






'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer





def 	_a(					UpperCamelCase__			:  Optional[int],		UpperCamelCase__			:  Optional[Any],		UpperCamelCase__			:  List[str],		UpperCamelCase__			:  Dict,		UpperCamelCase__			:  Dict=True,		UpperCamelCase__			:  str="pt"							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Tuple									={'''add_prefix_space''': True} if isinstance(UpperCamelCase__,		UpperCamelCase__							) and not line.startswith(''' '''							) else {}
      SCREAMING_SNAKE_CASE__       :			Any									=padding_side
      return tokenizer(
          [line],		max_length=UpperCamelCase__,		padding='''max_length''' if pad_to_max_length else None,		truncation=UpperCamelCase__,		return_tensors=UpperCamelCase__,		add_special_tokens=UpperCamelCase__,		**UpperCamelCase__,		)





def 	_a(					UpperCamelCase__			:  Dict,		UpperCamelCase__			:  List[Any],		UpperCamelCase__			:  Optional[int]=None,		):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			int									=input_ids.ne(UpperCamelCase__							).any(dim=0							)
      if attention_mask is None:
            return input_ids[:, keep_column_mask]
      else:
            return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 def __init__( self		:							Any   , __lowercase		:							Tuple   , __lowercase		:							List[Any]   , __lowercase		:							List[str]   , __lowercase		:							int   , __lowercase		:							Tuple="train"   , __lowercase		:							Tuple=None   , __lowercase		:							Union[str, Any]=None   , __lowercase		:							List[str]=None   , __lowercase		:							int=""   , )		->  Tuple:
       super().__init__()
       SCREAMING_SNAKE_CASE__       :			Optional[int]									=Path(__lowercase ).joinpath(type_path + '''.source''' )
       SCREAMING_SNAKE_CASE__       :			int									=Path(__lowercase ).joinpath(type_path + '''.target''' )
       SCREAMING_SNAKE_CASE__       :			Dict									=self.get_char_lens(self.src_file )
       SCREAMING_SNAKE_CASE__       :			str									=max_source_length
       SCREAMING_SNAKE_CASE__       :			Any									=max_target_length
       assert min(self.src_lens ) > 0, F"found empty line in {self.src_file}"
       SCREAMING_SNAKE_CASE__       :			Optional[int]									=tokenizer
       SCREAMING_SNAKE_CASE__       :			List[Any]									=prefix
       if n_obs is not None:
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]									=self.src_lens[:n_obs]
       SCREAMING_SNAKE_CASE__       :			List[str]									=src_lang
       SCREAMING_SNAKE_CASE__       :			int									=tgt_lang




 def __len__( self		:							Any )		->  Optional[Any]:
       return len(self.src_lens )




 def __getitem__( self		:							Tuple   , __lowercase		:							Dict )		->  Dict[str, torch.Tensor]:
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]									=index + 1  # linecache starts at 1
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]									=self.prefix + linecache.getline(str(self.src_file )   , __lowercase ).rstrip('''\n''' )
       SCREAMING_SNAKE_CASE__       :			str									=linecache.getline(str(self.tgt_file )   , __lowercase ).rstrip('''\n''' )
       assert source_line, F"empty source line for index {index}"
       assert tgt_line, F"empty tgt line for index {index}"

       # Need to add eos token manually for T5
       if isinstance(self.tokenizer   , __lowercase ):
             source_line += self.tokenizer.eos_token
             tgt_line += self.tokenizer.eos_token

       # Pad source and target to the right
       SCREAMING_SNAKE_CASE__       :			Tuple									=(
           self.tokenizer.question_encoder if isinstance(self.tokenizer   , __lowercase ) else self.tokenizer
       )
       SCREAMING_SNAKE_CASE__       :			Tuple									=self.tokenizer.generator if isinstance(self.tokenizer   , __lowercase ) else self.tokenizer

