code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
a = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'''text-classification'''... | 315 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
... | 315 | 1 |
"""simple docstring"""
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
a = {
'''iou_pred... | 315 |
"""simple docstring"""
import numpy
class lowercase_ :
'''simple docstring'''
def __init__( self : Dict , _UpperCAmelCase : numpy.ndarray , _UpperCAmelCase : numpy.ndarray ):
_A = input_array
# Random initial weigh... | 315 | 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 get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping... | 315 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
a = TypeVar('''T''')
class lowercase_ ( Generic[T] ):
'''simple docstring'''
def __init__( self : Any , _UpperCAmelCase ... | 315 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''kssteven/ibert-roberta-base''': '''https://huggingface... | 315 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
a = logging.get_logger(__name__)
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Any , *... | 315 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configura... | 315 |
"""simple docstring"""
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _snake_case ( _snake_case : str ) -> str:
'''simple docstring'''
return "".join(sorted(_snake_case ) )
def ... | 315 | 1 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''Salesforce/blip-vqa-base''': '''https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.j... | 315 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transfor... | 315 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : str , _snake_case : int ) -> str:
'''simple docstring'''
_A = [[] for _ in range(_snake_case )]
_A = key - 1
if key <= 0:
raise ValueError(... | 315 |
"""simple docstring"""
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
a = logging.getLogger(__name__)
a = 50 # max width of layer name... | 315 | 1 |
"""simple docstring"""
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def _snake_case ( _snake_case : str , _snake_case : str , **_snake_case : Dict ) -> Union[str, Any]:
'''simple docstring'... | 315 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
a = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlati... | 315 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json''',
}
class lowercase_ ... | 315 |
"""simple docstring"""
from collections.abc import Callable
def _snake_case ( _snake_case : Callable[[float], float] , _snake_case : float , _snake_case : float ) -> float:
'''simple docstring'''
_A = a
_A ... | 315 | 1 |
"""simple docstring"""
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
a = _sy... | 315 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(__lowerCAmel... | 315 | 1 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
a = logging.get_logger(__name__)
def _snake_case ( _snake_case : Dict , _snake_case : int ) -> ... | 315 |
"""simple docstring"""
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDepe... | 315 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def _snake_case ( _snake_case : list ) -> int:
'''simple docstring'''
if not postfix_notation:
return 0
_A = {'+', '-', '*', '/'}
_A ... | 315 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : tuple[int, int] , _snake_case : int ) -> list[tuple[int, int]]:
'''simple docstring'''
_A , _A = position
_A = [
... | 315 | 1 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : str ) -> list[int]:
'''simple docstring'''
return [ord(_snake_case ) - 96 for elem in plain]
def _snake_case ( _snake_case : ... | 315 |
"""simple docstring"""
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met... | 315 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : int ) -> int:
'''simple docstring'''
_A = abs(_snake_case )
_A = 0
while n > 0:
res += n % 10
n //= 10
return res
def _snake... | 315 |
"""simple docstring"""
def _snake_case ( _snake_case : int , _snake_case : int ) -> bool:
'''simple docstring'''
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 | 1 |
"""simple docstring"""
import os
import time
import numpy as np
import onnxruntime as ort
a = '''1'''
a = '''0'''
a = '''1'''
a = ort.SessionOptions()
a = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('''Create inference session...''')
