code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from __future__ import annotations
from functools import lru_cache
from math import ceil
__a = 1_00
__a = set(range(3, NUM_PRIMES, 2))
primes.add(2)
__a = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
p... | 66 |
"""simple docstring"""
from math import factorial
class lowerCamelCase :
'''simple docstring'''
def __init__( self: Optional[int] , snake_case: Dict , snake_case: int ) -> Tuple:
snake_case_ :List[Any] = real
if ... | 66 | 1 |
"""simple docstring"""
__a = "Tobias Carryer"
from time import time
class lowerCamelCase :
'''simple docstring'''
def __init__( self: int , snake_case: Any , snake_case: Any , snake_case: Optional[Any] , snake_case:... | 66 |
"""simple docstring"""
from __future__ import annotations
__a = 10
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Union[str, Any] = 1
snake_case_ :List[str] = max(_lowercase )
while placement <= max_digit:
# declare and initialize... | 66 | 1 |
"""simple docstring"""
def A_ ( _lowercase, _lowercase ):
'''simple docstring'''
snake_case_ :int = len(_lowercase )
print("""The following activities are selected:""" )
# The first activity is always selected
snake_case_ :Union[str, Any] = 0
pri... | 66 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__a = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerC... | 66 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {
"configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}
try:
if not is_torch_available():
... | 66 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy ... | 66 | 1 |
"""simple docstring"""
def A_ ( _lowercase = 3, _lowercase = 7, _lowercase = 1000000 ):
'''simple docstring'''
snake_case_ :List[Any] = 0
snake_case_ :Any = 1
for current_denominator in range(1, limit + 1 ):
snake_case_ :int = curr... | 66 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
"configuration_mobilebert": [
"MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 66 | 1 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_... | 66 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Union[str, Any] = os.path.join(args.tf_model_dir, """parameters.j... | 66 | 1 |
"""simple docstring"""
import math
def A_ ( _lowercase, _lowercase ):
'''simple docstring'''
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(_lowercase )
else:
if x == 0: # 0 raised to a... | 66 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
__a = pd.read_csv("sample_data.csv", header=None)
__a = ... | 66 | 1 |
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available,... | 66 |
"""simple docstring"""
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... | 66 | 1 |
"""simple docstring"""
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
e... | 66 |
"""simple docstring"""
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVision... | 66 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {
"configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
"feature_extraction_mctct": ["MCTCTFeatureExtractor"],
"processi... | 66 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 66 | 1 |
"""simple docstring"""
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 (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils impor... | 66 |
"""simple docstring"""
import math
class lowerCamelCase :
'''simple docstring'''
def lowerCAmelCase_ ( self: Tuple , snake_case: list[list[float]] , snake_case: list[int] ) -> int:
snake_case_ :Any = 0.0
sn... | 66 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCamelCase ( _lowerCAmelCase , unittest.TestCase ):
'''simple doc... | 66 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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... | 66 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_, snake_case_ :Any = analyze_text(_lowercase )
snake_case_ :str = li... | 66 |
"""simple docstring"""
import re
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Optional[int] = re.compile(
r"""^(?:0|94|\+94|0{2}94)""" r"""7(0|1|2|4|5|6|7|8)""" r"""(-| |)""" r"""\d{7}$""" )
return bool(re.search(_lowercase, _lowercase ) )
if __... | 66 | 1 |
"""simple docstring"""
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__a = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yong... | 66 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBert... | 66 | 1 |
"""simple docstring"""
def A_ ( _lowercase ):
'''simple docstring'''
if length <= 0 or not isinstance(_lowercase, _lowercase ):
raise ValueError("""Length must be a positive integer.""" )
return [n * (2 * n - 1) for n in range(_lowercase )]
if __name__ == "__main__... | 66 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
... | 66 | 1 |
"""simple docstring"""
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # no... | 66 |
"""simple docstring"""
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_bac... | 66 | 1 |
"""simple docstring"""
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
__a = logging.get_logger(__name__)
__a = OrderedDict(
[
# B... | 66 |
"""simple docstring"""
