code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def lowerCamelCase ( _snake_case ,_snake_case ,_snake_case ):
UpperCAmelCase__ : Union[str, Any] = ('dense.weight', 'attention.self.q... | 110 |
"""simple docstring"""
def lowerCamelCase ( _snake_case ,_snake_case ):
return int((input_a, input_a).count(0 ) == 0 )
def lowerCamelCase ( ):
assert and_gate(0 ,0 ) == 0
assert and_gate(0 ,1 ) == 0
assert and_gate(1 ,0 ) == 0
assert and_gate(1 ,1 ) == 1
if... | 110 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
... | 110 |
"""simple docstring"""
from typing import Any
class a :
def __init__( self , UpperCamelCase_ ):
UpperCAmelCase__ : Optional[Any] = data
UpperCAmelCase__ : List[str] = None
def __repr__( self ):
retur... | 110 | 1 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class a :
pass
| 110 |
"""simple docstring"""
import random
def lowerCamelCase ( _snake_case ):
UpperCAmelCase__ : Tuple = num - 1
UpperCAmelCase__ : Dict = 0
while s % 2 == 0:
UpperCAmelCase__ : Optional[int] = s // 2
t += 1
for _ in... | 110 | 1 |
"""simple docstring"""
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class a ( lowercase ):
# to overwrite at feature extractactor spec... | 110 |
"""simple docstring"""
from __future__ import annotations
UpperCamelCase__ = tuple[int, int, int]
UpperCamelCase__ = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
UpperCamelCase__ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
# ------------------------... | 110 | 1 |
"""simple docstring"""
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def lowerCamelCase ( _snake_case ,_snake_case ,_snake_case=1024 ,_snake_case=1024 ,_snake_case=False ,**_sn... | 110 |
"""simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
UpperCamelCase__ = logging.getLogger(__name__)
class a :
def __init__( self ):
... | 110 | 1 |
"""simple docstring"""
import re
def lowerCamelCase ( _snake_case ):
if len(re.findall('[ATCG]' ,_snake_case ) ) != len(_snake_case ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' ,'TAGC' ) )
if __name__ == "__main__":
import doctest
doc... | 110 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoForme... | 110 | 1 |
"""simple docstring"""
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
UpperCamelCase__ = '\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Simplification}... | 110 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase__ = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}
try:
if not is_torch... | 110 | 1 |
"""simple docstring"""
import warnings
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
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamel... | 110 |
"""simple docstring"""
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from t... | 110 | 1 |
"""simple docstring"""
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.... | 110 |
"""simple docstring"""
import torch
from diffusers import StableDiffusionPipeline
UpperCamelCase__ = 'path-to-your-trained-model'
UpperCamelCase__ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('cuda')
UpperCamelCase__ = 'A photo of sks dog in a buck... | 110 | 1 |
"""simple docstring"""
from typing import Dict, Iterable, Optional, 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, to_pil_image
from ...image_utils import (
... | 110 |
"""simple docstring"""
from __future__ import annotations
import queue
class a :
def __init__( self , UpperCamelCase_ ):
UpperCAmelCase__ : int = data
UpperCAmelCase__ : Dict = None
UpperCAmelCase__ : Optional... | 110 | 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 .tok... | 110 |
"""simple docstring"""
def lowerCamelCase ( _snake_case ,_snake_case ,_snake_case ):
if len(_snake_case ) != len(_snake_case ):
raise ValueError('The length of profit and weight must be same.' )
if max_weight <= 0:
raise ValueError('max_weight must greater than zero.' )
... | 110 | 1 |
"""simple docstring"""
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COM... | 110 |
"""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 a ( lowercase ... | 110 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, Attn... | 110 |
"""simple docstring"""
from itertools import permutations
def lowerCamelCase ( _snake_case ):
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
UpperCAmelCase__ : List[str] = ... | 110 | 1 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepI... | 110 |
"""simple docstring"""
def lowerCamelCase ( _snake_case ,_snake_case ):
UpperCAmelCase__ : Optional[int] = ''
for i in table:
res += inp[i - 1]
return res
def lowerCamelCase ( _snake_case ):
return data[1:] + data[0]
def lowerCamelCase ( ... | 110 | 1 |
"""simple docstring"""
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusion... | 110 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
class a :
def __init__( self , UpperCamelCase_ ):
UpperCAmelCase__ : str = value
UpperCAmelCase__ : Node | None = None
... | 110 | 1 |
"""simple docstring"""
def lowerCamelCase ( _snake_case ):
UpperCAmelCase__ : Any = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def lowerCamelCase ( _snake_case = 100 ):
UpperCAmelCase__ : List[Any] = ... | 110 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowerCamelCase ( _snake_case ):
UpperCAmelCase__ : int = [
'encoder.version',
'decoder.version',
'model.enco... | 110 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/r... | 110 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params... | 110 | 1 |
"""simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTester... | 110 |
"""simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTester... | 110 | 1 |
"""simple docstring"""
from collections.abc import Callable
class a :
def __init__( self , UpperCamelCase_ = None ):
