



import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Any     ,				_SCREAMING_SNAKE_CASE   :	List[str]     ,				_SCREAMING_SNAKE_CASE   :	List[Any]				)->int:
      _lowerCAmelCase 			=  AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  FlaxAutoModelForSeqaSeqLM.from_config(config=_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  checkpoints.load_tax_checkpoint(_SCREAMING_SNAKE_CASE				)

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

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

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

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

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

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

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

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

            _lowerCAmelCase 			=  tax_attention_layer_norm

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

            if split_mlp_wi:
                  _lowerCAmelCase 			=  tax_mlp_wi_a
                  _lowerCAmelCase 			=  tax_mlp_wi_a
            else:
                  _lowerCAmelCase 			=  tax_mlp_wi

            _lowerCAmelCase 			=  tax_mlp_wo
            _lowerCAmelCase 			=  tax_mlp_layer_norm

            _lowerCAmelCase 			=  flax_model_encoder_layer_block

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

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

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

      # Decoder
      for layer_index in range(config.num_layers				):
            _lowerCAmelCase 			=  f'''layers_{str(_SCREAMING_SNAKE_CASE				)}'''

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

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

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

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

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

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

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

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

            _lowerCAmelCase 			=  tax_pre_attention_layer_norm

            _lowerCAmelCase 			=  tax_enc_dec_attention_key
            _lowerCAmelCase 			=  tax_enc_dec_attention_out
            _lowerCAmelCase 			=  tax_enc_dec_attention_query
            _lowerCAmelCase 			=  tax_enc_dec_attention_value

            _lowerCAmelCase 			=  tax_cross_layer_norm

            if split_mlp_wi:
                  _lowerCAmelCase 			=  tax_mlp_wi_a
                  _lowerCAmelCase 			=  tax_mlp_wi_a
            else:
                  _lowerCAmelCase 			=  tax_mlp_wi

            _lowerCAmelCase 			=  tax_mlp_wo

            _lowerCAmelCase 			=  txa_mlp_layer_norm

            _lowerCAmelCase 			=  flax_model_decoder_layer_block

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

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

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

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

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


if __name__ == "__main__":
    UpperCAmelCase_									=							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."
    )
    UpperCAmelCase_									=							parser.parse_args()
    convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput





class 		UpperCAmelCase						(						snake_case_				):





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  dataset
         _lowerCAmelCase 			=  process
         _lowerCAmelCase 			=  params





   def __len__(	self			):
         return len(self.dataset			)





   def __getitem__(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  self.dataset[i]
         _lowerCAmelCase 			=  self.process(_lowerCAmelCase       ,     **self.params			)
         return processed






class 		UpperCAmelCase						(						snake_case_				):





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase=None			):
         _lowerCAmelCase 			=  loader
         _lowerCAmelCase 			=  infer
         _lowerCAmelCase 			=  params
         if loader_batch_size == 1:
               # Let's spare some time by deactivating altogether
               _lowerCAmelCase 			=  None
         _lowerCAmelCase 			=  loader_batch_size

         # Internal bookkeeping
         _lowerCAmelCase 			=  None
         _lowerCAmelCase 			=  None





   def __len__(	self			):
         return len(self.loader			)





   def __iter__(	self			):
         _lowerCAmelCase 			=  iter(self.loader			)
         return self





   def 	__lowerCAmelCase							(	self			):
         if isinstance(self._loader_batch_data       ,     torch.Tensor			):
               # Batch data is simple tensor, just fetch the slice
               _lowerCAmelCase 			=  self._loader_batch_data[self._loader_batch_index]
         else:
               # Batch data is assumed to be BaseModelOutput (or dict)
               _lowerCAmelCase 			=  {}
               for k, element in self._loader_batch_data.items():
                     if isinstance(_lowerCAmelCase       ,     _lowerCAmelCase			):
                           # Convert ModelOutput to tuple first
                           _lowerCAmelCase 			=  element.to_tuple()
                           if isinstance(element[0]       ,     torch.Tensor			):
                                 _lowerCAmelCase 			=  tuple(el[self._loader_batch_index].unsqueeze(0			) for el in element			)
                           elif isinstance(element[0]       ,     np.ndarray			):
                                 _lowerCAmelCase 			=  tuple(np.expand_dims(el[self._loader_batch_index]       ,     0			) for el in element			)
                           continue
                     if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(_lowerCAmelCase       ,     _lowerCAmelCase			):
                           # Those are stored as lists of tensors so need specific unbatching.
                           if isinstance(element[0]       ,     torch.Tensor			):
                                 _lowerCAmelCase 			=  tuple(el[self._loader_batch_index].unsqueeze(0			) for el in element			)
                           elif isinstance(element[0]       ,     np.ndarray			):
                                 _lowerCAmelCase 			=  tuple(np.expand_dims(el[self._loader_batch_index]       ,     0			) for el in element			)
                           continue
                     if element is None:
                           # This can happen for optional data that get passed around
                           _lowerCAmelCase 			=  None
                     elif isinstance(element[self._loader_batch_index]       ,     torch.Tensor			):
                           # Take correct batch data, but make it looked like batch_size=1
                           # For compatibility with other methods within transformers

                           _lowerCAmelCase 			=  element[self._loader_batch_index].unsqueeze(0			)
                     elif isinstance(element[self._loader_batch_index]       ,     np.ndarray			):
                           # Take correct batch data, but make it looked like batch_size=1
                           # For compatibility with other methods within transformers
                           _lowerCAmelCase 			=  np.expand_dims(element[self._loader_batch_index]       ,     0			)
                     else:
                           # This is typically a list, so no need to `unsqueeze`.
                           _lowerCAmelCase 			=  element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
               _lowerCAmelCase 			=  self._loader_batch_data.__class__(_lowerCAmelCase			)
         self._loader_batch_index += 1
         return result





   def 	__lowerCAmelCase							(	self			):
         if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
               # We are currently unrolling a batch so we just need to return
               # the current item within a batch
               return self.loader_batch_item()

