


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig

def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)						->							Dict:




       '''simple docstring'''






       _lowercase					:				int           =			TaConfig.from_pretrained(UpperCAmelCase_			)
       _lowercase					:				Optional[int]           =			FlaxTaForConditionalGeneration(config=UpperCAmelCase_			)
       _lowercase					:				Optional[int]           =			checkpoints.load_tax_checkpoint(UpperCAmelCase_			)

       _lowercase					:				List[Any]           =			'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

       # Encoder
       for layer_index in range(config.num_layers			):
              _lowercase					:				Dict           =			F'layers_{str(UpperCAmelCase_			)}'

              # Self-Attention
              _lowercase					:				Optional[Any]           =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
              _lowercase					:				List[Any]           =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
              _lowercase					:				Optional[int]           =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
              _lowercase					:				Optional[Any]           =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

              # Layer Normalization
              _lowercase					:				Any           =			tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

              if split_mlp_wi:
                     _lowercase					:				Tuple           =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                     _lowercase					:				Union[str, Any]           =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
              else:
                     _lowercase					:				List[Any]           =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

              _lowercase					:				List[str]           =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

              # Layer Normalization
              _lowercase					:				List[str]           =			tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

              # Assigning
              _lowercase					:				Dict           =			tax_attention_key
              _lowercase					:				Union[str, Any]           =			tax_attention_out
              _lowercase					:				Optional[Any]           =			tax_attention_query
              _lowercase					:				Optional[int]           =			tax_attention_value

              _lowercase					:				str           =			tax_attention_layer_norm

              if split_mlp_wi:
                     _lowercase					:				List[str]           =			tax_mlp_wi_a
                     _lowercase					:				Dict           =			tax_mlp_wi_a
              else:
                     _lowercase					:				Dict           =			tax_mlp_wi

              _lowercase					:				Any           =			tax_mlp_wo
              _lowercase					:				str           =			tax_mlp_layer_norm

       # Only for layer 0:
       _lowercase					:				List[str]           =			tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
       _lowercase					:				Optional[Any]           =			tax_encoder_rel_embedding

       # Assigning
       _lowercase					:				Union[str, Any]           =			tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
       _lowercase					:				Optional[int]           =			tax_encoder_norm

       # Decoder
       for layer_index in range(config.num_decoder_layers			):
              _lowercase					:				int           =			F'layers_{str(UpperCAmelCase_			)}'

              # Self-Attention
              _lowercase					:				Tuple           =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
              _lowercase					:				Tuple           =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
              _lowercase					:				Tuple           =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
              _lowercase					:				str           =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

              # Layer Normalization
              _lowercase					:				int           =			tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
                  '''scale'''
              ]

              # Encoder-Decoder-Attention
              _lowercase					:				List[str]           =			tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
                  '''kernel'''
              ]
              _lowercase					:				Optional[int]           =			tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
                  '''kernel'''
              ]
              _lowercase					:				Tuple           =			tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
                  '''kernel'''
              ]
              _lowercase					:				Optional[Any]           =			tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
                  '''kernel'''
              ]

              # Layer Normalization
              _lowercase					:				Optional[Any]           =			tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

              # MLP
              if split_mlp_wi:
                     _lowercase					:				List[str]           =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                     _lowercase					:				Optional[int]           =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
              else:
                     _lowercase					:				int           =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

              _lowercase					:				List[str]           =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

              # Layer Normalization
              _lowercase					:				List[Any]           =			tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

              # Assigning
              _lowercase					:				Dict           =			tax_attention_key
              _lowercase					:				str           =			tax_attention_out
              _lowercase					:				List[str]           =			tax_attention_query
              _lowercase					:				Union[str, Any]           =			tax_attention_value

              _lowercase					:				Dict           =			tax_pre_attention_layer_norm

              _lowercase					:				List[str]           =			tax_enc_dec_attention_key
              _lowercase					:				Tuple           =			tax_enc_dec_attention_out
              _lowercase					:				Dict           =			tax_enc_dec_attention_query
              _lowercase					:				Optional[int]           =			tax_enc_dec_attention_value

