'''simple docstring'''


import timeit

import numpy as np

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





def       lowerCAmelCase					(		UpperCamelCase__				:   int       ):


	"""simple docstring"""
	def wrapper(*UpperCamelCase__				:   Tuple    ,					**UpperCamelCase__				:   List[Any]       ):
		__UpperCAmelCase							=			timeit.default_timer()
		__UpperCAmelCase							=			func(*UpperCamelCase__    ,					**UpperCamelCase__       )
		__UpperCAmelCase							=			timeit.default_timer() - starttime
		return delta

	__UpperCAmelCase							=			func.__name__

	return wrapper





def       lowerCAmelCase					(		UpperCamelCase__				:   dict    ,					UpperCamelCase__				:   str=1_0_0    ,					UpperCamelCase__				:   Union[str, Any]=None       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[]
	__UpperCAmelCase							=			seq_shapes or {}
	for i in range(UpperCamelCase__       ):
		__UpperCAmelCase							=			{}
		for col_id, (k, v) in enumerate(features.items()       ):
			if isinstance(UpperCamelCase__    ,					_ArrayXD       ):
				__UpperCAmelCase							=			np.random.rand(*v.shape       ).astype(v.dtype       )
			elif isinstance(UpperCamelCase__    ,					datasets.Value       ):
				if v.dtype == "string":
					__UpperCAmelCase							=			'''The small grey turtle was surprisingly fast when challenged.'''
				else:
					__UpperCAmelCase							=			np.random.randint(1_0    ,					size=1       ).astype(v.dtype       ).item()
			elif isinstance(UpperCamelCase__    ,					datasets.Sequence       ):
				while isinstance(UpperCamelCase__    ,					datasets.Sequence       ):
					__UpperCAmelCase							=			v.feature
				__UpperCAmelCase							=			seq_shapes[k]
				__UpperCAmelCase							=			np.random.rand(*UpperCamelCase__       ).astype(v.dtype       )
			__UpperCAmelCase							=			data

		dummy_data.append((i, example)       )

	return dummy_data





def       lowerCAmelCase					(		UpperCamelCase__				:   Tuple    ,					UpperCamelCase__				:   Union[str, Any]    ,					UpperCamelCase__				:   Dict=1_0_0    ,					UpperCamelCase__				:   Optional[Any]=None       ):


	"""simple docstring"""
	__UpperCAmelCase							=			generate_examples(UpperCamelCase__    ,					num_examples=UpperCamelCase__    ,					seq_shapes=UpperCamelCase__       )

	with ArrowWriter(features=UpperCamelCase__    ,					path=UpperCamelCase__       ) as writer:
		for key, record in dummy_data:
			__UpperCAmelCase							=			features.encode_example(UpperCamelCase__       )
			writer.write(UpperCamelCase__       )

		__UpperCAmelCase				,			__UpperCAmelCase							=			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}."""       )

	__UpperCAmelCase							=			datasets.Dataset.from_file(filename=UpperCamelCase__    ,					info=datasets.DatasetInfo(features=UpperCamelCase__       )       )

	return dataset

'''simple docstring'''


from ...configuration_utils import PretrainedConfig






class 						A   (   UpperCAmelCase   ):
							a_        =      '''bert-generation'''





							def __init__(      self				:							str     ,		__a				:							str=5_0_3_5_8     ,		__a				:							int=1_0_2_4     ,		__a				:							Optional[Any]=2_4     ,		__a				:							Any=1_6     ,		__a				:							int=4_0_9_6     ,		__a				:							Any="gelu"     ,		__a				:							Union[str, Any]=0.1     ,		__a				:							Any=0.1     ,		__a				:							Union[str, Any]=5_1_2     ,		__a				:							int=0.0_2     ,		__a				:							str=1e-12     ,		__a				:							List[str]=0     ,		__a				:							Optional[int]=2     ,		__a				:							Tuple=1     ,		__a				:							str="absolute"     ,		__a				:							Optional[Any]=True     ,		**__a				:							Tuple     ,		)							->	Any:
								super().__init__(pad_token_id=__a     ,		bos_token_id=__a     ,		eos_token_id=__a     ,		**__a	)

								__UpperCAmelCase							=			vocab_size
								__UpperCAmelCase							=			hidden_size
								__UpperCAmelCase							=			num_hidden_layers
								__UpperCAmelCase							=			num_attention_heads
								__UpperCAmelCase							=			hidden_act
								__UpperCAmelCase							=			intermediate_size
								__UpperCAmelCase							=			hidden_dropout_prob
								__UpperCAmelCase							=			attention_probs_dropout_prob
								__UpperCAmelCase							=			max_position_embeddings
								__UpperCAmelCase							=			initializer_range
								__UpperCAmelCase							=			layer_norm_eps
								__UpperCAmelCase							=			position_embedding_type
								__UpperCAmelCase							=			use_cache
