.en''': '''https://openaipublic.azure... | 79 |
'''simple docstring'''
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : List[str] , __UpperCAmelCase : list[int] ):
'''simple docstring'''
_A = len(__UpperCAmelCase )
_A = [0] * len_array
if len_array... | 79 | 1 |
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, SchedulerOutput
def __snake_cas... | 367 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a :Dict = logging.get_logger(__name__)
__a :int = {
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'
),
'goo... | 329 | 0 |
"""simple docstring"""
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttentio... | 86 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers impor... | 86 | 1 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
a : List[str] = """\
@misc{chen... | 150 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
a : Dict = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 impl... | 150 | 1 |
from __future__ import annotations
def _UpperCAmelCase ( snake_case , snake_case , snake_case ):
"""simple docstring"""
if len(snake_case ) == 0:
raise ValueError("""find_max() arg is an empty sequence""" )
if (
left >= len(snake_case )
... | 82 | '''simple docstring'''
import os
def lowerCamelCase ( UpperCAmelCase__ : str = "input.txt" ) -> int:
with open(os.path.join(os.path.dirname(UpperCAmelCase__ ) , UpperCAmelCase__ ) ) as input_file:
lowercase_ : str = [
... | 239 | 0 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYa... | 364 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
re... | 135 | 0 |
'''simple docstring'''
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...tes... | 1 | '''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
SCREAMING_SNAKE_CASE_: Union[str, Any] =logging.get_logger(__name__)
class __A ( UpperCamelCase__ ):
def __init__(self : int , *__a : Dict , *... | 1 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A_ : Any = {
"""configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
"""tok... | 349 |
'''simple docstring'''
import argparse
import copy
def snake_case_ ( lowerCAmelCase_ )-> Dict:
'''simple docstring'''
_UpperCAmelCase : Dict = {}
with open(lowerCAmelCase_ ) as f:
for line in f:
if line.split()[0] ... | 349 | 1 |
"""simple docstring"""
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
UpperCAmelCase_ : Optional[int... | 91 |
"""simple docstring"""
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
UpperCAmelCase_ : Optional[int... | 91 | 1 |
A : Union[str, Any] = 'Input must be a string of 8 numbers plus letter'
A : int = 'TRWAGMYFPDXBNJZSQVHLCKE'
def __lowerCAmelCase ( a__ ) -> str:
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
__a = F"""Expected string as ... | 355 |
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_subprocess_async, require_cuda, require_multi_gpu
from ac... | 33 | 0 |
'''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
snake_case_ : List[Any] = logging.get_logger(__name__)
snake_case_ :... | 83 |
'''simple docstring'''
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,... | 83 | 1 |
"""simple docstring"""
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from tr... | 358 |
"""simple docstring"""
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class ... | 296 | 0 |
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
lowerCamelCase : int = ""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple... | 283 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__a : Any = {
"""configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
if not is_tokenizers_... | 210 | 0 |
'''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
if is_torch_available():
... | 111 |
'''simple docstring'''
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {name: getattr(transformers, name + 'Fast... | 111 | 1 |
"""simple docstring"""
from collections import defaultdict
def _snake_case ( UpperCAmelCase_ : str , UpperCAmelCase_ : str ):
A__ = first_str.lower().strip()
A__ = second_str.lower().strip()
# Remove whitespace
A__ ... | 335 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : Dict ): # noqa: E741
A__ = len(UpperCAmelCase_ )
A__ = 0
A__ = [0] * n
A__ = [False] * n
A__ = [False] * n
def dfs(UpperCAmelCase_ : ... | 335 | 1 |
"""simple docstring"""
from __future__ import annotations
def _A ( _a : int | float | str , _a : int | float | str ):
"""simple docstring"""
if nth_term == "":
return [""]
A = int(_a )
A ... | 77 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def _A ( _a : Callable , _a : float , _a : float , _a : float , _a : float ):
"""simple docstring"""
A = int... | 77 | 1 |
