




import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				''''''
    UpperCAmelCase__							  =				(
        None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
    )
    UpperCAmelCase__							  =				None  # compression type in fsspec. ex: "gzip"
    UpperCAmelCase__							  =				None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz


    def __init__(      self				:     Optional[int] ,			UpperCAmelCase__				:     str = "" ,			UpperCAmelCase__				:     Optional[str] = None ,			UpperCAmelCase__				:     Optional[dict] = None ,			**UpperCAmelCase__				:     List[Any])						->str:





           '''simple docstring'''

           super().__init__(self ,			**UpperCAmelCase__)
           # always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
           A__							=   fsspec.open(
               UpperCAmelCase__ ,			mode='''rb''' ,			protocol=UpperCAmelCase__ ,			compression=self.compression ,			client_kwargs={
                   '''requote_redirect_url''': False,  # see https://github.com/huggingface/datasets/pull/5459
                   '''trust_env''': True,  # Enable reading proxy env variables.
                   **(target_options or {}).pop('''client_kwargs''' ,			{}),  # To avoid issues if it was already passed.
               } ,			**(target_options or {}) ,			)
           A__							=   os.path.basename(self.file.path.split('''::''')[0])
           A__							=   (
               self.compressed_name[: self.compressed_name.rindex('''.''')]
               if '''.''' in self.compressed_name
               else self.compressed_name
           )
           A__							=   None


    @classmethod
    def 	SCREAMING_SNAKE_CASE							(      cls				:     List[Any] ,			UpperCAmelCase__				:     int)						->List[Any]:





           '''simple docstring'''

           return super()._strip_protocol(UpperCAmelCase__).lstrip('''/''')


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->Any:





           '''simple docstring'''

           if self.dir_cache is None:
                  A__							=   {**self.file.fs.info(self.file.path), '''name''': self.uncompressed_name}
                  A__							=   {f['''name''']: f}


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			UpperCAmelCase__				:     str)						->str:





           '''simple docstring'''

           return self.file.open().read()





    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     str = "rb" ,			UpperCAmelCase__				:     str=None ,			UpperCAmelCase__				:     List[Any]=True ,			UpperCAmelCase__				:     List[str]=None ,			**UpperCAmelCase__				:     Optional[int] ,			)						->Dict:





           '''simple docstring'''

           A__							=   self._strip_protocol(UpperCAmelCase__)
           if mode != "rb":
                  raise ValueError(f"""Tried to read with mode {mode} on file {self.file.path} opened with mode 'rb'""")
           return self.file.open()


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				'''bz2'''
    UpperCAmelCase__							  =				'''bz2'''
    UpperCAmelCase__							  =				'''.bz2'''


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				'''gzip'''
    UpperCAmelCase__							  =				'''gzip'''
    UpperCAmelCase__							  =				'''.gz'''


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				'''lz4'''
    UpperCAmelCase__							  =				'''lz4'''
    UpperCAmelCase__							  =				'''.lz4'''


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				'''xz'''
    UpperCAmelCase__							  =				'''xz'''
    UpperCAmelCase__							  =				'''.xz'''


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				'''zstd'''
    UpperCAmelCase__							  =				'''zstd'''
    UpperCAmelCase__							  =				'''.zst'''


    def __init__(      self				:     str ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     str = "rb" ,			UpperCAmelCase__				:     Optional[str] = None ,			UpperCAmelCase__				:     Optional[dict] = None ,			UpperCAmelCase__				:     int = DEFAULT_BLOCK_SIZE ,			**UpperCAmelCase__				:     Optional[int] ,			)						->str:





           '''simple docstring'''

           super().__init__(
               fo=UpperCAmelCase__ ,			mode=UpperCAmelCase__ ,			target_protocol=UpperCAmelCase__ ,			target_options=UpperCAmelCase__ ,			block_size=UpperCAmelCase__ ,			**UpperCAmelCase__ ,			)
           # We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
           #
           # File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
           #     out.close = close
           # AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
           #
           # see https://github.com/intake/filesystem_spec/issues/725
           A__							=   self.file.__enter__


           class     UpperCamelCase_   :

               '''simple docstring'''






               def __init__(      self				:     List[Any] ,			UpperCAmelCase__				:     int)						->Any:





                      '''simple docstring'''

                      A__							=   file_


               def __enter__(      self				:     int)						->str:





                      '''simple docstring'''

                      self._file.__enter__()
                      return self


               def __exit__(      self				:     str ,			*UpperCAmelCase__				:     Tuple ,			**UpperCAmelCase__				:     Optional[int])						->Optional[int]:





                      '''simple docstring'''

                      self._file.__exit__(*UpperCAmelCase__ ,			**UpperCAmelCase__)


               def __iter__(      self				:     Optional[int])						->Dict:





                      '''simple docstring'''

                      return iter(self._file)


               def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->str:





                      '''simple docstring'''

                      return next(self._file)





               def __getattr__(      self				:     int ,			UpperCAmelCase__				:     Any)						->Any:





                      '''simple docstring'''

                      return getattr(self._file ,			UpperCAmelCase__)

           def fixed_enter(*UpperCAmelCase__				:     Dict ,			**UpperCAmelCase__				:     Union[str, Any]):
                  return WrappedFile(_enter(*UpperCAmelCase__ ,			**UpperCAmelCase__))

           A__							=   fixed_enter








import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	Tuple:



       """simple docstring"""

       A__							=   AutoConfig.from_pretrained(lowercase_						)
       A__							=   FlaxAutoModelForSeqaSeqLM.from_config(config=lowercase_						)
       A__							=   checkpoints.load_tax_checkpoint(lowercase_						)

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

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

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

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

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

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

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

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

              A__							=   tax_attention_layer_norm

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

              if split_mlp_wi:
                     A__							=   tax_mlp_wi_a
                     A__							=   tax_mlp_wi_a
              else:
                     A__							=   tax_mlp_wi

              A__							=   tax_mlp_wo
              A__							=   tax_mlp_layer_norm

              A__							=   flax_model_encoder_layer_block

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

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

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

       # Decoder
       for layer_index in range(config.num_layers						):
              A__							=   f"""layers_{str(lowercase_						)}"""

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

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

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

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

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

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

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

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

              A__							=   tax_pre_attention_layer_norm

              A__							=   tax_enc_dec_attention_key
              A__							=   tax_enc_dec_attention_out
              A__							=   tax_enc_dec_attention_query
              A__							=   tax_enc_dec_attention_value

              A__							=   tax_cross_layer_norm

              if split_mlp_wi:
                     A__							=   tax_mlp_wi_a
                     A__							=   tax_mlp_wi_a
              else:
                     A__							=   tax_mlp_wi

              A__							=   tax_mlp_wo

              A__							=   txa_mlp_layer_norm

              A__							=   flax_model_decoder_layer_block

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

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

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

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

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


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


