code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase : Dict = {'configuration_xlnet': ['XLNET_P... | 267 |
import argparse
from collections import defaultdict
import yaml
lowerCAmelCase : Dict = 'docs/source/en/_toctree.yml'
def A_ ( a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = defaultdict(a )
for doc in model_doc:
... | 253 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase : Dict ={
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Confi... | 262 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
lowerCAmelCase : List[Any] = datasets.load_iris()
lowerCAmelCase : List[str] = np.array(data['data'])
lowerCAmelCase : Any = np.array(dat... | 253 | 0 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
__a = False
class lowercase__( unittest.TestCase ):
"""simple docstring"""
def ... | 30 |
import os
from typing import Dict, List, Tuple, TypeVar, Union
lowerCAmelCase : str = TypeVar('T')
lowerCAmelCase : Optional[Any] = Union[List[T], Tuple[T, ...]]
lowerCAmelCase : str = Union[T, List[T], Dict[str, T]]
lowerCAmelCase : Union[str, Any] = Uni... | 253 | 0 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
cl... | 195 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase : List[str] = logging.get_logger(__name__)
class _A ( __magic_name__ , __magic_nam... | 253 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
if number > 0:
raise ValueError('''input must be a negative integer''' )
A : Dict = len(bin(snake_case__ )[3:] )
A : List[Any] ... | 3 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__magic_name__)
class _A ( __magic_name__):
# `task` is not a ClassVar since we want it to be part of the `asdic... | 253 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
lowercase_ = logging.get_logger(__name__)
def lowercase ( lowerCAmelCase__ : str ) -> List[Any]:
if isinstance(low... | 45 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Tuple = logging.get_logger(__name__)
lowerCAmelCase : str = ... | 253 | 0 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowerCamelCase__ :
"""simple docstring"""
pass
| 115 |
lowerCAmelCase : str = '0.21.0'
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches... | 253 | 0 |
'''simple docstring'''
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class snake_case__ ( UpperCamelCase):
a_ = '''M-CLIP'''
def __init__( self : List[Any] , _A : List[str]=10_24 , _A : List[... | 304 |
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,
AutoencoderKL,
DDIMS... | 253 | 0 |
def lowerCAmelCase_ (lowerCAmelCase__: str , lowerCAmelCase__: Dict = False ):
"""simple docstring"""
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCAmelCase_: Union[str, Any] = F'Expected string as input, found {type(lo... | 147 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
lowerCAmelCase : Any = logging.get_logger(__n... | 253 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__A : Optional[Any] = logging.get_logger(__name__)
__A : str = {
'nielsr/canine-s': ... | 120 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import t... | 253 | 0 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see... | 49 |
import os
import numpy
import onnx
def A_ ( a , a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = a.name
SCREAMING_SNAKE_CASE_ : Dict = b.name
SCREAMING_SNAKE_CASE_ : Optional[int] = ''
SCREAMING_S... | 253 | 0 |
'''simple docstring'''
import logging
import os
from .state import PartialState
class lowerCAmelCase__ ( logging.LoggerAdapter ):
"""simple docstring"""
@staticmethod
def UpperCAmelCase__ ( __SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Any:
"""simple d... | 267 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase : Dict = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutL... | 253 | 0 |
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
_UpperCAmelCase : L... | 262 |
from jiwer import compute_measures
import datasets
lowerCAmelCase : Tuple = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation... | 253 | 0 |
def a ( snake_case__: Optional[int] , snake_case__: Union[str, Any] ):
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
raise ValueError('''iterations must be defined as integers''' )
if not isinstance(snake_case... | 30 |
def A_ ( a ):
"""simple docstring"""
return "".join(chr(ord(a ) - 3_2 ) if 'a' <= char <= 'z' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 253 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
... | 195 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowerCAmelCase : Any = logging.getLogger(__name__)
class _A ( __magic_name__):
def __init__( self , _SCREAMING_... | 253 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
