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 |
|---|---|---|---|---|
def snake_case_ ( lowerCAmelCase_ : int = 100 ):
__lowercase : Tuple = n * (n + 1) * (2 * n + 1) / 6
__lowercase : List[Any] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(f'''{solut... | 306 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects import ... | 306 | 1 |
from __future__ import annotations
from collections import Counter
from random import random
class lowerCAmelCase :
'''simple docstring'''
def __init__( self : Dict ) -> int:
"""simple docstring"""
__lowercase : Optional[Any] =... | 306 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm ... | 306 | 1 |
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
fro... | 306 |
from ...processing_utils import ProcessorMixin
class lowerCAmelCase ( __a ):
'''simple docstring'''
_A : List[str] = ['''image_processor''', '''feature_extractor''']
_A : List[Any] = '''TvltImageProcessor'''
_A : Optional[int] = '''TvltFeatureE... | 306 | 1 |
def snake_case_ ( lowerCAmelCase_ : list ):
def merge(lowerCAmelCase_ : list , lowerCAmelCase_ : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
... | 306 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...tes... | 306 | 1 |
from manim import *
class lowerCAmelCase ( __a ):
'''simple docstring'''
def lowerCAmelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase : str = Rectangle(height=0.5 , width... | 306 |
def snake_case_ ( lowerCAmelCase_ : int ):
__lowercase : int = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def snake_case_ ( lowerCAmelCase_ : int = 5000 ):
__lowercase : Optional[int] = [(i * (3 * i - 1)) // 2 for ... | 306 | 1 |
from __future__ import annotations
import math
def snake_case_ ( lowerCAmelCase_ : list , lowerCAmelCase_ : list ):
if len(lowerCAmelCase_ ) != 2 or len(a[0] ) != 2 or len(lowerCAmelCase_ ) != 2 or len(b[0] ) != 2:
raise Exception("""Matrices are not 2x2""" )
... | 306 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowerCAmelCase ( __a ):
'''simple docstring'''
_A :... | 306 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : List[str] = logging.get_logger(__name__)
lowerCamelCase : Optional[int] = {
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-single-n... | 306 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_datase... | 306 | 1 |
from math import factorial, pi
def snake_case_ ( lowerCAmelCase_ : float , lowerCAmelCase_ : int = 30 ):
if not isinstance(lowerCAmelCase_ , (int, float) ):
raise ValueError("""maclaurin_sin() requires either an int or float for theta""" )
if not isin... | 306 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
lowerCamelCase : str = {
'''facebook/nllb-moe-54B''': '''https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json''',
}
class ... | 306 | 1 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class lowerCAmelCase ( nn.Module ):
'''simple docstring'''
_A : int
_A : jnp.dtype = jnp.floataa
def lowerCAmelCase ( self : Optional[int] ) -> Tuple:
"""simpl... | 306 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase : Optional[Any] = {
'''configuration_poolformer''': [
'''POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''PoolFormerConfig''',
... | 306 | 1 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowerCA... | 306 |
from __future__ import annotations
def snake_case_ ( lowerCAmelCase_ : int ):
__lowercase : List[str] = 2
__lowercase : Union[str, Any] = []
while i * i <= n:
if n % i:
i += 1
else:
... | 306 | 1 |
from PIL import Image
def snake_case_ ( lowerCAmelCase_ : Image , lowerCAmelCase_ : float ):
def brightness(lowerCAmelCase_ : int ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("""level must be... | 306 |
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 import TFCamembertModel
@requi... | 306 | 1 |
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 TFModelTesterMixin, ids_tensor, random_attention_mas... | 306 |
def snake_case_ ( lowerCAmelCase_ : str , lowerCAmelCase_ : str ):
__lowercase : Optional[Any] = len(lowerCAmelCase_ )
__lowercase : str = len(lowerCAmelCase_ )
__lowercase : Optional[int] = [[False for _ in rang... | 306 | 1 |
from __future__ import annotations
def snake_case_ ( lowerCAmelCase_ : list[int | float] , lowerCAmelCase_ : int , lowerCAmelCase_ : int ):
if len(lowerCAmelCase_ ) == 0:
raise ValueError("""find_max() arg is an empty sequence""" )
if (
l... | 306 |
from scipy.stats import spearmanr
import datasets
lowerCamelCase : List[str] = '''
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 implying no correlation.