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,... | 66 | 1 |
"""simple docstring"""
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def A_ ( _lowercase, _lowercase, _lowercase, _lowercase, _lowercase ):
'''simple docstring'''
snake_case_ :Tuple = StableDiffu... | 66 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__a = {
"configuration_mask2former": [
"MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Mask2FormerConfig",
],
}
try:
... | 66 | 1 |
"""simple docstring"""
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... | 66 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_d... | 66 | 1 |
"""simple docstring"""
import enum
import shutil
import sys
__a , __a = shutil.get_terminal_size()
__a = {"UP": "A", "DOWN": "B", "RIGHT": "C", "LEFT": "D"}
class lowerCamelCase ( enum.Enum ):
'''simple docstring'''
_A : Dict = 0
_A : str ... | 66 |
"""simple docstring"""
from __future__ import annotations
from collections import Counter
from random import random
class lowerCamelCase :
'''simple docstring'''
def __init__( self: Tuple ) -> Optional[Any]:
snake_case_ :Optional[int] = {}
d... | 66 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def A_ ( _lowercase, _lowercase ):
'''simple docstring'''
return math.sqrt(sum(pow(a - b, 2 ) for a, b in zip(_lowercase, _lowercase ) ) )
def A_ ( _l... | 66 |
"""simple docstring"""
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run t... | 66 | 1 |
"""simple docstring"""
from math import isclose, sqrt
def A_ ( _lowercase, _lowercase, _lowercase ):
'''simple docstring'''
snake_case_ :List[Any] = point_y / 4 / point_x
snake_case_ :Any = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
sna... | 66 |
"""simple docstring"""
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_... | 66 | 1 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Any = [
"""encoder.version""",
"""decoder.versio... | 66 |
"""simple docstring"""
from math import factorial
class lowerCamelCase :
'''simple docstring'''
def __init__( self: Optional[int] , snake_case: Dict , snake_case: int ) -> Tuple:
snake_case_ :List[Any] = real
if ... | 66 | 1 |
"""simple docstring"""
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTeste... | 66 |
"""simple docstring"""
from __future__ import annotations
__a = 10
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Union[str, Any] = 1
snake_case_ :List[str] = max(_lowercase )
while placement <= max_digit:
# declare and initialize... | 66 | 1 |
"""simple docstring"""
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :int = {}
snake_case_ :List[Any] = job["""started_at"""]
snake_case_ :int = ... | 66 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__a = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerC... | 66 | 1 |
"""simple docstring"""
from math import isqrt, loga
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Tuple = [True] * max_number
for i in range(2, isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2, _lowercase, ... | 66 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy ... | 66 | 1 |
"""simple docstring"""
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transfor... | 66 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
"configuration_mobilebert": [
"MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 66 | 1 |
"""simple docstring"""
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
__a = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add_argument("--dpm", action="... | 66 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Union[str, Any] = os.path.join(args.tf_model_dir, """parameters.j... | 66 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json",
}
class lowerCamelCase ( _lowerCAmelCase ... | 66 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
__a = pd.read_csv("sample_data.csv", header=None)
__a = ... | 66 | 1 |
"""simple docstring"""
import logging
import os
from .state import PartialState
class lowerCamelCase ( logging.LoggerAdapter ):
'''simple docstring'''
@staticmethod
def lowerCAmelCase_ ( snake_case: Union[str, Any] ) -> Any:
snake_case_ :int ... | 66 |
"""simple docstring"""
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... | 66 | 1 |
"""simple docstring"""
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,... | 66 |
"""simple docstring"""
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVision... | 66 | 1 |
"""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
from transforme... | 66 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 66 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
__a = TypeVar("T")
class lowerCamelCase ( Generic[T] ):
'''simple docstring'''
def __init__( self: Optional[int] , sna... | 66 |
"""simple docstring"""
import math
class lowerCamelCase :
'''simple docstring'''
def lowerCAmelCase_ ( self: Tuple , snake_case: list[list[float]] , snake_case: list[int] ) -> int:
snake_case_ :Any = 0.0
sn... | 66 | 1 |
"""simple docstring"""
from __future__ import annotations
__a = 10
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Union[str, Any] = 1
snake_case_ :List[str] = max(_lowercase )