A : Dict = list(range(len(snake_case__ ) ) )
A : Tuple ... | 3 |
import os
def A_ ( a = "matrix.txt" ):
"""simple docstring"""
with open(os.path.join(os.path.dirname(a ) , a ) ) as in_file:
SCREAMING_SNAKE_CASE_ : Dict = in_file.read()
SCREAMING_SNAKE_CASE_ : Dict = [[int(a ) fo... | 253 | 0 |
"""simple docstring"""
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class __lowerCAmelCase :
'''simple docstring... | 45 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from to... | 253 | 0 |
"""simple docstring"""
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
... | 115 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def A_ ( a , a , a = 1 / sqrt(2 ) ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = tau * frequency / samplerate
SCREAMING_SNAKE_CASE_ : List[Any] ... | 253 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_UpperCamelCase : Optional[int] = {
'configuration_squeezebert': [
'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 304 |
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
lowerCAmelCase : ... | 253 | 0 |
import math
def lowerCAmelCase_ (lowerCAmelCase__: Dict ):
"""simple docstring"""
UpperCAmelCase_: List[str] = 0
UpperCAmelCase_: Dict = 0
while num > 0:
UpperCAmelCase_: Dict = num % 8
UpperCAmelCase_: ... | 147 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase : Any = ... | 253 | 0 |
'''simple docstring'''
from torch import nn
def UpperCamelCase_ ( A__ : int ):
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()... | 120 |
lowerCAmelCase : Optional[Any] = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
lowerCAmelCase ... | 253 | 0 |
from __future__ import annotations
from collections import namedtuple
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
__a = namedtuple('''result''' , '''name value''' )
if (voltage, current, power).count(0 ) != 1:
raise ValueError(... | 49 |
import math
def A_ ( a , a = 0 , a = 0 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = end or len(a )
for i in range(a , a ):
SCREAMING_SNAKE_CASE_ : List[Any] = i
SCREAMING_SNA... | 253 | 0 |
'''simple docstring'''
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
fro... | 267 |
import argparse
from collections import defaultdict
import yaml
lowerCAmelCase : Dict = 'docs/source/en/_toctree.yml'
def A_ ( a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = defaultdict(a )
for doc in model_doc:
... | 253 | 0 |
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..uti... | 262 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
lowerCAmelCase : List[Any] = datasets.load_iris()
lowerCAmelCase : List[str] = np.array(data['data'])
lowerCAmelCase : Any = np.array(dat... | 253 | 0 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def a ( snake_case__: List[str] = 3 ):
'''simple docstring'''
if isinstance(snake_case__ , snake_case__ ):
raise... | 30 |
import os
from typing import Dict, List, Tuple, TypeVar, Union
lowerCAmelCase : str = TypeVar('T')
lowerCAmelCase : Optional[Any] = Union[List[T], Tuple[T, ...]]
lowerCAmelCase : str = Union[T, List[T], Dict[str, T]]
lowerCAmelCase : Union[str, Any] = Uni... | 253 | 0 |
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
enable_full_determinism()
class ... | 195 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase : List[str] = logging.get_logger(__name__)
class _A ( __magic_name__ , __magic_nam... | 253 | 0 |
'''simple docstring'''
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
... | 3 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__magic_name__)
class _A ( __magic_name__):
# `task` is not a ClassVar since we want it to be part of the `asdic... | 253 | 0 |
"""simple docstring"""
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the... | 45 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Tuple = logging.get_logger(__name__)
lowerCAmelCase : str = ... | 253 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
UpperCAmelCase : str = False
class lowerCamelCase__ (... | 115 |
lowerCAmelCase : str = '0.21.0'
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches... | 253 | 0 |
'''simple docstring'''
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
... | 304 |
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,
AutoencoderKL,
DDIMS... | 253 | 0 |
def lowerCAmelCase_ (lowerCAmelCase__: str , lowerCAmelCase__: List[str] ):
"""simple docstring"""
if not (isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and isinstance(lowerCAmelCase__ , lowerCAmelCase__ )):
raise ValueError("""longest_... | 147 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