Positive corr... | 306 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase : Optional[int] = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''W... | 306 |
from __future__ import annotations
def snake_case_ ( lowerCAmelCase_ : str , lowerCAmelCase_ : str ):
__lowercase : Any = get_failure_array(lowerCAmelCase_ )
# 2) Step through text searching for pattern
__lowercase , __lowercase : Op... | 306 | 1 |
def snake_case_ ( lowerCAmelCase_ : int , lowerCAmelCase_ : int , lowerCAmelCase_ : int ):
__lowercase : Optional[Any] = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def... | 306 |
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,
resize,
to_channel... | 306 | 1 |
def snake_case_ ( lowerCAmelCase_ : int ):
__lowercase : Tuple = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def snake_case_ ( lowerCAmelCase_ : int = 100 ):
__lowercase : List[A... | 306 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def snake_case_ ( lowerCAmelCase_ : int , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : str ):
__lowercase : Tuple ... | 306 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
lowerCamelCase : Li... | 306 |
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as P... | 306 | 1 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartToke... | 306 |
def snake_case_ ( lowerCAmelCase_ : int = 200 ):
__lowercase : List[str] = [1, 2, 5, 10, 20, 50, 100, 200]
__lowercase : List[str] = [0] * (pence + 1)
__lowercase : Optional[Any] = 1 # base case: 1 way to make 0 pence
... | 306 | 1 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
lowerCamelCase : Optional[Any] = TypeVar('''T''')
def snake_case_ ( lowerCAmelCase_ : int ):
return (position - 1) // 2
def snake_case_ ( lowerCAmelCase_ : int ):
... | 306 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_co... | 306 | 1 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
impo... | 306 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logg... | 306 | 1 |
from ...processing_utils import ProcessorMixin
class lowerCAmelCase ( __a ):
'''simple docstring'''
_A : List[str] = ['''image_processor''', '''feature_extractor''']
_A : List[Any] = '''TvltImageProcessor'''
_A : Optional[int] = '''TvltFeatureE... | 306 |
def snake_case_ ( lowerCAmelCase_ : str , lowerCAmelCase_ : str ):
if len(lowerCAmelCase_ ) != len(lowerCAmelCase_ ):
raise ValueError("""String lengths must match!""" )
__lowercase : str = 0
for chara, chara in zip(lowerCAmelCase_ ... | 306 | 1 |
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy... | 306 |
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_availa... | 306 | 1 |
def snake_case_ ( lowerCAmelCase_ : list[int] ):
__lowercase : Any = len(lowerCAmelCase_ )
for i in range(lowerCAmelCase_ ):
for j in range(i + 1 , lowerCAmelCase_ ):
if numbers[j] < numbers[i]:
_... | 306 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects import ... | 306 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor... | 306 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm ... | 306 | 1 |
def snake_case_ ( lowerCAmelCase_ : int = 600851475143 ):
try:
__lowercase : Any = int(lowerCAmelCase_ )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castable to int.""" )
if n <= 0:
... | 306 |
from ...processing_utils import ProcessorMixin
class lowerCAmelCase ( __a ):
'''simple docstring'''
_A : List[str] = ['''image_processor''', '''feature_extractor''']
_A : List[Any] = '''TvltImageProcessor'''
_A : Optional[int] = '''TvltFeatureE... | 306 | 1 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_ava... | 306 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...tes... | 306 | 1 |
def snake_case_ ( lowerCAmelCase_ : int ):
__lowercase : str = generate_pascal_triangle(lowerCAmelCase_ )
for row_idx in range(lowerCAmelCase_ ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end... | 306 |
def snake_case_ ( lowerCAmelCase_ : int ):
__lowercase : int = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def snake_case_ ( lowerCAmelCase_ : int = 5000 ):
__lowercase : Optional[int] = [(i * (3 * i - 1)) // 2 for ... | 306 | 1 |
from ..utils import DummyObject, requires_backends
