import argparse
import collections
import importlib.util
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
__lowerCamelCase     :					str				  =						'src/diffusers'
__lowerCamelCase     :					Any				  =						'docs/source/en'
__lowerCamelCase     :					Any				  =						'.'



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Any					,	__UpperCamelCase       :			int					,	__UpperCamelCase       :			Optional[int]      )				->   Optional[int]:







  """simple docstring"""





  with open(snake_case__					,	"""r"""					,	encoding="""utf-8"""					,	newline="""\n"""      ) as f:
    SCREAMING_SNAKE_CASE__     				= f.readlines()
  # Find the start prompt.
  SCREAMING_SNAKE_CASE__     				= 0
  while not lines[start_index].startswith(snake_case__      ):
    start_index += 1
  start_index += 1

  SCREAMING_SNAKE_CASE__     				= start_index
  while not lines[end_index].startswith(snake_case__      ):
    end_index += 1
  end_index -= 1

  while len(lines[start_index]      ) <= 1:
    start_index += 1
  while len(lines[end_index]      ) <= 1:
    end_index -= 1
  end_index += 1
  return "".join(lines[start_index:end_index]      ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
__lowerCamelCase     :					Union[str, Any]				  =						'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
__lowerCamelCase     :					str				  =						re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
__lowerCamelCase     :					Optional[int]				  =						re.compile(r'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__lowerCamelCase     :					Any				  =						re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


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



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Any      )				->   Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""					,	snake_case__      )
  return [m.group(0      ) for m in matches]



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Optional[Any]					,	__UpperCamelCase       :			Any      )				->   Union[str, Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= 2 if text == """✅""" or text == """❌""" else len(snake_case__      )
  SCREAMING_SNAKE_CASE__     				= (width - text_length) // 2
  SCREAMING_SNAKE_CASE__     				= width - text_length - left_indent
  return " " * left_indent + text + " " * right_indent



def       __SCREAMING_SNAKE_CASE    (   )				->   Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  SCREAMING_SNAKE_CASE__     				= {
      name: config_mapping_names[code]
      for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
      if code in config_mapping_names
  }
  SCREAMING_SNAKE_CASE__     				= {name: config.replace("""ConfigMixin"""					,	""""""      ) for name, config in model_name_to_config.items()}

  # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
  SCREAMING_SNAKE_CASE__     				= collections.defaultdict(snake_case__      )
  SCREAMING_SNAKE_CASE__     				= collections.defaultdict(snake_case__      )
  SCREAMING_SNAKE_CASE__     				= collections.defaultdict(snake_case__      )
  SCREAMING_SNAKE_CASE__     				= collections.defaultdict(snake_case__      )
  SCREAMING_SNAKE_CASE__     				= collections.defaultdict(snake_case__      )

  # Let's lookup through all diffusers object (once).
  for attr_name in dir(snake_case__      ):
    SCREAMING_SNAKE_CASE__     				= None
    if attr_name.endswith("""Tokenizer"""      ):
      SCREAMING_SNAKE_CASE__     				= slow_tokenizers
      SCREAMING_SNAKE_CASE__     				= attr_name[:-9]
    elif attr_name.endswith("""TokenizerFast"""      ):
      SCREAMING_SNAKE_CASE__     				= fast_tokenizers
      SCREAMING_SNAKE_CASE__     				= attr_name[:-13]
    elif _re_tf_models.match(snake_case__      ) is not None:
      SCREAMING_SNAKE_CASE__     				= tf_models
      SCREAMING_SNAKE_CASE__     				= _re_tf_models.match(snake_case__      ).groups()[0]
    elif _re_flax_models.match(snake_case__      ) is not None:
      SCREAMING_SNAKE_CASE__     				= flax_models
      SCREAMING_SNAKE_CASE__     				= _re_flax_models.match(snake_case__      ).groups()[0]
    elif _re_pt_models.match(snake_case__      ) is not None:
      SCREAMING_SNAKE_CASE__     				= pt_models
      SCREAMING_SNAKE_CASE__     				= _re_pt_models.match(snake_case__      ).groups()[0]

    if lookup_dict is not None:
      while len(snake_case__      ) > 0:
        if attr_name in model_name_to_prefix.values():
          SCREAMING_SNAKE_CASE__     				= True
          break
        # Try again after removing the last word in the name
        SCREAMING_SNAKE_CASE__     				= """""".join(camel_case_split(snake_case__      )[:-1]      )

    # Let's build that table!
  SCREAMING_SNAKE_CASE__     				= list(model_name_to_config.keys()      )
  model_names.sort(key=str.lower      )
  SCREAMING_SNAKE_CASE__     				= ["""Model""", """Tokenizer slow""", """Tokenizer fast""", """PyTorch support""", """TensorFlow support""", """Flax Support"""]
  # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
  SCREAMING_SNAKE_CASE__     				= [len(snake_case__      ) + 2 for c in columns]
  SCREAMING_SNAKE_CASE__     				= max([len(snake_case__      ) for name in model_names]      ) + 2

