code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
... | 70 |
__UpperCamelCase: str = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__UpperCamelCase: Tuple = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def SCREAMING_SNAKE_CASE__ ( _lowercase : dict[int, list[int]] , _lowercase : int , _lowercase : list[bool] ) -> ... | 266 | 0 |
from collections import deque
from .hash_table import HashTable
class lowerCAmelCase__ ( _lowerCamelCase ):
def __init__( self : Dict , *__UpperCamelCase : Tuple , **__UpperCamelCase : List[Any] ) -> Union[str, Any]:
super().__init__(*__UpperC... | 712 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import A... | 224 | 0 |
def UpperCAmelCase__( __UpperCAmelCase : int ):
if p < 2:
raise ValueError('p should not be less than 2!' )
elif p == 2:
return True
__snake_case : str = 4
__snake_case : List[str] = (1 << p) - 1
for _ in range(p - 2 ):
... | 576 | import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
__magic_na... | 576 | 1 |
'''simple docstring'''
_snake_case : Tuple = """\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"... | 719 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' wh... | 493 | 0 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def _lowerCAmelCase ( lowerCamelCas... | 502 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCAmelCase__ ( unittest... | 250 | 0 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
__lowerCamelCase : int = logging.get_logger(__name__)
class A__ ( _lowerCAmelCase ):
def __init__( self , *A_ , **A_ ):
'''simple docstring'''
... | 708 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase : Tuple = logging.get_logger(__name__)
__lowerCamelCase : str = {
"""camembert-base""": """h... | 38 | 0 |
'''simple docstring'''
from math import log
from scipy.constants import Boltzmann, physical_constants
__UpperCAmelCase = 300 # TEMPERATURE (unit = K)
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , ):
"""simple docstring"""
if donor_conc <= 0:
... | 379 |
'''simple docstring'''
__UpperCAmelCase = """0.18.2"""
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion... | 379 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Optional[int] = {
"google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main... | 441 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_... | 441 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from... | 571 |
"""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 transformers imp... | 571 | 1 |
'''simple docstring'''
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobert... | 706 |
'''simple docstring'''
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
lowerCAmelCase : Optional[Any] = '''scheduler_config.json'''
class UpperCAmelCase__ ( UpperCame... | 39 | 0 |
SCREAMING_SNAKE_CASE : List[str] = {
"A": ".-", "B": "-...", "C": "-.-.", "D": "-..", "E": ".", "F": "..-.", "G": "--.",
"H": "....", "I": "..", "J": ".---", "K": "-.-", "L": ".-..", "M": "--", "N": "-.",
"O": "---", "P": ".--.", "Q": "--.-", "R": ".-.", "S": "...", "T": "-", "U": "..-",
... | 419 |
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, TypedSequence
from datasets.features import Ar... | 419 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailable()
except Optiona... | 713 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase = {
'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
'tokeniz... | 503 | 0 |
'''simple docstring'''
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def _UpperCamelCase (_lowerCamelCase : List[Any] )-> List[Any]:
'''simple docstring'''
return x + 2
class ... | 24 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
__lowerCAmelCase = TypeVar("T")
class __SCREAMING_SNAKE_CASE (Generic[T] ):
"""simple docstring"""
def __init__( self , UpperCamel... | 536 | 0 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
... | 361 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,... | 361 | 1 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accel... | 467 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCamelCase = {
'''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
... | 467 | 1 |
"""simple docstring"""
import argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _conver... | 712 |
"""simple docstring"""
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ ) -> float:
_validate_point(SCREAMING_SNAKE_CASE__ )
_validate_point(SCREAMING_SNAKE_CASE__ )
if len(SCREAMING_SNAKE_CASE__ ) != len(SCREAMING_SNAKE_CASE__ ):
raise ValueError("Both points ... | 370 | 0 |
from __future__ import annotations
def __UpperCAmelCase ( a_ , a_ , a_):
if days_between_payments <= 0:
raise ValueError('days_between_payments must be > 0')
if daily_interest_rate < 0:
raise ValueError('daily_interest_rate must be >= 0')