# Stores actual heap items.
UpperCAmelCase__ : list = []
# Stores indexes of each item for supporting updates and dele... | 110 |
"""simple docstring"""
UpperCamelCase__ = {
'meter': 'm',
'kilometer': 'km',
'megametre': 'Mm',
'gigametre': 'Gm',
'terametre': 'Tm',
'petametre': 'Pm',
'exametre': 'Em',
'zettametre': 'Zm',
'yottametre': 'Ym',
}
# Exponent of the factor(meter)
UpperCamelCase__ ... | 110 | 1 |
"""simple docstring"""
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class a :
def __init__( self , UpperCamelCase_ ):
UpperCAmelCase__ : int = data
UpperCAmelCase__ : Dict = [0x67... | 110 |
"""simple docstring"""
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallba... | 110 | 1 |
"""simple docstring"""
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class a ( lowercase ):
def __init__( self , Upp... | 110 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class a ( lowercase ):
UpperCamelCase : Union[str, Any] = """bert-generation"""
def __init__( self , UpperCamelCase_=50_358 , UpperCamelCase_=1_024 , UpperCamelCase_=24 ... | 110 | 1 |
"""simple docstring"""
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel,... | 110 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandin... | 110 | 1 |
"""simple docstring"""
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
# TODO Update this
UpperCamelCase__ = {
'facebook/esm-1b': 'https://... | 110 |
"""simple docstring"""
def lowerCamelCase ( _snake_case ,_snake_case ):
return int((input_a, input_a).count(0 ) == 0 )
def lowerCamelCase ( ):
assert and_gate(0 ,0 ) == 0
assert and_gate(0 ,1 ) == 0
assert and_gate(1 ,0 ) == 0
assert and_gate(1 ,1 ) == 1
if... | 110 | 1 |
"""simple docstring"""
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
UpperCamelCase__ = HUGGINGFACE_HUB_CACHE
UpperCamelCase__ = 'config.json'
UpperCamelCase__ = 'diffusion_pytorch_model.bin'
UpperCamelCase__ = 'diffusion_flax_model.msg... | 110 |
"""simple docstring"""
from typing import Any
class a :
def __init__( self , UpperCamelCase_ ):
UpperCAmelCase__ : Optional[Any] = data
UpperCAmelCase__ : List[str] = None
def __repr__( self ):
retur... | 110 | 1 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def lowerCamelCase ( _snake_case ):
UpperCAmelCase__ : Tuple = [
'encoder.version',
'decoder.version',
... | 110 |
"""simple docstring"""
import random
def lowerCamelCase ( _snake_case ):
UpperCAmelCase__ : Tuple = num - 1
UpperCAmelCase__ : Dict = 0
while s % 2 == 0:
UpperCAmelCase__ : Optional[int] = s // 2
t += 1
for _ in... | 110 | 1 |
"""simple docstring"""
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 110 |
"""simple docstring"""
from __future__ import annotations
UpperCamelCase__ = tuple[int, int, int]
UpperCamelCase__ = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
UpperCamelCase__ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
# ------------------------... | 110 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
... | 110 |
"""simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
UpperCamelCase__ = logging.getLogger(__name__)
class a :
def __init__( self ):
... | 110 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMode... | 110 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoForme... | 110 | 1 |
"""simple docstring"""
import random
def lowerCamelCase ( _snake_case ):
UpperCAmelCase__ : Tuple = num - 1
UpperCAmelCase__ : Dict = 0
while s % 2 == 0:
UpperCAmelCase__ : Optional[int] = s // 2
t += 1
for _ in... | 110 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase__ = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}
try:
if not is_torch... | 110 | 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
Uppe... | 110 |
"""simple docstring"""
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from t... | 110 | 1 |
"""simple docstring"""
import argparse
import json
import subprocess
def lowerCamelCase ( _snake_case ,_snake_case ):
UpperCAmelCase__ : str = []
UpperCAmelCase__ : Tuple = (
F'''curl -H "Accept: application/vnd.github+json" -H "Authorization: B... | 110 |