while placement <= max_digit:
# declare and initialize... | 66 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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... | 66 | 1 |
"""simple docstring"""
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Tuple = [int(_lowercase ) for i in ip_va_address.split(""".""" ) if i.isdigit()]
return len(_lowercase ) == 4 and all(0 <= int(_lowercase ) <= 254 for octet in octets )
if __name__ == "__main_... | 66 |
"""simple docstring"""
import re
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Optional[int] = re.compile(
r"""^(?:0|94|\+94|0{2}94)""" r"""7(0|1|2|4|5|6|7|8)""" r"""(-| |)""" r"""\d{7}$""" )
return bool(re.search(_lowercase, _lowercase ) )
if __... | 66 | 1 |
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors impor... | 66 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBert... | 66 | 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,
WavaVecaProcessor,
l... | 66 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
... | 66 | 1 |
"""simple docstring"""
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 ...t... | 66 |
"""simple docstring"""
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_bac... | 66 | 1 |
"""simple docstring"""
import os
def A_ ( ):
'''simple docstring'''
with open(os.path.dirname(_lowercase ) + """/p022_names.txt""" ) as file:
snake_case_ :Optional[Any] = str(file.readlines()[0] )
snake_case_ :str = names.replace("""\"""", """""" ... | 66 |
"""simple docstring"""
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,... | 66 | 1 |
"""simple docstring"""
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
__a = 1.054_571_817e-34 # unit of ℏ : J * s
__a = 3e8 # unit of c : m * s^-1
def A_ ( _lowercase, _lowercase, ... | 66 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__a = {
"configuration_mask2former": [
"MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Mask2FormerConfig",
],
}
try:
... | 66 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
"uclanlp/visualbert-vqa-pre": "https://hug... | 66 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_d... | 66 | 1 |
"""simple docstring"""
import math
import tensorflow as tf
from packaging import version
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Any = tf.convert_to_tensor(_lowercase )
snake_case_ :Optional[int] = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ),... | 66 |
"""simple docstring"""
from __future__ import annotations
from collections import Counter
from random import random
class lowerCamelCase :
'''simple docstring'''
def __init__( self: Tuple ) -> Optional[Any]:
snake_case_ :Optional[int] = {}
d... | 66 | 1 |
"""simple docstring"""
def A_ ( _lowercase = 100 ):
'''simple docstring'''
snake_case_ :Dict = (n * (n + 1) // 2) ** 2
snake_case_ :List[str] = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(F"""{solution() ... | 66 |
"""simple docstring"""
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run t... | 66 | 1 |
"""simple docstring"""
def A_ ( ):
'''simple docstring'''
snake_case_ :int = []
snake_case_ :int = 1
while len(_lowercase ) < 1e6:
constant.append(str(_lowercase ) )
i += 1
snake_case_ :str = """""".join(_lowercase )
... | 66 |
"""simple docstring"""
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_... | 66 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_... | 66 |
"""simple docstring"""
from math import factorial
class lowerCamelCase :
'''simple docstring'''
def __init__( self: Optional[int] , snake_case: Dict , snake_case: int ) -> Tuple:
snake_case_ :List[Any] = real
if ... | 66 | 1 |
"""simple docstring"""
def A_ ( _lowercase, _lowercase ):
'''simple docstring'''
if mass < 0:
raise ValueError("""The mass of a body cannot be negative""" )
return 0.5 * mass * abs(_lowercase ) * abs(_lowercase )
if __name__ == "__main__":
import doctest
... | 66 |
"""simple docstring"""
from __future__ import annotations
__a = 10
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Union[str, Any] = 1
snake_case_ :List[str] = max(_lowercase )
while placement <= max_digit:
# declare and initialize... | 66 | 1 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
... | 66 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__a = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerC... | 66 | 1 |
"""simple docstring"""
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :int = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def A_ ( _lowercase = 5000 ):
'''simple docstring'''
snake_case_ :Any = [(i * (3 * i - 1)) // 2 for i... | 66 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy ... | 66 | 1 |
"""simple docstring"""
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 loggi... | 66 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
"configuration_mobilebert": [
"MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 66 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils im... | 66 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Union[str, Any] = os.path.join(args.tf_model_dir, """parameters.j... | 66 | 1 |
"""simple docstring"""
