code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@requi... | 17 | '''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files", [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.js... | 17 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( a_: list[list[int]], a_: int, a_: int, a_: list[int] ):
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path... | 17 | '''simple docstring'''
from math import factorial
def __UpperCAmelCase ( a_: int = 100 ):
return sum(map(a_, str(factorial(a_ ) ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip()))) | 17 | 1 |
'''simple docstring'''
from __future__ import annotations
def __UpperCAmelCase ( a_: list ):
if not nums:
raise ValueError("List is empty" )
return sum(a_ ) / len(a_ )
if __name__ == "__main__":
import doctest
doctest.testmod() | 17 | '''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
__a = (3, 9, -11, 0, 7, 5, 1, -1)
__a = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCa... | 17 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_chann... | 17 | '''simple docstring'''
def __UpperCAmelCase ( a_: str ):
if not all(char in "01" for char in bin_string ):
raise ValueError("Non-binary value was passed to the function" )
if not bin_string:
raise ValueError("Empty string was passed to the function" )
_Upp... | 17 | 1 |
'''simple docstring'''
from __future__ import annotations
def __UpperCAmelCase ( a_: list[int | str] ):
create_state_space_tree(a_, [], 0, [0 for i in range(len(a_ ) )] )
def __UpperCAmelCase ( a_: list[int | str], a_: list[int | str], a_: int, a_: ... | 17 | '''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def __UpperCAmelCase ( a_: str ):
for param in module.parameters():
_UpperCAmelCase : Any = False
def __UpperCAmelCase ( ):
_UpperCAmelCa... | 17 | 1 |
'''simple docstring'''
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
__a = WebClient(token=os.environ['CI_SLACK_BOT_TOKEN'])
def __UpperCAmelCase ( a_: Di... | 17 | '''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[int] = (Eu... | 17 | 1 |
'''simple docstring'''
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 TFModelTesterMix... | 17 | '''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
_UpperCAmelCase : List[str] = str(bin(a_ ) )[2:] # remove the leading "0b"
_UpperCAmelCase... | 17 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( a_: float, a_: float ):
if mass < 0:
raise ValueError("The mass of a body cannot be negative" )
return 0.5 * mass * abs(a_ ) * abs(a_ )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True... | 17 | '''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def __UpperCAmelCase ( a_: int ):
# A local function to see if a dot lands in the circle.
def is_in_circle(a_: float, a_: float ) -> bo... | 17 | 1 |
'''simple docstring'''
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : int = ['''image_processor''', '''tokenizer''']
UpperCa... | 17 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__a = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Layo... | 17 | 1 |
'''simple docstring'''
__a = {
0: '0',
1: '1',
2: '2',
3: '3',
4: '4',
5: '5',
6: '6',
7: '7',
8: '8',
9: '9',
10: 'a',
11: 'b',
12: 'c',
13: 'd',
14: 'e',
15: 'f',
}
def __UpperCAmelCase ( a_: float ):
assert typ... | 17 | '''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
if not isinstance(a_, a_ ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(a_, a_ ) or not number >= 1:
raise ValueError(
"starting number must be\n ... | 17 | 1 |
'''simple docstring'''
import os
def __UpperCAmelCase ( a_: Tuple ):
_UpperCAmelCase : int = len(grid[0] )
_UpperCAmelCase : Tuple = len(a_ )
_UpperCAmelCase : str = 0
_UpperCAmelCase : st... | 17 | '''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
... | 17 | 1 |
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[int] = (Eu... | 17 | '''simple docstring'''
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class A__ ( pl.LightningModule ):
"""simple docstring"""
def __init__( self : Any , ... | 17 | 1 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
__a ... | 17 | '''simple docstring'''
from importlib import import_module
from .logging import get_logger
__a = get_logger(__name__)
class A__ :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : O... | 17 | 1 |
'''simple docstring'''
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
Autoencod... | 17 | '''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cas... | 17 | 1 |
'''simple docstring'''
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class A... | 17 | '''simple docstring'''
from __future__ import annotations
def __UpperCAmelCase ( a_: list[int] ):
if not nums:
return 0
_UpperCAmelCase : int = nums[0]
_UpperCAmelCase : Dict = 0
for num in nums[1:]:
_UpperCAmelCase ... | 17 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__a = {