              _lowercase					:				Dict           =			tax_cross_layer_norm

              if split_mlp_wi:
                     _lowercase					:				Union[str, Any]           =			tax_mlp_wi_a
                     _lowercase					:				Tuple           =			tax_mlp_wi_a
              else:
                     _lowercase					:				Dict           =			tax_mlp_wi

              _lowercase					:				Tuple           =			tax_mlp_wo

              _lowercase					:				Union[str, Any]           =			txa_mlp_layer_norm

       # Decoder Normalization
       _lowercase					:				str           =			tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
       _lowercase					:				Tuple           =			txa_decoder_norm

       # Only for layer 0:
       _lowercase					:				Tuple           =			tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
       _lowercase					:				Optional[int]           =			tax_decoder_rel_embedding

       # Token Embeddings
       _lowercase					:				str           =			tax_model['''target''']['''token_embedder''']['''embedding''']
       _lowercase					:				Optional[Any]           =			txa_token_embeddings

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

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


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






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__   (      A_      ,						A_      ,						A_		):



     '''simple docstring'''




     @register_to_config
     def __init__( self       :       Dict	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       float	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       str	,      UpperCamelCase       :       bool = False	,      ):





            """simple docstring"""



            super().__init__()

            _lowercase					:				Any           =			nn.Linear(UpperCamelCase	,      UpperCamelCase	,      bias=UpperCamelCase		)

            _lowercase					:				List[Any]           =			nn.Embedding(UpperCamelCase	,      UpperCamelCase		)
            _lowercase					:				Any           =			False

            _lowercase					:				Tuple           =			nn.Dropout(p=UpperCamelCase		)

            _lowercase					:				Any           =			TaConfig(
                d_model=UpperCamelCase	,      num_heads=UpperCamelCase	,      d_kv=UpperCamelCase	,      d_ff=UpperCamelCase	,      feed_forward_proj=UpperCamelCase	,      dropout_rate=UpperCamelCase	,      is_decoder=UpperCamelCase	,      is_encoder_decoder=UpperCamelCase	,      )
            _lowercase					:				Any           =			nn.ModuleList()
            for lyr_num in range(UpperCamelCase		):
                   _lowercase					:				Tuple           =			TaBlock(UpperCamelCase		)
                   self.encoders.append(UpperCamelCase		)

            _lowercase					:				List[Any]           =			TaLayerNorm(UpperCamelCase		)
            _lowercase					:				Optional[Any]           =			nn.Dropout(p=UpperCamelCase		)




     def   lowerCAmelCase_   ( self       :       int	,      UpperCamelCase       :       Optional[int]	,      UpperCamelCase       :       List[Any]		):





            """simple docstring"""



            _lowercase					:				Optional[int]           =			self.input_proj(UpperCamelCase		)

            # terminal relative positional encodings
            _lowercase					:				Optional[int]           =			encoder_inputs.shape[1]
            _lowercase					:				Optional[int]           =			torch.arange(UpperCamelCase	,      device=encoder_inputs.device		)

            _lowercase					:				Any           =			encoder_inputs_mask.sum(-1		)
            _lowercase					:				int           =			torch.roll(input_positions.unsqueeze(0		)	,      tuple(seq_lens.tolist()		)	,      dims=0		)
            x += self.position_encoding(UpperCamelCase		)

            _lowercase					:				Optional[int]           =			self.dropout_pre(UpperCamelCase		)

            # inverted the attention mask
            _lowercase					:				Optional[int]           =			encoder_inputs.size()
            _lowercase					:				List[Any]           =			self.get_extended_attention_mask(UpperCamelCase	,      UpperCamelCase		)

            for lyr in self.encoders:
                   _lowercase					:				Optional[int]           =			lyr(UpperCamelCase	,      UpperCamelCase		)[0]
            _lowercase					:				List[str]           =			self.layer_norm(UpperCamelCase		)

            return self.dropout_post(UpperCamelCase		), encoder_inputs_mask