
"""simple docstring"""
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a   :      List[str]       =				logging.get_logger(__name__)

_a   :      List[Any]       =				{}






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			Any	=       """fsmt_decoder"""
    def __init__(     self    ,     snake_case_=0    ,     snake_case_=0  ):
      super().__init__()
      _snake_case  :    Tuple            = vocab_size
      _snake_case  :    List[Any]            = bos_token_id






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			List[str]	=       """fsmt"""
    __lowercase				:			Optional[Any]	=       {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
    def __init__(     self    ,     snake_case_=["en", "de"]    ,     snake_case_=4_20_24    ,     snake_case_=4_20_24    ,     snake_case_="relu"    ,     snake_case_=10_24    ,     snake_case_=2_00    ,     snake_case_=10_24    ,     snake_case_=40_96    ,     snake_case_=12    ,     snake_case_=16    ,     snake_case_=0.0    ,     snake_case_=40_96    ,     snake_case_=12    ,     snake_case_=16    ,     snake_case_=0.0    ,     snake_case_=0.0    ,     snake_case_=0.1    ,     snake_case_=0.0    ,     snake_case_=0.02    ,     snake_case_=2    ,     snake_case_=True    ,     snake_case_=True    ,     snake_case_=False    ,     snake_case_=5    ,     snake_case_=1.0    ,     snake_case_=False    ,     snake_case_=True    ,     snake_case_=1    ,     snake_case_=0    ,     snake_case_=2    ,     snake_case_=2    ,     **snake_case_    ,     ):
      _snake_case  :    str            = langs
      _snake_case  :    Tuple            = src_vocab_size
      _snake_case  :    Tuple            = tgt_vocab_size
      _snake_case  :    Optional[Any]            = d_model  # encoder_embed_dim and decoder_embed_dim

      _snake_case  :    Any            = encoder_ffn_dim
      _snake_case  :    Optional[int]            = encoder_layers
      _snake_case  :    Union[str, Any]            = encoder_attention_heads
      _snake_case  :    List[Any]            = encoder_layerdrop
      _snake_case  :    List[Any]            = decoder_layerdrop
      _snake_case  :    Optional[Any]            = decoder_ffn_dim
      _snake_case  :    Tuple            = decoder_layers
      _snake_case  :    Tuple            = decoder_attention_heads
      _snake_case  :    str            = max_position_embeddings
      _snake_case  :    List[Any]            = init_std  # Normal(0, this parameter)
      _snake_case  :    Optional[Any]            = activation_function

      _snake_case  :    int            = DecoderConfig(vocab_size=snake_case_    ,     bos_token_id=snake_case_  )
      if "decoder" in common_kwargs:
        del common_kwargs["decoder"]

      _snake_case  :    List[str]            = scale_embedding  # scale factor will be sqrt(d_model) if True

      # 3 Types of Dropout
      _snake_case  :    Any            = attention_dropout
      _snake_case  :    Optional[int]            = activation_dropout
      _snake_case  :    Optional[Any]            = dropout

      _snake_case  :    List[Any]            = use_cache
      super().__init__(
          pad_token_id=snake_case_    ,     bos_token_id=snake_case_    ,     eos_token_id=snake_case_    ,     decoder_start_token_id=snake_case_    ,     is_encoder_decoder=snake_case_    ,     tie_word_embeddings=snake_case_    ,     forced_eos_token_id=snake_case_    ,     max_length=snake_case_    ,     num_beams=snake_case_    ,     length_penalty=snake_case_    ,     early_stopping=snake_case_    ,     **snake_case_    ,     )


    def        lowerCamelCase__						(     self  ):
      _snake_case  :    str            = copy.deepcopy(self.__dict__  )
      _snake_case  :    str            = self.decoder.to_dict()
      _snake_case  :    int            = self.__class__.model_type
      return output


"""simple docstring"""


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def        __a (		A     ,					A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				TaConfig.from_pretrained(A   )
     lowercase__								=				FlaxTaForConditionalGeneration(config=A   )
     lowercase__								=				checkpoints.load_tax_checkpoint(A   )

     lowercase__								=				"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

     # Encoder
     for layer_index in range(config.num_layers   ):
          lowercase__								=				f'''layers_{str(A   )}'''

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

          # Layer Normalization
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

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

          lowercase__								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

          # Assigning
          lowercase__								=				tax_attention_key
          lowercase__								=				tax_attention_out
          lowercase__								=				tax_attention_query
          lowercase__								=				tax_attention_value

          lowercase__								=				tax_attention_layer_norm

          if split_mlp_wi:
               lowercase__								=				tax_mlp_wi_a
               lowercase__								=				tax_mlp_wi_a
          else:
               lowercase__								=				tax_mlp_wi

          lowercase__								=				tax_mlp_wo
          lowercase__								=				tax_mlp_layer_norm

     # Only for layer 0:
     lowercase__								=				tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
     lowercase__								=				tax_encoder_rel_embedding

     # Assigning
     lowercase__								=				tax_model["target"]["encoder"]["encoder_norm"]["scale"]
     lowercase__								=				tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_decoder_layers   ):
          lowercase__								=				f'''layers_{str(A   )}'''

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

          # Layer Normalization
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
              "scale"
          ]

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

          # Layer Normalization
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

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

          lowercase__								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

          # Assigning
          lowercase__								=				tax_attention_key
          lowercase__								=				tax_attention_out
          lowercase__								=				tax_attention_query
          lowercase__								=				tax_attention_value

          lowercase__								=				tax_pre_attention_layer_norm

          lowercase__								=				tax_enc_dec_attention_key
          lowercase__								=				tax_enc_dec_attention_out
          lowercase__								=				tax_enc_dec_attention_query
          lowercase__								=				tax_enc_dec_attention_value

          lowercase__								=				tax_cross_layer_norm

          if split_mlp_wi:
               lowercase__								=				tax_mlp_wi_a
               lowercase__								=				tax_mlp_wi_a
          else:
               lowercase__								=				tax_mlp_wi

          lowercase__								=				tax_mlp_wo

          lowercase__								=				txa_mlp_layer_norm

     # Decoder Normalization
     lowercase__								=				tax_model["target"]["decoder"]["decoder_norm"]["scale"]
     lowercase__								=				txa_decoder_norm

     # Only for layer 0:
     lowercase__								=				tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
     lowercase__								=				tax_decoder_rel_embedding

     # Token Embeddings
     lowercase__								=				tax_model["target"]["token_embedder"]["embedding"]
     lowercase__								=				txa_token_embeddings

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

     flax_model.save_pretrained(A   )
     print("T5X Model was sucessfully converted!"   )


if __name__ == "__main__":
 lowerCAmelCase_:       List[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_:       Dict			     =       parser.parse_args()
 convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