a = ['''TensorrtExecutionProvider''... | 315 |
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : int ):
... | 315 | 1 |
"""simple docstring"""
import functools
from typing import Any
def _snake_case ( _snake_case : str , _snake_case : list[str] ) -> bool:
'''simple docstring'''
if not isinstance(_snake_case , _snake_case ) or len(_snake_case ... | 315 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
'''xlnet-large-cased''': '''https:/... | 315 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a = {
'''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvNextCon... | 315 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBe... | 315 | 1 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalD... | 315 |
"""simple docstring"""
from manim import *
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Dict ):
_A = Rectangle(height=0.5 , width=0.5 )
_A = Rectangle(height=0.46 , ... | 315 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_visi... | 315 |
"""simple docstring"""
def _snake_case ( _snake_case : list , _snake_case : int = 0 ) -> list:
'''simple docstring'''
_A = length or len(_snake_case )
_A = False
for i in range(length - 1 ):
... | 315 | 1 |
"""simple docstring"""
import os
def _snake_case ( _snake_case : str = "input.txt" ) -> int:
'''simple docstring'''
with open(os.path.join(os.path.dirname(_snake_case ) , _snake_case ) ) as input_file:
_A = [... | 315 |
"""simple docstring"""
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils im... | 315 | 1 |
"""simple docstring"""
import os
from collections.abc import Iterator
def _snake_case ( _snake_case : str = "." ) -> Iterator[str]:
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(_snake_case ):
_A = [d ... | 315 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : int , _snake_case : int ) -> list[list[int]]:
'''simple docstring'''
_A = []
create_all_state(1 , _snake_case , _snake_case ... | 315 | 1 |
"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteSche... | 315 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 10_00 ) -> int:
'''simple docstring'''
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 315 | 1 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def _snake_case ( _snake_case : Callable , _snake_case : float , _snake_case : float , _snake_case : float , _snake_case : float ) -> n... | 315 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
... | 315 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : float , _snake_case : float , _snake_case : float , _snake_case : float , _snake_case : float , ) -> float:
'''simple docstring'''
_A = [re... | 315 |
"""simple docstring"""
import numpy
class lowercase_ :
'''simple docstring'''
def __init__( self : Dict , _UpperCAmelCase : numpy.ndarray , _UpperCAmelCase : numpy.ndarray ):
_A = input_array
# Random initial weigh... | 315 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class lowercase_ ( __lowerCAmelCase ):
'''sim... | 315 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
a = TypeVar('''T''')
class lowercase_ ( Generic[T] ):
'''simple docstring'''
def __init__( self : Any , _UpperCAmelCase ... | 315 | 1 |
"""simple docstring"""
from collections import defaultdict
def _snake_case ( _snake_case : int ) -> int:
'''simple docstring'''
_A = 1
_A = True
for v in tree[start]:
if v not in visited:
ret... | 315 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
a = logging.get_logger(__name__)
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Any , *... | 315 | 1 |
"""simple docstring"""
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet impo... | 315 |
"""simple docstring"""
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _snake_case ( _snake_case : str ) -> str:
'''simple docstring'''
return "".join(sorted(_snake_case ) )
def ... | 315 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
'''simple docstring'''
_A = set()
# Replace all the whitespace in our sentence
_A = input_str.replace(' ' ... | 315 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transfor... | 315 | 1 |
"""simple docstring"""
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
a = HfArgumentParser(InitializationArguments)
a = parser.parse_args()
# Load codeparrot tokenizer trained for Python code ... | 315 |
"""simple docstring"""
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
a = logging.getLogger(__name__)
a = 50 # max width of layer name... | 315 | 1 |
"""simple docstring"""
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_atte... | 315 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
a = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlati... | 315 | 1 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _snake_case ( ) -> List[str]:
'''simple docstring'''
... | 315 |
"""simple docstring"""
from collections.abc import Callable
def _snake_case ( _snake_case : Callable[[float], float] , _snake_case : float , _snake_case : float ) -> float:
'''simple docstring'''
_A = a
_A ... | 315 | 1 |
"""simple docstring"""
import math
def _snake_case ( _snake_case : int ) -> list:
'''simple docstring'''
_A = [True] * n
_A = False
_A = False
_A = True
for i in range(3 , int(n**0.5 + 1... | 315 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(__lowerCAmel... | 315 | 1 |
"""simple docstring"""
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch... | 315 |
"""simple docstring"""
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDepe... | 315 | 1 |
"""simple docstring"""
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def _snake_case ( _snake_case : di... | 315 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : tuple[int, int] , _snake_case : int ) -> list[tuple[int, int]]:
'''simple docstring'''
_A , _A = position
_A = [
... | 315 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cud... | 315 |
"""simple docstring"""
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met... | 315 | 1 |
"""simple docstring"""
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met... | 315 |
"""simple docstring"""