"""simple docstring"""
import torch
from diffusers import StableDiffusionPipeline
UpperCamelCase__ = 'path-to-your-trained-model'
UpperCamelCase__ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('cuda')
UpperCamelCase__ = 'A photo of sks dog in a buck... | 110 | 1 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'CarlCochet/trajectory-transformer-halfcheetah-medium-v2': (
'https://huggingface.co/CarlCochet/trajectory-t... | 110 |
"""simple docstring"""
from __future__ import annotations
import queue
class a :
def __init__( self , UpperCamelCase_ ):
UpperCAmelCase__ : int = data
UpperCAmelCase__ : Dict = None
UpperCAmelCase__ : Optional... | 110 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase__ = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'feature_... | 110 |
"""simple docstring"""
def lowerCamelCase ( _snake_case ,_snake_case ,_snake_case ):
if len(_snake_case ) != len(_snake_case ):
raise ValueError('The length of profit and weight must be same.' )
if max_weight <= 0:
raise ValueError('max_weight must greater than zero.' )
... | 110 | 1 |
"""simple docstring"""
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
... | 110 |
"""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 a ( lowercase ... | 110 | 1 |
"""simple docstring"""
def lowerCamelCase ( _snake_case ):
UpperCAmelCase__ : List[str] = [0] * len(_snake_case )
for i in range(1 ,len(_snake_case ) ):
# use last results for better performance - dynamic programming
UpperCAmelCase__ : Optional[int] ... | 110 |
"""simple docstring"""
from itertools import permutations
def lowerCamelCase ( _snake_case ):
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
UpperCAmelCase__ : List[str] = ... | 110 | 1 |
"""simple docstring"""
def lowerCamelCase ( _snake_case = 3 ,_snake_case = 7 ,_snake_case = 1000000 ):
UpperCAmelCase__ : str = 0
UpperCAmelCase__ : Any = 1
for current_denominator in range(1 ,limit + 1 ):
UpperCAmelCase__ : Dict ... | 110 |
"""simple docstring"""
def lowerCamelCase ( _snake_case ,_snake_case ):
UpperCAmelCase__ : Optional[int] = ''
for i in table:
res += inp[i - 1]
return res
def lowerCamelCase ( _snake_case ):
return data[1:] + data[0]
def lowerCamelCase ( ... | 110 | 1 |
"""simple docstring"""
from collections import defaultdict
def lowerCamelCase ( _snake_case ):
UpperCAmelCase__ : int = 1
UpperCAmelCase__ : Tuple = True
for v in tree[start]:
if v not in visited:
ret += dfs(_snake_case )
if ret ... | 110 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
class a :
def __init__( self , UpperCamelCase_ ):
UpperCAmelCase__ : str = value
UpperCAmelCase__ : Node | None = None
... | 110 | 1 |
"""simple docstring"""
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import dis... | 110 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowerCamelCase ( _snake_case ):
UpperCAmelCase__ : int = [
'encoder.version',
'decoder.version',
'model.enco... | 110 | 1 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowerCamelCase ( _snake_case ):
UpperCAmelCase__ : int = [
'encoder.version',
'decoder.version',
'model.enco... | 110 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params... | 110 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ = {
'configuration_upernet': ['UperNetConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except... | 110 |
"""simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTester... | 110 | 1 |
"""simple docstring"""
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data... | 110 |
"""simple docstring"""
UpperCamelCase__ = {
'meter': 'm',
'kilometer': 'km',
'megametre': 'Mm',
'gigametre': 'Gm',
'terametre': 'Tm',
'petametre': 'Pm',
'exametre': 'Em',
'zettametre': 'Zm',
'yottametre': 'Ym',
}
# Exponent of the factor(meter)
UpperCamelCase__ ... | 110 | 1 |
"""simple docstring"""
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from t... | 110 |
"""simple docstring"""
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallba... | 110 | 1 |
"""simple docstring"""
from typing import Any
