
import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_="attention"   )		->	int:


  '''simple docstring'''






  lowercase__  :   Dict    		=      params[F'{prefix}/layers_{i}/{layer_name}/key/kernel']
  lowercase__  :   Dict    		=      params[F'{prefix}/layers_{i}/{layer_name}/out/kernel']
  lowercase__  :   Union[str, Any]    		=      params[F'{prefix}/layers_{i}/{layer_name}/query/kernel']
  lowercase__  :   List[Any]    		=      params[F'{prefix}/layers_{i}/{layer_name}/value/kernel']
  return k, o, q, v

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_=False   )		->	Dict:


  '''simple docstring'''






  if split_mlp_wi:
    lowercase__  :   List[str]    		=      params[F'{prefix}/layers_{i}/mlp/wi_0/kernel']
    lowercase__  :   Union[str, Any]    		=      params[F'{prefix}/layers_{i}/mlp/wi_1/kernel']
    lowercase__  :   Any    		=      (wi_a, wi_a)
  else:
    lowercase__  :   str    		=      params[F'{prefix}/layers_{i}/mlp/wi/kernel']

  lowercase__  :   List[Any]    		=      params[F'{prefix}/layers_{i}/mlp/wo/kernel']
  return wi, wo

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_   )		->	Dict:


  '''simple docstring'''






  return params[F'{prefix}/layers_{i}/{layer_name}/scale']

def 		UpperCamelCase	(      lowercase_ , *, lowercase_ , lowercase_   )		->	int:


  '''simple docstring'''






  lowercase__  :   Union[str, Any]    		=      traverse_util.flatten_dict(variables["""target"""]   )
  lowercase__  :   Any    		=      {"""/""".join(lowercase_   ): v for k, v in old.items()}

  # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
  lowercase__  :   Optional[int]    		=      """encoder/layers_0/mlp/wi_0/kernel""" in old
  print("""Split MLP:""" , lowercase_   )

  lowercase__  :   Union[str, Any]    		=      collections.OrderedDict()

  # Shared embeddings.
  lowercase__  :   List[Any]    		=      old["""token_embedder/embedding"""]

  # Encoder.
  for i in range(lowercase_   ):
    # Block i, layer 0 (Self Attention).
    lowercase__  :   str    		=      tax_layer_norm_lookup(lowercase_ , lowercase_ , """encoder""" , """pre_attention_layer_norm"""   )
    lowercase__		,    lowercase__		,    lowercase__		,    lowercase__  :   Optional[Any]    		=      tax_attention_lookup(lowercase_ , lowercase_ , """encoder""" , """attention"""   )
    lowercase__  :   Optional[int]    		=      layer_norm
    lowercase__  :   Optional[Any]    		=      k.T
    lowercase__  :   int    		=      o.T
    lowercase__  :   Optional[Any]    		=      q.T
    lowercase__  :   Any    		=      v.T

    # Block i, layer 1 (MLP).
    lowercase__  :   Any    		=      tax_layer_norm_lookup(lowercase_ , lowercase_ , """encoder""" , """pre_mlp_layer_norm"""   )
    lowercase__		,    lowercase__  :   str    		=      tax_mlp_lookup(lowercase_ , lowercase_ , """encoder""" , lowercase_   )
    lowercase__  :   List[Any]    		=      layer_norm
    if split_mlp_wi:
      lowercase__  :   List[Any]    		=      wi[0].T
      lowercase__  :   Optional[int]    		=      wi[1].T
    else:
      lowercase__  :   int    		=      wi.T
    lowercase__  :   Union[str, Any]    		=      wo.T

  lowercase__  :   Dict    		=      old[
      """encoder/relpos_bias/rel_embedding"""
  ].T
  lowercase__  :   str    		=      old["""encoder/encoder_norm/scale"""]

  if not is_encoder_only:
    # Decoder.
    for i in range(lowercase_   ):
      # Block i, layer 0 (Self Attention).
      lowercase__  :   List[str]    		=      tax_layer_norm_lookup(lowercase_ , lowercase_ , """decoder""" , """pre_self_attention_layer_norm"""   )
      lowercase__		,    lowercase__		,    lowercase__		,    lowercase__  :   Any    		=      tax_attention_lookup(lowercase_ , lowercase_ , """decoder""" , """self_attention"""   )
      lowercase__  :   Union[str, Any]    		=      layer_norm
      lowercase__  :   Dict    		=      k.T
      lowercase__  :   Dict    		=      o.T
      lowercase__  :   Optional[Any]    		=      q.T
      lowercase__  :   int    		=      v.T