class lowerCAmelCase ( metaclass=__a ):
'''simple docstring'''
_A : List[Any] = ['''speech''']
def __init__( self : str , *__a : Dict , **__a : int ) -> Optional[int]... | 306 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowerCAmelCase ( __a ):
'''simple docstring'''
_A :... | 306 | 1 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modelin... | 306 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_datase... | 306 | 1 |
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from t... | 306 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
lowerCamelCase : str = {
'''facebook/nllb-moe-54B''': '''https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json''',
}
class ... | 306 | 1 |
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,
resize,
to_channel... | 306 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase : Optional[Any] = {
'''configuration_poolformer''': [
'''POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''PoolFormerConfig''',
... | 306 | 1 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def snake_case_ ( lowerCAmelCase_ : str , lowerCAmelCase_ : str ):
__lowercase : Optional[Any] = list(lowerCAmelCase_ )
__lowercase : str = ... | 306 |
from __future__ import annotations
def snake_case_ ( lowerCAmelCase_ : int ):
__lowercase : List[str] = 2
__lowercase : Union[str, Any] = []
while i * i <= n:
if n % i:
i += 1
else:
... | 306 | 1 |
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils im... | 306 |
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 import TFCamembertModel
@requi... | 306 | 1 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHe... | 306 |
def snake_case_ ( lowerCAmelCase_ : str , lowerCAmelCase_ : str ):
__lowercase : Optional[Any] = len(lowerCAmelCase_ )
__lowercase : str = len(lowerCAmelCase_ )
__lowercase : Optional[int] = [[False for _ in rang... | 306 | 1 |
def snake_case_ ( lowerCAmelCase_ : int = 1000 ):
__lowercase : int = 3
__lowercase : str = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
resu... | 306 |
from scipy.stats import spearmanr
import datasets
lowerCamelCase : List[str] = '''
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 implying no correlation.
Positive corr... | 306 | 1 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def snake_case_ ( *lowerCAmelCase_ : List[str] ):
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
__lowercase : Optional[Any] = list... | 306 |
from __future__ import annotations
def snake_case_ ( lowerCAmelCase_ : str , lowerCAmelCase_ : str ):
__lowercase : Any = get_failure_array(lowerCAmelCase_ )
# 2) Step through text searching for pattern
__lowercase , __lowercase : Op... | 306 | 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.auto.modeling_auto i... | 306 |
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,
resize,
to_channel... | 306 | 1 |
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():
... | 306 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def snake_case_ ( lowerCAmelCase_ : int , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : str ):
__lowercase : Tuple ... | 306 | 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()
exce... | 306 |
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as P... | 306 | 1 |
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
lowerCamelCase : Optional[int] = logging.get_logger(__name__)
class lowerCAmelCase ( __a ):
'''simple docstring'... | 306 |
def snake_case_ ( lowerCAmelCase_ : int = 200 ):
__lowercase : List[str] = [1, 2, 5, 10, 20, 50, 100, 200]
__lowercase : List[str] = [0] * (pence + 1)
__lowercase : Optional[Any] = 1 # base case: 1 way to make 0 pence
... | 306 | 1 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFac... | 306 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_co... | 306 | 1 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def snake_case_ ( lowerCAmelCase_ : str = "" ):
__lowercase : Union[str, Any] = url or """https://www.imdb.com/chart/top/?ref_=nv_mv_250"""
__lowercase : Dict ... | 306 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logg... | 306 | 1 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def snake_case_ ( lowerCAmelCase_ : int = 3 ):
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("""number of qubits must b... | 306 |