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_si... | 66 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
__a = pd.read_csv("sample_data.csv", header=None)
__a = ... | 66 | 1 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
__a = True
except (ImportError, ModuleNotFoundError):
__a = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def A_ ( _lowercase )... | 66 |
"""simple docstring"""
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... | 66 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {
"configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
"processing_git": ["GitProcessor"],
}
try:
if not... | 66 |
"""simple docstring"""
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVision... | 66 | 1 |
"""simple docstring"""
def A_ ( _lowercase, _lowercase ):
'''simple docstring'''
return [sentence[i : i + ngram_size] for i in range(len(_lowercase ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 66 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 66 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_d... | 66 |
"""simple docstring"""
import math
class lowerCamelCase :
'''simple docstring'''
def lowerCAmelCase_ ( self: Tuple , snake_case: list[list[float]] , snake_case: list[int] ) -> int:
snake_case_ :Any = 0.0
sn... | 66 | 1 |
"""simple docstring"""
from __future__ import annotations
def A_ ( _lowercase, _lowercase ):
'''simple docstring'''
snake_case_ :Any = 0
snake_case_ :Optional[int] = len(_lowercase ) - 1
while i < j:
if nums[i] + nums[j] == target:
... | 66 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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... | 66 | 1 |
"""simple docstring"""
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
__a = logging.get_logger(__name__)
__a = r"\n Args:\n input_ids (`torch.LongTensor` of shape `(b... | 66 |
"""simple docstring"""
import re
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Optional[int] = re.compile(
r"""^(?:0|94|\+94|0{2}94)""" r"""7(0|1|2|4|5|6|7|8)""" r"""(-| |)""" r"""\d{7}$""" )
return bool(re.search(_lowercase, _lowercase ) )
if __... | 66 | 1 |
"""simple docstring"""
def A_ ( _lowercase = 100 ):
'''simple docstring'''
snake_case_ :Dict = n * (n + 1) * (2 * n + 1) / 6
snake_case_ :Tuple = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F"""{solu... | 66 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBert... | 66 | 1 |
"""simple docstring"""
def A_ ( _lowercase ):
'''simple docstring'''
if collection == []:
return []
# get some information about the collection
snake_case_ :Any = len(_lowercase )
snake_case_ :Optional[Any] = max(_lowercase )
snake_case_ ... | 66 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
... | 66 | 1 |
"""simple docstring"""
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__a = logging.get_logger(__name__)
__a ... | 66 |
"""simple docstring"""
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_bac... | 66 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def A_ ( _lowercase, _lowercase ):
'''simple docstring'''
snake_case_ :List[Any] = list(_lowercase )
snake_case_ :str = list(_lowercase )
... | 66 |
"""simple docstring"""
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,... | 66 | 1 |
"""simple docstring"""
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... | 66 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__a = {
"configuration_mask2former": [
"MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Mask2FormerConfig",
],
}
try:
... | 66 | 1 |
"""simple docstring"""
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertToke... | 66 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_d... | 66 | 1 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from ... | 66 |
"""simple docstring"""
from __future__ import annotations
from collections import Counter
from random import random
class lowerCamelCase :
'''simple docstring'''
def __init__( self: Tuple ) -> Optional[Any]:
snake_case_ :Optional[int] = {}
d... | 66 | 1 |
"""simple docstring"""
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 A_ ( _lowercase, _lowercase ):
'''simple d... | 66 |
"""simple docstring"""
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run t... | 66 | 1 |
"""simple docstring"""
from math import factorial
def A_ ( _lowercase = 20 ):
'''simple docstring'''
snake_case_ :List[str] = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
snake_case_ :Union[str, Any] = n // 2
re... | 66 |
"""simple docstring"""
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_... | 66 | 1 |
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
__a = "\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption tha... | 66 |
"""simple docstring"""
from math import factorial
class lowerCamelCase :
'''simple docstring'''
def __init__( self: Optional[int] , snake_case: Dict , snake_case: int ) -> Tuple:
snake_case_ :List[Any] = real
if ... | 66 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"
... | 66 |
"""simple docstring"""
from __future__ import annotations