"""simple docstring"""
a = {str(digit): digit**5 for digit in range(10)}
def _snake_case ( _snake_case : int ) -> int:
'''simple docstring'''
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_snake_case ) )
def _snak... | 315 |
"""simple docstring"""
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils im... | 315 | 1 |
'''simple docstring'''
from collections import deque
from .hash_table import HashTable
class A_ ( lowerCAmelCase_ ):
def __init__( self : Tuple , *snake_case_ : Tuple , **snake_case_ : Optional[int] ):
super().__init__(*snake_case_ , **snake_ca... | 156 |
'''simple docstring'''
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 :Tuple = '''\
'''
__SCREAMING_SNAKE_CASE :Union[str, Any] = ... | 156 | 1 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def lowerCamelCase__ ( a__ : str ) -> str:
if "model" in orig_key:
UpperCamelCase_ = orig_key.replace("""model.""" , """""" )
if "norm1" in orig_key:
U... | 122 |
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import ... | 122 | 1 |
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"""files""" , [
["""full:README.md""", """dataset_infos.json"""],
["""em... | 369 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
... | 77 | 0 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
... | 345 |
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
__UpperCamelCase : int = 299792458
# Symbols
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase : Optional[int] = symbols... | 307 | 0 |
from collections.abc import Callable
import numpy as np
def _A ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ):
lowercase__ = int(np.ceil((x_end - xa) / step_size ) )
lowercase__ = np.zeros((n + 1,) )
lowercase__ = ya
... | 360 |
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class lowerCAmelCase ( lowercase_ ):
def __init__( se... | 201 | 0 |
from random import shuffle
import tensorflow as tf
from numpy import array
def lowerCAmelCase ( _lowerCAmelCase : Tuple , _lowerCAmelCase : List[Any] ):
"""simple docstring"""
UpperCAmelCase__ = int(lowercase_ )
assert noofclusters < len(lo... | 169 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common ... | 14 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"""uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}... | 368 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutpu... | 100 | 0 |
"""simple docstring"""
from __future__ import annotations
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase ) -> bool:
"""simple docstring"""
if len(__UpperCamelCase ) == 0:
return False
lowerCAmelCase_ : Optional[int] = len(__Upper... | 241 |
"""simple docstring"""
lowercase__ = 0 # The first color of the flag.
lowercase__ = 1 # The second color of the flag.
lowercase__ = 2 # The third color of the flag.
lowercase__ = (red, white, blue)
def __lowerCamelCase ( __UpperCamelCase ) -> list:
"""... | 241 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
Dif... | 363 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
'''simple docstring'''
_a = 'SpeechT5FeatureExtractor'
_a = 'SpeechT5Tokenizer'
def __init__( self : D... | 272 | 0 |
"""simple docstring"""
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def lowercase ( _snake_case ... | 102 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
snake_case__ : int
snake_case__ : jnp.dtype = jnp.floataa
def SCREAMING_SNAKE_CASE ( self : str ) -> int:
... | 32 | 0 |
'''simple docstring'''
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter... | 264 |
'''simple docstring'''
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
_lowercase : Optional[Any] = logging.get_logger(__nam... | 264 | 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 (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaske... | 205 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
Be... | 205 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random... | 353 |
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_to... | 173 | 0 |
"""simple docstring"""
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def lowerCamelCase_ ():
_UpperCAmelCase , _UpperCAmelCase : str = 9, 14 # noqa: F841
_UpperCAmelCase : Optional[Any] = ... | 263 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase :int = logging.get_logger(__name__)
_lowerCAmelCase :Union[str, Any] = {
'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/co... | 263 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import loggi... | 362 |
"""simple docstring"""
from __future__ import annotations
def _a ( _snake_case , _snake_case = None , _snake_case = None ):
"""simple docstring"""
