






"""simple docstring"""

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowercase__					:						List[Any]												=			logging.get_logger(__name__)

lowercase__					:						Optional[int]												=			{
    '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json''',
    # See all Marian models at https://huggingface.co/models?filter=marian
}




class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Optional[Any]	      =	"""marian"""
 _lowerCAmelCase     :    Any	      =	["""past_key_values"""]
 _lowerCAmelCase     :    List[str]	      =	{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

 def __init__(  self	: List[str]						,  lowercase_	: Any=58101						,  lowercase_	: List[Any]=None						,  lowercase_	: List[str]=1024						,  lowercase_	: str=12						,  lowercase_	: Optional[Any]=4096						,  lowercase_	: Tuple=16						,  lowercase_	: int=12						,  lowercase_	: List[Any]=4096						,  lowercase_	: str=16						,  lowercase_	: Dict=0.0						,  lowercase_	: Dict=0.0						,  lowercase_	: Optional[int]=True						,  lowercase_	: List[str]=True						,  lowercase_	: List[str]="gelu"						,  lowercase_	: int=1024						,  lowercase_	: Any=0.1						,  lowercase_	: Tuple=0.0						,  lowercase_	: List[Any]=0.0						,  lowercase_	: Any=0.02						,  lowercase_	: Optional[Any]=58100						,  lowercase_	: Dict=False						,  lowercase_	: Union[str, Any]=58100						,  lowercase_	: Dict=0						,  lowercase_	: Optional[Any]=0						,  lowercase_	: str=True						,  **lowercase_	: Union[str, Any]						,  ):
     snake_case_				:  List[str]       =			vocab_size
     snake_case_				:  Any       =			decoder_vocab_size or vocab_size
     snake_case_				:  Dict       =			max_position_embeddings
     snake_case_				:  Optional[Any]       =			d_model
     snake_case_				:  Optional[int]       =			encoder_ffn_dim
     snake_case_				:  Tuple       =			encoder_layers
     snake_case_				:  Tuple       =			encoder_attention_heads
     snake_case_				:  Tuple       =			decoder_ffn_dim
     snake_case_				:  Dict       =			decoder_layers
     snake_case_				:  int       =			decoder_attention_heads
     snake_case_				:  str       =			dropout
     snake_case_				:  Tuple       =			attention_dropout
     snake_case_				:  Any       =			activation_dropout
     snake_case_				:  List[str]       =			activation_function
     snake_case_				:  List[Any]       =			init_std
     snake_case_				:  Union[str, Any]       =			encoder_layerdrop
     snake_case_				:  str       =			decoder_layerdrop
     snake_case_				:  Union[str, Any]       =			use_cache
     snake_case_				:  Tuple       =			encoder_layers
     snake_case_				:  List[str]       =			scale_embedding  # scale factor will be sqrt(d_model) if True
     snake_case_				:  int       =			share_encoder_decoder_embeddings
     super().__init__(
         pad_token_id=_SCREAMING_SNAKE_CASE						,  eos_token_id=_SCREAMING_SNAKE_CASE						,  is_encoder_decoder=_SCREAMING_SNAKE_CASE						,  decoder_start_token_id=_SCREAMING_SNAKE_CASE						,  forced_eos_token_id=_SCREAMING_SNAKE_CASE						,  **_SCREAMING_SNAKE_CASE						,  )





class  _UpperCAmelCase (    lowerCAmelCase__):

 @property
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
 def 		_snake_case      (  self	: Optional[int]       ):






     if self.task in ["default", "seq2seq-lm"]:
         snake_case_				:  Tuple       =			OrderedDict(
             [
                 ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
             ]       )

         if self.use_past:
             snake_case_				:  Dict       =			{0: '''batch'''}
             snake_case_				:  Optional[int]       =			{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
         else:
             snake_case_				:  Dict       =			{0: '''batch''', 1: '''decoder_sequence'''}
             snake_case_				:  Tuple       =			{0: '''batch''', 1: '''decoder_sequence'''}

         if self.use_past:
             self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE						,  direction='''inputs'''       )
     elif self.task == "causal-lm":
         # TODO: figure this case out.
         snake_case_				:  Tuple       =			OrderedDict(
             [
                 ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
             ]       )
         if self.use_past:
             snake_case_,			snake_case_				:  Any       =			self.num_layers
             for i in range(_SCREAMING_SNAKE_CASE       ):
                 snake_case_				:  str       =			{0: '''batch''', 2: '''past_sequence + sequence'''}
                 snake_case_				:  Union[str, Any]       =			{0: '''batch''', 2: '''past_sequence + sequence'''}
     else:
         snake_case_				:  List[Any]       =			OrderedDict(
             [
                 ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                 ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
             ]       )

