"""simple docstring"""




import argparse
import os
import re

import packaging.version


_a				:		Optional[Any]	   =       'examples/'
_a				:		int	   =       {
    'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
    'init': (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
    'setup': (re.compile(r'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), r'\1version="VERSION",'),
    'doc': (re.compile(r'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
_a				:		Any	   =       {
    'init': 'src/transformers/__init__.py',
    'setup': 'setup.py',
}
_a				:		List[str]	   =       'README.md'





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str      ,_lowerCamelCase						:	Any      ,_lowerCamelCase						:	Tuple     )	->				Optional[Any]:
  with open(_lowerCamelCase      ,"""r"""      ,encoding="""utf-8"""      ,newline="""\n"""     ) as f:
    _lowerCAmelCase						:	str        =					f.read()
  _lowerCAmelCase						:	List[Any]        =					REPLACE_PATTERNS[pattern]
  _lowerCAmelCase						:	Union[str, Any]        =					replace.replace("""VERSION"""      ,_lowerCamelCase     )
  _lowerCAmelCase						:	int        =					re_pattern.sub(_lowerCamelCase      ,_lowerCamelCase     )
  with open(_lowerCamelCase      ,"""w"""      ,encoding="""utf-8"""      ,newline="""\n"""     ) as f:
    f.write(_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[str]     )	->				Dict:
  for folder, directories, fnames in os.walk(_lowerCamelCase     ):
    # Removing some of the folders with non-actively maintained examples from the walk
    if "research_projects" in directories:
      directories.remove("""research_projects"""     )
    if "legacy" in directories:
      directories.remove("""legacy"""     )
    for fname in fnames:
      if fname.endswith(""".py"""     ):
        update_version_in_file(os.path.join(_lowerCamelCase      ,_lowerCamelCase     )      ,_lowerCamelCase      ,pattern="""examples"""     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[Any]      ,_lowerCamelCase						:	int=False     )	->				Tuple:
  for pattern, fname in REPLACE_FILES.items():
    update_version_in_file(_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase     )
  if not patch:
    update_version_in_examples(_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      )	->				List[Any]:
  _lowerCAmelCase						:	Any        =					"""🤗 Transformers currently provides the following architectures"""
  _lowerCAmelCase						:	List[Any]        =					"""1. Want to contribute a new model?"""
  with open(_lowerCamelCase      ,"""r"""      ,encoding="""utf-8"""      ,newline="""\n"""     ) as f:
    _lowerCAmelCase						:	Optional[int]        =					f.readlines()

  # Find the start of the list.
  _lowerCAmelCase						:	int        =					0
  while not lines[start_index].startswith(_start_prompt     ):
    start_index += 1
  start_index += 1

  _lowerCAmelCase						:	Dict        =					start_index
  # Update the lines in the model list.
  while not lines[index].startswith(_end_prompt     ):
    if lines[index].startswith("""1."""     ):
      _lowerCAmelCase						:	str        =					lines[index].replace(
          """https://huggingface.co/docs/transformers/main/model_doc"""      ,"""https://huggingface.co/docs/transformers/model_doc"""      ,)
    index += 1

  with open(_lowerCamelCase      ,"""w"""      ,encoding="""utf-8"""      ,newline="""\n"""     ) as f:
    f.writelines(_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      )	->				int:
  with open(REPLACE_FILES["""init"""]      ,"""r"""     ) as f:
    _lowerCAmelCase						:	Optional[int]        =					f.read()
  _lowerCAmelCase						:	List[Any]        =					REPLACE_PATTERNS["""init"""][0].search(_lowerCamelCase     ).groups()[0]
  return packaging.version.parse(_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Tuple=False     )	->				Tuple:
  _lowerCAmelCase						:	List[Any]        =					get_version()
  if patch and default_version.is_devrelease:
    raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!"""     )
  if default_version.is_devrelease:
    _lowerCAmelCase						:	List[str]        =					default_version.base_version
  elif patch:
    _lowerCAmelCase						:	int        =					f"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
  else:
    _lowerCAmelCase						:	Tuple        =					f"{default_version.major}.{default_version.minor + 1}.0"

