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 |
|---|---|---|---|---|
from ...configuration_utils import PretrainedConfig
a : Dict = {
'google/tapas-base-finetuned-sqa': (
'https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'
),
'google/tapas-base-finetuned-wtq': (
'https://huggingface.co/google/tap... | 354 |
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available... | 82 | 0 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn a... | 355 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_... | 82 | 0 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transfo... | 356 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
a : Optional[int] = {
'text_branch': 'text_model',
'audio_branch': 'audio_model.audio_encoder',
'attn': 'attention.self',
'se... | 82 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Optional[Any] = logging.get_logger(__name__)
a : Tuple = {
'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}
class ... | 357 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
a : List[str] = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
... | 82 | 0 |
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def lowerCAmelCase_ (lowerCAmelCase__: str , lowerCAmelCase__: str , lowerCAmelCase__: Optional[str] = None ):
"""simple docstring"""
i... | 358 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
a : Dict = datasets.logging.get_logger(__name__)
a : Any = '\\n@InProceedings{moosavi2019minimum,\n ... | 82 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Optional[Any] = logging.get_logger(__name__)
a : Dict = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resol... | 359 |
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin... | 82 | 0 |
from math import isqrt, loga
def lowerCAmelCase_ (lowerCAmelCase__: int ):
"""simple docstring"""
UpperCAmelCase_: Dict = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
... | 360 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : Optional[Any] = {
'facebook/encodec_24khz': 'https://huggingface.co/facebo... | 82 | 0 |
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoMo... | 361 |
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 import ConfigT... | 82 | 0 |
def lowerCAmelCase_ (lowerCAmelCase__: float , lowerCAmelCase__: float , lowerCAmelCase__: int ):
"""simple docstring"""
if principal <= 0:
raise Exception("""Principal borrowed must be > 0""" )
if rate_per_annum < 0:
raise E... | 362 |
from __future__ import annotations
def lowerCAmelCase_ (lowerCAmelCase__: list[float] ):
"""simple docstring"""
UpperCAmelCase_: Union[str, Any] = 0.00
UpperCAmelCase_: List[str] = 0
for resistor in resistors:
... | 82 | 0 |
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 ConfigTester... | 363 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
a : str ... | 82 | 0 |
def lowerCAmelCase_ (lowerCAmelCase__: List[str] , lowerCAmelCase__: Union[str, Any] ):
"""simple docstring"""
UpperCAmelCase_: str = [1]
for i in range(2 , lowerCAmelCase__ ):
factorials.append(factorials[-1] * i )
assert 0 ... | 364 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
a : Tuple = {'tokenization_tapex': ['TapexTokenizer']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
a : List[Any] = _LazyModule(__name__, globals()['_... | 82 | 0 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
... | 365 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_to... | 82 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
a : Dict = '2020.9.26'
a : Optional[Any] = 'xcodz-dot, cclaus, dhruvmanila'
def lowerCAmelCase_ (lowerCAmelCase__: float , lowerCAmelCase__: float , ... | 366 |
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 : List[... | 82 | 0 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transf... | 367 |
from __future__ import annotations
def lowerCAmelCase_ (lowerCAmelCase__: list[int | float] , lowerCAmelCase__: int , lowerCAmelCase__: int ):
"""simple docstring"""
if len(lowerCAmelCase__ ) == 0:
raise ValueError("""fin... | 82 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
a : List[str] = {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
'albert-large-v1': 'https://hugg... | 368 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
a : Optional[Any] = logging.get_logger(__na... | 82 | 0 |
"""simple docstring"""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import Schedule... | 369 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
f... | 82 | 0 |
def lowerCAmelCase_ (lowerCAmelCase__: list , lowerCAmelCase__: list , lowerCAmelCase__: int , lowerCAmelCase__: int , lowerCAmelCase__: int ):
"""simple docstring"""
if index == number_of_items:
return 0
UpperCAmelCas... | 370 |
from collections import defaultdict
class _a :
def __init__(self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> List[str]:
UpperCAmelCase_: Optional[int] = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
... | 82 | 0 |
from __future__ import annotations
def lowerCAmelCase_ (lowerCAmelCase__: list[int | float] , lowerCAmelCase__: int , lowerCAmelCase__: int ):
"""simple docstring"""
if len(lowerCAmelCase__ ) == 0:
raise ValueError("""fin... | 371 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _a ( unittest.TestCase , _lowerCAmelCase ):
def __snake_case (self ) -> Optional[int]:
UpperCAmelCase_: Optional[int] = l... | 82 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class snake_case__ :
"""simple docstring"""
low... | 83 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path"... | 83 | 1 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
'''simple docstring'''
while second != 0:
snake_case : int = first & second
first ^= second
snake_case : Dict = c << 1
return first
... | 83 |
'''simple docstring'''
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixi... | 83 | 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
lowercase__... | 83 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from di... | 83 | 1 |
'''simple docstring'''
# Algorithm for the pigeonhole sorting
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> Optional[Any]:
'''simple docstring'''
snake_case : int = min(SCREAMING_SNAKE_CASE__ ) # min() finds the minimum value
snake_case : int ... | 83 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mod... | 83 | 1 |
'''simple docstring'''
import os
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = "matrix.txt" ) -> int:
'''simple docstring'''
with open(os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ ) ) as in_file:
snake_case : Optional[Any] ... | 83 |
'''simple docstring'''
import requests
lowercase__ = "" # <-- Put your OpenWeatherMap appid here!
lowercase__ = "https://api.openweathermap.org/data/2.5/"
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = "Chicago" , SCREAMING_SNAKE_CASE__ = APPID ) -> dict:
'''sim... | 83 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class snake_case__ ( ... | 83 |
'''simple docstring'''
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartFor... | 83 | 1 |
'''simple docstring'''
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test impor... | 83 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = 50 ) -> int:
'''simple docstring'''
snake_case : Union[str, Any] = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(r... | 83 | 1 |
'''simple docstring'''
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
lowercase__ = TypeVar("KT")
lowercase__ = TypeVar("VT")
class snake_case__ ( Generic[KT, VT] ):
"""simple docstring"""
def __... | 83 |
'''simple docstring'''
class snake_case__ :
"""simple docstring"""
def __init__( self : List[Any] , UpperCamelCase__ : list[int] ) -> None:
"""simple docstring"""
snake_case : List[Any] = len(UpperCamelCase... | 83 | 1 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> int:
'''simple docstring'''
if num < 0:
raise ValueError('''Number should not be negative.''' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
... | 83 |
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ ... | 83 | 1 |
'''simple docstring'''
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate ... | 83 |
'''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(_... | 83 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_t... | 83 |
'''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 _UpperCamelCase... | 83 | 1 |
'''simple docstring'''
import random
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> List[str]:
'''simple docstring'''
snake_case : Optional[int] = a[left_index]
snake_case : Dict = left... | 83 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import MutableSequence
class snake_case__ :
"""simple docstring"""
def __init__( self : Dict , UpperCamelCase__ : int , UpperCamelCase__ : MutableSeque... | 83 | 1 |
'''simple docstring'''
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def _UpperCa... | 83 |
'''simple docstring'''
import os
def _UpperCamelCase ( ) -> List[Any]:
'''simple docstring'''
snake_case : int = os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE__ ) , '''num.txt''' )
with open(SCREAMING_SNAKE_CASE__ ) as file_hand:
return str(sum(int(... | 83 | 1 |
'''simple docstring'''
class snake_case__ :
"""simple docstring"""
def __init__( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
snake_case : int = ''''''
snake_case : List[str] = ''''''... | 83 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = 1 , SCREAMING_SNAKE_CASE__ = 1000 ) -> int:
'''simple docstring'''
snake_case : Dict = 1
snake_case : str = 0
for divide_by_number in range(SCREAMING_SNAKE_CASE__ , digit +... | 83 | 1 |
'''simple docstring'''