if start is None:
UpperCAmelCase = 0
if end is None:
UpperCAmelCase =... | 234 | 0 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
# TODO Update this
UpperCAmelCase = {
'''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b... | 195 |
import pprint
import requests
UpperCAmelCase = '''https://zenquotes.io/api'''
def UpperCAmelCase_ ( ):
return requests.get(API_ENDPOINT_URL + '/today' ).json()
def UpperCAmelCase_ ( ):
return requests.get(API_ENDPOINT_URL + '/random' ).json()
if __name__ ... | 195 | 1 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
UpperCamelCase__ = datasets.load_iris()
UpperCamelCase__ = np.array(data["data"])
UpperCamelCase__ = np.array(data["target"])
UpperCamelCase__ = data["target_name... | 87 |
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
f... | 87 | 1 |
"""simple docstring"""
def _snake_case ( lowerCamelCase__ : int , lowerCamelCase__ : int , lowerCamelCase__ : int ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
lowerCamelCase_ : Union[str, Any] ... | 144 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : int = logging.get_logger(__name__)
A__ : List[str] = {
'google/pix2struct-textcaps-base': (
'https://... | 144 | 1 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
__UpperCAmelCase : Dict = logging.get_logger... | 293 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def A__ ( SCREAMING_SNAKE_CASE__) -> list[list[float]]:
__snake_case: Any = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only wo... | 293 | 1 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_devi... | 207 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def a ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = ('''dense.weight''', '''attention.self.query''', ''... | 207 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__A = {'''processing_wav2vec2_with_lm''': ['''Wav2Vec2ProcessorWithLM''']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
__A = _LazyModule(__n... | 64 | """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
__A = logging.get_logger(__name__)
__A = {
'''google/efficie... | 64 | 1 |
"""simple docstring"""
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
f... | 224 |
"""simple docstring"""
def UpperCamelCase_ ( lowerCAmelCase__ : int = 100 ) -> int:
"""simple docstring"""
lowerCAmelCase_ : Any = (n * (n + 1) // 2) ** 2
lowerCAmelCase_ : Optional[int] = n * (n + 1) * (2 * n + 1) // 6
r... | 224 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase : int = {
'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
... | 366 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=A ):
"""simple docstring"""
__a = ["""keras_nlp"""]
def __init__( self : str , *UpperCamelCase : List[Any] , **UpperCamelCase ... | 320 | 0 |
import numpy as np
_a = [
['''a''', '''b''', '''c''', '''d''', '''e'''],
['''f''', '''g''', '''h''', '''i''', '''k'''],
['''l''', '''m''', '''n''', '''o''', '''p'''],
['''q''', '''r''', '''s''', '''t''', '''u'''],
['''v''', '''w''', '''x''', '''y''', '''z'''],
]
... | 39 |
"""simple docstring"""
def _lowercase ( ) -> int:
return 1
def _lowercase ( __snake_case ) -> int:
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def _lowercase ( __snake_case ) -> int:
return 0 if x < 0 else five_pence(x - 5 )... | 269 | 0 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
__a :Dict = False
__a :int = True
__a :Any = False
if __name__ == "__main__":
__a :Any = argparse.ArgumentParser()
pars... | 329 |
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... | 329 | 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, prepare_image_inputs
if is_torch_av... | 53 |
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
A_ : List[str] = {
'n_samples': 64,
'horizon': 32,
'num_inference_steps': 20,
'n_guide_steps': 2, # can set to 0 for faster sampling, does not use value network
'scale_grad_by_std... | 192 | 0 |
'''simple docstring'''
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def UpperCamelCase__ ( lowerC... | 361 |
'''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.apach... | 220 | 0 |
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[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_dif... | 102 |
"""simple docstring"""
from sklearn.metrics import recall_score
import datasets
SCREAMING_SNAKE_CASE : Dict = """
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true p... | 102 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def lowerCamelCase__ ( A : int , A : int , A : bool , A : list[int] , A : float ):
'''simple docstring'''
if depth < ... | 91 |
'''simple docstring'''
