


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig



def 			_lowerCAmelCase						(							lowerCAmelCase_							:int		,						lowerCAmelCase_							:List[Any]		,						lowerCAmelCase_							:str							)->Dict:
   '''simple docstring'''






   snake_case_					    =					TaConfig.from_pretrained(__a							)
   snake_case_					    =					FlaxTaForConditionalGeneration(config=__a							)
   snake_case_					    =					checkpoints.load_tax_checkpoint(__a							)

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

   # Encoder
   for layer_index in range(config.num_layers							):
      snake_case_					    =					F'''layers_{str(__a							)}'''

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

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

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

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

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

      # Assigning
      snake_case_					    =					tax_attention_key
      snake_case_					    =					tax_attention_out
      snake_case_					    =					tax_attention_query
      snake_case_					    =					tax_attention_value

      snake_case_					    =					tax_attention_layer_norm

      if split_mlp_wi:
         snake_case_					    =					tax_mlp_wi_a
         snake_case_					    =					tax_mlp_wi_a
      else:
         snake_case_					    =					tax_mlp_wi

      snake_case_					    =					tax_mlp_wo
      snake_case_					    =					tax_mlp_layer_norm

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

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

   # Decoder
   for layer_index in range(config.num_decoder_layers							):
      snake_case_					    =					F'''layers_{str(__a							)}'''

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

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

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

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

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

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

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

      # Assigning
      snake_case_					    =					tax_attention_key
      snake_case_					    =					tax_attention_out
      snake_case_					    =					tax_attention_query
      snake_case_					    =					tax_attention_value

      snake_case_					    =					tax_pre_attention_layer_norm

      snake_case_					    =					tax_enc_dec_attention_key
      snake_case_					    =					tax_enc_dec_attention_out
      snake_case_					    =					tax_enc_dec_attention_query
      snake_case_					    =					tax_enc_dec_attention_value

      snake_case_					    =					tax_cross_layer_norm

      if split_mlp_wi:
         snake_case_					    =					tax_mlp_wi_a
         snake_case_					    =					tax_mlp_wi_a
      else:
         snake_case_					    =					tax_mlp_wi

      snake_case_					    =					tax_mlp_wo

      snake_case_					    =					txa_mlp_layer_norm

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

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

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

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

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


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE	: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.'''
   )
   SCREAMING_SNAKE_CASE	:Union[str, Any]  		=     parser.parse_args()
   convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)








from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


SCREAMING_SNAKE_CASE	:int  		=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE	:Optional[Any]  		=     {
    '''google/long-t5-local-base''': '''https://huggingface.co/google/long-t5-local-base/blob/main/config.json''',
    '''google/long-t5-local-large''': '''https://huggingface.co/google/long-t5-local-large/blob/main/config.json''',
    '''google/long-t5-tglobal-base''': '''https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json''',
    '''google/long-t5-tglobal-large''': '''https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json''',
}


class     __lowerCAmelCase     ( a					):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		    =       'longt5'
  _SCREAMING_SNAKE_CASE		    =       ['past_key_values']
  _SCREAMING_SNAKE_CASE		    =       {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}



  def __init__(     self       :       int    ,				_lowerCAmelCase       :       str=3_2_1_2_8    ,				_lowerCAmelCase       :       Optional[Any]=5_1_2    ,				_lowerCAmelCase       :       List[str]=6_4    ,				_lowerCAmelCase       :       Optional[int]=2_0_4_8    ,				_lowerCAmelCase       :       Any=6    ,				_lowerCAmelCase       :       Any=None    ,				_lowerCAmelCase       :       str=8    ,				_lowerCAmelCase       :       List[str]=1_2_7    ,				_lowerCAmelCase       :       int=1_6    ,				_lowerCAmelCase       :       List[str]=3_2    ,				_lowerCAmelCase       :       Any=1_2_8    ,				_lowerCAmelCase       :       Union[str, Any]=0.1    ,				_lowerCAmelCase       :       Union[str, Any]=1e-6    ,				_lowerCAmelCase       :       str=1.0    ,				_lowerCAmelCase       :       Tuple="relu"    ,				_lowerCAmelCase       :       List[str]=True    ,				_lowerCAmelCase       :       Tuple="local"    ,				_lowerCAmelCase       :       Union[str, Any]=True    ,				_lowerCAmelCase       :       Optional[int]=0    ,				_lowerCAmelCase       :       str=1    ,				**_lowerCAmelCase       :       int    ,				)   -> Optional[int]:




     """simple docstring"""
     snake_case_					    =					vocab_size
     snake_case_					    =					d_model
     snake_case_					    =					d_kv
     snake_case_					    =					d_ff
     snake_case_					    =					num_layers
     # default = symmetry
     snake_case_					    =					num_decoder_layers if num_decoder_layers is not None else self.num_layers
     snake_case_					    =					num_heads
     snake_case_					    =					local_radius
     snake_case_					    =					global_block_size
     snake_case_					    =					relative_attention_num_buckets
     snake_case_					    =					relative_attention_max_distance
     snake_case_					    =					dropout_rate
     snake_case_					    =					layer_norm_epsilon
     snake_case_					    =					initializer_factor
     snake_case_					    =					feed_forward_proj
     snake_case_					    =					encoder_attention_type
     snake_case_					    =					use_cache

     snake_case_					    =					self.feed_forward_proj.split("-"  )
     snake_case_					    =					act_info[-1]
     snake_case_					    =					act_info[0] == "gated"

     if len(_lowerCAmelCase  ) > 1 and act_info[0] != "gated" or len(_lowerCAmelCase  ) > 2:
        raise ValueError(
            F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
            "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
            "'gated-gelu' or 'relu'"  )

     # for backwards compatibility
     if feed_forward_proj == "gated-gelu":
        snake_case_					    =					"gelu_new"

     super().__init__(
         pad_token_id=_lowerCAmelCase    ,				eos_token_id=_lowerCAmelCase    ,				is_encoder_decoder=_lowerCAmelCase    ,				**_lowerCAmelCase    ,				)






class     __lowerCAmelCase     ( a					):






  """simple docstring"""





  @property
  def 					lowerCAmelCase__  (     self       :       str  )   -> Mapping[str, Mapping[int, str]]:




     """simple docstring"""
     snake_case_					    =					{
         "input_ids": {0: "batch", 1: "encoder_sequence"},
         "attention_mask": {0: "batch", 1: "encoder_sequence"},
     }
     if self.use_past:
        snake_case_					    =					"past_encoder_sequence + sequence"
        snake_case_					    =					{0: "batch"}
        snake_case_					    =					{0: "batch", 1: "past_decoder_sequence + sequence"}
     else:
        snake_case_					    =					{0: "batch", 1: "decoder_sequence"}
        snake_case_					    =					{0: "batch", 1: "decoder_sequence"}

     if self.use_past:
        self.fill_with_past_key_values_(_lowerCAmelCase    ,				direction="inputs"  )

     return common_inputs



  @property
  def 					lowerCAmelCase__  (     self       :       List[Any]  )   -> int:




     """simple docstring"""
     return 1_3