def _snake_case ( _snake_case : int , _snake_case : int ) -> bool:
'''simple docstring'''
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 | 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
#
#... | 315 |
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : int ):
... | 315 | 1 |
"""simple docstring"""
import collections
import os
import re
from pathlib import Path
a = '''src/transformers'''
# Matches is_xxx_available()
a = re.compile(r'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
a = re.compile(r'''^_import_structure\s+=\s+\{([^\}]+)\}... | 315 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
'''xlnet-large-cased''': '''https:/... | 315 | 1 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__lowerCAmelCase )
class lowercase_ ( __lowerCAmelCase ):
'''simple ... | 315 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBe... | 315 | 1 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech... | 315 |
"""simple docstring"""
from manim import *
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Dict ):
_A = Rectangle(height=0.5 , width=0.5 )
_A = Rectangle(height=0.46 , ... | 315 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''facebook/deit-base-dist... | 315 |
"""simple docstring"""
def _snake_case ( _snake_case : list , _snake_case : int = 0 ) -> list:
'''simple docstring'''
_A = length or len(_snake_case )
_A = False
for i in range(length - 1 ):
... | 315 | 1 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
a = False
class lowercase_ ( unittest.TestCas... | 315 |
"""simple docstring"""
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils im... | 315 | 1 |
"""simple docstring"""
import torch
from transformers import AutoModel
class lowercase_ ( torch.nn.Module ):
'''simple docstring'''
def __init__( self : Any , _UpperCAmelCase : Optional[Any]="sayef/fsner-bert-base-uncased" ):
super(_UpperCAm... | 315 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : int , _snake_case : int ) -> list[list[int]]:
'''simple docstring'''
_A = []
create_all_state(1 , _snake_case , _snake_case ... | 315 | 1 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def _snake_case ( _snake_case : str = "https://www.worldometers.info/coronavirus" ) -> dict:
'''simple docstring'''
_A = BeautifulSoup(requests.get(_snake_case ).text ... | 315 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 10_00 ) -> int:
'''simple docstring'''
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 315 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
a = logging.get_logger(__name__)
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Any , *... | 315 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
... | 315 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
f... | 315 |
"""simple docstring"""
import numpy
class lowercase_ :
'''simple docstring'''
def __init__( self : Dict , _UpperCAmelCase : numpy.ndarray , _UpperCAmelCase : numpy.ndarray ):
_A = input_array
# Random initial weigh... | 315 | 1 |
"""simple docstring"""
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class lowercase_ ( __lowerCAmelCase , __lowerCAm... | 315 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
a = TypeVar('''T''')
class lowercase_ ( Generic[T] ):
'''simple docstring'''
def __init__( self : Any , _UpperCAmelCase ... | 315 | 1 |
"""simple docstring"""
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_availa... | 315 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
a = logging.get_logger(__name__)
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Any , *... | 315 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json''',
'''uclanlp/visualbert-vqa-pre''': '''htt... | 315 |
"""simple docstring"""
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _snake_case ( _snake_case : str ) -> str:
'''simple docstring'''
return "".join(sorted(_snake_case ) )
def ... | 315 | 1 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transfor... | 315 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transfor... | 315 | 1 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : tuple[int, int] , _snake_case : int ) -> list[tuple[int, int]]:
'''simple docstring'''
_A , _A = position
_A = [
... | 315 |
"""simple docstring"""
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
a = logging.getLogger(__name__)
a = 50 # max width of layer name... | 315 | 1 |
"""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_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we... | 315 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
a = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlati... | 315 | 1 |
"""simple docstring"""
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def _snake_case ( _snake_case : str ) -> Tuple:
'... | 315 |
"""simple docstring"""
from collections.abc import Callable
def _snake_case ( _snake_case : Callable[[float], float] , _snake_case : float , _snake_case : float ) -> float:
'''simple docstring'''
_A = a
_A ... | 315 | 1 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils ... | 315 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(__lowerCAmel... | 315 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_... | 315 |
"""simple docstring"""
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDepe... | 315 | 1 |
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _snake_case ( ) -> Optional[int]:
'''simple docstring'''
_A = ArgumentParser('Diffusers CLI tool' , usage='diffusers-cli <command> [<args>]' )
... | 315 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : tuple[int, int] , _snake_case : int ) -> list[tuple[int, int]]:
'''simple docstring'''
_A , _A = position
_A = [
... | 315 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _snake_case ( _snake_case : str , _snake_case : str ) -> str | Literal[False]:
'''simple docstring'''
_A ... | 315 |
"""simple docstring"""
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met... | 315 | 1 |
"""simple docstring"""
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_... | 315 |
"""simple docstring"""