class a :
def __init__( self , UpperCamelCase_ ):
UpperCAmelCase__ : Optional[Any] = data
UpperCAmelCase__ : List[str] = None
def __repr__( self ):
retur... | 110 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class a ( lowercase ):
UpperCamelCase : Union[str, Any] = """bert-generation"""
def __init__( self , UpperCamelCase_=50_358 , UpperCamelCase_=1_024 , UpperCamelCase_=24 ... | 110 | 1 |
"""simple docstring"""
def lowerCamelCase ( _snake_case = 100 ):
UpperCAmelCase__ : Union[str, Any] = (n * (n + 1) // 2) ** 2
UpperCAmelCase__ : Any = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f... | 110 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandin... | 110 | 1 |
"""simple docstring"""
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
UpperCamelCase__ = (
'This metric will be removed from the libr... | 110 |
"""simple docstring"""
def lowerCamelCase ( _snake_case ,_snake_case ):
return int((input_a, input_a).count(0 ) == 0 )
def lowerCamelCase ( ):
assert and_gate(0 ,0 ) == 0
assert and_gate(0 ,1 ) == 0
assert and_gate(1 ,0 ) == 0
assert and_gate(1 ,1 ) == 1
if... | 110 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class a ( unittest.TestCase ):
... | 110 |
"""simple docstring"""
from typing import Any
class a :
def __init__( self , UpperCamelCase_ ):
UpperCAmelCase__ : Optional[Any] = data
UpperCAmelCase__ : List[str] = None
def __repr__( self ):
retur... | 110 | 1 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a ( lowercase ):
UpperCamelCase : Dict = ["""image_processor""", """tokenizer"""]
UpperCamelCase : Union[str... | 110 |
"""simple docstring"""
import random
def lowerCamelCase ( _snake_case ):
UpperCAmelCase__ : Tuple = num - 1
UpperCAmelCase__ : Dict = 0
while s % 2 == 0:
UpperCAmelCase__ : Optional[int] = s // 2
t += 1
for _ in... | 110 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'facebook/xmod-base': '... | 110 |
"""simple docstring"""
from __future__ import annotations
UpperCamelCase__ = tuple[int, int, int]
UpperCamelCase__ = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
UpperCamelCase__ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
# ------------------------... | 110 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase__ = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiT... | 110 |
"""simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
UpperCamelCase__ = logging.getLogger(__name__)
class a :
def __init__( self ):
... | 110 | 1 |
"""simple docstring"""
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class a ( unittest.TestCase ):
UpperCamelCase : Optional[Any] = JukeboxTokenizer
UpperCamelCase : int = {... | 110 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoForme... | 110 | 1 |
"""simple docstring"""
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
UpperCamelCase__ = logg... | 110 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase__ = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}
try:
if not is_torch... | 110 | 1 |
"""simple docstring"""
UpperCamelCase__ = {
'Pillow': 'Pillow<10.0.0',
'accelerate': 'accelerate>=0.20.3',
'av': 'av==9.2.0',
'beautifulsoup4': 'beautifulsoup4',
'black': 'black~=23.1',
'codecarbon': 'codecarbon==1.2.0',
'cookiecutter': 'cookiecutter==1.7.3',
'dataclasses': ... | 110 |
"""simple docstring"""
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from t... | 110 | 1 |
"""simple docstring"""
import os
from pathlib import Path
def lowerCamelCase ( ):
from torch.utils.cpp_extension import load
UpperCAmelCase__ : str = Path(_snake_case ).resolve().parent.parent.parent / 'kernels' / 'deformable_detr'
UpperCAmelCase__ : List[Any] ... | 110 |
"""simple docstring"""
import torch
from diffusers import StableDiffusionPipeline
UpperCamelCase__ = 'path-to-your-trained-model'
UpperCamelCase__ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('cuda')
UpperCamelCase__ = 'A photo of sks dog in a buck... | 110 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase__ = {'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']... | 110 |
