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 requests
from bsa import BeautifulSoup
def __UpperCAmelCase ( a_: Optional[Any] = "https://www.worldometers.info/coronavirus" ):
_UpperCAmelCase : List[Any] = BeautifulSoup(requests.get(UpperCAmelCase__ ).text, "html.parser" ... | 362 | '''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 | 0 |
'''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... | 363 | '''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 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from tran... | 364 | '''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 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class A__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase_ : Dict = (PNDMScheduler,)
UpperCamelCase_ : ... | 365 | '''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 | 0 |
'''simple docstring'''
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A__ ( UpperCAmelCase_ , unittest.T... | 366 | '''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 | 0 |
'''simple docstring'''
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
__a = '%20'.join(argv[1:]) if len(argv) > 1 else quote(str(input('Search: ')))
... | 367 | '''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 | 0 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __UpperCAmelCase ( a_: Union[str, Any] ):
return "".join(sorted(lowerCAmelCase_ ) )
def __UpperCAmelCase ( a_: Tuple ):
retur... | 368 | '''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 | 0 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __UpperCAme... | 369 | '''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 | 0 |
'''simple docstring'''
def __UpperCAmelCase ( a_: Optional[int] ):
_UpperCAmelCase : Tuple = [0] * len(__lowerCAmelCase )
_UpperCAmelCase : List[str] = []
_UpperCAmelCase : Optional[Any] = [1] * len(__lowerCAmelCas... | 370 | '''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 | 0 |
'''simple docstring'''
def __UpperCAmelCase ( a_: List[str] ):
if not grid or not grid[0]:
raise TypeError("The grid does not contain the appropriate information" )
for cell_n in range(1, len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
_UpperCAme... | 371 | '''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 | 0 |
'''simple docstring'''
import unittest
from knapsack import knapsack as k
class A__ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : Optional[Any] ) -> int:
"""simple docstring"""
_UpperCAmelCase : ... | 350 | '''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 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
__a = '2020.9.26'
__a = 'xcodz-dot, cclaus, dhruvmanila'
def __UpperCAmelCase ( a_: float, a_: float, a_: float, a_: float, a_: float ):
if not all(isinstance(a_, (float, int) ) for val in l... | 351 | '''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 | 0 |
'''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
_... | 352 | '''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 | 0 |
'''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''']
UpperC... | 353 | '''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 | 0 |
'''simple docstring'''
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class A__ ( unittest.TestCase , UpperCamelCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : Dict ) -> int:
"""s... | 354 | '''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 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configu... | 355 | '''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 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Optional[int] = ['''keras_nlp''']
def __init__( self : Tuple ... | 356 | '''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 | 0 |
'''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... | 357 | '''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 | 0 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common import ... | 358 | '''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 | 0 |
'''simple docstring'''
from math import pi, sqrt
def __UpperCAmelCase ( a_: float ):
if num <= 0:
raise ValueError("math domain error" )
if num > 171.5:
raise OverflowError("math range error" )
elif num - int(a_ ) not in (0, 0.5):
raise NotImpl... | 359 | '''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 | 0 |
'''simple docstring'''
def __UpperCAmelCase ( a_: int, a_: int ):
return int(input_a == input_a == 0 )
def __UpperCAmelCase ( ):
print("Truth Table of NOR Gate:" )
print("| Input 1 | Input 2 | Output |" )
print(f"""| 0 | 0 ... | 360 | '''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 | 0 |
'''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 ne... | 361 | '''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 | 0 |
'''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... | 362 | '''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 | 0 |
'''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... | 363 | '''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 | 0 |
'''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 ... | 364 | '''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 | 0 |
'''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... | 365 | '''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 | 0 |
'''simple docstring'''
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
i... | 366 | '''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 | 0 |
'''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__":
... | 367 | '''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 | 0 |
'''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 ... | 368 | '''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 | 0 |
'''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__ ... | 369 | '''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 | 0 |
'''simple docstring'''
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
... | 370 | '''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 | 0 |
'''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... | 371 | '''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 | 0 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingS... | 350 | '''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 | 0 |
'''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... | 351 | '''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 | 0 |
'''simple docstring'''
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert... | 352 | '''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 | 0 |
'''simple docstring'''
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_p... | 353 | '''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 | 0 |
'''simple docstring'''
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transform... | 354 | '''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 | 0 |
'''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... | 355 | '''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 | 0 |
'''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 TFMod... | 356 | '''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 | 0 |
'''simple docstring'''
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_att... | 357 | '''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 | 0 |
'''simple docstring'''
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
__a = 500_000
__a , __a = os.path.split(__file__)
__a = os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('... | 358 | '''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 | 0 |
'''simple docstring'''
from math import pow, sqrt
def __UpperCAmelCase ( *a_: float ):
_UpperCAmelCase : List[Any] = len(a_ ) > 0 and all(value > 0.0 for value in values )
return result
def __UpperCAmelCase ( a_: float, a_: float ... | 359 | '''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 | 0 |
'''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... | 360 | '''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 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
... | 361 | '''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 | 0 |
'''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()))) | 362 | '''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 | 0 |
'''simple docstring'''
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionMod... | 363 | '''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 | 0 |
'''simple docstring'''
from manim import *
class A__ ( UpperCamelCase ):
"""simple docstring"""
def _lowerCAmelCase ( self : Dict ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : List[Any] = Rectangle(h... | 364 | '''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 | 0 |
'''simple docstring'''
import math
__a = 10
__a = 7
__a = BALLS_PER_COLOUR * NUM_COLOURS
def __UpperCAmelCase ( a_: int = 20 ):
_UpperCAmelCase : int = math.comb(a_, a_ )
_UpperCAmelCase : Tuple = math.com... | 365 | '''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 | 0 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __UpperCAmel... | 366 | '''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 | 0 |
