"""simple docstring"""




import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_a				:		Tuple	   =       'src/diffusers'
_a				:		List[Any]	   =       '.'


# This is to make sure the diffusers module imported is the one in the repo.
_a				:		List[Any]	   =       importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(DIFFUSERS_PATH, '__init__.py'),
    submodule_search_locations=[DIFFUSERS_PATH],
)
_a				:		List[Any]	   =       spec.loader.load_module()





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[int]      ,_lowerCamelCase						:	Optional[Any]     )	->				Tuple:
  return line.startswith(_lowerCamelCase     ) or len(_lowerCamelCase     ) <= 1 or re.search(r"""^\s*\)(\s*->.*:|:)\s*$"""      ,_lowerCamelCase     ) is not None





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[str]     )	->				int:
  _lowerCAmelCase						:	str        =					object_name.split("""."""     )
  _lowerCAmelCase						:	List[str]        =					0

  # First let's find the module where our object lives.
  _lowerCAmelCase						:	Optional[int]        =					parts[i]
  while i < len(_lowerCamelCase     ) and not os.path.isfile(os.path.join(_lowerCamelCase      ,f"{module}.py"     )     ):
    i += 1
    if i < len(_lowerCamelCase     ):
      _lowerCAmelCase						:	int        =					os.path.join(_lowerCamelCase      ,parts[i]     )
  if i >= len(_lowerCamelCase     ):
    raise ValueError(f"`object_name` should begin with the name of a module of diffusers but got {object_name}."     )

  with open(os.path.join(_lowerCamelCase      ,f"{module}.py"     )      ,"""r"""      ,encoding="""utf-8"""      ,newline="""\n"""     ) as f:
    _lowerCAmelCase						:	Dict        =					f.readlines()

  # Now let's find the class / func in the code!
  _lowerCAmelCase						:	Dict        =					""""""
  _lowerCAmelCase						:	str        =					0
  for name in parts[i + 1 :]:
    while (
        line_index < len(_lowerCamelCase     ) and re.search(rf"^{indent}(class|def)\s+{name}(\(|\:)"      ,lines[line_index]     ) is None
    ):
      line_index += 1
    indent += "    "
    line_index += 1

  if line_index >= len(_lowerCamelCase     ):
    raise ValueError(f" {object_name} does not match any function or class in {module}."     )

  # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
  _lowerCAmelCase						:	Union[str, Any]        =					line_index
  while line_index < len(_lowerCamelCase     ) and _should_continue(lines[line_index]      ,_lowerCamelCase     ):
    line_index += 1
  # Clean up empty lines at the end (if any).
  while len(lines[line_index - 1]     ) <= 1:
    line_index -= 1

  _lowerCAmelCase						:	Optional[int]        =					lines[start_index:line_index]
  return "".join(_lowerCamelCase     )


_a				:		Union[str, Any]	   =       re.compile(r'^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)')
_a				:		Union[str, Any]	   =       re.compile(r'^\s*(\S+)->(\S+)(\s+.*|$)')
_a				:		int	   =       re.compile(r'<FILL\s+[^>]*>')





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Any     )	->				Optional[int]:
  _lowerCAmelCase						:	int        =					code.split("""\n"""     )
  _lowerCAmelCase						:	Optional[Any]        =					0
  while idx < len(_lowerCamelCase     ) and len(lines[idx]     ) == 0:
    idx += 1
  if idx < len(_lowerCamelCase     ):
    return re.search(r"""^(\s*)\S"""      ,lines[idx]     ).groups()[0]
  return ""





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Union[str, Any]     )	->				Tuple:
  _lowerCAmelCase						:	List[str]        =					len(get_indent(_lowerCamelCase     )     ) > 0
  if has_indent:
    _lowerCAmelCase						:	str        =					f"class Bla:\n{code}"
  _lowerCAmelCase						:	Optional[int]        =					black.Mode(target_versions={black.TargetVersion.PYaa}      ,line_length=119      ,preview=_lowerCamelCase     )
  _lowerCAmelCase						:	List[str]        =					black.format_str(_lowerCamelCase      ,mode=_lowerCamelCase     )
  _lowerCAmelCase      ,     _lowerCAmelCase						:	Union[str, Any]        =					style_docstrings_in_code(_lowerCamelCase     )
  return result[len("""class Bla:\n"""     ) :] if has_indent else result





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Dict      ,_lowerCamelCase						:	Optional[int]=False     )	->				str:
  with open(_lowerCamelCase      ,"""r"""      ,encoding="""utf-8"""      ,newline="""\n"""     ) as f:
    _lowerCAmelCase						:	Optional[Any]        =					f.readlines()
  _lowerCAmelCase						:	List[str]        =					[]
  _lowerCAmelCase						:	Any        =					0
  # Not a for loop cause `lines` is going to change (if `overwrite=True`).
  while line_index < len(_lowerCamelCase     ):
    _lowerCAmelCase						:	str        =					_re_copy_warning.search(lines[line_index]     )
    if search is None:
      line_index += 1
      continue