         # We're out of items within a batch
         _lowerCAmelCase 			=  next(self.iterator			)
         _lowerCAmelCase 			=  self.infer(_lowerCAmelCase       ,     **self.params			)
         # We now have a batch of "inferred things".
         if self.loader_batch_size is not None:
               # Try to infer the size of the batch
               if isinstance(_lowerCAmelCase       ,     torch.Tensor			):
                     _lowerCAmelCase 			=  processed
               else:
                     _lowerCAmelCase 			=  list(processed.keys()			)[0]
                     _lowerCAmelCase 			=  processed[key]
               if isinstance(_lowerCAmelCase       ,     _lowerCAmelCase			):
                     _lowerCAmelCase 			=  len(_lowerCAmelCase			)
               else:
                     _lowerCAmelCase 			=  first_tensor.shape[0]
               if 0 < observed_batch_size < self.loader_batch_size:
                     # could be last batch so we can't unroll as many
                     # elements.
                     _lowerCAmelCase 			=  observed_batch_size
               # Setting internal index to unwrap the batch
               _lowerCAmelCase 			=  processed
               _lowerCAmelCase 			=  0
               return self.loader_batch_item()
         else:
               # We're not unrolling batches
               return processed






class 		UpperCAmelCase						(						snake_case_				):





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase=None			):
         super().__init__(_lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			)





   def __iter__(	self			):
         _lowerCAmelCase 			=  iter(self.loader			)
         _lowerCAmelCase 			=  None
         return self





   def 	__lowerCAmelCase							(	self			):
         if self.subiterator is None:
               _lowerCAmelCase 			=  self.infer(next(self.iterator			)       ,     **self.params			)
         try:
               # Try to return next item
               _lowerCAmelCase 			=  next(self.subiterator			)
         except StopIteration:
               # When a preprocess iterator ends, we can start lookig at the next item
               # ChunkIterator will keep feeding until ALL elements of iterator
               # all have created their subiterator and have been iterating against.
               #
               # Another way to look at it, is we're basically flattening lists of lists
               # into a single list, but with generators
               _lowerCAmelCase 			=  self.infer(next(self.iterator			)       ,     **self.params			)
               _lowerCAmelCase 			=  next(self.subiterator			)
         return processed






class 		UpperCAmelCase						(						snake_case_				):





   def __iter__(	self			):
         _lowerCAmelCase 			=  iter(self.loader			)
         return self





   def 	__lowerCAmelCase							(	self			):
         # Extremely similar to PipelineIterator in its unpacking mechanism
         # BUT, we have an extra required item which is the presence of `is_last`
         # That is because everything is flattened by `PipelineChunkIterator` we
         # need to keep track of how to regroup here in the original `process`
         # boundaries so that `process` and `postprocess` see the same data.

         # This iterator accumulates items (possibly while unbatching) until it
         # its a `is_last` and then just passes it on to the caller.
         _lowerCAmelCase 			=  False
         _lowerCAmelCase 			=  []
         if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
               while self._loader_batch_index < self.loader_batch_size:
                     _lowerCAmelCase 			=  self.loader_batch_item()
                     _lowerCAmelCase 			=  item.pop('''is_last'''			)
                     accumulator.append(_lowerCAmelCase			)
                     if is_last:
                           return accumulator

         while not is_last:
               _lowerCAmelCase 			=  self.infer(next(self.iterator			)       ,     **self.params			)
               if self.loader_batch_size is not None:
                     if isinstance(_lowerCAmelCase       ,     torch.Tensor			):
                           _lowerCAmelCase 			=  processed
                     else:
                           _lowerCAmelCase 			=  list(processed.keys()			)[0]
                           _lowerCAmelCase 			=  processed[key]
                     if isinstance(_lowerCAmelCase       ,     _lowerCAmelCase			):
                           _lowerCAmelCase 			=  len(_lowerCAmelCase			)
                     else:
                           _lowerCAmelCase 			=  first_tensor.shape[0]
                     if 0 < observed_batch_size < self.loader_batch_size:
                           # could be last batch so we can't unroll as many
                           # elements.
                           _lowerCAmelCase 			=  observed_batch_size
                     _lowerCAmelCase 			=  processed
                     _lowerCAmelCase 			=  0
                     while self._loader_batch_index < self.loader_batch_size:
                           _lowerCAmelCase 			=  self.loader_batch_item()
                           _lowerCAmelCase 			=  item.pop('''is_last'''			)
                           accumulator.append(_lowerCAmelCase			)
                           if is_last:
                                 return accumulator
               else:
                     _lowerCAmelCase 			=  processed
                     _lowerCAmelCase 			=  item.pop('''is_last'''			)
                     accumulator.append(_lowerCAmelCase			)
         return accumulator






class 		UpperCAmelCase						(						snake_case_				):





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  dataset
         _lowerCAmelCase 			=  key





   def __len__(	self			):
         return len(self.dataset			)





   def __getitem__(	self       ,     _lowerCAmelCase			):
         return self.dataset[i][self.key]






class 		UpperCAmelCase						(						snake_case_				):





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  dataset
         _lowerCAmelCase 			=  keya
         _lowerCAmelCase 			=  keya





   def __len__(	self			):
         return len(self.dataset			)





   def __getitem__(	self       ,     _lowerCAmelCase			):
         return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}