def snake_case_ ( lowerCAmelCase_ : str , lowerCAmelCase_ : str ):
if len(lowerCAmelCase_ ) != len(lowerCAmelCase_ ):
raise ValueError("""String lengths must match!""" )
__lowercase : str = 0
for chara, chara in zip(lowerCAmelCase_ ... | 306 | 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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
... | 306 |
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_availa... | 306 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResamp... | 306 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects import ... | 306 | 1 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test... | 306 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm ... | 306 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase : Optional[int] = {
'''configuration_altclip''': [
'''ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AltCLIPConfig''',
... | 306 |
from ...processing_utils import ProcessorMixin
class lowerCAmelCase ( __a ):
'''simple docstring'''
_A : List[str] = ['''image_processor''', '''feature_extractor''']
_A : List[Any] = '''TvltImageProcessor'''
_A : Optional[int] = '''TvltFeatureE... | 306 | 1 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(__a ) , '''Tatoeba directory do... | 306 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...tes... | 306 | 1 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...tes... | 306 |
def snake_case_ ( lowerCAmelCase_ : int ):
__lowercase : int = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def snake_case_ ( lowerCAmelCase_ : int = 5000 ):
__lowercase : Optional[int] = [(i * (3 * i - 1)) // 2 for ... | 306 | 1 |
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 306 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowerCAmelCase ( __a ):
'''simple docstring'''
_A :... | 306 | 1 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class lowerCAmelCase :
'''simple docstring'''
_A : List[str] = None
def lowerCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
... | 306 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_datase... | 306 | 1 |
import string
def snake_case_ ( lowerCAmelCase_ : str ):
for key in range(len(string.ascii_uppercase ) ):
__lowercase : Any = """"""
for symbol in message:
if symbol in string.ascii_uppercase:
__... | 306 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
lowerCamelCase : str = {
'''facebook/nllb-moe-54B''': '''https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json''',
}
class ... | 306 | 1 |
"""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,
)
if is_sentencepiece_available():
from ..ta... | 350 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase : Optional[Any] = {
'''configuration_poolformer''': [
'''POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''PoolFormerConfig''',
... | 306 | 0 |
from __future__ import annotations
lowerCamelCase : List[Any] = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
cl... | 351 |
from __future__ import annotations
def snake_case_ ( lowerCAmelCase_ : int ):
__lowercase : List[str] = 2
__lowercase : Union[str, Any] = []
while i * i <= n:
if n % i:
i += 1
else:
... | 306 | 0 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def snake_case_ ( lowerCAmelCase_ : int ):
__lowercase : Optional[int] ... | 352 |
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 import TFCamembertModel
@requi... | 306 | 0 |
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 logging
lowerCamelCase : Tuple ... | 353 |
def snake_case_ ( lowerCAmelCase_ : str , lowerCAmelCase_ : str ):
__lowercase : Optional[Any] = len(lowerCAmelCase_ )
__lowercase : str = len(lowerCAmelCase_ )
__lowercase : Optional[int] = [[False for _ in rang... | 306 | 0 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def snake_case_ ( lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Optional[int]=102... | 354 |
from scipy.stats import spearmanr
import datasets
lowerCamelCase : List[str] = '''
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 implying no correlation.
