




"""simple docstring"""

from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser






@dataclass
class        SCREAMING_SNAKE_CASE__   :
   snake_case			   =       field(
       metadata={"help": "The output directory where the model will be written."} ,				)
   snake_case			   =       field(
       metadata={
           "help": (
               "The encoder model checkpoint for weights initialization."
               "Don't set if you want to train an encoder model from scratch."
           )
       } ,				)
   snake_case			   =       field(
       metadata={
           "help": (
               "The decoder model checkpoint for weights initialization."
               "Don't set if you want to train a decoder model from scratch."
           )
       } ,				)
   snake_case			   =       field(
       default=UpperCAmelCase_ ,				metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"}      )
   snake_case			   =       field(
       default=UpperCAmelCase_ ,				metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"}      )






def     _A					( ):





       """simple docstring"""



       lowerCamelCase__          =						HfArgumentParser((ModelArguments,)    )
       ((lowerCamelCase__			)     ,				)          =						parser.parse_args_into_dataclasses()

       # Load pretrained model and tokenizer

       # Use explicit specified encoder config
       if model_args.encoder_config_name:
              lowerCamelCase__          =						AutoConfig.from_pretrained(model_args.encoder_config_name    )
       # Use pretrained encoder model's config
       else:
              lowerCamelCase__          =						AutoConfig.from_pretrained(model_args.encoder_model_name_or_path    )

       # Use explicit specified decoder config
       if model_args.decoder_config_name:
              lowerCamelCase__          =						AutoConfig.from_pretrained(model_args.decoder_config_name    )
       # Use pretrained decoder model's config
       else:
              lowerCamelCase__          =						AutoConfig.from_pretrained(model_args.decoder_model_name_or_path    )

       # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
       lowerCamelCase__          =						True
       lowerCamelCase__          =						True

       lowerCamelCase__          =						FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
           encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path							,     decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path							,     encoder_config=__UpperCamelCase							,     decoder_config=__UpperCamelCase							,     )

       # GPT2 only has bos/eos tokens but not decoder_start/pad tokens
       lowerCamelCase__          =						decoder_config.decoder_start_token_id
       lowerCamelCase__          =						decoder_config.pad_token_id
       if decoder_start_token_id is None:
              lowerCamelCase__          =						decoder_config.bos_token_id
       if pad_token_id is None:
              lowerCamelCase__          =						decoder_config.eos_token_id

       # This is necessary to make Flax's generate() work
       lowerCamelCase__          =						decoder_config.eos_token_id
       lowerCamelCase__          =						decoder_start_token_id
       lowerCamelCase__          =						pad_token_id

       lowerCamelCase__          =						AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path    )

       lowerCamelCase__          =						AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path    )
       lowerCamelCase__          =						tokenizer.convert_ids_to_tokens(model.config.pad_token_id    )

       model.save_pretrained(model_args.output_dir    )
       image_processor.save_pretrained(model_args.output_dir    )
       tokenizer.save_pretrained(model_args.output_dir    )


if __name__ == "__main__":
  main()











import argparse
from collections import defaultdict

import yaml


SCREAMING_SNAKE_CASE__   			=   '''docs/source/en/_toctree.yml'''


def A    (							__UpperCamelCase       )    ->   Optional[Any]:
			A__	  =     defaultdict(__UpperCamelCase       )
			for doc in model_doc:
						counts[doc["local"]] += 1
			A__	  =     [key for key, value in counts.items() if value > 1]

			A__	  =     []
			for duplicate_key in duplicates:
						A__	  =     list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key}       )
						if len(__UpperCamelCase       ) > 1:
									raise ValueError(
									    f'''{duplicate_key} is present several times in the documentation table of content at '''
									    '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
									    'others.'       )
						# Only add this once
						new_doc.append({'local': duplicate_key, 'title': titles[0]}       )

			# Add none duplicate-keys
			new_doc.extend([doc for doc in model_doc if counts[doc['local']] == 1]       )

			# Sort
			return sorted(__UpperCamelCase			,						key=lambda __UpperCamelCase       : s["title"].lower()       )


def A    (							__UpperCamelCase=False       )    ->   str:
			with open(__UpperCamelCase			,						encoding='utf-8'       ) as f:
						A__	  =     yaml.safe_load(f.read()       )

			# Get to the API doc
			A__	  =     0
			while content[api_idx]["title"] != "API":
						api_idx += 1
			A__	  =     content[api_idx]['sections']

			# Then to the model doc
			A__	  =     0
			while api_doc[model_idx]["title"] != "Models":
						model_idx += 1

			A__	  =     api_doc[model_idx]['sections']

			A__	  =     [(idx, section) for idx, section in enumerate(__UpperCamelCase       ) if 'sections' in section]
			A__	  =     False
			for idx, modality_doc in modalities_docs:
						A__	  =     modality_doc['sections']
						A__	  =     clean_model_doc_toc(__UpperCamelCase       )

						if old_modality_doc != new_modality_doc:
									A__	  =     True
									if overwrite:
												A__	  =     new_modality_doc

			if diff:
						if overwrite:
									A__	  =     model_doc
									A__	  =     api_doc
									with open(__UpperCamelCase			,						'w'			,						encoding='utf-8'       ) as f:
												f.write(yaml.dump(__UpperCamelCase			,						allow_unicode=__UpperCamelCase       )       )
						else:
									raise ValueError(
									    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'       )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   argparse.ArgumentParser()
							parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
							SCREAMING_SNAKE_CASE__   			=   parser.parse_args()

							check_model_doc(args.fix_and_overwrite)




