code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import numpy as np
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
return vector * sigmoid(UpperCAmelCase... | 605 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
... | 605 | 1 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
snake_case : Union[str, Any] = object()
# For specifying empty leaf dict `{}`
snake_case : Any = objec... | 605 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
snake_case : Optional[Any] = logging.get_logger(__name__)
... | 605 | 1 |
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, 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... | 605 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices... | 605 | 1 |
# limitations under the License.
# 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 .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
... | 605 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching bet... | 605 | 1 |
import numpy
class __lowercase :
"""simple docstring"""
def __init__( self , A_ , A_ )-> None:
_SCREAMING_SNAKE_CASE = input_array
# Random initial weights are assigned where first argument is the
# number of nodes i... | 605 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstrin... | 605 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
print(f'''Vertex\tShortest Distance from vertex {src}''' )
for i, d in enumerate(UpperCAmelCase__ ):
print(f'''{i}\t\t{d}''' )
... | 605 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if len(UpperCAmelCase__ ) == 0:
return array
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = min(UpperCAmelCase__ ), max(UpperCAmelCase__ ... | 605 | 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_attent... | 605 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
snake_case : Dict = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('3.7'... | 605 | 1 |
import requests
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = {'Content-Type': 'application/json'}
_SCREAMING_SNAKE_CASE = requests.post(UpperCAmelCase__ ,json={'text': message_body} ,headers=Upp... | 605 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = HfArgumentParser(UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = parser.parse_args_into_dataclas... | 605 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case : List[Any] = {
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
'InstructBlipQFo... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum < 0:
raise Exception('Rate of interest must be >= 0' )
... | 605 | 1 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
def merge(UpperCAmelCase__ ,UpperCAmelCase__ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yi... | 605 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"... | 605 | 1 |
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
if depth < 0:
raise ValueError('Depth cannot be less than 0' )
... | 605 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase )
class __lowercase ( UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE... | 605 | 1 |
import operator as op
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = lambda UpperCAmelCase__ ,UpperCAmelCase__ : int(x / y ) # noqa: E731 integer division operation
_SCREAMING... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
def merge(UpperCAmelCase__ ,UpperCAmelCase__ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yi... | 605 | 1 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ = 1000 ):
"""simple docstring"""
return sum(2 * a * ((a - 1) // 2) for a in range(3 ,n + 1 ) )
if __name__ == "__main__":
print(solution())
| 605 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
... | 605 | 1 |
from jiwer import compute_measures
import datasets
snake_case : Optional[Any] = '\\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... | 605 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case : int = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
... | 605 | 1 |
import unittest
import numpy as np
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ = None ,):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = np.shape(UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = np.shape(UpperC... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ = 1000 ):
"""simple docstring"""
return sum(2 * a * ((a - 1) // 2) for a in range(3 ,n + 1 ) )
if __name__ == "__main__":
print(solution())
| 605 | 1 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum < 0:
raise Exception('Rate of interest must be >= 0' )
... | 605 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
snake_case : List[str] = {
'configuration_roberta_prelayernorm': [
'ROBERTA_PRELAYERNORM_PRETR... | 605 | 1 |
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common ... | 605 |
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_simplify,
... | 605 | 1 |
from math import ceil, sqrt
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ = 100_0000 ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 0
for outer_width in range(3 ,(limit // 4) + 2 ):
if outer_width**2 > limit:
_SCREAMING_SNAKE_CASE = ... | 605 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
snake_case : Tuple = (7_20, 12_80) # Height, Width
snake_case : List[Any] = (0.4, 0.6) # if height or width lower than this scale, drop it.