       SCREAMING_SNAKE_CASE__       :			Any									=encode_line(__lowercase   , __lowercase   , self.max_source_length   , '''right''' )
       SCREAMING_SNAKE_CASE__       :			Dict									=encode_line(__lowercase   , __lowercase   , self.max_target_length   , '''right''' )

       SCREAMING_SNAKE_CASE__       :			Union[str, Any]									=source_inputs['''input_ids'''].squeeze()
       SCREAMING_SNAKE_CASE__       :			Optional[Any]									=target_inputs['''input_ids'''].squeeze()
       SCREAMING_SNAKE_CASE__       :			Dict									=source_inputs['''attention_mask'''].squeeze()
       return {
           "input_ids": source_ids,
           "attention_mask": src_mask,
           "decoder_input_ids": target_ids,
       }




 @staticmethod
 def        __magic_name__		( __lowercase		:							Dict )		->  Optional[int]:
       return [len(__lowercase ) for x in Path(__lowercase ).open().readlines()]




 def        __magic_name__		( self		:							List[str]   , __lowercase		:							Tuple )		->  Dict[str, torch.Tensor]:
       SCREAMING_SNAKE_CASE__       :			str									=torch.stack([x['''input_ids'''] for x in batch] )
       SCREAMING_SNAKE_CASE__       :			List[Any]									=torch.stack([x['''attention_mask'''] for x in batch] )
       SCREAMING_SNAKE_CASE__       :			List[Any]									=torch.stack([x['''decoder_input_ids'''] for x in batch] )
       SCREAMING_SNAKE_CASE__       :			List[str]									=(
           self.tokenizer.generator.pad_token_id
           if isinstance(self.tokenizer   , __lowercase )
           else self.tokenizer.pad_token_id
       )
       SCREAMING_SNAKE_CASE__       :			Dict									=(
           self.tokenizer.question_encoder.pad_token_id
           if isinstance(self.tokenizer   , __lowercase )
           else self.tokenizer.pad_token_id
       )
       SCREAMING_SNAKE_CASE__       :			str									=trim_batch(__lowercase   , __lowercase )
       SCREAMING_SNAKE_CASE__       :			int									=trim_batch(__lowercase   , __lowercase   , attention_mask=__lowercase )
       SCREAMING_SNAKE_CASE__       :			Optional[int]									={
           '''input_ids''': source_ids,
           '''attention_mask''': source_mask,
           '''decoder_input_ids''': y,
       }
       return batch


a_					=	getLogger(__name__)





def 	_a(					UpperCamelCase__			:  List[List]							):




      '''simple docstring'''






      return list(itertools.chain.from_iterable(UpperCamelCase__							)							)





def 	_a(					UpperCamelCase__			:  str							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Union[str, Any]									=get_git_info()
      save_json(UpperCamelCase__,		os.path.join(UpperCamelCase__,		'''git_log.json'''							)							)





def 	_a(					UpperCamelCase__			:  Tuple,		UpperCamelCase__			:  int,		UpperCamelCase__			:  List[Any]=4,		**UpperCamelCase__			:  Tuple							):




      '''simple docstring'''






      with open(UpperCamelCase__,		'''w'''							) as f:
            json.dump(UpperCamelCase__,		UpperCamelCase__,		indent=UpperCamelCase__,		**UpperCamelCase__							)





def 	_a(					UpperCamelCase__			:  Optional[int]							):




      '''simple docstring'''






      with open(UpperCamelCase__							) as f:
            return json.load(UpperCamelCase__							)





def 	_a(					):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Dict									=git.Repo(search_parent_directories=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Optional[Any]									={
          '''repo_id''': str(UpperCamelCase__							),
          '''repo_sha''': str(repo.head.object.hexsha							),
          '''repo_branch''': str(repo.active_branch							),
          '''hostname''': str(socket.gethostname()							),
      }
      return repo_infos





def 	_a(					UpperCamelCase__			:  Callable,		UpperCamelCase__			:  Iterable							):