     return common_inputs

 @property
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
 def 		_snake_case      (  self	: List[Any]       ):






     if self.task in ["default", "seq2seq-lm"]:
         snake_case_				:  List[str]       =			super().outputs
     else:
         snake_case_				:  Dict       =			super(_SCREAMING_SNAKE_CASE						,  self       ).outputs
         if self.use_past:
             snake_case_,			snake_case_				:  Any       =			self.num_layers
             for i in range(_SCREAMING_SNAKE_CASE       ):
                 snake_case_				:  Optional[int]       =			{0: '''batch''', 2: '''past_sequence + sequence'''}
                 snake_case_				:  Tuple       =			{0: '''batch''', 2: '''past_sequence + sequence'''}
     return common_outputs

 def 		_snake_case      (  self	: str						,  lowercase_	: str						,  lowercase_	: List[str] = -1						,  lowercase_	: List[str] = -1						,  lowercase_	: Dict = False						,  lowercase_	: Dict = None						,  ):
     snake_case_				:  Dict       =			self._generate_dummy_inputs_for_encoder_and_decoder(
         _SCREAMING_SNAKE_CASE						,  _SCREAMING_SNAKE_CASE						,  _SCREAMING_SNAKE_CASE						,  _SCREAMING_SNAKE_CASE						,  _SCREAMING_SNAKE_CASE       )

     # Generate decoder inputs
     snake_case_				:  Optional[int]       =			seq_length if not self.use_past else 1
     snake_case_				:  Any       =			self._generate_dummy_inputs_for_encoder_and_decoder(
         _SCREAMING_SNAKE_CASE						,  _SCREAMING_SNAKE_CASE						,  _SCREAMING_SNAKE_CASE						,  _SCREAMING_SNAKE_CASE						,  _SCREAMING_SNAKE_CASE       )
     snake_case_				:  List[str]       =			{f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
     snake_case_				:  List[Any]       =			dict(**_SCREAMING_SNAKE_CASE						,  **_SCREAMING_SNAKE_CASE       )

     if self.use_past:
         if not is_torch_available():
             raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''       )
         else:
             import torch
         snake_case_,			snake_case_				:  Any       =			common_inputs['''input_ids'''].shape
         snake_case_				:  int       =			common_inputs['''decoder_input_ids'''].shape[1]
         snake_case_,			snake_case_				:  Any       =			self.num_attention_heads
         snake_case_				:  List[str]       =			(
             batch,
             num_encoder_attention_heads,
             encoder_seq_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )
         snake_case_				:  List[str]       =			decoder_seq_length + 3
         snake_case_				:  Any       =			(
             batch,
             num_decoder_attention_heads,
             decoder_past_length,
             self._config.hidden_size // num_decoder_attention_heads,
         )

         snake_case_				:  List[Any]       =			torch.cat(
             [common_inputs['''decoder_attention_mask'''], torch.ones(_SCREAMING_SNAKE_CASE						,  _SCREAMING_SNAKE_CASE       )]						,  dim=1       )

         snake_case_				:  Optional[int]       =			[]
         # If the number of encoder and decoder layers are present in the model configuration, both are considered
         snake_case_,			snake_case_				:  Tuple       =			self.num_layers
         snake_case_				:  List[str]       =			min(_SCREAMING_SNAKE_CASE						,  _SCREAMING_SNAKE_CASE       )
         snake_case_				:  str       =			max(_SCREAMING_SNAKE_CASE						,  _SCREAMING_SNAKE_CASE       ) - min_num_layers
         snake_case_				:  Union[str, Any]       =			'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

         for _ in range(_SCREAMING_SNAKE_CASE       ):
             common_inputs["past_key_values"].append(
                 (
                     torch.zeros(_SCREAMING_SNAKE_CASE       ),
                     torch.zeros(_SCREAMING_SNAKE_CASE       ),
                     torch.zeros(_SCREAMING_SNAKE_CASE       ),
                     torch.zeros(_SCREAMING_SNAKE_CASE       ),
                 )       )
         # TODO: test this.
         snake_case_				:  Any       =			encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
         for _ in range(_SCREAMING_SNAKE_CASE						,  _SCREAMING_SNAKE_CASE       ):
             common_inputs["past_key_values"].append((torch.zeros(_SCREAMING_SNAKE_CASE       ), torch.zeros(_SCREAMING_SNAKE_CASE       ))       )
     return common_inputs

 def 		_snake_case      (  self	: str						,  lowercase_	: str						,  lowercase_	: int = -1						,  lowercase_	: Dict = -1						,  lowercase_	: List[str] = False						,  lowercase_	: Optional[Any] = None						,  ):
     snake_case_				:  Union[str, Any]       =			self._generate_dummy_inputs_for_encoder_and_decoder(
         _SCREAMING_SNAKE_CASE						,  _SCREAMING_SNAKE_CASE						,  _SCREAMING_SNAKE_CASE						,  _SCREAMING_SNAKE_CASE						,  _SCREAMING_SNAKE_CASE       )