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import ... | 91 | 1 |
import random
from .binary_exp_mod import bin_exp_mod
def A ( a_ ,a_=1_000 ) -> Optional[Any]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
__UpperCamelCase : List[An... | 71 |
'''simple docstring'''
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowercase_ = logging.getLogger(__name__)
if __name__ == "__m... | 58 | 0 |
"""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 lowerCamelCase ( lowerCAmelCase_... | 366 |
"""simple docstring"""
from math import factorial, radians
def a__ ( lowerCAmelCase , lowerCAmelCase = 18 , lowerCAmelCase = 10 ) -> float:
UpperCAmelCase__ : List[Any] = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Converting from degrees to radians
... | 166 | 0 |
'''simple docstring'''
lowerCAmelCase_ : Tuple = '\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
lowerCAmelCase_ : str = [{'type': 'code... | 63 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : Dict = logging.get_logger(__name__)
lowerCAmelCase_ : int = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacod... | 63 | 1 |
class A__ :
"""simple docstring"""
def __init__( self) -> Optional[Any]:
'''simple docstring'''
a__ : Any = {}
def __lowercase ( self) -> None:
'''simple docstring'''
... | 363 |
from __future__ import annotations
from collections.abc import Callable
def A_ ( A__ , A__ , A__ , A__ = 100 , ) -> float:
a__ : Dict = x_start
a__ : Any = fnc(A__ )
a__ : Optional[int] = 0.0
for _ in range(A__ ... | 225 | 0 |
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
lowerCamelCase__ = datasets.utils.logging.get_logger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE ( ... | 302 |
import string
import numpy
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
return b if a == 0 else greatest_common_divisor(b % a , _SCREAMING_SNAKE_CASE )
class SCREAMING_SNAKE_CASE :
__lowerCame... | 302 | 1 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
UpperCamelCase = (720, 1280) # Height, Width
UpperCamelCase = (0.4, 0.6) # if height or width lower than this scale, drop it.
UpperCamelCase ... | 351 |
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
UpperCamelCase = [
"""Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"""... | 65 | 0 |
from collections.abc import Sequence
from queue import Queue
class lowercase :
def __init__( self , snake_case , snake_case , snake_case , snake_case=None , snake_case=None ):
snake_case_ = start
snake_case_ = end
... | 285 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = {}
sn... | 285 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE__ : Any = {"configuration_beit": ["BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BeitCon... | 359 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Tuple = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.... | 339 | 0 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=5 ) -> Optional[int]:
'''simple docstring'''
assert masked_input.count('<mask>' ) == 1
SCREAMING_SNAKE_CASE_... | 176 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interl... | 176 | 1 |
'''simple docstring'''
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch... | 338 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[Any] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100On... | 338 | 1 |
from collections.abc import Iterable
from typing import Any
class A :
def __init__(self , lowerCAmelCase = None ):
__lowercase= value
__lowercase= None # Added in order to delete a node easier
__lowercase= None
__lowercase= None
... | 295 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor,... | 295 | 1 |
"""simple docstring"""
import itertools
import string
from collections.abc import Generator, Iterable
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Generator[tuple[str, ...], None, None]:
snake_case_ = iter(_SCREAMING_SNAKE_CASE )
while True:
... | 233 |
"""simple docstring"""
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noq... | 233 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow... | 91 |
"""simple docstring"""
def _A (__a = 50 ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
... | 91 | 1 |
'''simple docstring'''
from timeit import timeit
a_ : Union[str, Any] = {
"MALAYALAM": True,
"String": False,
"rotor": True,
"level": True,
"A": True,
"BB": True,
"ABC": False,
"amanaplanacanalpanama": True, # "a man a plan a canal panama... | 350 |
'''simple docstring'''
from timeit import timeit
def _A (lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
if number < 0:
raise ValueError('the value of input must not be negative' )
_a = 0
while nu... | 104 | 0 |