snake_case... | 605 | 1 |
from __future__ import annotations
import unittest
from transformers import 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_mask
from ..... | 605 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class __lowercase ( UpperCamelCase ):
"""simple d... | 605 | 1 |
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 loggin... | 605 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_commo... | 605 | 1 |
from __future__ import annotations
import time
import numpy as np
snake_case : Union[str, Any] = [8, 5, 9, 7]
snake_case : Optional[int] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
snake_case : Union[str, Any] ... | 605 |
from __future__ import annotations
from typing import Any
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if not postfix_notation:
return 0
_SCREAMING_SNAKE_CASE = {'+', '-', '*', '/'}
_SCREAMING_SNAKE_CASE = []
f... | 605 | 1 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
def __magic_name__ ( self )-> List[str]:
_SCREAMING_SNAKE_CASE = [
... | 605 |
from __future__ import annotations
class __lowercase :
"""simple docstring"""
def __init__( self , A_ )-> None:
_SCREAMING_SNAKE_CASE = data
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
de... | 605 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if len(UpperCAmelCase__ ) == 0:
return array
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = min(UpperCAmelCase__ ), max(UpperCAmelCase__ ... | 605 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
... | 605 | 1 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached... | 605 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
snake_case : Optional[Any] = logging.get_logger(__name__)
... | 605 | 1 |
from typing import Dict, Optional
import numpy as np
import datasets
snake_case : str = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or m... | 605 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices... | 605 | 1 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ = False ):
"""simple docstring"""
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
... | 605 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching bet... | 605 | 1 |
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
retu... | 605 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstrin... | 605 | 1 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, ... | 605 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if len(UpperCAmelCase__ ) == 0:
return array
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = min(UpperCAmelCase__ ), max(UpperCAmelCase__ ... | 605 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if len(UpperCAmelCase__ ) < 2:
raise ValueError('Monogons and Digons are not polygons in the Euclidean space' )
if any(i <= 0 for i in nums ):
... | 605 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
snake_case : Dict = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('3.7'... | 605 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case : List[str] = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available():
... | 605 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = HfArgumentParser(UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = parser.parse_args_into_dataclas... | 605 | 1 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = cva.get... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum < 0:
raise Exception('Rate of interest must be >= 0' )
... | 605 | 1 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_com... | 605 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"... | 605 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenizatio... | 605 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase )
class __lowercase ( UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE... | 605 | 1 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from acceler... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
def merge(UpperCAmelCase__ ,UpperCAmelCase__ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yi... | 605 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_commo... | 605 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
... | 605 | 1 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowercase ( UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = "ClapFeatureExtractor"
SCREAMING_SNAKE_CASE : Option... | 605 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case : int = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
... | 605 | 1 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if n_term == "":
return []
_SCREAMING_SNAKE_CASE = []
for temp in range(int(UpperCAmelCase__ ) ):
series.append(f'''1/{temp + 1}''' if series else '1' )
return ser... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ = 1000 ):
"""simple docstring"""
return sum(2 * a * ((a - 1) // 2) for a in range(3 ,n + 1 ) )
if __name__ == "__main__":
print(solution())
| 605 | 1 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ = 0 ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = length or len(UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
... | 605 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
snake_case : List[str] = {
'configuration_roberta_prelayernorm': [
'ROBERTA_PRELAYERNORM_PRETR... | 605 | 1 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
if index == number_of_items:
return 0
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAK... | 605 |
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_simplify,
... | 605 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer
... | 605 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
snake_case : Tuple = (7_20, 12_80) # Height, Width
snake_case : List[Any] = (0.4, 0.6) # if height or width lower than this scale, drop it.
snake_case... | 605 | 1 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
snake_case : List[Any] = logging.get... | 605 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class __lowercase ( UpperCamelCase ):
"""simple d... | 605 | 1 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class __lowercase ( UpperCamelCase ):
"""simple docstring"""
de... | 605 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_commo... | 605 | 1 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from acceler... | 605 |
from __future__ import annotations
from typing import Any
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if not postfix_notation:
return 0
_SCREAMING_SNAKE_CASE = {'+', '-', '*', '/'}
_SCREAMING_SNAKE_CASE = []
f... | 605 | 1 |
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
imp... | 605 |
from __future__ import annotations
class __lowercase :
"""simple docstring"""
def __init__( self , A_ )-> None:
_SCREAMING_SNAKE_CASE = data
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
de... | 605 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
... | 605 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
... | 605 | 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_avail... | 605 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
snake_case : Optional[Any] = logging.get_logger(__name__)
... | 605 | 1 |
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class __lowercase ( UpperCamelCase ):
"""simple docstring"""
def __init__( self , A_="" , A_="train" )-> str:
assert os.path.isdir(A_ )
... | 605 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices... | 605 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
snake_case : Dict = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextC... | 605 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching bet... | 605 | 1 |
import torch
from diffusers import DiffusionPipeline
class __lowercase ( UpperCamelCase ):
"""simple docstring"""
def __init__( self , A_ , A_ )-> Optional[Any]:
super().__init__()
self.register_modules(unet=A_ , s... | 605 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstrin... | 605 | 1 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class __lowercase :
"""simple docstring"""
def __init__( self , A_ )-> Optional[Any]:
_SCREAMING_SNAKE_CASE = list_of_points
# Degree determines the fl... | 605 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if len(UpperCAmelCase__ ) == 0:
return array
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = min(UpperCAmelCase__ ), max(UpperCAmelCase__ ... | 605 | 1 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ = 1 ,UpperCAmelCase__ = 1000 ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = 0
for divide_by_number in range(UpperCAmelCase__ ,digit + 1 ):
_SCREAMING_SNAKE_CASE = []
... | 605 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
snake_case : Dict = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('3.7'... | 605 | 1 |
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
snake_case : Tuple = logging... | 605 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = HfArgumentParser(UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = parser.parse_args_into_dataclas... | 605 | 1 |
# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum < 0:
raise Exception('Rate of interest must be >= 0' )
... | 605 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_nump... | 605 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"... | 605 | 1 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class __lowercase ( UpperCamelCase , unittest.TestC... | 605 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase )
class __lowercase ( UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE... | 605 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase )
class __lowercase ( UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
def merge(UpperCAmelCase__ ,UpperCAmelCase__ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yi... | 605 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenizatio... | 605 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
... | 605 | 1 |
from __future__ import annotations
snake_case : Any = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,):
"""simple docs... | 605 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case : int = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
... | 605 | 1 |
import os
def SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
with open(os.path.dirname(UpperCAmelCase__ ) + '/p022_names.txt' ) as file:
_SCREAMING_SNAKE_CASE = str(file.readlines()[0] )
_SCREAMING_SNAKE_CASE = names.replace('"' ,'' ).sp... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ = 1000 ):
"""simple docstring"""
return sum(2 * a * ((a - 1) // 2) for a in range(3 ,n + 1 ) )
if __name__ == "__main__":
print(solution())
| 605 | 1 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ = 10 ,UpperCAmelCase__ = 22 ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = range(1 ,UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = range(1 ,UpperCAmelCase__ )
return sum(
1 for power in powers for base i... | 605 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
snake_case : List[str] = {
'configuration_roberta_prelayernorm': [
'ROBERTA_PRELAYERNORM_PRETR... | 605 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
if b == 0:
return (1, 0)
((_SCREAMING_SNAKE_CASE) , (_SCREAMING_SNAKE_CASE)) = extended_euclid(UpperCAmelCase__ ,a % b )
... | 605 |
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_simplify,
... | 605 | 1 |
from __future__ import annotations
from typing import Any
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if not postfix_notation:
return 0
_SCREAMING_SNAKE_CASE = {'+', '-', '*', '/'}
_SCREAMING_SNAKE_CASE = []
f... | 605 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
snake_case : Tuple = (7_20, 12_80) # Height, Width
snake_case : List[Any] = (0.4, 0.6) # if height or width lower than this scale, drop it.
snake_case... | 605 | 1 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
return abs(UpperCAmelCase__ ) if a == 0 else greatest_common_divisor(b % a ,UpperCAmelCase__ )
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ):
""... | 605 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class __lowercase ( UpperCamelCase ):
"""simple d... | 605 | 1 |
from sklearn.metrics import fa_score
import datasets
snake_case : Optional[int] = '\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'
snake_case : Dict = '\nArgs:\n ... | 605 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_commo... | 605 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase )
class __lowercase ( UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : ... | 605 |
from __future__ import annotations
from typing import Any
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if not postfix_notation:
return 0
_SCREAMING_SNAKE_CASE = {'+', '-', '*', '/'}
_SCREAMING_SNAKE_CASE = []
f... | 605 | 1 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
snake_case : int = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership funct... | 605 |
from __future__ import annotations
class __lowercase :
"""simple docstring"""
def __init__( self , A_ )-> None:
_SCREAMING_SNAKE_CASE = data
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
de... | 605 | 1 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils ... | 605 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
... | 605 | 1 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __lowercase ( UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = (EulerDiscre... | 605 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
snake_case : Optional[Any] = logging.get_logger(__name__)
... | 605 | 1 |
import json
import sys
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
with open(UpperCAmelCase__ ,encoding='utf-8' ) as f:
_SCREAMING_SNAKE_CASE = json.load(UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = ['... | 605 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices... | 605 | 1 |
from collections.abc import Sequence
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ = False ):
"""simple docstring"""
if not arr:
return 0
_SCREAMING_SNAKE_CASE = 0 if allow_empty_subarrays else float('-inf' )
_SCREAMING_SNAKE_CASE... | 605 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching bet... | 605 | 1 |
import argparse
from collections import defaultdict
import yaml
snake_case : List[str] = 'docs/source/en/_toctree.yml'
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = defaultdict(UpperCAmelCase__ )
... | 605 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstrin... | 605 | 1 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torc... | 605 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if len(UpperCAmelCase__ ) == 0:
return array
