'''simple docstring'''
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM



def  __lowerCamelCase       (      _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  int							,    _UpperCamelCase  :  Optional[int]					):


      '''simple docstring'''


      UpperCAmelCase_            =  AutoConfig.from_pretrained(_UpperCamelCase					)
      UpperCAmelCase_            =  FlaxAutoModelForSeqaSeqLM.from_config(config=_UpperCamelCase					)
      UpperCAmelCase_            =  checkpoints.load_tax_checkpoint(_UpperCamelCase					)

      UpperCAmelCase_            =  '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

      if config.model_type == "t5":
            UpperCAmelCase_            =  '''SelfAttention'''
      if config.model_type == "longt5" and config.encoder_attention_type == "local":
            UpperCAmelCase_            =  '''LocalSelfAttention'''
      elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            UpperCAmelCase_            =  '''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					):
            UpperCAmelCase_            =  F"""layers_{str(_UpperCamelCase					)}"""

            # Self-Attention
            UpperCAmelCase_            =  tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
            UpperCAmelCase_            =  tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
            UpperCAmelCase_            =  tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
            UpperCAmelCase_            =  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":
                  UpperCAmelCase_            =  tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

            # Layer Normalization
            UpperCAmelCase_            =  tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

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

            UpperCAmelCase_            =  tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

            # Layer Normalization
            UpperCAmelCase_            =  tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

            # Assigning
            UpperCAmelCase_            =  flax_model.params['''encoder''']['''block'''][str(_UpperCamelCase					)]['''layer''']
            UpperCAmelCase_            =  tax_attention_key
            UpperCAmelCase_            =  tax_attention_out
            UpperCAmelCase_            =  tax_attention_query
            UpperCAmelCase_            =  tax_attention_value

            UpperCAmelCase_            =  tax_attention_layer_norm

            # Global input layer norm
            if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                  UpperCAmelCase_            =  tax_global_layer_norm

            if split_mlp_wi:
                  UpperCAmelCase_            =  tax_mlp_wi_a
                  UpperCAmelCase_            =  tax_mlp_wi_a
            else:
                  UpperCAmelCase_            =  tax_mlp_wi

            UpperCAmelCase_            =  tax_mlp_wo
            UpperCAmelCase_            =  tax_mlp_layer_norm

            UpperCAmelCase_            =  flax_model_encoder_layer_block

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

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

      # Assigning
      UpperCAmelCase_            =  tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
      UpperCAmelCase_            =  tax_encoder_norm

      # Decoder
      for layer_index in range(config.num_layers					):
            UpperCAmelCase_            =  F"""layers_{str(_UpperCamelCase					)}"""

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

            # Layer Normalization
            UpperCAmelCase_            =  tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
                '''scale'''
            ]

            # Encoder-Decoder-Attention
            UpperCAmelCase_            =  tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
            UpperCAmelCase_            =  tax_enc_dec_attention_module['''key''']['''kernel''']
            UpperCAmelCase_            =  tax_enc_dec_attention_module['''out''']['''kernel''']
            UpperCAmelCase_            =  tax_enc_dec_attention_module['''query''']['''kernel''']
            UpperCAmelCase_            =  tax_enc_dec_attention_module['''value''']['''kernel''']

            # Layer Normalization
            UpperCAmelCase_            =  tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

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

            UpperCAmelCase_            =  tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

            # Layer Normalization
            UpperCAmelCase_            =  tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

            # Assigning
            UpperCAmelCase_            =  flax_model.params['''decoder''']['''block'''][str(_UpperCamelCase					)]['''layer''']
            UpperCAmelCase_            =  tax_attention_key
            UpperCAmelCase_            =  tax_attention_out
            UpperCAmelCase_            =  tax_attention_query
            UpperCAmelCase_            =  tax_attention_value

            UpperCAmelCase_            =  tax_pre_attention_layer_norm

            UpperCAmelCase_            =  tax_enc_dec_attention_key
            UpperCAmelCase_            =  tax_enc_dec_attention_out
            UpperCAmelCase_            =  tax_enc_dec_attention_query
            UpperCAmelCase_            =  tax_enc_dec_attention_value

            UpperCAmelCase_            =  tax_cross_layer_norm

            if split_mlp_wi:
                  UpperCAmelCase_            =  tax_mlp_wi_a
                  UpperCAmelCase_            =  tax_mlp_wi_a
            else:
                  UpperCAmelCase_            =  tax_mlp_wi

            UpperCAmelCase_            =  tax_mlp_wo

            UpperCAmelCase_            =  txa_mlp_layer_norm

            UpperCAmelCase_            =  flax_model_decoder_layer_block

      # Decoder Normalization
      UpperCAmelCase_            =  tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
      UpperCAmelCase_            =  txa_decoder_norm

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

      # Token Embeddings
      UpperCAmelCase_            =  tax_model['''target''']['''token_embedder''']['''embedding''']
      UpperCAmelCase_            =  txa_token_embeddings