Positive corr... | 306 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : List[Any] = logging.get_logger(__name__)
lowerCamelCase : Tuple = {
'''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config... | 355 |
from __future__ import annotations
def snake_case_ ( lowerCAmelCase_ : str , lowerCAmelCase_ : str ):
__lowercase : Any = get_failure_array(lowerCAmelCase_ )
# 2) Step through text searching for pattern
__lowercase , __lowercase : Op... | 306 | 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
lowerCamelCase : str = logging.get_logger(__name__)
lowerCamelCase : Optional[Any] = {
... | 356 |
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,
resize,
to_channel... | 306 | 0 |
from __future__ import annotations
import math
def snake_case_ ( lowerCAmelCase_ : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all ... | 357 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def snake_case_ ( lowerCAmelCase_ : int , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : str ):
__lowercase : Tuple ... | 306 | 0 |
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def snake_case_ ( lowerCAmelCase_ : List[str] ):
__lowercase : ... | 358 |
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as P... | 306 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
lowerCamelCase : Optional[int] = {
"""edbeeching/decision-transformer-gym-hopper-medium""": (
"""https://huggi... | 359 |
def snake_case_ ( lowerCAmelCase_ : int = 200 ):
__lowercase : List[str] = [1, 2, 5, 10, 20, 50, 100, 200]
__lowercase : List[str] = [0] * (pence + 1)
__lowercase : Optional[Any] = 1 # base case: 1 way to make 0 pence
... | 306 | 0 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase ( A_ ):
'''simple docstring'''
_A : Optional[int] = (DDPMParallelScheduler,)
def lowerCAmelCase ( self : str , **__a... | 360 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_co... | 306 | 0 |
from __future__ import annotations
from typing import Any
class lowerCAmelCase :
'''simple docstring'''
def __init__( self : Any , __a : List[Any] , __a : Any , __a : Optional[Any] = 0 ) -> Tuple:
"""simple doc... | 361 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logg... | 306 | 0 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def snake_case_ ( ):
__lowercase : Optional[Any] = {
'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'],
... | 362 |
def snake_case_ ( lowerCAmelCase_ : str , lowerCAmelCase_ : str ):
if len(lowerCAmelCase_ ) != len(lowerCAmelCase_ ):
raise ValueError("""String lengths must match!""" )
__lowercase : str = 0
for chara, chara in zip(lowerCAmelCase_ ... | 306 | 0 |
import math
def snake_case_ ( lowerCAmelCase_ : Optional[int] ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples o... | 363 |
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_availa... | 306 | 0 |
from __future__ import annotations
from typing import Any
def snake_case_ ( lowerCAmelCase_ : int ):
create_state_space_tree(a_ , [] , 0 )
def snake_case_ ( lowerCAmelCase_ : Any , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : str ):
... | 364 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects import ... | 306 | 0 |
import os
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 logging
lowerCamelCase : Optional[int] = logging.get_logger(__name__)
lowerCamelCase : Any ... | 365 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm ... | 306 | 0 |
def snake_case_ ( lowerCAmelCase_ : int , lowerCAmelCase_ : int ):
return int((input_a, input_a).count(0 ) != 0 )
def snake_case_ ( ):
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
assert nand_gate(1 , 0 ) == 1
... | 366 |
from ...processing_utils import ProcessorMixin
class lowerCAmelCase ( __a ):
'''simple docstring'''
_A : List[str] = ['''image_processor''', '''feature_extractor''']
_A : List[Any] = '''TvltImageProcessor'''
_A : Optional[int] = '''TvltFeatureE... | 306 | 0 |
import os
import sys
import unittest
lowerCamelCase : Tuple = 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_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object... | 367 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...tes... | 306 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
loggi... | 368 |
def snake_case_ ( lowerCAmelCase_ : int ):
__lowercase : int = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def snake_case_ ( lowerCAmelCase_ : int = 5000 ):
__lowercase : Optional[int] = [(i * (3 * i - 1)) // 2 for ... | 306 | 0 |
from functools import reduce
lowerCamelCase : List[Any] = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
... | 369 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowerCAmelCase ( __a ):
'''simple docstring'''