    # There is some copied code here, let's retrieve the original.
    _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	Union[str, Any]        =					search.groups()
    _lowerCAmelCase						:	Union[str, Any]        =					find_code_in_diffusers(_lowerCamelCase     )
    _lowerCAmelCase						:	Tuple        =					get_indent(_lowerCamelCase     )

    _lowerCAmelCase						:	Dict        =					line_index + 1 if indent == theoretical_indent else line_index + 2
    _lowerCAmelCase						:	List[str]        =					theoretical_indent
    _lowerCAmelCase						:	Union[str, Any]        =					start_index

    # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
    _lowerCAmelCase						:	str        =					True
    while line_index < len(_lowerCamelCase     ) and should_continue:
      line_index += 1
      if line_index >= len(_lowerCamelCase     ):
        break
      _lowerCAmelCase						:	Any        =					lines[line_index]
      _lowerCAmelCase						:	List[Any]        =					_should_continue(_lowerCamelCase      ,_lowerCamelCase     ) and re.search(f"^{indent}# End copy"      ,_lowerCamelCase     ) is None
    # Clean up empty lines at the end (if any).
    while len(lines[line_index - 1]     ) <= 1:
      line_index -= 1

    _lowerCAmelCase						:	List[Any]        =					lines[start_index:line_index]
    _lowerCAmelCase						:	Union[str, Any]        =					"""""".join(_lowerCamelCase     )

    # Remove any nested `Copied from` comments to avoid circular copies
    _lowerCAmelCase						:	Any        =					[line for line in theoretical_code.split("""\n"""     ) if _re_copy_warning.search(_lowerCamelCase     ) is None]
    _lowerCAmelCase						:	Tuple        =					"""\n""".join(_lowerCamelCase     )

    # Before comparing, use the `replace_pattern` on the original code.
    if len(_lowerCamelCase     ) > 0:
      _lowerCAmelCase						:	Union[str, Any]        =					replace_pattern.replace("""with"""      ,""""""     ).split(""","""     )
      _lowerCAmelCase						:	Union[str, Any]        =					[_re_replace_pattern.search(_lowerCamelCase     ) for p in patterns]
      for pattern in patterns:
        if pattern is None:
          continue
        _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	Tuple        =					pattern.groups()
        _lowerCAmelCase						:	Union[str, Any]        =					re.sub(_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase     )
        if option.strip() == "all-casing":
          _lowerCAmelCase						:	Union[str, Any]        =					re.sub(obja.lower()      ,obja.lower()      ,_lowerCamelCase     )
          _lowerCAmelCase						:	str        =					re.sub(obja.upper()      ,obja.upper()      ,_lowerCamelCase     )

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
      _lowerCAmelCase						:	Dict        =					blackify(lines[start_index - 1] + theoretical_code     )
      _lowerCAmelCase						:	str        =					theoretical_code[len(lines[start_index - 1]     ) :]

    # Test for a diff and act accordingly.
    if observed_code != theoretical_code:
      diffs.append([object_name, start_index]     )
      if overwrite:
        _lowerCAmelCase						:	int        =					lines[:start_index] + [theoretical_code] + lines[line_index:]
        _lowerCAmelCase						:	int        =					start_index + 1

  if overwrite and len(_lowerCamelCase     ) > 0:
    # Warn the user a file has been modified.
    print(f"Detected changes, rewriting {filename}."     )
    with open(_lowerCamelCase      ,"""w"""      ,encoding="""utf-8"""      ,newline="""\n"""     ) as f:
      f.writelines(_lowerCamelCase     )
  return diffs





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	bool = False     )	->				Tuple:
  _lowerCAmelCase						:	str        =					glob.glob(os.path.join(_lowerCamelCase      ,"""**/*.py"""     )      ,recursive=_lowerCamelCase     )
  _lowerCAmelCase						:	int        =					[]
  for filename in all_files:
    _lowerCAmelCase						:	Optional[Any]        =					is_copy_consistent(_lowerCamelCase      ,_lowerCamelCase     )
    diffs += [f"- {filename}: copy does not match {d[0]} at line {d[1]}" for d in new_diffs]
  if not overwrite and len(_lowerCamelCase     ) > 0:
    _lowerCAmelCase						:	List[Any]        =					"""\n""".join(_lowerCamelCase     )
    raise Exception(
        """Found the following copy inconsistencies:\n"""
        + diff
        + """\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."""     )


if __name__ == "__main__":
     _a				:		Optional[int]	   =       argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     _a				:		Dict	   =       parser.parse_args()

     check_copies(args.fix_and_overwrite)





"""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)