"""simple docstring"""
from __future__ import annotations
import queue
class a :
def __init__( self , UpperCamelCase_ ):
UpperCAmelCase__ : int = data
UpperCAmelCase__ : Dict = None
UpperCAmelCase__ : Optional... | 110 | 1 |
"""simple docstring"""
import flax.linen as nn
import jax
import jax.numpy as jnp
class a ( nn.Module ):
UpperCamelCase : int
UpperCamelCase : jnp.dtype = jnp.floataa
def __snake_case ( self ):
UpperCAmelCase__ : int... | 110 |
"""simple docstring"""
def lowerCamelCase ( _snake_case ,_snake_case ,_snake_case ):
if len(_snake_case ) != len(_snake_case ):
raise ValueError('The length of profit and weight must be same.' )
if max_weight <= 0:
raise ValueError('max_weight must greater than zero.' )
... | 110 | 1 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowerCamelCase ( _snake_case ):
def wrapper(*_snake_case ,**_snake_case ):
UpperCAmelCase__ : Any = ... | 110 |
"""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 a ( lowercase ... | 110 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils i... | 110 |
"""simple docstring"""
from itertools import permutations
def lowerCamelCase ( _snake_case ):
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
UpperCAmelCase__ : List[str] = ... | 110 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ = {
'configuration_roberta': ['ROBERTA_PRETRAINED_CO... | 110 |
"""simple docstring"""
def lowerCamelCase ( _snake_case ,_snake_case ):
UpperCAmelCase__ : Optional[int] = ''
for i in table:
res += inp[i - 1]
return res
def lowerCamelCase ( _snake_case ):
return data[1:] + data[0]
def lowerCamelCase ( ... | 110 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ = {
'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
'tokenization_mvp':... | 110 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
class a :
def __init__( self , UpperCamelCase_ ):
UpperCAmelCase__ : str = value
UpperCAmelCase__ : Node | None = None
... | 110 | 1 |
"""simple docstring"""
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
UpperCamelCase__ = argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--... | 110 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowerCamelCase ( _snake_case ):
UpperCAmelCase__ : int = [
'encoder.version',
'decoder.version',
'model.enco... | 110 | 1 |
"""simple docstring"""
def lowerCamelCase ( _snake_case ):
if len(_snake_case ) < 2:
return collection
def circle_sort_util(_snake_case ,_snake_case ,_snake_case ) -> bool:
UpperCAmelCase__ : Optional[Any] = False
if low == high:
re... | 110 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params... | 110 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase__ = {
'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MA... | 110 |
"""simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTester... | 110 | 1 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
UpperCamelCase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
class a ( low... | 110 |
"""simple docstring"""
UpperCamelCase__ = {
'meter': 'm',
'kilometer': 'km',
'megametre': 'Mm',
'gigametre': 'Gm',
'terametre': 'Tm',
'petametre': 'Pm',
'exametre': 'Em',
'zettametre': 'Zm',
'yottametre': 'Ym',
}
# Exponent of the factor(meter)
UpperCamelCase__ ... | 110 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
... | 110 |
"""simple docstring"""
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallba... | 110 | 1 |
"""simple docstring"""
import math
def lowerCamelCase ( _snake_case ,_snake_case ):
UpperCAmelCase__ : Dict = len(_snake_case )
UpperCAmelCase__ : List[str] = int(math.floor(math.sqrt(_snake_case ) ) )
UpperCAmelCase__ : Optional[Any] ... | 110 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class a ( lowercase ):
UpperCamelCase : Union[str, Any] = """bert-generation"""
def __init__( self , UpperCamelCase_=50_358 , UpperCamelCase_=1_024 , UpperCamelCase_=24 ... | 110 | 1 |
"""simple docstring"""