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configurati... | 83 |
'''simple docstring'''
from collections.abc import Generator
def _UpperCamelCase ( ) -> Generator[int, None, None]:
'''simple docstring'''
snake_case ,snake_case : Tuple = 0, 1
while True:
snake_case ,snake_case : List[Any] = b, a + b
yie... | 83 | 1 |
'''simple docstring'''
from typing import List
import numpy as np
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> int:
'''simple docstring'''
snake_case : Any = {key: len(SCREAMING_SNAKE_CASE__ ) for key, value in gen_kwargs.items() if isinstance(SCREAMING_SNAK... | 83 |
'''simple docstring'''
print((lambda quine: quine % quine)("print((lambda quine: quine %% quine)(%r))"))
| 83 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ = {
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InformerConf... | 83 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax ... | 83 | 1 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def _UpperCamelCase ( SCREAMING_SNAK... | 83 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
... | 83 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowercase__ = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_available():
... | 83 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case__ ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowerCamelCase = ["""image_processor""", """to... | 83 | 1 |
'''simple docstring'''
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
... | 83 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> int:
'''simple docstring'''
if num < 0:
raise ValueError('''Number should not be negative.''' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
... | 83 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class snake_case__ :
"""simple docstring"""
lowerCamelCase = 42
lowerCamelCase ... | 83 |
'''simple docstring'''
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ... | 83 | 1 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}
class ... | 83 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> list:
'''simple docstring'''
snake_case : Any = len(SCREAMING_SNAKE_CASE__ )
for i in range(1 , SCREAMING_SNAKE_CASE__ ):
snake_case : Optional[int] = collection[i]
snak... | 83 | 1 |
'''simple docstring'''
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S",... | 83 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path"... | 83 | 1 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> float:
'''simple docstring'''
return 10 - x * x
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> float:
'''simple docstring'''
if equation(SCREAMING_S... | 83 |
'''simple docstring'''
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixi... | 83 | 1 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
lo... | 83 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from di... | 83 | 1 |
'''simple docstring'''
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_avail... | 83 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mod... | 83 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
lowercase__ = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MA... | 83 |
'''simple docstring'''
import requests
lowercase__ = "" # <-- Put your OpenWeatherMap appid here!
lowercase__ = "https://api.openweathermap.org/data/2.5/"
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = "Chicago" , SCREAMING_SNAKE_CASE__ = APPID ) -> dict:
'''sim... | 83 | 1 |
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy a... | 83 |
'''simple docstring'''
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartFor... | 83 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class snake_case__ ( metaclass=__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowerCamelCase = ["""note_seq"""]
def __init__( self : List[Any] , *UpperCam... | 83 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = 50 ) -> int:
'''simple docstring'''
snake_case : Union[str, Any] = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(r... | 83 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import loggin... | 83 |
'''simple docstring'''
class snake_case__ :
"""simple docstring"""
def __init__( self : List[Any] , UpperCamelCase__ : list[int] ) -> None:
"""simple docstring"""
snake_case : List[Any] = len(UpperCamelCase... | 83 | 1 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> bool:
'''simple docstring'''
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("Program to check whether a number is a Perfect number or... | 83 |
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ ... | 83 | 1 |
'''simple docstring'''
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to... | 83 |
'''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(_... | 83 | 1 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = 50 ) -> int:
'''simple docstring'''