lowerCAmelCase : Any = logging.get_logger(__n... | 253 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,... | 120 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import t... | 253 | 0 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
__a = StableDiffusionPipeline.from_pretrained(_... | 49 |
import os
import numpy
import onnx
def A_ ( a , a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = a.name
SCREAMING_SNAKE_CASE_ : Dict = b.name
SCREAMING_SNAKE_CASE_ : Optional[int] = ''
SCREAMING_S... | 253 | 0 |
'''simple docstring'''
UpperCAmelCase : Optional[Any] = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transforme... | 267 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase : Dict = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutL... | 253 | 0 |
from statistics import mean, stdev
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ = 3 )-> Any:
lowerCAmelCase_ : Optional[Any] = min(lowerCAmelCase_ )
lowerCAmelCase_ : Any = max(lowerCAmelCase_ )
# normalize data
ret... | 262 |
from jiwer import compute_measures
import datasets
lowerCAmelCase : Tuple = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation... | 253 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_... | 30 |
def A_ ( a ):
"""simple docstring"""
return "".join(chr(ord(a ) - 3_2 ) if 'a' <= char <= 'z' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 253 | 0 |
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub_u... | 195 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowerCAmelCase : Any = logging.getLogger(__name__)
class _A ( __magic_name__):
def __init__( self , _SCREAMING_... | 253 | 0 |
'''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 : Union[str, Any] = logging.get_logger(__name... | 3 |
import os
def A_ ( a = "matrix.txt" ):
"""simple docstring"""
with open(os.path.join(os.path.dirname(a ) , a ) ) as in_file:
SCREAMING_SNAKE_CASE_ : Dict = in_file.read()
SCREAMING_SNAKE_CASE_ : Dict = [[int(a ) fo... | 253 | 0 |
"""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 ... | 45 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from to... | 253 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self : List[Any] , UpperCamelCase : Tuple = None... | 115 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def A_ ( a , a , a = 1 / sqrt(2 ) ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = tau * frequency / samplerate
SCREAMING_SNAKE_CASE_ : List[Any] ... | 253 | 0 |
'''simple docstring'''
from __future__ import annotations
import os
from collections.abc import Mapping
_UpperCamelCase : Any = tuple[int, int]
class snake_case__ :
def __init__( self : int , _A : List[str] , _A : Optional[Any] ) ... | 304 |
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
lowerCAmelCase : ... | 253 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a : str = {
'configuration_efficientformer': [
'EFFICIENTFORMER_PRETRAINED_CONFIG_ARCH... | 147 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase : Any = ... | 253 | 0 |
'''simple docstring'''
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batc... | 120 |
lowerCAmelCase : Optional[Any] = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
lowerCAmelCase ... | 253 | 0 |
import torch
from diffusers import DiffusionPipeline
class _A ( __UpperCAmelCase ):
def __init__( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Tuple):
'''simple docstring'''
super()._... | 49 |
import math
def A_ ( a , a = 0 , a = 0 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = end or len(a )
for i in range(a , a ):
SCREAMING_SNAKE_CASE_ : List[Any] = i
SCREAMING_SNA... | 253 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import MutableSequence
class lowerCAmelCase__ :
"""simple docstring"""
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : ... | 267 |
import argparse
from collections import defaultdict
import yaml
lowerCAmelCase : Dict = 'docs/source/en/_toctree.yml'
def A_ ( a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = defaultdict(a )
for doc in model_doc:
... | 253 | 0 |
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class snake_case__( UpperCAmelCase__ ):
'''simple docstring'''
def __lt__( self , __lowercase ) -> List[str]:
... | 262 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
lowerCAmelCase : List[Any] = datasets.load_iris()
lowerCAmelCase : List[str] = np.array(data['data'])
lowerCAmelCase : Any = np.array(dat... | 253 | 0 |
import os
import sys
import unittest
__a = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_model_... | 30 |
import os
from typing import Dict, List, Tuple, TypeVar, Union
lowerCAmelCase : str = TypeVar('T')
lowerCAmelCase : Optional[Any] = Union[List[T], Tuple[T, ...]]