'''simple docstring'''
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute... | 97 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if ... | 245 | 0 |
'''simple docstring'''
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler... | 13 |
'''simple docstring'''
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
__UpperCamelCase = re.compile(R"^(?P<major>\d+)" R"\.(?P<minor>\d+)" R"\.(?P<patch>\d+)$")
... | 13 | 1 |
'''simple docstring'''
from collections import deque
from .hash_table import HashTable
class _a ( __a ):
def __init__( self : Union[str, Any] , *lowercase : Tuple , **lowercase : Optional[int] ):
'''simple docstring'''
super().__init__(*low... | 34 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
__lowerCAmelCase = logging.get_logger(__name__)
class UpperCAmelCase__ ( lowercase__ ):
"""simple docstring"""
def __init__( self : Tuple ... | 271 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
_UpperCAmelCase : str ... | 352 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import requ... | 9 | 0 |
'''simple docstring'''
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_... | 22 |
'''simple docstring'''
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
__A = logging.get_logger(__name__)
class A ( _... | 164 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( lowercase : int ) -> int:
"""simple docstring"""
assert (
isinstance(lowercase , lowercase ) and number_of_steps > 0
), F'number_of_steps needs to be positive integer, your input {number_of_steps}'
i... | 112 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__snake_case = {
"""configuration_conditional_detr""": [
"""CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""C... | 112 | 1 |
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowercase_ ( unittest.TestCase ):
""... | 338 |
from __future__ import annotations
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , lowerCAmelCase_ : int ):
"""simple docstring"""
_A: List[str] = data
_A: Node | None = None
_A: Node... | 121 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase = {
"""configuration_longformer""": [
"""LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_M... | 192 | 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 = logging.get_logger(__name__)
_UpperCAmelCase = {"... | 192 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...s... | 194 |
# flake8: noqa
# Lint as: python3
lowerCAmelCase__ = [
'''VerificationMode''',
'''Version''',
'''disable_progress_bar''',
'''enable_progress_bar''',
'''is_progress_bar_enabled''',
'''experimental''',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar... | 130 | 0 |
"""simple docstring"""
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
_a = logging.get_logger(__name__)
class A_ :
'''simple docstring'''
def __in... | 23 |
"""simple docstring"""
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_a = {
'E': 12.70,
'T': 9.06,
'A': 8.17,
'O': 7.51,
'I': 6.97,
'N': 6.75,
'S': 6.33,
'H': 6.09,
'R': 5.99,
'D': 4.25,
'L': 4.03,
'C': 2.78,
'U': 2... | 23 | 1 |
"""simple docstring"""
def lowercase_ ( _snake_case ,_snake_case ):
return 1 if input_a == input_a else 0
def lowercase_ ( ):
assert xnor_gate(0 ,0 ) == 1
assert xnor_gate(0 ,1 ) == 0
assert xnor_gate(1 ,0 ) == 0
assert xnor_gate(1 ... | 25 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
Seg... | 277 | 0 |
def _lowercase ( UpperCamelCase_ ) -> int:
'''simple docstring'''
assert (
isinstance(UpperCamelCase_ , UpperCamelCase_ ) and number_of_steps > 0
), F'number_of_steps needs to be positive integer, your input {number_of_steps}'
if number_of_steps == 1:
... | 169 |
from __future__ import annotations
from collections.abc import Generator
def _lowercase ( ) -> Generator[int, None, None]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = {}
SCREAMING_SNAKE_CASE__ = 2
while True:
SCREAMING_SNAKE_CASE... | 169 | 1 |
from __future__ import annotations
import pandas as pd
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> list[int]:
"""simple docstring"""
A__ = [0] * no_of_processes
A__ = [0] * no_of_processes
# Copy the burs... | 14 |
import os
from distutils.util import strtobool
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
for e in env_keys:
lowerCAmelCase__ : Union[str, Any] = int(os.environ.get(SCREAMING_SNAKE_CASE_ , -1 ) )
... | 212 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_ver... | 166 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available... | 166 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_d... | 35 |
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
