
"""simple docstring"""






import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD





def __A						(   a_ :							Optional[int]     )->							List[str]:




		'''simple docstring'''






		def wrapper(*a_ :							List[str]		,		**a_ :							Dict     ):
				SCREAMING_SNAKE_CASE       :     int				= timeit.default_timer()
				SCREAMING_SNAKE_CASE       :     Any				= func(*a_		,		**a_     )
				SCREAMING_SNAKE_CASE       :     str				= timeit.default_timer() - starttime
				return delta

		SCREAMING_SNAKE_CASE       :     int				= func.__name__

		return wrapper





def __A						(   a_ :							dict		,		a_ :							str=1_00		,		a_ :							Optional[int]=None     )->							Tuple:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= []
		SCREAMING_SNAKE_CASE       :     Optional[int]				= seq_shapes or {}
		for i in range(a_     ):
				SCREAMING_SNAKE_CASE       :     List[Any]				= {}
				for col_id, (k, v) in enumerate(features.items()     ):
						if isinstance(a_		,		_ArrayXD     ):
								SCREAMING_SNAKE_CASE       :     Optional[Any]				= np.random.rand(*v.shape     ).astype(v.dtype     )
						elif isinstance(a_		,		datasets.Value     ):
								if v.dtype == "string":
										SCREAMING_SNAKE_CASE       :     str				= '''The small grey turtle was surprisingly fast when challenged.'''
								else:
										SCREAMING_SNAKE_CASE       :     Union[str, Any]				= np.random.randint(10		,		size=1     ).astype(v.dtype     ).item()
						elif isinstance(a_		,		datasets.Sequence     ):
								while isinstance(a_		,		datasets.Sequence     ):
										SCREAMING_SNAKE_CASE       :     Optional[int]				= v.feature
								SCREAMING_SNAKE_CASE       :     List[Any]				= seq_shapes[k]
								SCREAMING_SNAKE_CASE       :     Any				= np.random.rand(*a_     ).astype(v.dtype     )
						SCREAMING_SNAKE_CASE       :     Tuple				= data

				dummy_data.append((i, example)     )

		return dummy_data





def __A						(   a_ :							List[Any]		,		a_ :							Dict		,		a_ :							List[str]=1_00		,		a_ :							Union[str, Any]=None     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[Any]				= generate_examples(a_		,		num_examples=a_		,		seq_shapes=a_     )

		with ArrowWriter(features=a_		,		path=a_     ) as writer:
				for key, record in dummy_data:
						SCREAMING_SNAKE_CASE       :     int				= features.encode_example(a_     )
						writer.write(a_     )

				SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Any				= writer.finalize()

		if not num_final_examples == num_examples:
				raise ValueError(
				    F"Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."     )

		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= datasets.Dataset.from_file(filename=a_		,		info=datasets.DatasetInfo(features=a_     )     )

		return dataset







"""simple docstring"""






from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__ :       Tuple						     =			{
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__ :       Union[str, Any]						     =			[
					    "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "MCTCTForCTC",
					    "MCTCTModel",
					    "MCTCTPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
					from .feature_extraction_mctct import MCTCTFeatureExtractor
					from .processing_mctct import MCTCTProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
					import sys

					lowerCamelCase__ :       Tuple						     =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