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def lowerCamelCase ( _snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case = None ,_snake_case = None ... | 110 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandin... | 110 | 1 |
"""simple docstring"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
UpperCamelCase__ = {'UserAgent': UserAgent().random}
def lowerCamelCase ( _snake_case ):
UpperCAmelCase__ : List[str] = ... | 110 |
"""simple docstring"""
def lowerCamelCase ( _snake_case ,_snake_case ):
return int((input_a, input_a).count(0 ) == 0 )
def lowerCamelCase ( ):
assert and_gate(0 ,0 ) == 0
assert and_gate(0 ,1 ) == 0
assert and_gate(1 ,0 ) == 0
assert and_gate(1 ,1 ) == 1
if... | 110 | 1 |
"""simple docstring"""
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
class a ( lowercase ):
UpperCamelCase ... | 110 |
"""simple docstring"""
from typing import Any
class a :
def __init__( self , UpperCamelCase_ ):
UpperCAmelCase__ : Optional[Any] = data
UpperCAmelCase__ : List[str] = None
def __repr__( self ):
retur... | 110 | 1 |
"""simple docstring"""
UpperCamelCase__ = 'Tobias Carryer'
from time import time
class a :
def __init__( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=int(time() ) ): # noqa: B008
UpperCAmelCase__ : Optional[Any... | 110 |
"""simple docstring"""
import random
def lowerCamelCase ( _snake_case ):
UpperCAmelCase__ : Tuple = num - 1
UpperCAmelCase__ : Dict = 0
while s % 2 == 0:
UpperCAmelCase__ : Optional[int] = s // 2
t += 1
for _ in... | 110 | 1 |
"""simple docstring"""
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_... | 110 |
"""simple docstring"""
from __future__ import annotations
UpperCamelCase__ = tuple[int, int, int]
UpperCamelCase__ = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
UpperCamelCase__ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
# ------------------------... | 110 | 1 |
"""simple docstring"""
import fire
from utils import calculate_rouge, save_json
def lowerCamelCase ( _snake_case ,_snake_case ,_snake_case=None ,**_snake_case ):
UpperCAmelCase__ : List[str] = [x.strip() for x in open(_snake_case ).readlines()]
UpperCAmelCase__ : ... | 110 |
"""simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
UpperCamelCase__ = logging.getLogger(__name__)
class a :
def __init__( self ):
... | 110 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class a ( lowercase ):
UpperCamelCase : Optional[int] = (IPNDMScheduler,)
UpperCamelCase : Optional[int] ... | 110 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoForme... | 110 | 1 |
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils i... | 110 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase__ = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}
try:
if not is_torch... | 110 | 1 |
"""simple docstring"""
from random import shuffle
import tensorflow as tf
from numpy import array
def lowerCamelCase ( _snake_case ,_snake_case ):
UpperCAmelCase__ : Optional[int] = int(_snake_case )
assert noofclusters < len(_snake_case )
# Find out the dimensionalit... | 110 |
"""simple docstring"""
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from t... | 110 | 1 |
"""simple docstring"""
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import Accelerat... | 110 |
"""simple docstring"""
import torch
from diffusers import StableDiffusionPipeline
UpperCamelCase__ = 'path-to-your-trained-model'
UpperCamelCase__ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('cuda')
UpperCamelCase__ = 'A photo of sks dog in a buck... | 110 | 1 |
"""simple docstring"""
from __future__ import annotations
UpperCamelCase__ = tuple[int, int, int]
UpperCamelCase__ = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
UpperCamelCase__ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
# ------------------------... | 110 |
"""simple docstring"""
from __future__ import annotations
import queue
class a :
def __init__( self , UpperCamelCase_ ):
UpperCAmelCase__ : int = data
UpperCAmelCase__ : Dict = None
UpperCAmelCase__ : Optional... | 110 | 1 |
"""simple docstring"""
def lowerCamelCase ( _snake_case ):
UpperCAmelCase__ : Union[str, Any] = [0] * len(_snake_case )