_SCREAMING_SNAKE_CASE = datasets.utils.logging.get_logger(__name__)
class lowerCAmelCase_ ( folder_based_builde... | 158 | 0 |
from typing import Any
import numpy as np
def UpperCAmelCase_ (_lowerCAmelCase : np.ndarray ):
return np.array_equal(_lowerCAmelCase , matrix.conjugate().T )
def UpperCAmelCase_ (_lowerCAmelCase : np.ndarray , _lowerCAmelCase : np.ndarray ):
... | 171 |
def UpperCAmelCase_ (_lowerCAmelCase : int = 1_00 ):
__UpperCamelCase : Tuple = n * (n + 1) * (2 * n + 1) / 6
__UpperCamelCase : List[str] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F"""{s... | 171 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
a_ : Any = logging.get_logger(__name__)
class __UpperCamelCase ( lowerCamelCase__ ):
def __init__( self, *lowerCAmelCase, **lowerC... | 75 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 75 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__A =logging.get_logger(__name__)
__A ... | 283 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A ={
'configuration_table_transformer': [
'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TableTransformerConfig',
'Tab... | 283 | 1 |
'''simple docstring'''
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime... | 141 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_... | 141 | 1 |
import os
__A = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}
def __A ( _lowercase ):
'''simple docstring'''
_A = 0
_A = 0
while index < len(_lowercase ) - 1:
_A = SYMBOLS[numerals[index]]
_A... | 75 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IM... | 75 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 137 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
a_ : Tuple = logging.getLogger()
def lowerCa... | 137 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedCla... | 363 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pi... | 238 | 0 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 84 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _A ( ):
"""simple docstring"""
with offline(OfflineSimulationMode.CONNECT... | 104 | 0 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : str = "" ) -> dict[str, float]:
"""simple docstring"""
UpperCamelCase :Dict = url or """https://www.imdb.com/chart/top/... | 365 |
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : Optional[int] ) -> Any:
"""simple docstring"""
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
... | 62 | 0 |
'''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
import torch
fro... | 254 | import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from... | 348 | 0 |
'''simple docstring'''
import os
from math import logaa
def UpperCamelCase_( snake_case : str = "base_exp.txt" ):
'''simple docstring'''
snake_case_ = 0
snake_case_ = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(snake_case... | 92 |
'''simple docstring'''
def UpperCamelCase_( snake_case : int = 1_0_0_0_0_0_0 ):
'''simple docstring'''
snake_case_ = set(range(3 , snake_case , 2 ) )
primes.add(2 )
for p in range(3 , snake_case , 2 ... | 92 | 1 |
import os
from distutils.util import strtobool
def lowerCAmelCase__ ( a__: Tuple , a__: Tuple ) -> Any:
'''simple docstring'''
for e in env_keys:
_UpperCAmelCase = int(os.environ.get(a__ , -1 ) )
if val >= 0:
... | 329 |
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
lower... | 329 | 1 |
"""simple docstring"""
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gp... | 352 | import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.con... | 258 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase__ = {
"configuration_wav2vec2": ["WAV_2_VEC_2_PRETR... | 96 |
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_inpu... | 184 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.... | 361 |
'''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.org/... | 4 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE : Optional[Any] = {
'''configuration_trajectory_transformer''': [
'''TRAJECTORY_TRANSFORM... | 183 |
"""simple docstring"""
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
_SCREAMING_SNAKE_CASE : Any = importlib.util.find... | 183 | 1 |
"""simple docstring"""
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class a ( lowerCAmelCase_ , lowerCAmelCase_ ):
@register_to_config
def __init__( self : Dict , *,
__lowerCA... | 30 | """simple docstring"""
def __UpperCAmelCase ( lowercase = 10_00 ):
"""simple docstring"""
_UpperCAmelCase = 2**power
_UpperCAmelCase = 0
while n:
_UpperCAmelCase , _UpperCAmelCase = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input... | 30 | 1 |
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