def _snake_case ( _snake_case : int , _snake_case : int ) -> bool:
'''simple docstring'''
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {
'''configuration_xlm_roberta_xl''': [
'''XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XLMRobertaXLConfig''',
'''XLMRobertaX... | 315 |
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : int ):
... | 315 | 1 |
"""simple docstring"""
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
a = get_tests... | 315 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
'''xlnet-large-cased''': '''https:/... | 315 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _snake_case ( _snake_case : int , _snake_case :... | 315 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBe... | 315 | 1 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from tran... | 315 |
"""simple docstring"""
from manim import *
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Dict ):
_A = Rectangle(height=0.5 , width=0.5 )
_A = Rectangle(height=0.46 , ... | 315 | 1 |
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : int ):
... | 315 |
"""simple docstring"""
def _snake_case ( _snake_case : list , _snake_case : int = 0 ) -> list:
'''simple docstring'''
_A = length or len(_snake_case )
_A = False
for i in range(length - 1 ):
... | 315 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : list[int] ) -> list[int]:
'''simple docstring'''
_A = len(_snake_case )
for i in range(_snake_case ):
for j in range(i + 1 , _snake_case ):
... | 315 |
"""simple docstring"""
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils im... | 315 | 1 |
"""simple docstring"""
from __future__ import annotations
class lowercase_ :
'''simple docstring'''
def __init__( self : str , _UpperCAmelCase : list[list[int]] ):
_A = TypeError(
'Matrices must be formed from a list of zero or more... | 315 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : int , _snake_case : int ) -> list[list[int]]:
'''simple docstring'''
_A = []
create_all_state(1 , _snake_case , _snake_case ... | 315 | 1 |
"""simple docstring"""
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
... | 315 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 10_00 ) -> int:
'''simple docstring'''
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 315 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a = {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiTOn... | 315 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
... | 315 | 1 |
"""simple docstring"""
from collections.abc import Callable
def _snake_case ( _snake_case : Callable[[float], float] , _snake_case : float , _snake_case : float ) -> float:
'''simple docstring'''
_A = a
_A ... | 315 |
"""simple docstring"""
import numpy
class lowercase_ :
'''simple docstring'''
def __init__( self : Dict , _UpperCAmelCase : numpy.ndarray , _UpperCAmelCase : numpy.ndarray ):
_A = input_array
# Random initial weigh... | 315 | 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... | 315 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
a = TypeVar('''T''')
class lowercase_ ( Generic[T] ):
'''simple docstring'''
def __init__( self : Any , _UpperCAmelCase ... | 315 | 1 |
"""simple docstring"""
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
a = {
'''User-Agent''': '''Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'''
''' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582'... | 315 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
a = logging.get_logger(__name__)
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Any , *... | 315 | 1 |
"""simple docstring"""
a = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def _snake_case ( ... | 315 |
"""simple docstring"""
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _snake_case ( _snake_case : str ) -> str:
'''simple docstring'''
return "".join(sorted(_snake_case ) )
def ... | 315 | 1 |
"""simple docstring"""
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDepe... | 315 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transfor... | 315 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import Patchi... | 315 |
"""simple docstring"""
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
a = logging.getLogger(__name__)
a = 50 # max width of layer name... | 315 | 1 |
"""simple docstring"""
class lowercase_ :
'''simple docstring'''
def __init__( self : Optional[Any] ):
_A = {}
def lowerCAmelCase_ ( self : Dict ):
print(self.vertex )
for i in self.vertex:
print(_UpperCAmelCase , ... | 315 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
a = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlati... | 315 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from... | 315 |
"""simple docstring"""
from collections.abc import Callable
def _snake_case ( _snake_case : Callable[[float], float] , _snake_case : float , _snake_case : float ) -> float:
'''simple docstring'''
_A = a
_A ... | 315 | 1 |
"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config impor... | 315 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(__lowerCAmel... | 315 | 1 |
"""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 ... | 315 |
"""simple docstring"""
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDepe... | 315 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from... | 315 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : tuple[int, int] , _snake_case : int ) -> list[tuple[int, int]]:
'''simple docstring'''
_A , _A = position
_A = [
... | 315 | 1 |
"""simple docstring"""
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a = logging.get_logger(__name__)
a = ... | 315 |
"""simple docstring"""
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met... | 315 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
... | 315 |
"""simple docstring"""
def _snake_case ( _snake_case : int , _snake_case : int ) -> bool:
'''simple docstring'''
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 | 1 |
"""simple docstring"""
import math
def _snake_case ( _snake_case : int ) -> bool:
'''simple docstring'''