     if self.use_past:
         if not is_torch_available():
             raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''       )
         else:
             import torch
         snake_case_,			snake_case_				:  Union[str, Any]       =			common_inputs['''input_ids'''].shape
         # Not using the same length for past_key_values
         snake_case_				:  Tuple       =			seqlen + 2
         snake_case_,			snake_case_				:  str       =			self.num_layers
         snake_case_,			snake_case_				:  Optional[int]       =			self.num_attention_heads
         snake_case_				:  int       =			(
             batch,
             num_encoder_attention_heads,
             past_key_values_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )

         snake_case_				:  Union[str, Any]       =			common_inputs['''attention_mask'''].dtype
         snake_case_				:  Optional[int]       =			torch.cat(
             [common_inputs['''attention_mask'''], torch.ones(_SCREAMING_SNAKE_CASE						,  _SCREAMING_SNAKE_CASE						,  dtype=_SCREAMING_SNAKE_CASE       )]						,  dim=1       )
         snake_case_				:  List[Any]       =			[
             (torch.zeros(_SCREAMING_SNAKE_CASE       ), torch.zeros(_SCREAMING_SNAKE_CASE       )) for _ in range(_SCREAMING_SNAKE_CASE       )
         ]
     return common_inputs

 def 		_snake_case      (  self	: Union[str, Any]						,  lowercase_	: Union[str, Any]						,  lowercase_	: int = -1						,  lowercase_	: List[Any] = -1						,  lowercase_	: List[Any] = False						,  lowercase_	: str = None						,  ):
     snake_case_				:  Union[str, Any]       =			compute_effective_axis_dimension(
         _SCREAMING_SNAKE_CASE						,  fixed_dimension=OnnxConfig.default_fixed_batch						,  num_token_to_add=0       )

     # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
     snake_case_				:  Optional[int]       =			tokenizer.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE       )
     snake_case_				:  List[str]       =			compute_effective_axis_dimension(
         _SCREAMING_SNAKE_CASE						,  fixed_dimension=OnnxConfig.default_fixed_sequence						,  num_token_to_add=_SCREAMING_SNAKE_CASE       )

     # Generate dummy inputs according to compute batch and sequence
     snake_case_				:  List[str]       =			[''' '''.join([tokenizer.unk_token]       ) * seq_length] * batch_size
     snake_case_				:  Tuple       =			dict(tokenizer(_SCREAMING_SNAKE_CASE						,  return_tensors=_SCREAMING_SNAKE_CASE       )       )
     return common_inputs

 def 		_snake_case      (  self	: List[str]						,  lowercase_	: Optional[int]						,  lowercase_	: Union[str, Any] = -1						,  lowercase_	: Tuple = -1						,  lowercase_	: Optional[Any] = False						,  lowercase_	: List[Any] = None						,  ):






     if self.task in ["default", "seq2seq-lm"]:
         snake_case_				:  Union[str, Any]       =			self._generate_dummy_inputs_for_default_and_seqaseq_lm(
             _SCREAMING_SNAKE_CASE						,  batch_size=_SCREAMING_SNAKE_CASE						,  seq_length=_SCREAMING_SNAKE_CASE						,  is_pair=_SCREAMING_SNAKE_CASE						,  framework=_SCREAMING_SNAKE_CASE       )

     else:
         snake_case_				:  Optional[int]       =			self._generate_dummy_inputs_for_causal_lm(
             _SCREAMING_SNAKE_CASE						,  batch_size=_SCREAMING_SNAKE_CASE						,  seq_length=_SCREAMING_SNAKE_CASE						,  is_pair=_SCREAMING_SNAKE_CASE						,  framework=_SCREAMING_SNAKE_CASE       )

     return common_inputs

 def 		_snake_case      (  self	: Optional[Any]						,  lowercase_	: List[str]						,  lowercase_	: str						,  lowercase_	: List[str]						,  lowercase_	: Optional[int]       ):






     if self.task in ["default", "seq2seq-lm"]:
         snake_case_				:  Dict       =			super()._flatten_past_key_values_(_SCREAMING_SNAKE_CASE						,  _SCREAMING_SNAKE_CASE						,  _SCREAMING_SNAKE_CASE						,  _SCREAMING_SNAKE_CASE       )
     else:
         snake_case_				:  int       =			super(_SCREAMING_SNAKE_CASE						,  self       )._flatten_past_key_values_(
             _SCREAMING_SNAKE_CASE						,  _SCREAMING_SNAKE_CASE						,  _SCREAMING_SNAKE_CASE						,  _SCREAMING_SNAKE_CASE       )