'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MobileViTConfi... | 17 | '''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__a ... | 17 | 1 |
'''simple docstring'''
from sklearn.metrics import mean_squared_error
import datasets
__a = '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M... | 17 | '''simple docstring'''
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="session" )
def __UpperCAmelCase ... | 17 | 1 |
'''simple docstring'''
from manim import *
class A__ ( UpperCamelCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : Dict ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : List[Any] = Rectangle(height=0... | 17 | '''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@requi... | 17 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( a_: str ):
return " ".join(
"".join(word[::-1] ) if len(a_ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words('Hey wollef ... | 17 | '''simple docstring'''
import unittest
import numpy as np
import requests
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
... | 17 | 1 |
'''simple docstring'''
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... | 17 | '''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'huggingface/time-series-transformer-tourism-monthly': (
'https://huggingface.co/huggingface... | 17 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( a_: List[str] ):
_UpperCAmelCase : List[str] = len(a_ )
while cur > 1:
# Find the maximum number in arr
_UpperCAmelCase : int = arr.index(max(arr[0:cur] ) )
# Reve... | 17 | '''simple docstring'''
import baseaa
def __UpperCAmelCase ( a_: str ):
return baseaa.baaencode(string.encode("utf-8" ) )
def __UpperCAmelCase ( a_: bytes ):
return baseaa.baadecode(a_ ).decode("utf-8" )
if __name__ == "__main__":
... | 17 | 1 |
'''simple docstring'''
import os
from math import logaa
def __UpperCAmelCase ( a_: str = "base_exp.txt" ):
_UpperCAmelCase : float = 0
_UpperCAmelCase : Union[str, Any] = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(a_ ... | 17 | '''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ... | 17 | 1 |
'''simple docstring'''
import baseaa
def __UpperCAmelCase ( a_: str ):
return baseaa.baaencode(string.encode("utf-8" ) )
def __UpperCAmelCase ( a_: bytes ):
return baseaa.baadecode(a_ ).decode("utf-8" )
if __name__ == "__main__":
... | 17 | '''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files", [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.js... | 17 | 1 |
'''simple docstring'''
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionMod... | 17 | '''simple docstring'''
from math import factorial
def __UpperCAmelCase ( a_: int = 100 ):
return sum(map(a_, str(factorial(a_ ) ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip()))) | 17 | 1 |
'''simple docstring'''
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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
#
# ... | 17 | '''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
__a = (3, 9, -11, 0, 7, 5, 1, -1)
__a = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCa... | 17 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__a = {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConf... | 17 | '''simple docstring'''
def __UpperCAmelCase ( a_: str ):
if not all(char in "01" for char in bin_string ):
raise ValueError("Non-binary value was passed to the function" )
if not bin_string:
raise ValueError("Empty string was passed to the function" )
_Upp... | 17 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( a_: str ):
if not all(char in "01" for char in bin_string ):
raise ValueError("Non-binary value was passed to the function" )
if not bin_string:
raise ValueError("Empty string was passed to the function" )
_Upp... | 17 | '''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def __UpperCAmelCase ( a_: str ):
for param in module.parameters():
_UpperCAmelCase : Any = False
def __UpperCAmelCase ( ):
_UpperCAmelCa... | 17 | 1 |
'''simple docstring'''
import unittest
import numpy as np
def __UpperCAmelCase ( a_: np.ndarray, a_: np.ndarray, a_: np.ndarray, a_: np.ndarray | None = None, ):
_UpperCAmelCase : Optional[Any] = np.shape(a_ )
_UpperCAmelCase : List[Any] = ... | 17 | '''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[int] = (Eu... | 17 | 1 |
'''simple docstring'''
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
__a = TypeVar('T')
class A__ ( Generic[T] ):
"""simple doc... | 17 | '''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
_UpperCAmelCase : List[str] = str(bin(a_ ) )[2:] # remove the leading "0b"
_UpperCAmelCase... | 17 | 1 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common... | 17 | '''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def __UpperCAmelCase ( a_: int ):