snake_case : Union[str, Any] = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(r... | 83 |
'''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 _UpperCamelCase... | 83 | 1 |
'''simple docstring'''
from math import factorial
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = 100 ) -> int:
'''simple docstring'''
return sum(map(SCREAMING_SNAKE_CASE__ , str(factorial(SCREAMING_SNAKE_CASE__ ) ) ) )
if __name__ == "__main__":
print(solution(int... | 83 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import MutableSequence
class snake_case__ :
"""simple docstring"""
def __init__( self : Dict , UpperCamelCase__ : int , UpperCamelCase__ : MutableSeque... | 83 | 1 |
'''simple docstring'''
lowercase__ = {str(digit): digit**5 for digit in range(1_0)}
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> int:
'''simple docstring'''
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(SCREAMING_SNAKE_CASE__ ) )
def _UpperCamelCase ... | 83 |
'''simple docstring'''
import os
def _UpperCamelCase ( ) -> List[Any]:
'''simple docstring'''
snake_case : int = os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE__ ) , '''num.txt''' )
with open(SCREAMING_SNAKE_CASE__ ) as file_hand:
return str(sum(int(... | 83 | 1 |
'''simple docstring'''
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
lowercase__ = logging.get_logger(__name__)
... | 83 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = 1 , SCREAMING_SNAKE_CASE__ = 1000 ) -> int:
'''simple docstring'''
snake_case : Dict = 1
snake_case : str = 0
for divide_by_number in range(SCREAMING_SNAKE_CASE__ , digit +... | 83 | 1 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = 1000 ) -> int:
'''simple docstring'''
snake_case : Optional[Any] = 3
snake_case : Union[str, Any] = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15... | 83 |
'''simple docstring'''
from collections.abc import Generator
def _UpperCamelCase ( ) -> Generator[int, None, None]:
'''simple docstring'''
snake_case ,snake_case : Tuple = 0, 1
while True:
snake_case ,snake_case : List[Any] = b, a + b
yie... | 83 | 1 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> tuple[float, float]:
'''simple docstring'''
if not len(SCREAMING_SNAKE_CASE__ ) == len(SCREAMING_SNAKE_CASE__ ) == 3:
raise ValueError('''Please enter a valid equation.''' )
if eq... | 83 |
'''simple docstring'''
print((lambda quine: quine % quine)("print((lambda quine: quine %% quine)(%r))"))
| 83 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"andreasmadsen/efficient_... | 83 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax ... | 83 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ ... | 83 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
... | 83 | 1 |
'''simple docstring'''
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_... | 83 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case__ ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowerCamelCase = ["""image_processor""", """to... | 83 | 1 |
'''simple docstring'''
from PIL import Image
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Image:
'''simple docstring'''
snake_case : Any = (259 * (level + 255)) / (255 * (259 - level))
def contrast(SCREAMING_SNAKE_CASE__ ) -> ... | 83 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> int:
'''simple docstring'''
if num < 0:
raise ValueError('''Number should not be negative.''' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
... | 83 | 1 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus imp... | 83 |
'''simple docstring'''
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ... | 83 | 1 |
'''simple docstring'''
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from t... | 83 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> list:
'''simple docstring'''
snake_case : Any = len(SCREAMING_SNAKE_CASE__ )
for i in range(1 , SCREAMING_SNAKE_CASE__ ):
snake_case : Optional[int] = collection[i]
snak... | 83 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enabl... | 83 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path"... | 83 | 1 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
lowercase__ = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowercase__ = [ord(letter) for letter in string.ascii_lo... | 83 |
'''simple docstring'''
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixi... | 83 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
"uclanlp/visualbert... | 83 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from di... | 83 | 1 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax ... | 83 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mod... | 83 | 1 |
'''simple docstring'''
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAva... | 83 |
'''simple docstring'''
import requests
lowercase__ = "" # <-- Put your OpenWeatherMap appid here!