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = min(UpperCAmelCase__ ), max(UpperCAmelCase__ ... | 605 | 1 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
_SCREAMING_SNAKE_CASE = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b... | 605 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
snake_case : Dict = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('3.7'... | 605 | 1 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if not isinstance(UpperCAmelCase__ ,UpperCAmelCase__ ):
raise TypeError('Input value must be an \'int\' type' )
_SCREAMING_SNAKE_CASE = 0
while number:
position += 1
... | 605 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = HfArgumentParser(UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = parser.parse_args_into_dataclas... | 605 | 1 |
import inspect
import unittest
from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum < 0:
raise Exception('Rate of interest must be >= 0' )
... | 605 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case : Dict = {
'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
'processing_git': ['GitProcessor'],
}
try:
... | 605 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"... | 605 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import lo... | 605 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase )
class __lowercase ( UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE... | 605 | 1 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if any(not isinstance(UpperCAmelCase__ ,UpperCAmelCase__ ) or x < 0 for x in sequence ):
raise TypeError('Sequence must be list of non-negative integers' )
for _ in range(len(UpperCAmelC... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
def merge(UpperCAmelCase__ ,UpperCAmelCase__ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yi... | 605 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case : int = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
... | 605 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
... | 605 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenizatio... | 605 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case : int = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
... | 605 | 1 |
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ = 1000 ):
"""simple docstring"""
return sum(2 * a * ((a - 1) // 2) for a in range(3 ,n + 1 ) )
if __name__ == "__main__":
print(solution())
| 605 | 1 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
snake_case : Dict = '\\n@inproceedings{popovic-2015-chrf,\n title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n author = "Popovi{\'c}, Maja",\n booktitle = "Pr... | 605 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
snake_case : List[str] = {
'configuration_roberta_prelayernorm': [
'ROBERTA_PRELAYERNORM_PRETR... | 605 | 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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN... | 605 |
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_simplify,
... | 605 | 1 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ = 1000 ):
"""simple docstring"""
return sum(e for e in range(3 ,UpperCAmelCase__ ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 605 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
snake_case : Tuple = (7_20, 12_80) # Height, Width
snake_case : List[Any] = (0.4, 0.6) # if height or width lower than this scale, drop it.
snake_case... | 605 | 1 |
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
... | 605 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class __lowercase ( UpperCamelCase ):
"""simple d... | 605 | 1 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"... | 605 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_commo... | 605 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
snake_case : int = {
'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MobileVi... | 605 |
from __future__ import annotations
from typing import Any
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if not postfix_notation:
return 0
_SCREAMING_SNAKE_CASE = {'+', '-', '*', '/'}
_SCREAMING_SNAKE_CASE = []
f... | 605 | 1 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
Com... | 605 |
from __future__ import annotations
class __lowercase :
"""simple docstring"""
def __init__( self , A_ )-> None:
_SCREAMING_SNAKE_CASE = data
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
de... | 605 | 1 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ..... | 605 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
... | 605 | 1 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowercase ( UpperCamelCase ):
"""s... | 605 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
snake_case : Optional[Any] = logging.get_logger(__name__)
... | 605 | 1 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [int(UpperCAmelCase__ ) for i in ip_va_address.split('.' ) if i.isdigit()]
return len(UpperCAmelCase__ ) == 4 and all(0 <= int(UpperCAmelCase__ ) <= 254 for octet i... | 605 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices... | 605 | 1 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
if exponent == 1:
return base
if exponent % 2 == 0:
_SCREAMING_SNAKE_CASE = _modexpt(UpperCAmelCase__ ,exponent // 2 ,UpperCAmelCase__ ) % modu... | 605 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching bet... | 605 | 1 |
import argparse
import copy
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = {}
with open(UpperCAmelCase__ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
... | 605 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstrin... | 605 | 1 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from .... | 605 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if len(UpperCAmelCase__ ) == 0:
return array
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = min(UpperCAmelCase__ ), max(UpperCAmelCase__ ... | 605 | 1 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowercase ( ... | 605 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
snake_case : Dict = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('3.7'... | 605 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless require... | 605 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = HfArgumentParser(UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = parser.parse_args_into_dataclas... | 605 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case : List[Any] = {
'configuration_blenderbot_small': [
'... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum < 0:
raise Exception('Rate of interest must be >= 0' )
... | 605 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 605 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"... | 605 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
snake_case : List[str] = {
'configuration_roberta_prelayernorm': [
'ROBERTA_PRELAYERNORM_PRETR... | 605 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase )
class __lowercase ( UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE... | 605 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 605 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
def merge(UpperCAmelCase__ ,UpperCAmelCase__ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yi... | 605 | 1 |
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