# A local function to see if a dot lands in the circle.
def is_in_circle(a_: float, a_: float ) -> bo... | 17 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAmelCase ( a_: Optional[Any], a_: Dict, a_: Any ):
# Initialise... | 17 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__a = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Layo... | 17 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__a = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}
... | 17 | '''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
if not isinstance(a_, a_ ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(a_, a_ ) or not number >= 1:
raise ValueError(
"starting number must be\n ... | 17 | 1 |
'''simple docstring'''
class A__ :
"""simple docstring"""
def __init__( self : Optional[int] , lowerCAmelCase__ : str = "" , lowerCAmelCase__ : bool = False ) -> None:
"""simple docstring"""
_UpperCAmelCase : dict[... | 17 | '''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
... | 17 | 1 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
... | 17 | '''simple docstring'''
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class A__ ( pl.LightningModule ):
"""simple docstring"""
def __init__( self : Any , ... | 17 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
ge... | 17 | '''simple docstring'''
from importlib import import_module
from .logging import get_logger
__a = get_logger(__name__)
class A__ :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : O... | 17 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Tuple = ['''torch''', '''scipy''']
def __init__( self : List[str] , *lowerCAmelCase__... | 17 | '''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cas... | 17 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
return int((input_a, input_a).count(0 ) == 0 )
def __UpperCAmelCase ( ):
assert and_gate(0, 0 ) == 0
assert and_gate(0, 1 ) == 0
assert and_gate(1, 0 ) == 0
assert a... | 17 | '''simple docstring'''
from __future__ import annotations
def __UpperCAmelCase ( a_: list[int] ):
if not nums:
return 0
_UpperCAmelCase : int = nums[0]
_UpperCAmelCase : Dict = 0
for num in nums[1:]:
_UpperCAmelCase ... | 17 | 1 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common i... | 17 | '''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__a ... | 17 | 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 A__ ( UpperCamelCase ... | 17 | '''simple docstring'''
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="session" )
def __UpperCAmelCase ... | 17 | 1 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def __UpperCAmelCase ( a_: str, a_: Dict, a_: Optional[int], a_: int ):
_UpperCAmelCase : str = s.rsplit(a_, a_ )
return ... | 17 | '''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@requi... | 17 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__a = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Layo... | 17 | '''simple docstring'''
import unittest
import numpy as np
import requests
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
... | 17 | 1 |
'''simple docstring'''
import os
from collections.abc import Iterator
def __UpperCAmelCase ( a_: str = "." ):
for dir_path, dir_names, filenames in os.walk(a_ ):
_UpperCAmelCase : Tuple = [d for d in dir_names if d != "scripts" and d[0] not in "._"]
... | 17 | '''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'huggingface/time-series-transformer-tourism-monthly': (
'https://huggingface.co/huggingface... | 17 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
if not isinstance(a_, a_ ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(a_, a_ ) or not number >= 1:
raise ValueError(
"starting number must be\n ... | 17 | '''simple docstring'''
import baseaa
def __UpperCAmelCase ( a_: str ):
return baseaa.baaencode(string.encode("utf-8" ) )
def __UpperCAmelCase ( a_: bytes ):
return baseaa.baadecode(a_ ).decode("utf-8" )
if __name__ == "__main__":
... | 17 | 1 |
'''simple docstring'''
__a = 65_521
def __UpperCAmelCase ( a_: str ):
_UpperCAmelCase : List[str] = 1
_UpperCAmelCase : str = 0
for plain_chr in plain_text:
_UpperCAmelCase : Union[str, Any] = (a + o... | 17 | '''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ... | 17 | 1 |
'''simple docstring'''
__a = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def __UpperCAmelCase ( a_: Dict, a_: Optional[int], a_: Any, a_: Optional[Any] ):
# Return Tru... | 17 | '''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files", [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.js... | 17 | 1 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
__a = '\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive co... | 17 | '''simple docstring'''
from math import factorial
def __UpperCAmelCase ( a_: int = 100 ):
return sum(map(a_, str(factorial(a_ ) ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip()))) | 17 | 1 |
'''simple docstring'''
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenat... | 17 | '''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
__a = (3, 9, -11, 0, 7, 5, 1, -1)
__a = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCa... | 17 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( a_: list[int] ):
if not nums: # Makes sure that the list is not empty
raise ValueError("List is empty" )
_UpperCAmelCase : List[Any] = sum(a_ ) / len(a_ ) # Calculate the average
return sum(a... | 17 | '''simple docstring'''
def __UpperCAmelCase ( a_: str ):
if not all(char in "01" for char in bin_string ):
raise ValueError("Non-binary value was passed to the function" )
if not bin_string:
raise ValueError("Empty string was passed to the function" )
_Upp... | 17 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
'google/fnet-large': 'https://huggingface.co/googl... | 17 | '''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def __UpperCAmelCase ( a_: str ):
for param in module.parameters():
_UpperCAmelCase : Any = False
def __UpperCAmelCase ( ):
_UpperCAmelCa... | 17 | 1 |
'''simple docstring'''
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
fr... | 17 | '''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[int] = (Eu... | 17 | 1 |
'''simple docstring'''
from ... import PretrainedConfig
__a = {
'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Dict = NEZHA_... | 17 | '''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
_UpperCAmelCase : List[str] = str(bin(a_ ) )[2:] # remove the leading "0b"
_UpperCAmelCase... | 17 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
f... | 17 | '''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def __UpperCAmelCase ( a_: int ):