  # Now let's ask nicely if that's the right one.
  _lowerCAmelCase						:	Any        =					input(f"Which version are you releasing? [{default_version}]"     )
  if len(_lowerCamelCase     ) == 0:
    _lowerCAmelCase						:	Optional[Any]        =					default_version

  print(f"Updating version to {version}."     )
  global_version_update(_lowerCamelCase      ,patch=_lowerCamelCase     )
  if not patch:
    print("""Cleaning main README, don't forget to run `make fix-copies`."""     )
    clean_main_ref_in_model_list()





def 							SCREAMING_SNAKE_CASE					(      )	->				Optional[Any]:
  _lowerCAmelCase						:	Dict        =					get_version()
  _lowerCAmelCase						:	List[Any]        =					f"{current_version.major}.{current_version.minor + 1}.0.dev0"
  _lowerCAmelCase						:	Optional[int]        =					current_version.base_version

  # Check with the user we got that right.
  _lowerCAmelCase						:	int        =					input(f"Which version are we developing now? [{dev_version}]"     )
  if len(_lowerCamelCase     ) == 0:
    _lowerCAmelCase						:	List[str]        =					dev_version

  print(f"Updating version to {version}."     )
  global_version_update(_lowerCamelCase     )
  print("""Cleaning main README, don't forget to run `make fix-copies`."""     )
  clean_main_ref_in_model_list()


if __name__ == "__main__":
     _a				:		Any	   =       argparse.ArgumentParser()
     parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
     parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
     _a				:		Any	   =       parser.parse_args()
     if not args.post_release:
          pre_release_work(patch=args.patch)
     elif args.patch:
          print('Nothing to do after a patch :-)')
     else:
          post_release_work()





"""simple docstring"""




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[str]      ,_lowerCamelCase						:	Any      ,_lowerCamelCase						:	Optional[Any]     )	->				str:
  _lowerCAmelCase						:	str        =					AutoConfig.from_pretrained(_lowerCamelCase     )
  _lowerCAmelCase						:	int        =					FlaxAutoModelForSeqaSeqLM.from_config(config=_lowerCamelCase     )
  _lowerCAmelCase						:	Any        =					checkpoints.load_tax_checkpoint(_lowerCamelCase     )

  _lowerCAmelCase						:	Tuple        =					"""wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

  if config.model_type == "t5":
    _lowerCAmelCase						:	Tuple        =					"""SelfAttention"""
  if config.model_type == "longt5" and config.encoder_attention_type == "local":
    _lowerCAmelCase						:	Optional[Any]        =					"""LocalSelfAttention"""
  elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    _lowerCAmelCase						:	Union[str, Any]        =					"""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						:	Tuple        =					f"layers_{str(_lowerCamelCase     )}"

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

    # Layer Normalization
    _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

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

    _lowerCAmelCase						:	Tuple        =					tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

    # Layer Normalization
    _lowerCAmelCase						:	Tuple        =					tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

    # Assigning
    _lowerCAmelCase						:	Any        =					flax_model.params["""encoder"""]["""block"""][str(_lowerCamelCase     )]["""layer"""]
    _lowerCAmelCase						:	Any        =					tax_attention_key
    _lowerCAmelCase						:	str        =					tax_attention_out
    _lowerCAmelCase						:	Union[str, Any]        =					tax_attention_query
    _lowerCAmelCase						:	Optional[Any]        =					tax_attention_value

    _lowerCAmelCase						:	List[str]        =					tax_attention_layer_norm

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

    if split_mlp_wi:
      _lowerCAmelCase						:	Dict        =					tax_mlp_wi_a
      _lowerCAmelCase						:	List[Any]        =					tax_mlp_wi_a
    else:
      _lowerCAmelCase						:	List[str]        =					tax_mlp_wi