if princ... | 198 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Distribu... | 73 | 0 |
UpperCamelCase_ = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def _UpperCAmelCase ( UpperCamelCase: int ):
"""simple docstring"""
__lowerCAmelCase = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_... | 718 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class a ( __UpperCAmelCase ):
@staticmethod
@abstractmethod
def UpperCAmelCase__ ( snake_case__ : ArgumentParser ):
"""simple docstring"""
raise NotImplementedError(... | 376 | 0 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, an... | 6 |
'''simple docstring'''
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vi... | 374 | 0 |
print((lambda quine: quine % quine)("print((lambda quine: quine %% quine)(%r))"))
| 713 |
from ...processing_utils import ProcessorMixin
class a_ ( lowerCamelCase_ ):
"""simple docstring"""
__UpperCAmelCase = ['image_processor', 'feature_extractor']
__UpperCAmelCase = 'TvltImageProcessor'
__UpperCAmelCase = 'TvltFeatureExtractor'
def __i... | 252 | 0 |
from __future__ import annotations
import math
def _UpperCamelCase (a__ :int ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives,... | 619 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils impo... | 619 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
a : int = logging.get_logger(__name__)
a : Dict = {'''vocab_file'... | 527 |
from itertools import count
def lowercase_ ( _UpperCamelCase = 50 ):
'''simple docstring'''
__lowercase = [1] * min_block_length
for n in count(_UpperCamelCase ):
fill_count_functions.append(1 )
for block_length in range(_UpperCamelCase , n + 1 ):
... | 527 | 1 |
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
snake_case__ : Optional[Any] = '\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language... | 408 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla... | 408 | 1 |
"""simple docstring"""
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
SCREAMING_SNAKE_CASE : Optional[int] = logging.getLog... | 229 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Optional[Any] = {
"""asapp/sew-d-tiny-100... | 229 | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__a: List[Any] = (720, 1280) # Height, Width
__a: str = (0.4, 0.6) # if height or width lower than this scale, drop it.
__a: Optional[Any] = ... | 108 |
"""simple docstring"""
def a ( __UpperCAmelCase : int , __UpperCAmelCase : int ) -> int:
while b:
__magic_name__, __magic_name__: Optional[int] = b, a % b
return a
def a ( __UpperCAmelCase : ... | 96 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 706 |
"""simple docstring"""
from __future__ import annotations
class __SCREAMING_SNAKE_CASE :
def __init__( self : Any , snake_case : str , snake_case : str ):
'''simple docstring'''
A__ , A__ : List[s... | 498 | 0 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class ... | 554 |
"""simple docstring"""
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/ch... | 554 | 1 |
def __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_):
'''simple docstring'''
lowerCamelCase_ : int = [[] for _ in range(A__)]
lowerCamelCase_ : Dict = key - 1
if key <= 0:
raise ValueError("Height of grid can't be 0 or negative")
i... | 713 |
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowerCAmelCase__ ( __lowerCamelCase ):
"""simple docstring"""
__UpperCAmelCase : Dict = '''EncodecFeatureExtr... | 73 | 0 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=False ) -> List[str]:
UpperCAmelCase_ = OmegaConf.load(__SCREAMING_SNAKE_CASE )
if displ... | 579 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_co... | 360 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTest... | 705 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase = {
"""configuration_mobilebert""": [
... | 562 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
"""SenseTime/deformable-detr""": """https://huggingface.co/sensetime/deformable-d... | 590 | """simple docstring"""
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize("repo_id" , ["canonical_dataset_name", "org-name/dataset-name"] )
@pytest.mark.parametrize("path" , ["filename.csv", "filename with blanks.csv"] )
@pytest.mark.parame... | 646 | 0 |
'''simple docstring'''
def _a ( lowerCamelCase_ ):
snake_case : Optional[int] =[1]
snake_case , snake_case , snake_case : int =0, 0, 0
snake_case : Union[str, Any] =ugly_nums[ia] * 2
snake_case : Uni... | 136 |
'''simple docstring'''
def _a ( lowerCamelCase_ = 1_00 ):
snake_case : List[Any] =0
snake_case : List[str] =0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name_... | 136 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor,... | 674 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case :