# A local function to see if a dot lands in the circle.
def is_in_circle(a_: float, a_: float ) -> bo... | 17 | 1 |
'''simple docstring'''
import os
import sys
import unittest
__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_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, fin... | 17 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__a = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Layo... | 17 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_dete... | 17 | '''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
if not isinstance(a_, a_ ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(a_, a_ ) or not number >= 1:
raise ValueError(
"starting number must be\n ... | 17 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class A__ :
"""simple docstring"""
def __init__( self : Any , lowerCAmelCase__ : int ) -> None:
"""simple docstring"""
_UpperCAmelCase : List... | 17 | '''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
... | 17 | 1 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def __UpperCAmelCase ( a_: bytes ):
if len(a_ ) != 32:
raise ValueError("Input must be of length 32" )
_UpperCAmelCase : Optional[int] = B""
for i in [3, 2, ... | 17 | '''simple docstring'''
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class A__ ( pl.LightningModule ):
"""simple docstring"""
def __init__( self : Any , ... | 17 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( a_: list, a_: list ):
_validate_point(a_ )
_validate_point(a_ )
if len(a_ ) != len(a_ ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(a - b ) for... | 17 | '''simple docstring'''
from importlib import import_module
from .logging import get_logger
__a = get_logger(__name__)
class A__ :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : O... | 17 | 1 |
'''simple docstring'''
from math import sqrt
def __UpperCAmelCase ( a_: int ):
_UpperCAmelCase : Optional[int] = 0
for i in range(1, int(sqrt(a_ ) + 1 ) ):
if n % i == 0 and i != sqrt(a_ ):
total += i + n // i
elif i... | 17 | '''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cas... | 17 | 1 |
'''simple docstring'''
from __future__ import annotations
def __UpperCAmelCase ( a_: list[int] ):
if not nums:
return 0
_UpperCAmelCase : int = nums[0]
_UpperCAmelCase : Dict = 0
for num in nums[1:]:
_UpperCAmelCase ... | 17 | '''simple docstring'''
from __future__ import annotations
def __UpperCAmelCase ( a_: list[int] ):
if not nums:
return 0
_UpperCAmelCase : int = nums[0]
_UpperCAmelCase : Dict = 0
for num in nums[1:]:
_UpperCAmelCase ... | 17 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase )
class A__ ( UpperCamelCase ):
"""simple docstring"""... | 17 | '''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__a ... | 17 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 17 | '''simple docstring'''
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="session" )
def __UpperCAmelCase ... | 17 | 1 |
'''simple docstring'''
__a = 8.3_1_4_4_5_9_8
def __UpperCAmelCase ( a_: float, a_: float ):
if temperature < 0:
raise Exception("Temperature cannot be less than 0 K" )
if molar_mass <= 0:
raise Exception("Molar mass cannot be less than or equal to 0 kg/mol... | 17 | '''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@requi... | 17 | 1 |
'''simple docstring'''
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class A__ ( pl.LightningModule ):
"""simple docstring"""
def __init__( self : Any , ... | 17 | '''simple docstring'''
import unittest
import numpy as np
import requests
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
... | 17 | 1 |
'''simple docstring'''
# Copyright 2022 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
#
#... | 17 | '''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'huggingface/time-series-transformer-tourism-monthly': (
'https://huggingface.co/huggingface... | 17 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( a_: float, a_: float ):
if density <= 0:
raise ValueError("Impossible fluid density" )
if bulk_modulus <= 0:
raise ValueError("Impossible bulk modulus" )
return (bulk_modulus / density) ** 0.5
if __name__ == "__m... | 17 | '''simple docstring'''
import baseaa
def __UpperCAmelCase ( a_: str ):
return baseaa.baaencode(string.encode("utf-8" ) )
def __UpperCAmelCase ( a_: bytes ):
return baseaa.baadecode(a_ ).decode("utf-8" )
if __name__ == "__main__":
... | 17 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class A__ ( UpperCamelCase ):
"""s... | 17 | '''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ... | 17 | 1 |
'''simple docstring'''
from __future__ import annotations
def __UpperCAmelCase ( a_: list[int] ):
if len(a_ ) == 0:
return array
_UpperCAmelCase , _UpperCAmelCase : List[str] = min(a_ ), max(a_ )
# Compute the variables
_Up... | 17 | '''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files", [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.js... | 17 | 1 |