 @property
 def 		_snake_case      (  self	: Any       ):
     return 1E-4










"""simple docstring"""

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def __lowercase    (							_a	,    _a	,    _a					):
    snake_case_				:  Tuple       =			AutoConfig.from_pretrained(_a					)
    snake_case_				:  Tuple       =			FlaxAutoModelForSeqaSeqLM.from_config(config=_a					)
    snake_case_				:  Union[str, Any]       =			checkpoints.load_tax_checkpoint(_a					)

    snake_case_				:  Optional[int]       =			'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

    if config.model_type == "t5":
        snake_case_				:  str       =			'''SelfAttention'''
    if config.model_type == "longt5" and config.encoder_attention_type == "local":
        snake_case_				:  List[str]       =			'''LocalSelfAttention'''
    elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        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					):
        snake_case_				:  Any       =			f"layers_{str(_a					)}"

        # Self-Attention
        snake_case_				:  Dict       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
        snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
        snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
        snake_case_				:  Optional[int]       =			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":
            snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

        # Layer Normalization
        snake_case_				:  List[str]       =			tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

        if split_mlp_wi:
            snake_case_				:  int       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            snake_case_				:  List[str]       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            snake_case_				:  int       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

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

        # Layer Normalization
        snake_case_				:  Optional[Any]       =			tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        snake_case_				:  Optional[Any]       =			flax_model.params['''encoder''']['''block'''][str(_a					)]['''layer''']
        snake_case_				:  List[str]       =			tax_attention_key
        snake_case_				:  Optional[Any]       =			tax_attention_out
        snake_case_				:  Any       =			tax_attention_query
        snake_case_				:  str       =			tax_attention_value

        snake_case_				:  Dict       =			tax_attention_layer_norm

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

        if split_mlp_wi:
            snake_case_				:  Any       =			tax_mlp_wi_a
            snake_case_				:  List[Any]       =			tax_mlp_wi_a
        else:
            snake_case_				:  Union[str, Any]       =			tax_mlp_wi

        snake_case_				:  List[Any]       =			tax_mlp_wo
        snake_case_				:  int       =			tax_mlp_layer_norm

        snake_case_				:  Any       =			flax_model_encoder_layer_block

    # Only for layer 0:
    snake_case_				:  Optional[int]       =			tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
    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":
        snake_case_				:  List[str]       =			tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
        snake_case_				:  Tuple       =			tax_encoder_global_rel_embedding

    # Assigning
    snake_case_				:  Dict       =			tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
    snake_case_				:  Any       =			tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_layers					):
        snake_case_				:  Tuple       =			f"layers_{str(_a					)}"

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

        # Layer Normalization
        snake_case_				:  str       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
            '''scale'''
        ]

        # Encoder-Decoder-Attention
        snake_case_				:  Any       =			tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
        snake_case_				:  Optional[Any]       =			tax_enc_dec_attention_module['''key''']['''kernel''']
        snake_case_				:  str       =			tax_enc_dec_attention_module['''out''']['''kernel''']
        snake_case_				:  Union[str, Any]       =			tax_enc_dec_attention_module['''query''']['''kernel''']
        snake_case_				:  List[str]       =			tax_enc_dec_attention_module['''value''']['''kernel''']

        # Layer Normalization
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

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

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

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

        # Assigning
        snake_case_				:  Dict       =			flax_model.params['''decoder''']['''block'''][str(_a					)]['''layer''']
        snake_case_				:  int       =			tax_attention_key
        snake_case_				:  List[Any]       =			tax_attention_out
        snake_case_				:  Any       =			tax_attention_query
        snake_case_				:  Dict       =			tax_attention_value

        snake_case_				:  str       =			tax_pre_attention_layer_norm

        snake_case_				:  Any       =			tax_enc_dec_attention_key
        snake_case_				:  str       =			tax_enc_dec_attention_out
        snake_case_				:  int       =			tax_enc_dec_attention_query
        snake_case_				:  Any       =			tax_enc_dec_attention_value

        snake_case_				:  Optional[Any]       =			tax_cross_layer_norm

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

        snake_case_				:  Dict       =			tax_mlp_wo

        snake_case_				:  List[Any]       =			txa_mlp_layer_norm

        snake_case_				:  Optional[int]       =			flax_model_decoder_layer_block

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

    # Only for layer 0:
    snake_case_				:  str       =			tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
    snake_case_				:  Optional[Any]       =			tax_decoder_rel_embedding

    # Token Embeddings
    snake_case_				:  Union[str, Any]       =			tax_model['''target''']['''token_embedder''']['''embedding''']
    snake_case_				:  Optional[int]       =			txa_token_embeddings

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

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


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