  # Build the table per se
  SCREAMING_SNAKE_CASE__     				= """|""" + """|""".join([_center_text(snake_case__					,	snake_case__      ) for c, w in zip(snake_case__					,	snake_case__      )]      ) + """|\n"""
  # Use ":-----:" format to center-aligned table cell texts
  table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths]      ) + "|\n"

  SCREAMING_SNAKE_CASE__     				= {True: """✅""", False: """❌"""}
  for name in model_names:
    SCREAMING_SNAKE_CASE__     				= model_name_to_prefix[name]
    SCREAMING_SNAKE_CASE__     				= [
        name,
        check[slow_tokenizers[prefix]],
        check[fast_tokenizers[prefix]],
        check[pt_models[prefix]],
        check[tf_models[prefix]],
        check[flax_models[prefix]],
    ]
    table += "|" + "|".join([_center_text(snake_case__					,	snake_case__      ) for l, w in zip(snake_case__					,	snake_case__      )]      ) + "|\n"
  return table



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Union[str, Any]=False      )				->   List[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= _find_text_in_file(
      filename=os.path.join(snake_case__					,	"""index.mdx"""      )					,	start_prompt="""<!--This table is updated automatically from the auto modules"""					,	end_prompt="""<!-- End table-->"""					,	)
  SCREAMING_SNAKE_CASE__     				= get_model_table_from_auto_modules()

  if current_table != new_table:
    if overwrite:
      with open(os.path.join(snake_case__					,	"""index.mdx"""      )					,	"""w"""					,	encoding="""utf-8"""					,	newline="""\n"""      ) as f:
        f.writelines(lines[:start_index] + [new_table] + lines[end_index:]      )
    else:
      raise ValueError(
          """The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this."""      )


if __name__ == "__main__":
   __lowerCamelCase     :					List[Any]				  =						argparse.ArgumentParser()
   parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
   __lowerCamelCase     :					str				  =						parser.parse_args()

   check_model_table(args.fix_and_overwrite)


import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification





def        lowerCAmelCase       (				snake_case__   :      Union[str, Any] )->			List[str]:
    A_								=					[
        "encoder.version",
        "decoder.version",
        "model.encoder.version",
        "model.decoder.version",
        "_float_tensor",
        "decoder.output_projection.weight",
    ]
    for k in ignore_keys:
        state_dict.pop(snake_case__				,					snake_case__ )





def        lowerCAmelCase       (				snake_case__   :      Dict )->			str:
    A_	,	A_								=					emb.weight.shape
    A_								=					nn.Linear(snake_case__				,					snake_case__				,					bias=snake_case__ )
    A_								=					emb.weight.data
    return lin_layer





def        lowerCAmelCase       (				snake_case__   :      Any				,					snake_case__   :      Optional[int]="uclanlp/plbart-base"				,					snake_case__   :      Any=False				,					snake_case__   :      str=False )->			str:
    A_								=					torch.load(snake_case__				,					map_location="cpu" )["model"]
    remove_ignore_keys_(snake_case__ )
    A_								=					state_dict["encoder.embed_tokens.weight"].shape[0]

    A_								=					PLBartConfig.from_pretrained(snake_case__				,					vocab_size=snake_case__ )

    A_								=					state_dict["decoder.embed_tokens.weight"]
    if not classification:
        A_								=					PLBartForConditionalGeneration(snake_case__ )
        model.model.load_state_dict(snake_case__ )
        if finetuned:
            A_								=					make_linear_from_emb(model.model.shared )

    else:
        A_								=					{}
        for key, value in state_dict.copy().items():
            if key.startswith("classification_heads.sentence_classification_head" ):
                A_								=					value
                state_dict.pop(snake_case__ )
        A_								=					PLBartForSequenceClassification(snake_case__ )
        model.model.load_state_dict(snake_case__ )
        model.classification_head.load_state_dict(snake_case__ )

    return model


if __name__ == "__main__":
     __magic_name__  :  int     			=   argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('fairseq_path', type=str, help='model.pt on local filesystem.')
     parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
     parser.add_argument(
         '--hf_config',
         default='uclanlp/plbart-base',
         type=str,
         help='Which huggingface architecture to use: plbart-base',
     )
     parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
     parser.add_argument(
         '--classification', action='store_true', help='whether the model is a classification checkpoint'
     )
     __magic_name__  :  Union[str, Any]     			=   parser.parse_args()
     __magic_name__  :  List[str]     			=   convert_fairseq_plbart_checkpoint_from_disk(
         args.fairseq_path,
         hf_config_path=args.hf_config,
         finetuned=args.finetuned,
         classification=args.classification,
     )
     model.save_pretrained(args.pytorch_dump_folder_path)