lowerCAmelCase : str = Union[T, List[T], Dict[str, T]]
lowerCAmelCase : Union[str, Any] = Uni... | 253 | 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
from ...test_mo... | 195 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase : List[str] = logging.get_logger(__name__)
class _A ( __magic_name__ , __magic_nam... | 253 | 0 |
'''simple docstring'''
class A :
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
A : List[Any] = name
A ... | 3 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__magic_name__)
class _A ( __magic_name__):
# `task` is not a ClassVar since we want it to be part of the `asdic... | 253 | 0 |
"""simple docstring"""
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowercase ( lowerCAmelCase__ : str ) -> Dict:
__a = {}... | 45 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Tuple = logging.get_logger(__name__)
lowerCAmelCase : str = ... | 253 | 0 |
"""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
UpperCAmelCase : int = logging.get_logger(__name__)
UpperCAmel... | 115 |
lowerCAmelCase : str = '0.21.0'
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches... | 253 | 0 |
'''simple docstring'''
_UpperCamelCase : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
... | 304 |
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,
AutoencoderKL,
DDIMS... | 253 | 0 |
from __future__ import annotations
import math
import random
from typing import Any
class _a :
def __init__(self ) -> str:
UpperCAmelCase_: list[Any] = []
UpperCAmelCase_: int = 0
UpperCAmelCase_: int = 0
d... | 147 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
lowerCAmelCase : Any = logging.get_logger(__n... | 253 | 0 |
'''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_... | 120 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import t... | 253 | 0 |
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class _A :
def __init__( self : Any , __SCREAMING_SNAKE_CASE : Union[str, Any]):
'''simple do... | 49 |
import os
import numpy
import onnx
def A_ ( a , a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = a.name
SCREAMING_SNAKE_CASE_ : Dict = b.name
SCREAMING_SNAKE_CASE_ : Optional[int] = ''
SCREAMING_S... | 253 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
UpperCAmelCase : Tuple = {
'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_AR... | 267 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase : Dict = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutL... | 253 | 0 |
import unittest
from knapsack import knapsack as k
class snake_case__( unittest.TestCase ):
'''simple docstring'''
def lowercase_ ( self ) -> int:
lowerCAmelCase_ : Dict = 0
lowerCAmelCase_ : Dict = [0]
... | 262 |
from jiwer import compute_measures
import datasets
lowerCAmelCase : Tuple = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation... | 253 | 0 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipeli... | 30 |
def A_ ( a ):
"""simple docstring"""
return "".join(chr(ord(a ) - 3_2 ) if 'a' <= char <= 'z' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 253 | 0 |
from ... import PretrainedConfig
UpperCAmelCase = {
'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : int = NEZHA_PRETRAINED_CONFIG_ARCH... | 195 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowerCAmelCase : Any = logging.getLogger(__name__)
class _A ( __magic_name__):
def __init__( self , _SCREAMING_... | 253 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
'''huggingface/time-series-transformer-tourism-monthly''': (
... | 254 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {
'''configuration_trajectory_transformer''': [
'''TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Tr... | 254 | 1 |
'''simple docstring'''
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _A ( __SCREAMING_SNAKE_CASE ):
_SCREAMING_SNAKE_CASE : List[str] = CustomTokenizer
pass
| 254 |
'''simple docstring'''
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def lowercase_ ( lowerCAmelCase__ : str , lowerCAmelCase__ : str , lowerCAmelCase__ : Optional[str] = None ):
... | 254 | 1 |
'''simple docstring'''
_UpperCamelCase = 6_5521
def lowercase_ ( lowerCAmelCase__ : str ):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = 1
__UpperCAmelCase : int = 0
for plain_chr in plain_text:
__Upper... | 254 |
'''simple docstring'''
from collections.abc import Sequence
def lowercase_ ( lowerCAmelCase__ : Sequence[float] , lowerCAmelCase__ : float ):
"""simple docstring"""
return sum(c * (x**i) for i, c in enumerate(lowerCAmelCase__ ) )
def ... | 254 | 1 |
'''simple docstring'''
from math import isqrt, loga
def lowercase_ ( lowerCAmelCase__ : int ):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
... | 254 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
_UpperCamelCase = {'''vocab_file''': '''vocab.txt''', '''tokenizer_f... | 254 | 1 |
'''simple docstring'''
def lowercase_ ( lowerCAmelCase__ : int ):
"""simple docstring"""
__UpperCAmelCase : list[list[int]] = [[0 for _ in range(lowerCAmelCase__ )] for _ in range(m + 1 )]
for i in range(m + 1 ):
__UpperCAmelCa... | 254 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def lowercase_ ( lowerCAmelCase__ : Union[str, Any] ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = FileLock(str(tmpdi... | 254 | 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 impo... | 254 |
'''simple docstring'''