_A :... | 306 | 0 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import repli... | 370 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_datase... | 306 | 0 |
"""simple docstring"""
from collections import deque
class lowerCAmelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , __a : str , __a : int , __a : int ) -> Union[str, Any]:
"""simple docst... | 371 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
lowerCamelCase : str = {
'''facebook/nllb-moe-54B''': '''https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json''',
}
class ... | 306 | 0 |
"""simple docstring"""
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class lowerCAmelCase ( __lowercase ):
'''simp... | 350 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase : Optional[Any] = {
'''configuration_poolformer''': [
'''POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''PoolFormerConfig''',
... | 306 | 0 |
from pathlib import Path
import fire
from tqdm import tqdm
def snake_case_ ( lowerCAmelCase_ : List[str]="ro" , lowerCAmelCase_ : int="en" , lowerCAmelCase_ : int="wmt16" , lowerCAmelCase_ : int=None ):
try:
import datasets
except (ModuleNotFoun... | 351 |
from __future__ import annotations
def snake_case_ ( lowerCAmelCase_ : int ):
__lowercase : List[str] = 2
__lowercase : Union[str, Any] = []
while i * i <= n:
if n % i:
i += 1
else:
... | 306 | 0 |
from __future__ import annotations
import os
from typing import Any
import requests
lowerCamelCase : Any = """https://api.github.com"""
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
lowerCamelCase : Tuple = BASE_URL + """/user"""
# https://... | 352 |
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 import TFCamembertModel
@requi... | 306 | 0 |
from __future__ import annotations
from math import pi, sqrt
def snake_case_ ( lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Tuple ):
if inductance <= 0:
raise ValueError("""Inductance cannot be 0 or negative""" )
elif capacitance <= 0:
... | 353 |
def snake_case_ ( lowerCAmelCase_ : str , lowerCAmelCase_ : str ):
__lowercase : Optional[Any] = len(lowerCAmelCase_ )
__lowercase : str = len(lowerCAmelCase_ )
__lowercase : Optional[int] = [[False for _ in rang... | 306 | 0 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase ( __a ):
'''simple docstring'''
_A : int = (UnCLIPScheduler,)
def lowerCAmelCase ( self : Optional[int] , **__a : Union... | 354 |
from scipy.stats import spearmanr
import datasets
lowerCamelCase : List[str] = '''
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 implying no correlation.
Positive corr... | 306 | 0 |
from collections.abc import Callable
def snake_case_ ( lowerCAmelCase_ : Callable[[float], float] , lowerCAmelCase_ : float , lowerCAmelCase_ : float ):
__lowercase : float = a
__lowercase : float = b
if function(snake_case_... | 355 |
from __future__ import annotations
def snake_case_ ( lowerCAmelCase_ : str , lowerCAmelCase_ : str ):
__lowercase : Any = get_failure_array(lowerCAmelCase_ )
# 2) Step through text searching for pattern
__lowercase , __lowercase : Op... | 306 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
lowerCamelCase : List[Any] = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''albert-large-v1''': '''... | 356 |
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,
resize,
to_channel... | 306 | 0 |
def snake_case_ ( lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : str ):
if index == r:
for j in ... | 357 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def snake_case_ ( lowerCAmelCase_ : int , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : str ):
__lowercase : Tuple ... | 306 | 0 |
def snake_case_ ( lowerCAmelCase_ : Union[str, Any] ):
if len(lowerCAmelCase_ ) <= 1:
return [tuple(lowerCAmelCase_ )]
__lowercase : List[str] = []
def generate(lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : int ):
... | 358 |
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as P... | 306 | 0 |
"""simple docstring"""
import math
def snake_case_ ( lowerCAmelCase_ : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all mult... | 359 |
def snake_case_ ( lowerCAmelCase_ : int = 200 ):
__lowercase : List[str] = [1, 2, 5, 10, 20, 50, 100, 200]
__lowercase : List[str] = [0] * (pence + 1)
__lowercase : Optional[Any] = 1 # base case: 1 way to make 0 pence
... | 306 | 0 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
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 l... | 360 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_co... | 306 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import tor... | 361 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logg... | 306 | 0 |
def snake_case_ ( lowerCAmelCase_ : Dict ):
