"""simple docstring"""
import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig





def  lowerCAmelCase__     (      _UpperCamelCase							:					Any			,      _UpperCamelCase							:					Union[str, Any]			,      _UpperCamelCase							:					Tuple       )       ->					List[Any]:





						"""simple docstring"""






						snake_case             =      TaConfig.from_pretrained(__lowercase       )
						snake_case             =      FlaxTaForConditionalGeneration(config=__lowercase       )
						snake_case             =      checkpoints.load_tax_checkpoint(__lowercase       )

						snake_case             =      '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

						# Encoder
						for layer_index in range(config.num_layers       ):
												snake_case             =      f"""layers_{str(__lowercase       )}"""

												# Self-Attention
												snake_case             =      tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
												snake_case             =      tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
												snake_case             =      tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
												snake_case             =      tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

												# Layer Normalization
												snake_case             =      tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

												if split_mlp_wi:
																		snake_case             =      tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
																		snake_case             =      tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
												else:
																		snake_case             =      tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

												snake_case             =      tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

												# Layer Normalization
												snake_case             =      tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

												# Assigning
												snake_case             =      tax_attention_key
												snake_case             =      tax_attention_out
												snake_case             =      tax_attention_query
												snake_case             =      tax_attention_value

												snake_case             =      tax_attention_layer_norm

												if split_mlp_wi:
																		snake_case             =      tax_mlp_wi_a
																		snake_case             =      tax_mlp_wi_a
												else:
																		snake_case             =      tax_mlp_wi

												snake_case             =      tax_mlp_wo
												snake_case             =      tax_mlp_layer_norm

						# Only for layer 0:
						snake_case             =      tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
						snake_case             =      tax_encoder_rel_embedding

						# Assigning
						snake_case             =      tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
						snake_case             =      tax_encoder_norm

						# Decoder
						for layer_index in range(config.num_decoder_layers       ):
												snake_case             =      f"""layers_{str(__lowercase       )}"""

												# Self-Attention
												snake_case             =      tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
												snake_case             =      tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
												snake_case             =      tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
												snake_case             =      tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

												# Layer Normalization
												snake_case             =      tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
												    '''scale'''
												]

												# Encoder-Decoder-Attention
												snake_case             =      tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
												    '''kernel'''
												]
												snake_case             =      tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
												    '''kernel'''
												]
												snake_case             =      tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
												    '''kernel'''
												]
												snake_case             =      tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
												    '''kernel'''
												]

												# Layer Normalization
												snake_case             =      tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

												# MLP
												if split_mlp_wi:
																		snake_case             =      tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
																		snake_case             =      tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
												else:
																		snake_case             =      tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

												snake_case             =      tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

												# Layer Normalization
												snake_case             =      tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

												# Assigning
												snake_case             =      tax_attention_key
												snake_case             =      tax_attention_out
												snake_case             =      tax_attention_query
												snake_case             =      tax_attention_value

												snake_case             =      tax_pre_attention_layer_norm

												snake_case             =      tax_enc_dec_attention_key
												snake_case             =      tax_enc_dec_attention_out
												snake_case             =      tax_enc_dec_attention_query
												snake_case             =      tax_enc_dec_attention_value

												snake_case             =      tax_cross_layer_norm

												if split_mlp_wi:
																		snake_case             =      tax_mlp_wi_a
																		snake_case             =      tax_mlp_wi_a
												else:
																		snake_case             =      tax_mlp_wi

												snake_case             =      tax_mlp_wo

												snake_case             =      txa_mlp_layer_norm

						# Decoder Normalization
						snake_case             =      tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
						snake_case             =      txa_decoder_norm

						# Only for layer 0:
						snake_case             =      tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
						snake_case             =      tax_decoder_rel_embedding

						# Token Embeddings
						snake_case             =      tax_model['''target''']['''token_embedder''']['''embedding''']
						snake_case             =      txa_token_embeddings

						# LM Head (only in v1.1 checkpoints)
						if "logits_dense" in tax_model["target"]["decoder"]:
												snake_case             =      tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

						flax_model.save_pretrained(__lowercase       )
						print('T5X Model was sucessfully converted!'       )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__       =	argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the TX5 checkpoint."
					)
					parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of T5 model.")
					parser.add_argument(
					    "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
					)
					SCREAMING_SNAKE_CASE__       =	parser.parse_args()
					convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)








'''simple docstring'''


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 						lowerCAmelCase_	(       UpperCAmelCase_							):







  '''simple docstring'''

  UpperCamelCase_			:      Optional[int]										=				"""AutoFeatureExtractor"""
  UpperCamelCase_			:      List[Any]										=				"""Speech2Text2Tokenizer"""



  def __init__( self				:							str					,  SCREAMING_SNAKE_CASE_				:							str					,  SCREAMING_SNAKE_CASE_				:							int	)			->		Any:







       '''simple docstring'''



       super().__init__(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
       A:		Union[str, Any]          =		self.feature_extractor
       A:		Dict          =		False



  def __call__( self				:							Any					,  *SCREAMING_SNAKE_CASE_				:							Any					,  **SCREAMING_SNAKE_CASE_				:							Optional[int]	)			->		Optional[Any]:







       '''simple docstring'''



       if self._in_target_context_manager:
            return self.current_processor(*SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_	)

       if "raw_speech" in kwargs:
            warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''	)
            A:		List[str]          =		kwargs.pop('''raw_speech'''	)
       else:
            A:		Any          =		kwargs.pop('''audio'''					,  SCREAMING_SNAKE_CASE_	)
       A:		List[Any]          =		kwargs.pop('''sampling_rate'''					,  SCREAMING_SNAKE_CASE_	)
       A:		Optional[int]          =		kwargs.pop('''text'''					,  SCREAMING_SNAKE_CASE_	)
       if len(SCREAMING_SNAKE_CASE_	) > 0:
            A:		str          =		args[0]
            A:		Tuple          =		args[1:]

       if audio is None and text is None:
            raise ValueError('''You need to specify either an `audio` or `text` input to process.'''	)

       if audio is not None:
            A:		Tuple          =		self.feature_extractor(SCREAMING_SNAKE_CASE_					,  *SCREAMING_SNAKE_CASE_					,  sampling_rate=SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_	)
       if text is not None:
            A:		List[Any]          =		self.tokenizer(SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_	)

       if text is None:
            return inputs
       elif audio is None:
            return encodings
       else:
            A:		str          =		encodings['''input_ids''']
            return inputs



  def 					_snake_case     ( self				:							Tuple					,  *SCREAMING_SNAKE_CASE_				:							int					,  **SCREAMING_SNAKE_CASE_				:							Union[str, Any]	)			->		str:







       '''simple docstring'''



       return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_	)



  def 					_snake_case     ( self				:							Optional[int]					,  *SCREAMING_SNAKE_CASE_				:							str					,  **SCREAMING_SNAKE_CASE_				:							int	)			->		Union[str, Any]:







       '''simple docstring'''



       return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_	)




  @contextmanager
  def 					_snake_case     ( self				:							str	)			->		int:







       '''simple docstring'''



       warnings.warn(
           '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
           '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
           '''your audio inputs, or in a separate call.'''	)
       A:		List[Any]          =		True
       A:		List[str]          =		self.tokenizer
       yield
       A:		List[Any]          =		self.feature_extractor
       A:		int          =		False