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
_UpperCamelCase = ver... | 254 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def lowercase_ ( lowerCAmelCase__ : Dict ):
"""simple docstring"""
if "img_encoder.pos_embed" in... | 254 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
_UpperCamelCase = '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''
_UpperCamelCase = '''
Args:
... | 254 | 1 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils imp... | 254 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {
'''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}
try:
if n... | 254 | 1 |
'''simple docstring'''
def lowercase_ ( lowerCAmelCase__ : str , lowerCAmelCase__ : list[str] ):
"""simple docstring"""
__UpperCAmelCase : Tuple = """"""
for word_or_phrase in separated:
if not isinstance(lowerCAmelCase__ , lowe... | 254 |
'''simple docstring'''
def lowercase_ ( lowerCAmelCase__ : str ):
"""simple docstring"""
return credit_card_number.startswith(("""34""", """35""", """37""", """4""", """5""", """6""") )
def lowercase_ ( lowerCAmelCase__ : str ):
"... | 254 | 1 |
'''simple docstring'''
from math import pi, sqrt
def lowercase_ ( lowerCAmelCase__ : float ):
"""simple docstring"""
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" )
... | 254 |
'''simple docstring'''
import qiskit
def lowercase_ ( lowerCAmelCase__ : int , lowerCAmelCase__ : int ):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum... | 254 | 1 |
'''simple docstring'''
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 ( __SCREAMING_SNAKE_C... | 254 |
'''simple docstring'''
def lowercase_ ( lowerCAmelCase__ : int ):
"""simple docstring"""
__UpperCAmelCase : list[list[int]] = [[0 for _ in range(lowerCAmelCase__ )] for _ in range(m + 1 )]
for i in range(m + 1 ):
__UpperCAmelCa... | 254 | 1 |
'''simple docstring'''
import qiskit
def lowercase_ ( lowerCAmelCase__ : int , lowerCAmelCase__ : int ):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum... | 254 |
'''simple docstring'''
_UpperCamelCase = '''
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
_UpperCamelCase = [{'''type''': '''code''', '''content''': INSTALL... | 254 | 1 |
'''simple docstring'''
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
... | 254 |
'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .token... | 254 | 1 |
'''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 SchedulerMixin
... | 254 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCamelCase = {
'''configuration_bridgetower''': [
'''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Brid... | 254 | 1 |
'''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,
)
f... | 254 |
'''simple docstring'''
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.vers... | 254 | 1 |
'''simple docstring'''
def lowercase_ ( lowerCAmelCase__ : int , lowerCAmelCase__ : int ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
__UpperCAmelCase : Dict = ... | 254 |
'''simple docstring'''
from pathlib import Path
import fire
def lowercase_ ( lowerCAmelCase__ : str , lowerCAmelCase__ : str , lowerCAmelCase__ : int ):
"""simple docstring"""
__UpperCAmelCase : List[str] = Path(lowerCAmelCase... | 254 | 1 |
'''simple docstring'''
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipeli... | 254 |
'''simple docstring'''
def lowercase_ ( lowerCAmelCase__ : int = 50 ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_l... | 254 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase_ ( lowerCAmelCase__ : List[Any] , lowerCAmelCase__ ... | 254 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenizat... | 254 | 1 |
'''simple docstring'''
import string
import numpy
def lowercase_ ( lowerCAmelCase__ : int , lowerCAmelCase__ : int ):
"""simple docstring"""
return b if a == 0 else greatest_common_divisor(b % a , lowerCAmelCase__ )
class _A :
... | 254 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {
'''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}
try:
if not is_t... | 254 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import TypedDict
class _A ( __SCREAMING_SNAKE_CASE ):
_SCREAMING_SNAKE_CASE : str
_SCREAMING_SNAKE_CASE : int
def lowercase_ ( lowerCAmelCase__ : str ):
"""simple docstring""... | 254 |
'''simple docstring'''
import math
import os
import sys
def lowercase_ ( lowerCAmelCase__ : str ):
"""simple docstring"""
__UpperCAmelCase : Any = """"""
try:
with open(lowerCAmelCase__ , """rb""" ) as binary_file:
_... | 254 | 1 |
'''simple docstring'''
from pathlib import Path
import fire
def lowercase_ ( lowerCAmelCase__ : str , lowerCAmelCase__ : str , lowerCAmelCase__ : int ):
"""simple docstring"""
__UpperCAmelCase : List[str] = Path(lowerCAmelCase... | 254 |
'''simple docstring'''
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_in... | 254 | 1 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transf... | 254 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...te... | 254 | 1 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
... | 254 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {
'''configuration_trajectory_transformer''': [
'''TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Tr... | 254 | 1 |
'''simple docstring'''
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
... | 254 |