'''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... | 367 | '''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 | 0 |
'''simple docstring'''
def __UpperCAmelCase ( a_: str ):
_UpperCAmelCase : Optional[int] = [], []
while len(a_ ) > 1:
_UpperCAmelCase : Dict = min(a_ ), max(a_ )
start.append(a_ )
end.append(a_ )
... | 368 | '''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 | 0 |
'''simple docstring'''
def __UpperCAmelCase ( a_: list[int] ):
if not numbers:
return 0
if not isinstance(a_, (list, tuple) ) or not all(
isinstance(a_, a_ ) for number in numbers ):
raise ValueError("numbers must be an iterable of integers" ... | 369 | '''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 | 0 |
'''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... | 370 | '''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 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImage... | 371 | '''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 | 0 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import t... | 350 | '''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 | 0 |
'''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... | 351 | '''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 | 0 |
'''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 ) -> int:
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError("run pip in... | 352 | '''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 | 0 |
'''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... | 353 | '''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 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
f... | 354 | '''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 | 0 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : List[str] = (DDPMScheduler,)
def _lowerCAmelCase ( se... | 355 | '''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 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MA... | 356 | '''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 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Tuple = ['''torch''', '''scipy''']
def __init__( self : List[str] , *lowerCAmelCase__... | 357 | '''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 | 0 |
'''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()))) | 358 | '''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 | 0 |
'''simple docstring'''
import math
def __UpperCAmelCase ( a_: int ):
assert isinstance(a_, a_ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not number % 2:
... | 359 | '''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 | 0 |
'''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... | 360 | '''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 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, 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_tenso... | 361 | '''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 | 0 |
'''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... | 362 | '''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 | 0 |
'''simple docstring'''
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
__a = logging.getLogger()
def ... | 363 | '''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 | 0 |
'''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 __UpperCAm... | 364 | '''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 | 0 |
'''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"""... | 365 | '''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 | 0 |
'''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... | 366 | '''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 | 0 |
'''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]:
... | 367 | '''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 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.jso... | 368 | '''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 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
__a = logging.get_logger(__name__)
class A__ ( UpperCamelCase ):
"""simple docstring"""
def __init__( self : str , ... | 369 | '''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 | 0 |
'''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... | 371 | '''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 | 0 |
'''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... | 350 | '''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 | 0 |
'''simple docstring'''
from math import factorial
__a = {str(d): factorial(d) for d in range(10)}
def __UpperCAmelCase ( a_: int ):
return sum(DIGIT_FACTORIAL[d] for d in str(a_ ) )
def __UpperCAmelCase ( ):
_UpperCAmelCase : Option... | 351 | '''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 | 0 |
'''simple docstring'''
import pickle
import numpy as np
from matplotlib import pyplot as plt
class A__ :
"""simple docstring"""
def __init__( self : Optional[int] , lowerCAmelCase__ : int , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ ... | 352 | '''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 | 0 |
'''simple docstring'''
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.uti... | 353 | '''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 | 0 |
'''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(abs(x ... | 354 | '''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 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, Bert... | 355 | '''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 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a = {
'configuration_groupvit': [
'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'GroupViTConfig',
'Gro... | 356 | '''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 | 0 |
'''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 ... | 357 | '''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 | 0 |
'''simple docstring'''
import os
def __UpperCAmelCase ( a_: Tuple ):
_UpperCAmelCase : int = len(grid[0] )
_UpperCAmelCase : Tuple = len(a_ )
_UpperCAmelCase : str = 0
_UpperCAmelCase : str = ... | 358 | '''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 | 0 |
'''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... | 359 | '''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 | 0 |
'''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __UpperCAmelCase ( ):
_UpperCAmelCase : str = HfArgumentParser(a_ )
_UpperCAmelCase : Optional[Any] = parser.parse_args... | 360 | '''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 | 0 |
'''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... | 361 | '''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 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def __UpperCAmelCase ( a_: float, a_: float, a_: float ):
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resista... | 362 | '''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 | 0 |
'''simple docstring'''
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def __UpperCAmelCase ( a_: int ):
return 1 / (1 + np.exp(-z ))
def __UpperCAmelCase ( a_: Optional[int], a_: int ):
return (-y * np.log(a_ ... | 363 | '''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 | 0 |
'''simple docstring'''
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class A__ :
"""simple docstring"""
@prop... | 364 | '''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 | 0 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_av... | 365 | '''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 | 0 |
'''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... | 366 | '''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 | 0 |
'''simple docstring'''
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data imp... | 367 | '''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 | 0 |
'''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']}
... | 368 | '''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 | 0 |
'''simple docstring'''
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def __UpperCAmelCase ( a_: Tuple, a_: Any=(), a_: int=None, a_: Dict="no", a_: Dict="2950... | 369 | '''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 | 0 |
'''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... | 370 | '''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 | 0 |
'''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
... | 371 | '''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 | 0 |
'''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 ... | 350 | '''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 | 0 |
'''simple docstring'''
class A__ :
"""simple docstring"""
def __init__( self : Optional[int] , lowerCAmelCase__ : str = "" , lowerCAmelCase__ : bool = False ) -> None:
"""simple docstring"""
_UpperCAmelCase : dict[... | 351 | '''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 | 0 |
'''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_... | 352 | '''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 | 0 |
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