assert isinstance(_snake_case , _snake_case ) and (
number >= 0
), "'number' must been an int and positive"
if 1 ... | 315 |
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : int ):
... | 315 | 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
... | 315 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
'''xlnet-large-cased''': '''https:/... | 315 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a = logging.get_logger(__name__)
a = {'''vocab_file''': '''sentencepiece.model'''}
... | 315 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBe... | 315 | 1 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _snake_case ( ) -> List[Any]:
'''simple docstring'''
_A = HfArgumentParser(_snake_case )
_A = parser.parse_args_into_... | 315 |
"""simple docstring"""
from manim import *
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Dict ):
_A = Rectangle(height=0.5 , width=0.5 )
_A = Rectangle(height=0.46 , ... | 315 | 1 |
"""simple docstring"""
import heapq as hq
import math
from collections.abc import Iterator
class lowercase_ :
'''simple docstring'''
def __init__( self : int , _UpperCAmelCase : Union[str, Any] ):
_A = str(id_ )
_A = None
... | 315 |
"""simple docstring"""
def _snake_case ( _snake_case : list , _snake_case : int = 0 ) -> list:
'''simple docstring'''
_A = length or len(_snake_case )
_A = False
for i in range(length - 1 ):
... | 315 | 1 |
"""simple docstring"""
a = {str(digit): digit**5 for digit in range(10)}
def _snake_case ( _snake_case : int ) -> int:
'''simple docstring'''
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_snake_case ) )
def _snak... | 315 |
"""simple docstring"""
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils im... | 315 | 1 |
"""simple docstring"""
from collections import defaultdict
from math import ceil, sqrt
def _snake_case ( _snake_case : int = 1_00_00_00 , _snake_case : int = 10 ) -> int:
'''simple docstring'''
_A = defaultdict(_snake_case ... | 315 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : int , _snake_case : int ) -> list[list[int]]:
'''simple docstring'''
_A = []
create_all_state(1 , _snake_case , _snake_case ... | 315 | 1 |
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumen... | 315 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 10_00 ) -> int:
'''simple docstring'''
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 315 | 1 |
"""simple docstring"""
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainin... | 315 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowercase_ ( nn.Module ):
'''simple docstring'''
UpperCAmelCase : int
... | 315 | 1 |
"""simple docstring"""
import copy
import re
class lowercase_ :
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = '''hp'''
UpperCAmelCase : str = {}
UpperCAmelCase : List[Any] = None
@classmethod
def lowerCAmelCase_ ( cls : ... | 315 |
"""simple docstring"""
import numpy
class lowercase_ :
'''simple docstring'''
def __init__( self : Dict , _UpperCAmelCase : numpy.ndarray , _UpperCAmelCase : numpy.ndarray ):
_A = input_array
# Random initial weigh... | 315 | 1 |
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mode... | 315 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
a = TypeVar('''T''')
class lowercase_ ( Generic[T] ):
'''simple docstring'''
def __init__( self : Any , _UpperCAmelCase ... | 315 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
impo... | 315 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
a = logging.get_logger(__name__)
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Any , *... | 315 | 1 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_... | 315 |
"""simple docstring"""
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _snake_case ( _snake_case : str ) -> str:
'''simple docstring'''
return "".join(sorted(_snake_case ) )
def ... | 315 | 1 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
a = '''\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMNLP},
year={2016}... | 315 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transfor... | 315 | 1 |
"""simple docstring"""
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def _snake_case ( ) -> tuple[list[int], int]:
'''simple docstring'''
_A = [randint(-10_00 , 10_00 ) f... | 315 |
"""simple docstring"""
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
a = logging.getLogger(__name__)
a = 50 # max width of layer name... | 315 | 1 |
"""simple docstring"""
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
a = logging.get_logger(__name__)
class ... | 315 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
a = '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlati... | 315 | 1 |
"""simple docstring"""
import numpy as np
from transformers import Pipeline
def _snake_case ( _snake_case : Optional[int] ) -> str:
'''simple docstring'''
_A = np.max(_snake_case , axis=-1 , keepdims=_snake_case )
_A ... | 315 |
"""simple docstring"""
from collections.abc import Callable
def _snake_case ( _snake_case : Callable[[float], float] , _snake_case : float , _snake_case : float ) -> float:
'''simple docstring'''
_A = a
_A ... | 315 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : str ) -> str:
'''simple docstring'''
return "".join(chr(ord(_snake_case ) - 32 ) if 'a' <= char <= 'z' else char for char in word )
if __name__ == "__main__":
from doctest imp... | 315 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(__lowerCAmel... | 315 | 1 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowercase_ ( ... | 315 |
"""simple docstring"""
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDepe... | 315 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : list , _snake_case : int = 0 ) -> list:
'''simple docstring'''
_A = length or len(_snake_case )
_A = False
for i in range(length - 1 ):
... | 315 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : tuple[int, int] , _snake_case : int ) -> list[tuple[int, int]]:
'''simple docstring'''
_A , _A = position
_A = [
... | 315 | 1 |
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