    _lowerCAmelCase						:	str        =					tax_mlp_wo
    _lowerCAmelCase						:	Optional[Any]        =					tax_mlp_layer_norm

    _lowerCAmelCase						:	Any        =					flax_model_encoder_layer_block

  # Only for layer 0:
  _lowerCAmelCase						:	Union[str, Any]        =					tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  _lowerCAmelCase						:	Optional[Any]        =					tax_encoder_rel_embedding

  # Side/global relative position_bias + layer norm
  if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    _lowerCAmelCase						:	List[str]        =					tax_model["""target"""]["""encoder"""]["""side_relpos_bias"""]["""rel_embedding"""].T
    _lowerCAmelCase						:	Optional[int]        =					tax_encoder_global_rel_embedding

  # Assigning
  _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
  _lowerCAmelCase						:	Any        =					tax_encoder_norm

  # Decoder
  for layer_index in range(config.num_layers     ):
    _lowerCAmelCase						:	Optional[int]        =					f"layers_{str(_lowerCamelCase     )}"

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

    # Layer Normalization
    _lowerCAmelCase						:	Optional[Any]        =					tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
        """scale"""
    ]

    # Encoder-Decoder-Attention
    _lowerCAmelCase						:	List[Any]        =					tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]
    _lowerCAmelCase						:	List[str]        =					tax_enc_dec_attention_module["""key"""]["""kernel"""]
    _lowerCAmelCase						:	List[Any]        =					tax_enc_dec_attention_module["""out"""]["""kernel"""]
    _lowerCAmelCase						:	List[str]        =					tax_enc_dec_attention_module["""query"""]["""kernel"""]
    _lowerCAmelCase						:	Dict        =					tax_enc_dec_attention_module["""value"""]["""kernel"""]

    # Layer Normalization
    _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

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

    _lowerCAmelCase						:	Optional[int]        =					tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

    # Layer Normalization
    _lowerCAmelCase						:	Optional[int]        =					tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

    # Assigning
    _lowerCAmelCase						:	str        =					flax_model.params["""decoder"""]["""block"""][str(_lowerCamelCase     )]["""layer"""]
    _lowerCAmelCase						:	int        =					tax_attention_key
    _lowerCAmelCase						:	List[str]        =					tax_attention_out
    _lowerCAmelCase						:	Optional[Any]        =					tax_attention_query
    _lowerCAmelCase						:	Dict        =					tax_attention_value

    _lowerCAmelCase						:	str        =					tax_pre_attention_layer_norm

    _lowerCAmelCase						:	List[Any]        =					tax_enc_dec_attention_key
    _lowerCAmelCase						:	List[Any]        =					tax_enc_dec_attention_out
    _lowerCAmelCase						:	Tuple        =					tax_enc_dec_attention_query
    _lowerCAmelCase						:	Any        =					tax_enc_dec_attention_value

    _lowerCAmelCase						:	Dict        =					tax_cross_layer_norm

    if split_mlp_wi:
      _lowerCAmelCase						:	Dict        =					tax_mlp_wi_a
      _lowerCAmelCase						:	int        =					tax_mlp_wi_a
    else:
      _lowerCAmelCase						:	Optional[int]        =					tax_mlp_wi

    _lowerCAmelCase						:	Dict        =					tax_mlp_wo

    _lowerCAmelCase						:	List[Any]        =					txa_mlp_layer_norm

    _lowerCAmelCase						:	Optional[Any]        =					flax_model_decoder_layer_block

  # Decoder Normalization
  _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
  _lowerCAmelCase						:	List[str]        =					txa_decoder_norm

  # Only for layer 0:
  _lowerCAmelCase						:	Optional[int]        =					tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  _lowerCAmelCase						:	Union[str, Any]        =					tax_decoder_rel_embedding

  # Token Embeddings
  _lowerCAmelCase						:	Optional[int]        =					tax_model["""target"""]["""token_embedder"""]["""embedding"""]
  _lowerCAmelCase						:	Optional[int]        =					txa_token_embeddings

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

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


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




