


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig

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


							'''simple docstring'''





							_UpperCAmelCase					=    TaConfig.from_pretrained(lowerCamelCase__ )
							_UpperCAmelCase					=    FlaxTaForConditionalGeneration(config=lowerCamelCase__ )
							_UpperCAmelCase					=    checkpoints.load_tax_checkpoint(lowerCamelCase__ )

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

							# Encoder
							for layer_index in range(config.num_layers ):
														_UpperCAmelCase					=    F'''layers_{str(lowerCamelCase__ )}'''

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

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

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

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

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

														# Assigning
														_UpperCAmelCase					=    tax_attention_key
														_UpperCAmelCase					=    tax_attention_out
														_UpperCAmelCase					=    tax_attention_query
														_UpperCAmelCase					=    tax_attention_value

														_UpperCAmelCase					=    tax_attention_layer_norm

														if split_mlp_wi:
																					_UpperCAmelCase					=    tax_mlp_wi_a
																					_UpperCAmelCase					=    tax_mlp_wi_a
														else:
																					_UpperCAmelCase					=    tax_mlp_wi

														_UpperCAmelCase					=    tax_mlp_wo
														_UpperCAmelCase					=    tax_mlp_layer_norm

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

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

							# Decoder
							for layer_index in range(config.num_decoder_layers ):
														_UpperCAmelCase					=    F'''layers_{str(lowerCamelCase__ )}'''

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

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

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

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

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

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

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

														# Assigning
														_UpperCAmelCase					=    tax_attention_key
														_UpperCAmelCase					=    tax_attention_out
														_UpperCAmelCase					=    tax_attention_query
														_UpperCAmelCase					=    tax_attention_value

														_UpperCAmelCase					=    tax_pre_attention_layer_norm

														_UpperCAmelCase					=    tax_enc_dec_attention_key
														_UpperCAmelCase					=    tax_enc_dec_attention_out
														_UpperCAmelCase					=    tax_enc_dec_attention_query
														_UpperCAmelCase					=    tax_enc_dec_attention_value

														_UpperCAmelCase					=    tax_cross_layer_norm

														if split_mlp_wi:
																					_UpperCAmelCase					=    tax_mlp_wi_a
																					_UpperCAmelCase					=    tax_mlp_wi_a
														else:
																					_UpperCAmelCase					=    tax_mlp_wi

														_UpperCAmelCase					=    tax_mlp_wo

														_UpperCAmelCase					=    txa_mlp_layer_norm

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

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

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

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

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


if __name__ == "__main__":
					lowerCAmelCase__						:Any         =							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.'''
					)
					lowerCAmelCase__						:int         =							parser.parse_args()
					convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




import warnings

from .generation import GenerationMixin

class 	__a   (       UpperCAmelCase    ):
		# warning at import time
		warnings.warn(
		    'Importing `GenerationMixin` from `src/transformers/generation_utils.py` is deprecated and will '
		    'be removed in Transformers v5. Import as `from transformers import GenerationMixin` instead.'					,   UpperCAmelCase					,   )