def __init__( self :Optional[Any] , _lowerCamelCase :int ):
__SCREAMING_SNAKE_CASE : int = num_of_nodes
__SCREAMING_SNAKE_CASE : list[lis... | 674 | 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
lowercase = '''<<<<<<< This should probably be modified because it mentions:... | 707 |
"""simple docstring"""
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
... | 24 | 0 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def UpperCamelCase_( snake_case__: Dict ... | 146 |
def UpperCamelCase_( snake_case__: float ) -> float:
if edge <= 0 or not isinstance(snake_case__ , snake_case__ ):
raise ValueError('Length must be a positive.' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def UpperCamelCase_( snake_case__: float ) ... | 146 | 1 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowerCamelCase ... | 700 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : List[Any] = logging.get_logger(__name__)
A_ : Dict = {
"microsoft/unispeech-large-1500h-cv": (
"https:... | 616 | 0 |
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Dict ):
SCREAMING_SNAKE_CASE : Optional[int] = {}
def _A ( self : str ):
print(self.vertex )
for i in self.vertex:
... | 62 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 63 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProc... | 714 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _A( snake_case__ ):
"""simple docstring"""
@staticmethod
@abstractmethod
def UpperCAmelCase_ ( _A ):
raise NotImplementedError()
@abstractmethod
def UpperCAmelCas... | 77 | 0 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
snake_case : Optional[Any] = {
"<": operator.lt,
"<=": operator.le,
"==": operator.eq,
"!=": operator.ne,
">=": operator.ge,
">": operator.gt,
}
def ... | 124 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( _snake_case : int , _snake_case : Union[str, Any] ) ... | 124 | 1 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
UpperCAmelCase_ : int = {
"vocab... | 704 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : Tuple ... | 424 | 0 |
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_lowercase : Any ={
"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.... | 136 |
from importlib import import_module
from .logging import get_logger
_lowercase : Optional[int] =get_logger(__name__)
class snake_case__ :
"""simple docstring"""
def __init__( self , __lowercase , __lowercase=None ) -> Dict:
... | 136 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase__ : Optional[int] = {
'''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig... | 704 |
"""simple docstring"""
import math
import sys
import cva
import numpy as np
def __lowercase ( _a , _a ):
# For applying gaussian function for each element in matrix.
snake_case_ : Dict = math.sqrt(_a )
snake_case_ : Tuple = 1 / (sigma * math.sqrt(2 * mat... | 485 | 0 |
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_aligned_output_features_output_indices
... | 16 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase : Dict = {'configuration_fnet': ['FNET_PRETRA... | 49 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamelCase_ : Optional[Any] = logging.get_logger(__name__)
lowerCamelCase_ : Optional[int] ... | 345 |
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_an... | 345 | 1 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.... | 475 |
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 lowercase__ ( __SCREAMING_SNAKE_CASE ):... | 475 | 1 |
import math
def _lowerCAmelCase ( _lowerCAmelCase ):
'''simple docstring'''
return math.sqrt(_lowerCAmelCase ) * math.sqrt(_lowerCAmelCase ) == num
def _lowerCAmelCase ( _lowerCAmelCase ):
'''simple docstring'''
A_ : Optional[int] = 0
A_ : ... | 481 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
logging.s... | 481 | 1 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
def update_area_of_max_square(__UpperCAmelCase, __UpperCAmelCase ) -> int:
# BASE CASE
if row >= rows or col >= cols:
... | 640 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[Any] = logging.get_logger(__name__)
a : List[str] = {
'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-... | 640 | 1 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, lo... | 286 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltCLIPConfig""",
"""AltCLIPTextConfig""",
... | 286 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
W... | 49 |
'''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 __lowerCamelCase ( SCREAMING_SNAK... | 421 | 0 |
def a (lowerCAmelCase__ ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 209 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def a (lowerCAmelCase__ ):
__a = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 matrices
if len(lowerCAme... | 209 | 1 |
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 require_torch, require_vision