lowercase__ = "https://api.openweathermap.org/data/2.5/"
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = "Chicago" , SCREAMING_SNAKE_CASE__ = APPID ) -> dict:
'''sim... | 83 | 1 |
'''simple docstring'''
import os
def _UpperCamelCase ( ) -> List[Any]:
'''simple docstring'''
snake_case : int = os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE__ ) , '''num.txt''' )
with open(SCREAMING_SNAKE_CASE__ ) as file_hand:
return str(sum(int(... | 83 |
'''simple docstring'''
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartFor... | 83 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
lowercase__ = "\\n\n"
lowercase__ = "\nPerplexity (PPL) is one of the most common metri... | 83 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = 50 ) -> int:
'''simple docstring'''
snake_case : Union[str, Any] = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(r... | 83 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowercase__ = {
"configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", "LongT5OnnxConfig"],
}
try:
... | 83 |
'''simple docstring'''
class snake_case__ :
"""simple docstring"""
def __init__( self : List[Any] , UpperCamelCase__ : list[int] ) -> None:
"""simple docstring"""
snake_case : List[Any] = len(UpperCamelCase... | 83 | 1 |
'''simple docstring'''
lowercase__ = 9.8_06_65
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = g ) -> float:
'''simple docstring'''
if fluid_density <= 0:
raise ValueError('''Impossible fluid density''' )
i... | 83 |
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ ... | 83 | 1 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_... | 83 |
'''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(_... | 83 | 1 |
'''simple docstring'''
import numpy as np
class snake_case__ :
"""simple docstring"""
def __init__( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
snake_case : Optional[Any] = (0, 0)
snake_... | 83 |
'''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 _UpperCamelCase... | 83 | 1 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = 1 , SCREAMING_SNAKE_CASE__ = 1000 ) -> int:
'''simple docstring'''
snake_case : Dict = 1
snake_case : str = 0
for divide_by_number in range(SCREAMING_SNAKE_CASE__ , digit +... | 83 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import MutableSequence
class snake_case__ :
"""simple docstring"""
def __init__( self : Dict , UpperCamelCase__ : int , UpperCamelCase__ : MutableSeque... | 83 | 1 |
'''simple docstring'''
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ... | 83 |
'''simple docstring'''
import os
def _UpperCamelCase ( ) -> List[Any]:
'''simple docstring'''
snake_case : int = os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE__ ) , '''num.txt''' )
with open(SCREAMING_SNAKE_CASE__ ) as file_hand:
return str(sum(int(... | 83 | 1 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
... | 83 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = 1 , SCREAMING_SNAKE_CASE__ = 1000 ) -> int:
'''simple docstring'''
snake_case : Dict = 1
snake_case : str = 0
for divide_by_number in range(SCREAMING_SNAKE_CASE__ , digit +... | 83 | 1 |
'''simple docstring'''
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
lowercase__ = 5_0_0_0_0_0
lowercase__ , lowercase__ = os.path.split(__file__)
lowercase__ = os.path.join(RESULTS_... | 83 |
'''simple docstring'''
from collections.abc import Generator
def _UpperCamelCase ( ) -> Generator[int, None, None]:
'''simple docstring'''
snake_case ,snake_case : Tuple = 0, 1
while True:
snake_case ,snake_case : List[Any] = b, a + b
yie... | 83 | 1 |
'''simple docstring'''
class snake_case__ :
"""simple docstring"""
def __init__( self : Tuple , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Any , UpperCamelCase__ : str ) -> Optional[Any]:
"""simple... | 83 |
'''simple docstring'''
print((lambda quine: quine % quine)("print((lambda quine: quine %% quine)(%r))"))
| 83 | 1 |
'''simple docstring'''
from __future__ import annotations
from statistics import mean
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> list[int]:
'''simple docstring'''
snake_case : str = [0] * no_of_pr... | 83 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax ... | 83 | 1 |
'''simple docstring'''
lowercase__ = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .features import Ar... | 83 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
... | 83 | 1 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> bool:
'''simple docstring'''
if num < 0:
return False
snake_case : int = num
snake_case : int = 0
while num > 0:
snake_case : Optional[Any] = rev_num *... | 83 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case__ ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowerCamelCase = ["""image_processor""", """to... | 83 | 1 |
'''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(_... | 83 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> int:
'''simple docstring'''
if num < 0:
raise ValueError('''Number should not be negative.''' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