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

      flax_model.save_pretrained(_UpperCamelCase					)
      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__  :      int        =					parser.parse_args()
      convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)






'''simple docstring'''
from typing import List, Optional, Union

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


lowercase__  :      int        =					logging.get_logger(__name__)

lowercase__  :      List[str]        =					{
    "huggingface/time-series-transformer-tourism-monthly": (
        "https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json"
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	'''time_series_transformer'''
  lowerCAmelCase__										=	{
      '''hidden_size''': '''d_model''',
      '''num_attention_heads''': '''encoder_attention_heads''',
      '''num_hidden_layers''': '''encoder_layers''',
  }






  def __init__(      self			:       Optional[int]  ,							UpperCAmelCase__			:       Optional[int] = None  ,							UpperCAmelCase__			:       Optional[int] = None  ,							UpperCAmelCase__			:       str = "student_t"  ,							UpperCAmelCase__			:       str = "nll"  ,							UpperCAmelCase__			:       int = 1  ,							UpperCAmelCase__			:       List[int] = [1, 2, 3, 4, 5, 6, 7]  ,							UpperCAmelCase__			:       Optional[Union[str, bool]] = "mean"  ,							UpperCAmelCase__			:       int = 0  ,							UpperCAmelCase__			:       int = 0  ,							UpperCAmelCase__			:       int = 0  ,							UpperCAmelCase__			:       int = 0  ,							UpperCAmelCase__			:       Optional[List[int]] = None  ,							UpperCAmelCase__			:       Optional[List[int]] = None  ,							UpperCAmelCase__			:       int = 32  ,							UpperCAmelCase__			:       int = 32  ,							UpperCAmelCase__			:       int = 2  ,							UpperCAmelCase__			:       int = 2  ,							UpperCAmelCase__			:       int = 2  ,							UpperCAmelCase__			:       int = 2  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       str = "gelu"  ,							UpperCAmelCase__			:       int = 64  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       int = 100  ,							UpperCAmelCase__			:       float = 0.02  ,							UpperCAmelCase__			:       Union[str, Any]=True  ,							**UpperCAmelCase__			:       Tuple  ,							)				->Optional[int]:
        # time series specific configuration
        UpperCAmelCase_            =  prediction_length
        UpperCAmelCase_            =  context_length or prediction_length
        UpperCAmelCase_            =  distribution_output
        UpperCAmelCase_            =  loss
        UpperCAmelCase_            =  input_size
        UpperCAmelCase_            =  num_time_features
        UpperCAmelCase_            =  lags_sequence
        UpperCAmelCase_            =  scaling
        UpperCAmelCase_            =  num_dynamic_real_features
        UpperCAmelCase_            =  num_static_real_features
        UpperCAmelCase_            =  num_static_categorical_features
        if cardinality and num_static_categorical_features > 0:
              if len(UpperCAmelCase__  ) != num_static_categorical_features:
                    raise ValueError(
                        '''The cardinality should be a list of the same length as `num_static_categorical_features`'''  )
              UpperCAmelCase_            =  cardinality
        else:
              UpperCAmelCase_            =  [0]
        if embedding_dimension and num_static_categorical_features > 0:
              if len(UpperCAmelCase__  ) != num_static_categorical_features:
                    raise ValueError(
                        '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''  )
              UpperCAmelCase_            =  embedding_dimension
        else:
              UpperCAmelCase_            =  [min(50  ,							(cat + 1) // 2  ) for cat in self.cardinality]
        UpperCAmelCase_            =  num_parallel_samples

        # Transformer architecture configuration
        UpperCAmelCase_            =  input_size * len(UpperCAmelCase__  ) + self._number_of_features
        UpperCAmelCase_            =  d_model
        UpperCAmelCase_            =  encoder_attention_heads
        UpperCAmelCase_            =  decoder_attention_heads
        UpperCAmelCase_            =  encoder_ffn_dim
        UpperCAmelCase_            =  decoder_ffn_dim
        UpperCAmelCase_            =  encoder_layers
        UpperCAmelCase_            =  decoder_layers

        UpperCAmelCase_            =  dropout
        UpperCAmelCase_            =  attention_dropout
        UpperCAmelCase_            =  activation_dropout
        UpperCAmelCase_            =  encoder_layerdrop
        UpperCAmelCase_            =  decoder_layerdrop

        UpperCAmelCase_            =  activation_function
        UpperCAmelCase_            =  init_std

        UpperCAmelCase_            =  use_cache

        super().__init__(is_encoder_decoder=UpperCAmelCase__  ,							**UpperCAmelCase__  )






  @property
  def  lowerCAmelCase__ (      self			:       List[str]  )				->int:
        return (
            sum(self.embedding_dimension  )
            + self.num_dynamic_real_features
            + self.num_time_features
            + self.num_static_real_features
            + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
        )





