



'''simple docstring'''


import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class      __UpperCamelCase       (_UpperCAmelCase			,   _UpperCAmelCase			,   _UpperCAmelCase			):







	@register_to_config
	def __init__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase = False ,				)    ->    Optional[int]:
							'''simple docstring'''






							super().__init__()

							lowercase        =							nn.Embedding(_lowerCAmelCase ,				_lowerCAmelCase						)

							lowercase        =							nn.Embedding(_lowerCAmelCase ,				_lowerCAmelCase						)
							lowercase        =							False

							lowercase        =							nn.Dropout(p=_lowerCAmelCase						)

							lowercase        =							TaConfig(
							    vocab_size=_lowerCAmelCase ,				d_model=_lowerCAmelCase ,				num_heads=_lowerCAmelCase ,				d_kv=_lowerCAmelCase ,				d_ff=_lowerCAmelCase ,				dropout_rate=_lowerCAmelCase ,				feed_forward_proj=_lowerCAmelCase ,				is_decoder=_lowerCAmelCase ,				is_encoder_decoder=_lowerCAmelCase ,				)

							lowercase        =							nn.ModuleList()
							for lyr_num in range(_lowerCAmelCase						):
													lowercase        =							TaBlock(_lowerCAmelCase						)
													self.encoders.append(_lowerCAmelCase						)

							lowercase        =							TaLayerNorm(_lowerCAmelCase						)
							lowercase        =							nn.Dropout(p=_lowerCAmelCase						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase						)    ->    str:
							'''simple docstring'''






							lowercase        =							self.token_embedder(_lowerCAmelCase						)

							lowercase        =							encoder_input_tokens.shape[1]
							lowercase        =							torch.arange(_lowerCAmelCase ,				device=encoder_input_tokens.device						)
							x += self.position_encoding(_lowerCAmelCase						)

							lowercase        =							self.dropout_pre(_lowerCAmelCase						)

							# inverted the attention mask
							lowercase        =							encoder_input_tokens.size()
							lowercase        =							self.get_extended_attention_mask(_lowerCAmelCase ,				_lowerCAmelCase						)

							for lyr in self.encoders:
													lowercase        =							lyr(_lowerCAmelCase ,				_lowerCAmelCase						)[0]
							lowercase        =							self.layer_norm(_lowerCAmelCase						)

							return self.dropout_post(_lowerCAmelCase						), encoder_inputs_mask







'''simple docstring'''


from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							HfArgumentParser(lowercase_						)
						lowercase        =							parser.parse_args_into_dataclasses()[0]
						lowercase        =							TensorFlowBenchmark(args=lowercase_						)
						try:
												lowercase        =							parser.parse_args_into_dataclasses()[0]
						except ValueError as e:
												lowercase        =							"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
												lowercase        =							""" """.join(str(lowercase_						).split(""" """						)[:-1]						)
												lowercase        =							""""""
												lowercase        =							eval(str(lowercase_						).split(""" """						)[-1]						)
												lowercase        =							[]
												for arg in depreciated_args:
																		# arg[2:] removes '--'
																		if arg[2:] in TensorFlowBenchmark.deprecated_args:
																								# arg[5:] removes '--no_'
																								full_error_msg += arg_error_msg.format(arg[5:]						)
																		else:
																								wrong_args.append(lowercase_						)
												if len(lowercase_						) > 0:
																		lowercase        =							full_error_msg + begin_error_msg + str(lowercase_						)
												raise ValueError(lowercase_						)
						benchmark.run()


if __name__ == "__main__":
						main()