__a = 10
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Union[str, Any] = 1
snake_case_ :List[str] = max(_lowercase )
while placement <= max_digit:
# declare and initialize... | 66 | 1 |
"""simple docstring"""
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
__a = {
"text_branch": "text_model",
"audio_branch": "audio_model.audio_encoder",
"attn": "attention.self",
"self.proj": "outpu... | 66 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__a = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerC... | 66 | 1 |
"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def A_ ( _lowercase ):
'''simple docstrin... | 66 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy ... | 66 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
"configuration_mobilebert": [
"MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 66 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
"configuration_mobilebert": [
"MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 66 | 1 |
"""simple docstring"""
__a = [0, 2, 4, 6, 8]
__a = [1, 3, 5, 7, 9]
def A_ ( _lowercase, _lowercase, _lowercase, _lowercase ):
'''simple docstring'''
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
... | 66 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Union[str, Any] = os.path.join(args.tf_model_dir, """parameters.j... | 66 | 1 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def A_ ( _lowercase ):
'''simple docstring'''
return np.maximum(0, _lowercase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 66 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
__a = pd.read_csv("sample_data.csv", header=None)
__a = ... | 66 | 1 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def A_ ( _lowercase ):
'''simple docstring'''
return ConvertCommand(
args.model_type, args.tf_checkpoint, args.pytorch_dump_output,... | 66 |
"""simple docstring"""
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... | 66 | 1 |
"""simple docstring"""
from jiwer import compute_measures
import datasets
__a = "\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation ... | 66 |
"""simple docstring"""
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVision... | 66 | 1 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__a = logging.get_logger(__name__)
def A_ ( _lowercase ):
'''simple docstring'''
snake_... | 66 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 66 | 1 |
"""simple docstring"""
def A_ ( _lowercase, _lowercase ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
snake_case_ :Optional[Any] = str(bin(_lowercase ) )[2:] # remove the leading "0b"
sna... | 66 |
"""simple docstring"""
import math
class lowerCamelCase :
'''simple docstring'''
def lowerCAmelCase_ ( self: Tuple , snake_case: list[list[float]] , snake_case: list[int] ) -> int:
snake_case_ :Any = 0.0
sn... | 66 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__a = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerC... | 66 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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... | 66 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class lowerCamelCase :
'''simple docstring'''
_A : torch.Tensor # [batch_size x 3]
_A : torch.Tensor # [batch_size x 3]
_A : torch.Tensor # [b... | 66 |
"""simple docstring"""
import re
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Optional[int] = re.compile(
r"""^(?:0|94|\+94|0{2}94)""" r"""7(0|1|2|4|5|6|7|8)""" r"""(-| |)""" r"""\d{7}$""" )
return bool(re.search(_lowercase, _lowercase ) )
if __... | 66 | 1 |
"""simple docstring"""
def A_ ( _lowercase, _lowercase ):
'''simple docstring'''
return 1 if input_a == input_a else 0
def A_ ( ):
'''simple docstring'''
assert xnor_gate(0, 0 ) == 1
assert xnor_gate(0, 1 ) == 0
assert xnor_gate(1, 0 ... | 66 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBert... | 66 | 1 |
"""simple docstring"""
import re
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Optional[int] = re.compile(
r"""^(?:0|94|\+94|0{2}94)""" r"""7(0|1|2|4|5|6|7|8)""" r"""(-| |)""" r"""\d{7}$""" )
return bool(re.search(_lowercase, _lowercase ) )
if __... | 66 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
... | 66 | 1 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
__a = "\\n@misc{chen2021evaluating,\n title={Evaluating Large Lan... | 66 |
"""simple docstring"""
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_bac... | 66 | 1 |
"""simple docstring"""
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def A_ ( ):
'''simple docstring'''
snake_case_, snake_case_ :Tuple = 9, 14 # noqa: F841
snake_case_ :Optional[Any] = [
[0... | 66 |
"""simple docstring"""
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,... | 66 | 1 |
"""simple docstring"""
def A_ ( _lowercase, _lowercase ):
'''simple docstring'''
if number < 0 or shift_amount < 0:
raise ValueError("""both inputs must be positive integers""" )
snake_case_ :Union[str, Any] = str(bin(_lowercase ) )
binary_number += "0"... | 66 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__a = {
"configuration_mask2former": [
"MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Mask2FormerConfig",
],
}
try:
... | 66 | 1 |
"""simple docstring"""
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@requir... | 66 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_d... | 66 | 1 |
"""simple docstring"""
def A_ ( _lowercase ):
'''simple docstring'''
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(_lowercase ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("doctest").testmod()
| 66 |
"""simple docstring"""
from __future__ import annotations
from collections import Counter
from random import random
class lowerCamelCase :
'''simple docstring'''
def __init__( self: Tuple ) -> Optional[Any]:
snake_case_ :Optional[int] = {}
d... | 66 | 1 |
"""simple docstring"""
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 A_ ( _lowercase, _lowercase, _lowercase, _lowercase, _lowercase, _lowercase, _lowercase ):
'''sim... | 66 |
"""simple docstring"""
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run t... | 66 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__a = {
"configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXJapaneseConfig"],
"tokenizat... | 66 |
"""simple docstring"""
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_... | 66 | 1 |
"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__a = 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 refe... | 66 |
"""simple docstring"""
from math import factorial
class lowerCamelCase :
'''simple docstring'''
def __init__( self: Optional[int] , snake_case: Dict , snake_case: int ) -> Tuple:
snake_case_ :List[Any] = real
if ... | 66 | 1 |
"""simple docstring"""
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :int = len(_lowercase )
snake_case_ :List[Any] = len(matrix[0] )
snake_case_ :Optional[int] = min(_lowercase, _lowercase )
for row in range(_lowercase ):
... | 66 |
"""simple docstring"""
from __future__ import annotations
__a = 10
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Union[str, Any] = 1
snake_case_ :List[str] = max(_lowercase )
while placement <= max_digit:
# declare and initialize... | 66 | 1 |
"""simple docstring"""
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODE... | 66 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__a = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerC... | 66 | 1 |
"""simple docstring"""
from graphs.minimum_spanning_tree_kruskal import kruskal
def A_ ( ):
'''simple docstring'''
snake_case_ :int = 9
snake_case_ :Tuple = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
... | 66 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy ... | 66 | 1 |
"""simple docstring"""
from random import randint
from tempfile import TemporaryFile
import numpy as np
def A_ ( _lowercase, _lowercase, _lowercase ):
'''simple docstring'''
snake_case_ :Dict = 0
if start < end:
snake_case_ :Dict = randint(_lowerca... | 66 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
"configuration_mobilebert": [
"MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 66 | 1 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"BridgeTower/bridgetower-base": "https://huggingface.co/BridgeTower/bridgetower-base/blob/main/con... | 66 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Union[str, Any] = os.path.join(args.tf_model_dir, """parameters.j... | 66 | 1 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowerCamelCase :
'''simple docstring'''
pass
| 66 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
__a = pd.read_csv("sample_data.csv", header=None)
__a = ... | 66 | 1 |
"""simple docstring"""
import operator as op
def A_ ( _lowercase ):
'''simple docstring'''
snake_case_ :Tuple = []
snake_case_ :Dict = lambda _lowercase, _lowercase : int(x / y ) # noqa: E731 integer division operation
snake_case_ :List[str] = {
... | 66 |
"""simple docstring"""
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... | 66 | 1 |
"""simple docstring"""
def A_ ( _lowercase, _lowercase ):
'''simple docstring'''
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(_lowercase, int(b / 2 ) ) * actual_power(_lowercase, int(b / 2 ) )
else:
return a * actual_... | 66 |
"""simple docstring"""
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVision... | 66 | 1 |
"""simple docstring"""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__a = logging.get_logger(__name__)
__a = ... | 66 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 66 | 1 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__a = logging.get_logger(__name__)
__a = {
"speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
# See all M-CTC-T models at https:... | 66 |
"""simple docstring"""
import math
class lowerCamelCase :
'''simple docstring'''
def lowerCAmelCase_ ( self: Tuple , snake_case: list[list[float]] , snake_case: list[int] ) -> int:
snake_case_ :Any = 0.0
sn... | 66 | 1 |
"""simple docstring"""
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_sp... | 66 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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... | 66 | 1 |
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