'''simple docstring'''
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
Stable... | 17 | '''simple docstring'''
from math import factorial
def __UpperCAmelCase ( a_: int = 100 ):
return sum(map(a_, str(factorial(a_ ) ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip()))) | 17 | 1 |
'''simple docstring'''
from math import factorial
def __UpperCAmelCase ( a_: int = 100 ):
return sum(int(a_ ) for x in str(factorial(a_ ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip()))) | 17 | '''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
__a = (3, 9, -11, 0, 7, 5, 1, -1)
__a = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCa... | 17 | 1 |
'''simple docstring'''
import itertools
import string
from collections.abc import Generator, Iterable
def __UpperCAmelCase ( a_: Iterable[str], a_: int ):
_UpperCAmelCase : List[str] = iter(a_ )
while True:
_UpperCAmelCase : int = ... | 17 | '''simple docstring'''
def __UpperCAmelCase ( a_: str ):
if not all(char in "01" for char in bin_string ):
raise ValueError("Non-binary value was passed to the function" )
if not bin_string:
raise ValueError("Empty string was passed to the function" )
_Upp... | 17 | 1 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
_... | 17 | '''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def __UpperCAmelCase ( a_: str ):
for param in module.parameters():
_UpperCAmelCase : Any = False
def __UpperCAmelCase ( ):
_UpperCAmelCa... | 17 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.... | 17 | '''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[int] = (Eu... | 17 | 1 |
'''simple docstring'''
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from tran... | 17 | '''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
_UpperCAmelCase : List[str] = str(bin(a_ ) )[2:] # remove the leading "0b"
_UpperCAmelCase... | 17 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class A__ :
"""simple docstring"""
def __init__( self : Optional[int] , lowerCAmelCase__ : Any ) -> str:
"""simple docstring... | 17 | '''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def __UpperCAmelCase ( a_: int ):
# A local function to see if a dot lands in the circle.
def is_in_circle(a_: float, a_: float ) -> bo... | 17 | 1 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def __UpperCAmelCase ( a_: int="ro", a_: str="en", a_: str="wmt16", a_: Dict=None ):
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError("run pip install data... | 17 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__a = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Layo... | 17 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_im... | 17 | '''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
if not isinstance(a_, a_ ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(a_, a_ ) or not number >= 1:
raise ValueError(
"starting number must be\n ... | 17 | 1 |
'''simple docstring'''
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def __UpperCAmelCase ( a_: Tuple, a_: Optional[Any]=None ):
_UpperCAmelCase : int = None
if tok... | 17 | '''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
... | 17 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.uti... | 17 | '''simple docstring'''
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class A__ ( pl.LightningModule ):
"""simple docstring"""
def __init__( self : Any , ... | 17 | 1 |
'''simple docstring'''
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __UpperCAmelCase ( ):
_UpperCAmelCase : Optional[int] = [randint(-1_000, 1_000 ) for i in range(10 )]
_U... | 17 | '''simple docstring'''
from importlib import import_module
from .logging import get_logger
__a = get_logger(__name__)
class A__ :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : O... | 17 | 1 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaag... | 17 | '''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cas... | 17 | 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_MOD... | 17 | '''simple docstring'''
from __future__ import annotations
def __UpperCAmelCase ( a_: list[int] ):
if not nums:
return 0
_UpperCAmelCase : int = nums[0]
_UpperCAmelCase : Dict = 0
for num in nums[1:]:
_UpperCAmelCase ... | 17 | 1 |
'''simple docstring'''
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__a = models.Sequential()
#... | 17 | '''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__a ... | 17 | 1 |
'''simple docstring'''
__a = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
__a = [{'typ... | 17 | '''simple docstring'''
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="session" )
def __UpperCAmelCase ... | 17 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__a = '\\n\n'
__a = '\nPerplexity (PPL) is one of the most common metrics for evaluating lang... | 17 | '''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@requi... | 17 | 1 |
'''simple docstring'''