UpperCAmelCase__ : Any = []
UpperCAmelCase__ : Any = []
UpperCAmelCase__ : Union[str, Any] = ... | 110 |
"""simple docstring"""
def lowerCamelCase ( _snake_case ,_snake_case ,_snake_case ):
if len(_snake_case ) != len(_snake_case ):
raise ValueError('The length of profit and weight must be same.' )
if max_weight <= 0:
raise ValueError('max_weight must greater than zero.' )
... | 110 | 1 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def lowerCamelCase ( _snake_case ):
UpperCAmelCase__ : Optional[Any] = [
'encoder.version',
'decoder.version',
'mo... | 110 |
"""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 a ( lowercase ... | 110 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_v... | 110 |
"""simple docstring"""
from itertools import permutations
def lowerCamelCase ( _snake_case ):
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
UpperCAmelCase__ : List[str] = ... | 110 | 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_determ... | 110 |
"""simple docstring"""
def lowerCamelCase ( _snake_case ,_snake_case ):
UpperCAmelCase__ : Optional[int] = ''
for i in table:
res += inp[i - 1]
return res
def lowerCamelCase ( _snake_case ):
return data[1:] + data[0]
def lowerCamelCase ( ... | 110 | 1 |
"""simple docstring"""
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCam... | 110 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
class a :
def __init__( self , UpperCamelCase_ ):
UpperCAmelCase__ : str = value
UpperCAmelCase__ : Node | None = None
... | 110 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
def lowerCamelCase ( ):
UpperCAmelCase__ : dict[int, int] = {}
UpperCAmelCase__ : Any = 2
while True:
UpperCAmelCase__ : Optional[int] ... | 110 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowerCamelCase ( _snake_case ):
UpperCAmelCase__ : int = [
'encoder.version',
'decoder.version',
'model.enco... | 110 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
UpperCamelCase__ = 'https://www.indeed.co.in/jobs?q=mobile+app+development&l='
def lowerCamelCase ( _snake_case = "mumbai" ):
UpperCAmelCase__ : ... | 110 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params... | 110 | 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 h... | 110 |
"""simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTester... | 110 | 1 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from tran... | 110 |
"""simple docstring"""
UpperCamelCase__ = {
'meter': 'm',
'kilometer': 'km',
'megametre': 'Mm',
'gigametre': 'Gm',
'terametre': 'Tm',
'petametre': 'Pm',
'exametre': 'Em',
'zettametre': 'Zm',
'yottametre': 'Ym',
}
# Exponent of the factor(meter)
UpperCamelCase__ ... | 110 | 1 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import FocalNetConfig
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_co... | 110 |
"""simple docstring"""
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallba... | 110 | 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
if is_fla... | 110 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class a ( lowercase ):
UpperCamelCase : Union[str, Any] = """bert-generation"""
def __init__( self , UpperCamelCase_=50_358 , UpperCamelCase_=1_024 , UpperCamelCase_=24 ... | 110 | 1 |
"""simple docstring"""
UpperCamelCase__ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def lowerCamelCase ( _snake_case ):
# Make sure the supplied data is a bytes-like object
if not isinstance(_snake_case ,_snake_case ):
UpperCAmelCase__ : Tuple ... | 110 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandin... | 110 | 1 |
"""simple docstring"""
def lowerCamelCase ( _snake_case ,_snake_case ):
UpperCAmelCase__ : Union[str, Any] = len(_snake_case )
UpperCAmelCase__ : Dict = len(_snake_case )
UpperCAmelCase__ : str = [[False for _ in range(m + 1 )] fo... | 110 |
"""simple docstring"""
def lowerCamelCase ( _snake_case ,_snake_case ):
return int((input_a, input_a).count(0 ) == 0 )
def lowerCamelCase ( ):
assert and_gate(0 ,0 ) == 0
assert and_gate(0 ,1 ) == 0
assert and_gate(1 ,0 ) == 0