      # Block i, layer 1 (Cross Attention).
      lowercase__  :   List[Any]    		=      tax_layer_norm_lookup(lowercase_ , lowercase_ , """decoder""" , """pre_cross_attention_layer_norm"""   )
      lowercase__		,    lowercase__		,    lowercase__		,    lowercase__  :   Tuple    		=      tax_attention_lookup(lowercase_ , lowercase_ , """decoder""" , """encoder_decoder_attention"""   )
      lowercase__  :   Optional[int]    		=      layer_norm
      lowercase__  :   Optional[Any]    		=      k.T
      lowercase__  :   Any    		=      o.T
      lowercase__  :   str    		=      q.T
      lowercase__  :   str    		=      v.T

      # Block i, layer 2 (MLP).
      lowercase__  :   Dict    		=      tax_layer_norm_lookup(lowercase_ , lowercase_ , """decoder""" , """pre_mlp_layer_norm"""   )
      lowercase__		,    lowercase__  :   Optional[Any]    		=      tax_mlp_lookup(lowercase_ , lowercase_ , """decoder""" , lowercase_   )
      lowercase__  :   List[Any]    		=      layer_norm
      if split_mlp_wi:
        lowercase__  :   str    		=      wi[0].T
        lowercase__  :   Any    		=      wi[1].T
      else:
        lowercase__  :   int    		=      wi.T
      lowercase__  :   List[str]    		=      wo.T

    lowercase__  :   List[str]    		=      old["""decoder/decoder_norm/scale"""]
    lowercase__  :   Union[str, Any]    		=      old[
        """decoder/relpos_bias/rel_embedding"""
    ].T

    # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
    if "decoder/logits_dense/kernel" in old:
      lowercase__  :   Any    		=      old["""decoder/logits_dense/kernel"""].T

  return new

def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	int:


  '''simple docstring'''






  lowercase__  :   Optional[Any]    		=      collections.OrderedDict([(k, torch.from_numpy(v.copy()   )) for (k, v) in converted_params.items()]   )

  # Add what is missing.
  if "encoder.embed_tokens.weight" not in state_dict:
    lowercase__  :   List[Any]    		=      state_dict["""shared.weight"""]

  if not is_encoder_only:
    if "decoder.embed_tokens.weight" not in state_dict:
      lowercase__  :   Any    		=      state_dict["""shared.weight"""]

    if "lm_head.weight" not in state_dict:  # For old 1.0 models.
      print("""Using shared word embeddings as lm_head."""   )
      lowercase__  :   Optional[Any]    		=      state_dict["""shared.weight"""]

  return state_dict

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_   )		->	Any:


  '''simple docstring'''






  lowercase__  :   Union[str, Any]    		=      checkpoints.load_tax_checkpoint(lowercase_   )
  lowercase__  :   Optional[int]    		=      convert_tax_to_pytorch(lowercase_ , num_layers=config.num_layers , is_encoder_only=lowercase_   )
  lowercase__  :   str    		=      make_state_dict(lowercase_ , lowercase_   )
  model.load_state_dict(lowercase_ , strict=lowercase_   )

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_ = False   )		->	Dict:


  '''simple docstring'''






  lowercase__  :   Optional[int]    		=      TaConfig.from_json_file(lowercase_   )
  print(F'Building PyTorch model from configuration: {config}'   )
  # Non-v1.1 checkpoints could also use T5Model, but this works for all.
  # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
  if is_encoder_only:
    lowercase__  :   List[Any]    		=      TaEncoderModel(lowercase_   )
  else:
    lowercase__  :   Optional[Any]    		=      TaForConditionalGeneration(lowercase_   )

  # Load weights from tf checkpoint
  load_tax_weights_in_ta(lowercase_ , lowercase_ , lowercase_ , lowercase_   )

  # Save pytorch-model
  print(F'Save PyTorch model to {pytorch_dump_path}'   )
  model.save_pretrained(lowercase_   )

  # Verify that we can load the checkpoint.
  model.from_pretrained(lowercase_   )
  print("""Done"""   )


if __name__ == "__main__":
     lowerCamelCase__    :		List[str]          =   argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
     # Required parameters
     parser.add_argument(
         """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
     )
     parser.add_argument(
         """--config_file""",
         default=None,
         type=str,
         required=True,
         help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
     )
     parser.add_argument(
         """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     parser.add_argument(
         """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
     )
     lowerCamelCase__    :		Optional[int]          =   parser.parse_args()
     convert_tax_checkpoint_to_pytorch(
         args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
     )








from abc import ABC, abstractmethod
from argparse import ArgumentParser



class       _snake_case   (	UpperCAmelCase_							):


 @staticmethod
 @abstractmethod
 def      lowercase__     (					SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   raise NotImplementedError()



 @abstractmethod
 def      lowercase__     (					self):


   '''simple docstring'''




   raise NotImplementedError()