__lowercase : Optional[Any] = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def snake_case_ ( lowerCAmelCase_ : Any = 5000 ):
__lowercase : Optional[int] = [(i * ... | 362 |
def snake_case_ ( lowerCAmelCase_ : str , lowerCAmelCase_ : str ):
if len(lowerCAmelCase_ ) != len(lowerCAmelCase_ ):
raise ValueError("""String lengths must match!""" )
__lowercase : str = 0
for chara, chara in zip(lowerCAmelCase_ ... | 306 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase : str = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
... | 363 |
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_availa... | 306 | 0 |
def snake_case_ ( lowerCAmelCase_ : list , lowerCAmelCase_ : int = 0 ):
__lowercase : Optional[Any] = length or len(_a )
__lowercase : Any = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
... | 364 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects import ... | 306 | 0 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
lowerCamelCase : str = 50_0000
lowerCamelCase : Optional[int] = os.path.split(__file__)
lowerCamelCase : Union[str, Any] = os.path.join(RESULTS_BASEPATH,... | 365 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm ... | 306 | 0 |
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
lowerCamelCase : List[Any] = 6_37_81_37.0
lowerCamelCase : str = 6_35_67_52.31_42_45
lowerCamelCase : Dict = 6_37_81_37
def snake_case_ ( lowerCAmelCase_ : float , lowe... | 366 |
from ...processing_utils import ProcessorMixin
class lowerCAmelCase ( __a ):
'''simple docstring'''
_A : List[str] = ['''image_processor''', '''feature_extractor''']
_A : List[Any] = '''TvltImageProcessor'''
_A : Optional[int] = '''TvltFeatureE... | 306 | 0 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
lowerCamelCase : int = ... | 367 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...tes... | 306 | 0 |
def snake_case_ ( lowerCAmelCase_ : int , lowerCAmelCase_ : int ):
return int((input_a, input_a).count(0 ) == 0 )
def snake_case_ ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) == 0
... | 368 |
def snake_case_ ( lowerCAmelCase_ : int ):
__lowercase : int = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def snake_case_ ( lowerCAmelCase_ : int = 5000 ):
__lowercase : Optional[int] = [(i * (3 * i - 1)) // 2 for ... | 306 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
lowerCamelCase : Optional[Any] = {
"distilbert-bas... | 369 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowerCAmelCase ( __a ):
'''simple docstring'''
_A :... | 306 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import tor... | 370 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_datase... | 306 | 0 |
"""simple docstring"""
from __future__ import annotations
def snake_case_ ( lowerCAmelCase_ : List[str] ):
__lowercase : Optional[Any] = str(_lowerCAmelCase )
return len(_lowerCAmelCase ) == 9 and set(_lowerCAmelCase ) == set("""123456789""" )
def snake_case_ ... | 371 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
lowerCamelCase : str = {
'''facebook/nllb-moe-54B''': '''https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json''',
}
class ... | 306 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase ( self : Li... | 350 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase : Optional[Any] = {
'''configuration_poolformer''': [
'''POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''PoolFormerConfig''',
... | 306 | 0 |
from __future__ import annotations
lowerCamelCase : Union[str, Any] = 1.6021E-19 # units = C
def snake_case_ ( lowerCAmelCase_ : str , lowerCAmelCase_ : Any , lowerCAmelCase_ : Union[str, Any] , ):
if (conductivity, electron_conc, mobility).count(0 ) != 1:
... | 351 |
from __future__ import annotations
def snake_case_ ( lowerCAmelCase_ : int ):
__lowercase : List[str] = 2
__lowercase : Union[str, Any] = []
while i * i <= n:
if n % i:
i += 1
else:
... | 306 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : int = logging.get_logger(__name__)
lowerCamelCase : List[str] = {
"""edbeeching/decision-transformer-gym-hopper-medium""": (
"""https://huggingface.co/edbeeching/decision-transformer-g... | 352 |
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 import TFCamembertModel
@requi... | 306 | 0 |
import os
import time
import numpy as np
import onnxruntime as ort
lowerCamelCase : Tuple = '''1'''
lowerCamelCase : Optional[Any] = '''0'''
lowerCamelCase : List[Any] = '''1'''
lowerCamelCase : Union[str, Any] = ort.SessionOptions()
lowerCamelCase : int = ort.GraphOpt... | 353 |
def snake_case_ ( lowerCAmelCase_ : str , lowerCAmelCase_ : str ):
__lowercase : Optional[Any] = len(lowerCAmelCase_ )
__lowercase : str = len(lowerCAmelCase_ )
__lowercase : Optional[int] = [[False for _ in rang... | 306 | 0 |
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