




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM

def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[Any]			,							lowercase			: Optional[Any]			,							lowercase			: Dict  ):


      '''simple docstring'''



      lowerCamelCase_	  =					AutoConfig.from_pretrained(lowercase  )
      lowerCamelCase_	  =					FlaxAutoModelForSeqaSeqLM.from_config(config=lowercase  )
      lowerCamelCase_	  =					checkpoints.load_tax_checkpoint(lowercase  )

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

      if config.model_type == "t5":
            lowerCamelCase_	  =					'SelfAttention'
      if config.model_type == "longt5" and config.encoder_attention_type == "local":
            lowerCamelCase_	  =					'LocalSelfAttention'
      elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            lowerCamelCase_	  =					'TransientGlobalSelfAttention'
      else:
            raise ValueError(
                'Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'
                ' attribute with a value from [\'local\', \'transient-global].'  )

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

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

            # Global input layer norm
            if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                  lowerCamelCase_	  =					tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

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

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

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

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

            # Assigning
            lowerCamelCase_	  =					flax_model.params['encoder']['block'][str(lowercase  )]['layer']
            lowerCamelCase_	  =					tax_attention_key
            lowerCamelCase_	  =					tax_attention_out
            lowerCamelCase_	  =					tax_attention_query
            lowerCamelCase_	  =					tax_attention_value

            lowerCamelCase_	  =					tax_attention_layer_norm

            # Global input layer norm
            if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                  lowerCamelCase_	  =					tax_global_layer_norm

            if split_mlp_wi:
                  lowerCamelCase_	  =					tax_mlp_wi_a
                  lowerCamelCase_	  =					tax_mlp_wi_a
            else:
                  lowerCamelCase_	  =					tax_mlp_wi

            lowerCamelCase_	  =					tax_mlp_wo
            lowerCamelCase_	  =					tax_mlp_layer_norm

            lowerCamelCase_	  =					flax_model_encoder_layer_block

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

      # Side/global relative position_bias + layer norm
      if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            lowerCamelCase_	  =					tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
            lowerCamelCase_	  =					tax_encoder_global_rel_embedding

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

      # Decoder
      for layer_index in range(config.num_layers  ):
            lowerCamelCase_	  =					f"""layers_{str(lowercase  )}"""

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

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

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

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

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

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

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

            # Assigning
            lowerCamelCase_	  =					flax_model.params['decoder']['block'][str(lowercase  )]['layer']
            lowerCamelCase_	  =					tax_attention_key
            lowerCamelCase_	  =					tax_attention_out
            lowerCamelCase_	  =					tax_attention_query
            lowerCamelCase_	  =					tax_attention_value

            lowerCamelCase_	  =					tax_pre_attention_layer_norm

            lowerCamelCase_	  =					tax_enc_dec_attention_key
            lowerCamelCase_	  =					tax_enc_dec_attention_out
            lowerCamelCase_	  =					tax_enc_dec_attention_query
            lowerCamelCase_	  =					tax_enc_dec_attention_value

            lowerCamelCase_	  =					tax_cross_layer_norm

            if split_mlp_wi:
                  lowerCamelCase_	  =					tax_mlp_wi_a
                  lowerCamelCase_	  =					tax_mlp_wi_a
            else:
                  lowerCamelCase_	  =					tax_mlp_wi

            lowerCamelCase_	  =					tax_mlp_wo

            lowerCamelCase_	  =					txa_mlp_layer_norm

            lowerCamelCase_	  =					flax_model_decoder_layer_block

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

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

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

      # LM Head (only in v1.1 and LongT5 checkpoints)
      if "logits_dense" in tax_model["target"]["decoder"]:
            lowerCamelCase_	  =					tax_model['target']['decoder']['logits_dense']['kernel']

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


if __name__ == "__main__":
 lowerCamelCase       :	Union[str, Any]									=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the T5X checkpoint."
 )
 parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of LongT5/T5 model.")
 parser.add_argument(
     "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
 )
 lowerCamelCase       :	Dict									=     parser.parse_args()
 convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)









from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate







@dataclass(frozen=UpperCamelCase     )
class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		field(default='''language-modeling'''							,   metadata={'''include_in_asdict_even_if_is_default''': True}     )

 UpperCamelCase		     =		Features({'''text''': Value('''string'''     )}     )
 UpperCamelCase		     =		Features({}     )
 UpperCamelCase		     =		"text"

 @property
 def        a__				(       self      :						List[Any]						)     ->			Dict[str, str]:

       """simple docstring"""






       return {self.text_column: "text"}