'''simple docstring'''
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def lowercase_ ( lowerCAmelCase__ : str , lowerCAmelCase__ : str , lowerCAmelCase__ : Optional[str] = None ):
... | 254 | 1 |
'''simple docstring'''
from collections.abc import Sequence
def lowercase_ ( lowerCAmelCase__ : Sequence[float] , lowerCAmelCase__ : float ):
"""simple docstring"""
return sum(c * (x**i) for i, c in enumerate(lowerCAmelCase__ ) )
def ... | 254 |
'''simple docstring'''
from collections.abc import Sequence
def lowercase_ ( lowerCAmelCase__ : Sequence[float] , lowerCAmelCase__ : float ):
"""simple docstring"""
return sum(c * (x**i) for i, c in enumerate(lowerCAmelCase__ ) )
def ... | 254 | 1 |
'''simple docstring'''
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class _A ( __SCREAMING_SNAKE_CASE ):
_SCREAMING_SNAKE_CASE : Tuple = ["image_processor", "tokenizer"]
_SCREAMING_SNAKE_CASE : List[Any]... | 254 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
_UpperCamelCase = {'''vocab_file''': '''vocab.txt''', '''tokenizer_f... | 254 | 1 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transfo... | 254 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def lowercase_ ( lowerCAmelCase__ : Union[str, Any] ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = FileLock(str(tmpdi... | 254 | 1 |
'''simple docstring'''
def lowercase_ ( lowerCAmelCase__ : list[list[int | float]] ):
"""simple docstring"""
__UpperCAmelCase : str = len(lowerCAmelCase__ )
__UpperCAmelCase : Union[str, Any] = len(matrix[0] )
__UpperCAmelC... | 254 |
'''simple docstring'''
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
_UpperCamelCase = ver... | 254 | 1 |
'''simple docstring'''
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stab... | 254 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
_UpperCamelCase = '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''
_UpperCamelCase = '''
Args:
... | 254 | 1 |
'''simple docstring'''
def lowercase_ ( lowerCAmelCase__ : int = 50 ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_l... | 254 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {
'''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}
try:
if n... | 254 | 1 |
'''simple docstring'''
import math
def lowercase_ ( lowerCAmelCase__ : float , lowerCAmelCase__ : float ):
"""simple docstring"""
if initial_intensity < 0:
raise ValueError("""The value of intensity cannot be negative""" )
# handling... | 254 |
'''simple docstring'''
def lowercase_ ( lowerCAmelCase__ : str ):
"""simple docstring"""
return credit_card_number.startswith(("""34""", """35""", """37""", """4""", """5""", """6""") )
def lowercase_ ( lowerCAmelCase__ : str ):
"... | 254 | 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,
)
... | 254 |
'''simple docstring'''
import qiskit
def lowercase_ ( lowerCAmelCase__ : int , lowerCAmelCase__ : int ):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum... | 254 | 1 |
'''simple docstring'''
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.proce... | 254 |
'''simple docstring'''
def lowercase_ ( lowerCAmelCase__ : int ):
"""simple docstring"""
__UpperCAmelCase : list[list[int]] = [[0 for _ in range(lowerCAmelCase__ )] for _ in range(m + 1 )]
for i in range(m + 1 ):
__UpperCAmelCa... | 254 | 1 |
'''simple docstring'''
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn... | 254 |
'''simple docstring'''
_UpperCamelCase = '''
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
_UpperCamelCase = [{'''type''': '''code''', '''content''': INSTALL... | 254 | 1 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTes... | 254 |
'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .token... | 254 | 1 |
'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchau... | 254 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCamelCase = {
'''configuration_bridgetower''': [
'''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Brid... | 254 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaV... | 254 |
'''simple docstring'''
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.vers... | 254 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
Stabl... | 254 |
'''simple docstring'''
from pathlib import Path
import fire
def lowercase_ ( lowerCAmelCase__ : str , lowerCAmelCase__ : str , lowerCAmelCase__ : int ):
"""simple docstring"""
__UpperCAmelCase : List[str] = Path(lowerCAmelCase... | 254 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class _A :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = 0 ) -> None:
'''simple docstring'''
__UpperCAmelCase , __Upper... | 254 |
'''simple docstring'''
def lowercase_ ( lowerCAmelCase__ : int = 50 ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_l... | 254 | 1 |
'''simple docstring'''
def lowercase_ ( lowerCAmelCase__ : int ):
"""simple docstring"""
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) or number < 0:
raise ValueError("""Input must be a non-negative integer""" )
__UpperCAmelCase... | 254 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenizat... | 254 | 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/L... | 254 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {
'''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}
try:
if not is_t... | 254 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.