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class A__ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : Optional[Any] ) -> Optional[int]:
... | 17 | '''simple docstring'''
import unittest
import numpy as np
import requests
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
... | 17 | 1 |
'''simple docstring'''
from __future__ import annotations
class A__ :
"""simple docstring"""
def __init__( self : Tuple , lowerCAmelCase__ : list[list[int]] ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : Dic... | 17 | '''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'huggingface/time-series-transformer-tourism-monthly': (
'https://huggingface.co/huggingface... | 17 | 1 |
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
__a = get_logger(__name__)
class A__ :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : O... | 17 | '''simple docstring'''
import baseaa
def __UpperCAmelCase ( a_: str ):
return baseaa.baaencode(string.encode("utf-8" ) )
def __UpperCAmelCase ( a_: bytes ):
return baseaa.baadecode(a_ ).decode("utf-8" )
if __name__ == "__main__":
... | 17 | 1 |
'''simple docstring'''
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def __UpperCAmelCase ( *a_: str ):
if not isinstance(a_, a_ ):
_UpperCAmelCase : Optional[int] = list(a_ )
... | 17 | '''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ... | 17 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__a = {
'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}
try:
if not is_torch_a... | 17 | '''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files", [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.js... | 17 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {
'configuration_upernet': ['UperNetConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Optiona... | 17 | '''simple docstring'''
from math import factorial
def __UpperCAmelCase ( a_: int = 100 ):
return sum(map(a_, str(factorial(a_ ) ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip()))) | 17 | 1 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizer... | 17 | '''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
__a = (3, 9, -11, 0, 7, 5, 1, -1)
__a = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCa... | 17 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
__a = (3, 9, -11, 0, 7, 5, 1, -1)
__a = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class A__ :
"""simple docstring"""
UpperCamelCa... | 17 | '''simple docstring'''
def __UpperCAmelCase ( a_: str ):
if not all(char in "01" for char in bin_string ):
raise ValueError("Non-binary value was passed to the function" )
if not bin_string:
raise ValueError("Empty string was passed to the function" )
_Upp... | 17 | 1 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cas... | 17 | '''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def __UpperCAmelCase ( a_: str ):
for param in module.parameters():
_UpperCAmelCase : Any = False
def __UpperCAmelCase ( ):
_UpperCAmelCa... | 17 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation i... | 17 | '''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[int] = (Eu... | 17 | 1 |
'''simple docstring'''
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.uti... | 17 | '''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
_UpperCAmelCase : List[str] = str(bin(a_ ) )[2:] # remove the leading "0b"
_UpperCAmelCase... | 17 | 1 |
'''simple docstring'''
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
__a = '\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted a... | 17 | '''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def __UpperCAmelCase ( a_: int ):
# A local function to see if a dot lands in the circle.
def is_in_circle(a_: float, a_: float ) -> bo... | 17 | 1 |
'''simple docstring'''
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLen... | 17 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__a = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Layo... | 17 | 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... | 17 | '''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
if not isinstance(a_, a_ ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(a_, a_ ) or not number >= 1:
raise ValueError(
"starting number must be\n ... | 17 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( a_: int = 10, a_: int = 1_000, a_: bool = True ):
assert (
isinstance(a_, a_ )
and isinstance(a_, a_ )
and isinstance(a_, a_ )
), "Invalid type of value(s) specified to function!"
if min_val > max_val... | 17 | '''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
... | 17 | 1 |
'''simple docstring'''
from __future__ import annotations
import time
__a = list[tuple[int, int]]
__a = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0,... | 17 | '''simple docstring'''
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class A__ ( pl.LightningModule ):
"""simple docstring"""
def __init__( self : Any , ... | 17 | 1 |
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