assert and_gate(1 ,1 ) == 1
if... | 110 | 1 |
"""simple docstring"""
import argparse
UpperCamelCase__ = 'docs/source/_static/js/custom.js'
def lowerCamelCase ( _snake_case ):
with open(_snake_case ,encoding='utf-8' ,newline='\n' ) as f:
UpperCAmelCase__ : List[str] = f.readlines()
UpperCAmelCase__... | 110 |
"""simple docstring"""
from typing import Any
class a :
def __init__( self , UpperCamelCase_ ):
UpperCAmelCase__ : Optional[Any] = data
UpperCAmelCase__ : List[str] = None
def __repr__( self ):
retur... | 110 | 1 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def lowerCamelCase ( ):
UpperCAmelCase__ : Dict = ArgumentParser(
description=(
'... | 110 |
"""simple docstring"""
import random
def lowerCamelCase ( _snake_case ):
UpperCAmelCase__ : Tuple = num - 1
UpperCAmelCase__ : Dict = 0
while s % 2 == 0:
UpperCAmelCase__ : Optional[int] = s // 2
t += 1
for _ in... | 110 | 1 |
"""simple docstring"""
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def lowerCamelCase ( _snake_case ,_snake_case=None ):
UpperCAmelCase__ : int = None
if token is not None:
... | 110 |
"""simple docstring"""
from __future__ import annotations
UpperCamelCase__ = tuple[int, int, int]
UpperCamelCase__ = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
UpperCamelCase__ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
# ------------------------... | 110 | 1 |
"""simple docstring"""
def lowerCamelCase ( ):
UpperCAmelCase__ : Union[str, Any] = 0
for i in range(1 ,1001 ):
total += i**i
return str(_snake_case )[-10:]
if __name__ == "__main__":
print(solution())
| 110 |
"""simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
UpperCamelCase__ = logging.getLogger(__name__)
class a :
def __init__( self ):
... | 110 | 1 |
"""simple docstring"""
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def lowerCamelCase ( _snake_case ,_snake_case ,_snake_case = "x" ,_snake_case = 10**-10 ,_snake_case = 1 ,):
UpperCAmelCase__ : int = symbols(_snake_case )
... | 110 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoForme... | 110 | 1 |
"""simple docstring"""
from __future__ import annotations
import queue
class a :
def __init__( self , UpperCamelCase_ ):
UpperCAmelCase__ : int = data
UpperCAmelCase__ : Dict = None
UpperCAmelCase__ : Optional... | 110 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase__ = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}
try:
if not is_torch... | 110 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class a ( lowercase ):
UpperCamelCase : Union[str, Any] = """bert-generation"""
def __init__( self , UpperCamelCase_=50_358 , UpperCamelCase_=1_024 , UpperCamelCase_=24 ... | 110 |
"""simple docstring"""
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from t... | 110 | 1 |
"""simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
UpperCamelCase__ = logging.getLogger(__name__)
class a :
def __init__( self ):
... | 110 |
"""simple docstring"""
import torch
from diffusers import StableDiffusionPipeline
UpperCamelCase__ = 'path-to-your-trained-model'
UpperCamelCase__ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('cuda')
UpperCamelCase__ = 'A photo of sks dog in a buck... | 110 | 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
from ...... | 110 |
"""simple docstring"""
from __future__ import annotations
import queue
class a :
def __init__( self , UpperCamelCase_ ):
UpperCAmelCase__ : int = data
UpperCAmelCase__ : Dict = None
UpperCAmelCase__ : Optional... | 110 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerT... | 110 |
"""simple docstring"""
def lowerCamelCase ( _snake_case ,_snake_case ,_snake_case ):
if len(_snake_case ) != len(_snake_case ):
raise ValueError('The length of profit and weight must be same.' )
if max_weight <= 0:
raise ValueError('max_weight must greater than zero.' )
... | 110 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_si... | 110 |
"""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 a ( lowercase ... | 110 | 1 |
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