      '''simple docstring'''






      return list(map(UpperCamelCase__,		UpperCamelCase__							)							)





def 	_a(					UpperCamelCase__			:  List[Any],		UpperCamelCase__			:  str							):




      '''simple docstring'''






      with open(UpperCamelCase__,		'''wb'''							) as f:
            return pickle.dump(UpperCamelCase__,		UpperCamelCase__							)





def 	_a(					UpperCamelCase__			:  int							):




      '''simple docstring'''






      def remove_articles(UpperCamelCase__			:  Tuple							):
            return re.sub(R'''\b(a|an|the)\b''',		''' ''',		UpperCamelCase__							)

      def white_space_fix(UpperCamelCase__			:  Tuple							):
            return " ".join(text.split()							)

      def remove_punc(UpperCamelCase__			:  Union[str, Any]							):
            SCREAMING_SNAKE_CASE__       :			List[Any]									=set(string.punctuation							)
            return "".join(ch for ch in text if ch not in exclude							)

      def lower(UpperCamelCase__			:  Tuple							):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase__							)							)							)							)





def 	_a(					UpperCamelCase__			:  List[Any],		UpperCamelCase__			:  List[str]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Optional[Any]									=normalize_answer(UpperCamelCase__							).split()
      SCREAMING_SNAKE_CASE__       :			int									=normalize_answer(UpperCamelCase__							).split()
      SCREAMING_SNAKE_CASE__       :			Optional[Any]									=Counter(UpperCamelCase__							) & Counter(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Tuple									=sum(common.values()							)
      if num_same == 0:
            return 0
      SCREAMING_SNAKE_CASE__       :			Tuple									=1.0 * num_same / len(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			str									=1.0 * num_same / len(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Dict									=(2 * precision * recall) / (precision + recall)
      return fa





def 	_a(					UpperCamelCase__			:  str,		UpperCamelCase__			:  str							):




      '''simple docstring'''






      return normalize_answer(UpperCamelCase__							) == normalize_answer(UpperCamelCase__							)





def 	_a(					UpperCamelCase__			:  List[str],		UpperCamelCase__			:  List[str]							):




      '''simple docstring'''






      assert len(UpperCamelCase__							) == len(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Optional[int]									=0
      for hypo, pred in zip(UpperCamelCase__,		UpperCamelCase__							):
            em += exact_match_score(UpperCamelCase__,		UpperCamelCase__							)
      if len(UpperCamelCase__							) > 0:
            em /= len(UpperCamelCase__							)
      return {"em": em}





def 	_a(					UpperCamelCase__			:  Dict							):




      '''simple docstring'''






      return model_prefix.startswith('''rag'''							)







def 	_a(					UpperCamelCase__			:  Optional[Any],		UpperCamelCase__			:  Optional[int],		UpperCamelCase__			:  Tuple							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Optional[int]									={p: p for p in extra_params}
      # T5 models don't have `dropout` param, they have `dropout_rate` instead
      SCREAMING_SNAKE_CASE__       :			Optional[Any]									='''dropout_rate'''
      for p in extra_params:
            if getattr(UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__							):
                  if not hasattr(UpperCamelCase__,		UpperCamelCase__							) and not hasattr(UpperCamelCase__,		equivalent_param[p]							):
                        logger.info('''config doesn\'t have a `{}` attribute'''.format(UpperCamelCase__							)							)
                        delattr(UpperCamelCase__,		UpperCamelCase__							)
                        continue
                  SCREAMING_SNAKE_CASE__       :			Optional[int]									=p if hasattr(UpperCamelCase__,		UpperCamelCase__							) else equivalent_param[p]
                  setattr(UpperCamelCase__,		UpperCamelCase__,		getattr(UpperCamelCase__,		UpperCamelCase__							)							)
                  delattr(UpperCamelCase__,		UpperCamelCase__							)
      return hparams, config