from transformers.utils imp... | 233 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : Dict = {
"""configuration_blip_2""": [
"""BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Blip2Config""",
"""Blip2QFormerConfig""",
"""Blip2Visi... | 233 | 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_avai... | 30 | '''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : float = 1 / sqrt(2 ) ) -> IIRFilter:
lowercase_ : str ... | 30 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 2 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ :List[str] = logging.get_logger(__name__)
UpperCAmelCase__ :Union[str, Any] = {
"""BAAI/AltCLIP""": """htt... | 150 | 0 |
"""simple docstring"""
import re
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : str ):
"""simple docstring"""
if len(re.findall("""[ATCG]""" , SCREAMING_SNAKE_CASE__ ) ) != len(SCREAMING_SNAKE_CASE__ ):
raise ValueError("""Invalid Strand""" )
... | 48 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ):
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 48 | 1 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _UpperCamel... | 578 |
'''simple docstring'''
import pytest
UpperCamelCase__ : Tuple = '''__dummy_dataset1__'''
UpperCamelCase__ : Dict = '''
import json
import os
import datasets
REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_... | 578 | 1 |
'''simple docstring'''
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenizati... | 719 |
'''simple docstring'''
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def ... | 216 | 0 |
from collections.abc import Callable
import numpy as np
def _lowerCamelCase ( __A : Optional[int] , __A : Union[str, Any] , __A : Dict , __A : Union[str, Any] , __A : Optional[int] ) -> Union[str, Any]:
_UpperCAmelCase : Union... | 485 |
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
import pyarrow as p... | 431 | 0 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned""... | 721 |
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def _a ( SCREAMING_SNAKE_CASE_ : ... | 552 | 0 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"SenseTime/deformable-detr": "https://huggin... | 308 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_pro... | 308 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.u... | 721 |
"""simple docstring"""
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_devi... | 239 | 0 |
"""simple docstring"""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
... | 510 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowerCAmelCase = {
'configuration_wav2vec2': ['WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Wav2Vec2Co... | 585 | 0 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTeste... | 143 |
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class __lowercase ( enum.Enum ... | 143 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_SCREAMING_SNAKE_CASE = {
'''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFo... | 502 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( lowerCamelCase_ : list[int | str] ):
create_state_space_tree(lowerCamelCase_ , [] , 0 , [0 for i in range(len(lowerCamelCase_ ) )] )
def _lowerCAmel... | 502 | 1 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
lowercase_ = 'Usage of script: script_name <size_of_canvas:int>'
lowercase_ = [0] * 1_0_0 + [1] * 1_0
random.shuffle(choice)
def UpperCamelCase__ ( SCREAMING_SNAKE_C... | 230 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
'InstructBlipQFormerConfig',
'Instruct... | 230 | 1 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
UpperCAmelCase__ = logging.get_logger(__name__)
class snake_case_ ( __UpperCamelCase ):
"""simple docstring"""
def __init__(self: str , *__... | 351 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_... | 98 | 0 |
"""simple docstring"""
import sys
from collections import defaultdict
class lowercase_ :
'''simple docstring'''
def __init__( self : Union[str, Any] ):
_A = []
def lowerCAmelCase_ ( self : int , _UpperCAmelCase : str ):
return self.node_pos... | 720 |
"""simple docstring"""
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import Nested... | 505 | 0 |
"""simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
SCREAMING_SNAKE_CASE : Dict = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0... | 260 |
"""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 FlaxStableDiffus... | 260 | 1 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class __lowerCAmelCase ( nn.Module ):
def __init__( self , lowerCAmelCase = 16 , lowerCAmelCase = 88 , lowerCAmelC... | 380 |
import requests
lowercase_ = 'YOUR API KEY'
def a ( A__ : str , A__ : str = giphy_api_key ) -> list:
"""simple docstring"""
_lowercase ='+'.join(query.split() )