... | 83 | 1 |
'''simple docstring'''
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> Dict:
'''simple docstring'''
for param in module.parameters():
snake_case : Optional[int] = False
de... | 83 |
'''simple docstring'''
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ... | 83 | 1 |
'''simple docstring'''
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
lowercase__ = {
"facebook/maskformer-swin-base-... | 83 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> list:
'''simple docstring'''
snake_case : Any = len(SCREAMING_SNAKE_CASE__ )
for i in range(1 , SCREAMING_SNAKE_CASE__ ):
snake_case : Optional[int] = collection[i]
snak... | 83 | 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
lowercase__ = logging.get_logger(__name__)
lowercase__ = ... | 83 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path"... | 83 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class snake_case__ ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def __init__( self : Dict , ... | 83 |
'''simple docstring'''
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixi... | 83 | 1 |
'''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 TFModelTesterMi... | 83 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from di... | 83 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.... | 83 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mod... | 83 | 1 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> None:
'''simple docstring'''
snake_case : Dict = generate_pascal_triangle(SCREAMING_SNAKE_CASE__ )
for row_idx in range(SCREAMING_SNAKE_CASE__ ):
# Print left spaces
for _ in range(num_rows... | 83 |
'''simple docstring'''
import requests
lowercase__ = "" # <-- Put your OpenWeatherMap appid here!
lowercase__ = "https://api.openweathermap.org/data/2.5/"
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = "Chicago" , SCREAMING_SNAKE_CASE__ = APPID ) -> dict:
'''sim... | 83 | 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.conf... | 83 |
'''simple docstring'''
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartFor... | 83 | 1 |
'''simple docstring'''
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassification... | 83 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = 50 ) -> int:
'''simple docstring'''
snake_case : Union[str, Any] = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(r... | 83 | 1 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
lowercase__ = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
def _UpperCamel... | 83 |
'''simple docstring'''
class snake_case__ :
"""simple docstring"""
def __init__( self : List[Any] , UpperCamelCase__ : list[int] ) -> None:
"""simple docstring"""
snake_case : List[Any] = len(UpperCamelCase... | 83 | 1 |
'''simple docstring'''
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartFor... | 83 |
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ ... | 83 | 1 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = 1000 ) -> int:
'''simple docstring'''
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 83 |
'''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(_... | 83 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ = {
"configuration_megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
}
try:
if not is_torch_ava... | 83 |
'''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 _UpperCamelCase... | 83 | 1 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
... | 83 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import MutableSequence
class snake_case__ :
"""simple docstring"""
def __init__( self : Dict , UpperCamelCase__ : int , UpperCamelCase__ : MutableSeque... | 83 | 1 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
f... | 83 |
'''simple docstring'''
import os
def _UpperCamelCase ( ) -> List[Any]:
'''simple docstring'''
snake_case : int = os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE__ ) , '''num.txt''' )
with open(SCREAMING_SNAKE_CASE__ ) as file_hand:
return str(sum(int(... | 83 | 1 |
'''simple docstring'''
import cmath
import math
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> complex:
'''simple docstring'''
snake_case : Union[str, Any] = math.radians(S... | 83 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ = 1 , SCREAMING_SNAKE_CASE__ = 1000 ) -> int:
'''simple docstring'''
snake_case : Dict = 1
snake_case : str = 0
for divide_by_number in range(SCREAMING_SNAKE_CASE__ , digit +... | 83 | 1 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> Optional[int]:
'''simple docstring'''
snake_case : Any = [
... | 83 |
'''simple docstring'''
from collections.abc import Generator
def _UpperCamelCase ( ) -> Generator[int, None, None]:
'''simple docstring'''
snake_case ,snake_case : Tuple = 0, 1
while True:
snake_case ,snake_case : List[Any] = b, a + b
yie... | 83 | 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_t... | 83 |
'''simple docstring'''
print((lambda quine: quine % quine)("print((lambda quine: quine %% quine)(%r))"))
| 83 | 1 |
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