'''simple docstring'''
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





def 	_a(					UpperCamelCase__			:  str,		UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  List[str]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Any													=AutoConfig.from_pretrained(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=FlaxAutoModelForSeqaSeqLM.from_config(config=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=checkpoints.load_tax_checkpoint(UpperCamelCase__							)

      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													='''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

      if config.model_type == "t5":
            SCREAMING_SNAKE_CASE__       :			str													='''SelfAttention'''
      if config.model_type == "longt5" and config.encoder_attention_type == "local":
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													='''LocalSelfAttention'''
      elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            SCREAMING_SNAKE_CASE__       :			Dict													='''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							):
            SCREAMING_SNAKE_CASE__       :			Dict													=f"layers_{str(UpperCamelCase__							)}"

            # Self-Attention
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			str													=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			List[Any]													=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":
                  SCREAMING_SNAKE_CASE__       :			str													=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                  SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
            else:
                  SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

            SCREAMING_SNAKE_CASE__       :			int													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

            # Assigning
            SCREAMING_SNAKE_CASE__       :			Tuple													=flax_model.params['''encoder''']['''block'''][str(UpperCamelCase__							)]['''layer''']
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tax_attention_key
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=tax_attention_out
            SCREAMING_SNAKE_CASE__       :			int													=tax_attention_query
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_attention_value

            SCREAMING_SNAKE_CASE__       :			Dict													=tax_attention_layer_norm

            # Global input layer norm
            if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                  SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tax_global_layer_norm

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			List[str]													=tax_mlp_wi_a
                  SCREAMING_SNAKE_CASE__       :			Any													=tax_mlp_wi_a
            else:
                  SCREAMING_SNAKE_CASE__       :			Tuple													=tax_mlp_wi

            SCREAMING_SNAKE_CASE__       :			int													=tax_mlp_wo
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tax_mlp_layer_norm

            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=flax_model_encoder_layer_block

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

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

      # Assigning
      SCREAMING_SNAKE_CASE__       :			List[str]													=tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
      SCREAMING_SNAKE_CASE__       :			int													=tax_encoder_norm

      # Decoder
      for layer_index in range(config.num_layers							):
            SCREAMING_SNAKE_CASE__       :			int													=f"layers_{str(UpperCamelCase__							)}"

            # Self-Attention
            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			int													=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
                '''scale'''
            ]

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

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			str													=tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

            # MLP
            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			Tuple													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                  SCREAMING_SNAKE_CASE__       :			str													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
            else:
                  SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

            # Assigning
            SCREAMING_SNAKE_CASE__       :			Tuple													=flax_model.params['''decoder''']['''block'''][str(UpperCamelCase__							)]['''layer''']
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_attention_key
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_attention_out
            SCREAMING_SNAKE_CASE__       :			int													=tax_attention_query
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_attention_value

            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_pre_attention_layer_norm

            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_enc_dec_attention_key
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=tax_enc_dec_attention_out
            SCREAMING_SNAKE_CASE__       :			str													=tax_enc_dec_attention_query
            SCREAMING_SNAKE_CASE__       :			str													=tax_enc_dec_attention_value

            SCREAMING_SNAKE_CASE__       :			Optional[int]													=tax_cross_layer_norm

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			List[str]													=tax_mlp_wi_a
                  SCREAMING_SNAKE_CASE__       :			int													=tax_mlp_wi_a
            else:
                  SCREAMING_SNAKE_CASE__       :			List[str]													=tax_mlp_wi

            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_mlp_wo

            SCREAMING_SNAKE_CASE__       :			str													=txa_mlp_layer_norm

            SCREAMING_SNAKE_CASE__       :			List[Any]													=flax_model_decoder_layer_block

      # Decoder Normalization
      SCREAMING_SNAKE_CASE__       :			Any													=tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=txa_decoder_norm

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

      # Token Embeddings
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_model['''target''']['''token_embedder''']['''embedding''']
      SCREAMING_SNAKE_CASE__       :			List[Any]													=txa_token_embeddings

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

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


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