_lowercase =F'''https://api.giphy.com/v1/gifs/search?q={for... | 380 | 1 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Acc... | 412 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
... | 412 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
UpperCAmelCase__ ={
"configuration_speecht5": [
"SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SPEECHT5_PRE... | 704 |
"""simple docstring"""
def lowerCAmelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : str ):
"""simple docstring"""
assert x is not None
assert y is not None
__lowercase = len(UpperCamelCase__ )
__lowercase = len(UpperCamelCase__ )
# dec... | 442 | 0 |
'''simple docstring'''
import requests
def lowerCamelCase__ ( __lowerCamelCase : str , __lowerCamelCase : str ):
'''simple docstring'''
_UpperCAmelCase : str ={'Content-Type': 'application/json'}
_UpperCAmelCase : int =requests.post(__l... | 446 |
'''simple docstring'''
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
lowercase ='\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex ... | 446 | 1 |
"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config ... | 544 |
"""simple docstring"""
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = 0
for ch in input_str:
UpperCamelCase = ord(_SCREAMING_SNAKE_CASE )
UpperCamelCase = pow(2 , _SCREAMING_SNAKE_CASE )
# If we already turned on bit for curre... | 544 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
__snake_case = {
'''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}
tr... | 1 |
from collections.abc import Generator
from math import sin
def _A ( _lowercase ) -> bytes:
"""simple docstring"""
if len(_lowercase ) != 32:
raise ValueError('Input must be of length 32' )
__UpperCamelCase = B''
for i in [3, 2, 1, 0]:
... | 1 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__magic_name__ : List[str] = {
'configuration_mobilenet_v2': [
'MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'MobileNetV2Conf... | 715 |
import math
def lowerCAmelCase ( snake_case__ : float , snake_case__ : float )-> float:
return math.pow(snake_case__ , 2 ) - a
def lowerCAmelCase ( snake_case__ : float )-> float:
return 2 * x
def ... | 608 | 0 |
'''simple docstring'''
__lowerCAmelCase = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
__lowerCAmelCase... | 585 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowerCAmelCase = {
'configuration_wav2vec2': ['WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Wav2Vec2Co... | 585 | 1 |
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
lowerC... | 161 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCame... | 161 | 1 |
'''simple docstring'''
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_fla... | 525 |
from ..utils import DummyObject, requires_backends
class _UpperCamelCase ( metaclass=__snake_case ):
"""simple docstring"""
lowerCAmelCase = ['note_seq']
def __init__( self , *a__ , **a__ ) -> Optional[int]:
requires_backends(self , ["""n... | 641 | 0 |
'''simple docstring'''
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class _UpperCamelCase ( _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE:List[Any] = (KDPMaDiscreteS... | 126 |
'''simple docstring'''
import qiskit
def lowerCAmelCase_ ( a : int , a : int ):
a__ = qiskit.Aer.get_backend('aer_simulator' )
a__ = qiskit.QuantumCircuit(4 , 2 )
# encode inputs in qubits 0 and 1
if bita ==... | 126 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCamelCase : Optional[int] = logging.get_logger(__name__)
_UpperCamelC... | 284 | """simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.js... | 363 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : int = logging.get_logger(__name__)
__A : Optional[int] = {
'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/reso... | 711 |
'''simple docstring'''
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentPa... | 126 | 0 |
"""simple docstring"""
from math import ceil
def lowerCamelCase ( _UpperCamelCase : Dict , _UpperCamelCase : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
__UpperCAmelCase : Tuple = list(range(0 , _UpperCamelCase ) )
__Uppe... | 139 |
"""simple docstring"""
def lowerCamelCase ( _UpperCamelCase : int = 1_0_0_0_0_0_0 ) -> int:
'''simple docstring'''
__UpperCAmelCase : List[str] = 1
__UpperCAmelCase : Union[str, Any] = 1
__UpperCAmelCase : Optional[Any] = {1: 1}
... | 139 | 1 |
"""simple docstring"""
from __future__ import annotations
def UpperCamelCase ( _A , _A ) -> str:
if len(_A ) < k or k < 0:
raise ValueError("""Invalid Input""" )
lowercase : Tuple = sum(array[:k] )
for i in range(len(_A ) - k ):... | 718 |
"""simple docstring"""
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def UpperCamelCase ( _A , _A=7 ) -> List[str]:
lowercase : Any = None
if token is not None:
lowercase ... | 348 | 0 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}
class _lowerCAmelCase ( __a ):
_lowercase ='''mvp'''
... | 290 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
_A = datasets.utils.logging.get_logger(__name__)
class _lowerCAmelCase ( folder_based_builder.FolderBasedBuilderConfig ):
_lowercase =Non... | 290 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
... | 79 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
... | 79 | 1 |
'''simple docstring'''
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__UpperCAmelCase =logging.getLogger(... | 546 | '''simple docstring'''
def __lowerCAmelCase ( UpperCamelCase__ ) -> bool:
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(UpperCamelCase__ ) == 0:
r... | 546 | 1 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.uti... | 718 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,... | 457 | 0 |
'''simple docstring'''
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from... | 517 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers impo... | 517 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...scheduler... | 556 |
"""simple docstring"""
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
SCREAMING_SNAKE_CASE = ... | 556 | 1 |
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuratio... | 206 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
cen... | 206 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( lowerCamelCase_ : List[str] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = [1]
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 0, 0, 0
SCREAMING_SNAKE_CASE_ : ... | 703 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 685 | 0 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils impor... | 17 |
def lowerCamelCase_ ( UpperCAmelCase__ = 100 ):
"""simple docstring"""
a_ = (n * (n + 1) // 2) ** 2
a_ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(F'''{solution() = }''') | 483 | 0 |
from __future__ import annotations
def __snake_case ( _UpperCamelCase , _UpperCamelCase ) -> List[str]:
# Checks if the entire collection has been sorted
if len(_UpperCamelCase ) <= 1 or n <= 1:
return
insert_next(_UpperCamelCase , n - 1 )
rec_insertion_sort(_UpperCa... | 346 |
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class UpperCAmelCase ( unittest.TestCase ):
def _A ( self: Dict ):
_a = inspect.getfile(accelerate.t... | 346 | 1 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class lowerCAmelCase_ ( T... | 10 | import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_lowerCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
# This is the reference code that ... | 10 | 1 |
'''simple docstring'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
if not grid or not grid[0]:
raise TypeError("""The grid does not contain the appropriate information""" )
for cell_n in range(1 , len(grid[0] ) ):
... | 0 |
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_s... | 0 | 1 |
import os
import sys
import unittest
a_ : List[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_map... | 623 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCA... | 623 | 1 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
def __init__( self ):
lowerCamelCase__ = ''''''
lowerCamelCase__ = ''''''
lowerCamelCase__ = ... | 705 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
A_ = Lock()
def __UpperCamelCase ( a, a, a, a, a, a, a) ->Optional[int]:
global process_lock
# we perform n swaps since ... | 360 | 0 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCAmelCase_ ( __lowercase... | 14 | def lowerCAmelCase__ ( a__ ) ->int:
'''simple docstring'''
if not isinstance(a__ , a__ ) or number < 0:
raise ValueError("Input must be a non-negative integer" )
_UpperCamelCase = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
... | 547 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__SCREAMING_SNAKE_CASE : str ={
'''configuration_clip... | 72 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Tuple ={
'''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', ... | 72 | 1 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def A ( lowercase__ : str , lowercase__ : complex , lowercase__ : str = "x" , lowercase__ : float = 10**-10 , lowercase__ : int = 1 , ) -> complex:
UpperCamelCase__ :Optional[int] = symbo... | 45 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import Bac... | 496 | 0 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def UpperCamelCase_( _A :List[Any] , _A ... | 185 |
def UpperCamelCase_( _A :List[str] , _A :Tuple , _A :Any , _A :Tuple , _A :Optional[int] , _A :str )-> List[str]:
if index == r:
for j in range(_A ):
print(data[j] , end=" " )
print(" " )
return
# When no more elem... | 185 | 1 |
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