

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase      :					int					    =      logging.get_logger(__name__)

_lowerCamelCase      :					List[str]					    =      {
    '''microsoft/git-base''': '''https://huggingface.co/microsoft/git-base/resolve/main/config.json''',
}





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    """git_vision_model"""

       def __init__(							self ,   lowercase__=7_6_8 ,   lowercase__=3_0_7_2 ,   lowercase__=1_2 ,   lowercase__=1_2 ,   lowercase__=3 ,   lowercase__=2_2_4 ,   lowercase__=1_6 ,   lowercase__="quick_gelu" ,   lowercase__=1E-5 ,   lowercase__=0.0 ,   lowercase__=0.02 ,   **lowercase__ ,   ):







             '''simple docstring'''



             super().__init__(**lowercase__     )

             __A    =hidden_size
             __A    =intermediate_size
             __A    =num_hidden_layers
             __A    =num_attention_heads
             __A    =num_channels
             __A    =patch_size
             __A    =image_size
             __A    =initializer_range
             __A    =attention_dropout
             __A    =layer_norm_eps
             __A    =hidden_act


       @classmethod
       def     __UpperCamelCase     (							cls ,   lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



             cls._set_token_in_kwargs(lowercase__     )

             __A    ,		__A    =cls.get_config_dict(lowercase__ ,   **lowercase__     )

             # get the vision config dict if we are loading from GITConfig
             if config_dict.get('''model_type'''     ) == "git":
                   __A    =config_dict['''vision_config''']

             if "model_type" in config_dict and hasattr(cls ,   '''model_type'''     ) and config_dict["model_type"] != cls.model_type:
                   logger.warning(
                       f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
                       f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''     )

             return cls.from_dict(lowercase__ ,   **lowercase__     )





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    """git"""

       def __init__(							self ,   lowercase__=None ,   lowercase__=3_0_5_2_2 ,   lowercase__=7_6_8 ,   lowercase__=6 ,   lowercase__=1_2 ,   lowercase__=3_0_7_2 ,   lowercase__="gelu" ,   lowercase__=0.1 ,   lowercase__=0.1 ,   lowercase__=1_0_2_4 ,   lowercase__=0.02 ,   lowercase__=1E-12 ,   lowercase__=0 ,   lowercase__="absolute" ,   lowercase__=True ,   lowercase__=False ,   lowercase__=1_0_1 ,   lowercase__=1_0_2 ,   lowercase__=None ,   **lowercase__ ,   ):







             '''simple docstring'''



             super().__init__(bos_token_id=lowercase__ ,   eos_token_id=lowercase__ ,   pad_token_id=lowercase__ ,   **lowercase__     )

             if vision_config is None:
                   __A    ={}
                   logger.info('''vision_config is None. initializing the GitVisionConfig with default values.'''     )

             __A    =GitVisionConfig(**lowercase__     )
             __A    =vocab_size
             __A    =hidden_size
             __A    =num_hidden_layers
             __A    =num_attention_heads
             __A    =hidden_act
             __A    =intermediate_size
             __A    =hidden_dropout_prob
             __A    =attention_probs_dropout_prob
             __A    =max_position_embeddings
             __A    =initializer_range
             __A    =layer_norm_eps
             __A    =position_embedding_type
             __A    =use_cache
             __A    =tie_word_embeddings
             __A    =num_image_with_embedding

             __A    =bos_token_id
             __A    =eos_token_id


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =copy.deepcopy(self.__dict__     )
             __A    =self.vision_config.to_dict()
             __A    =self.__class__.model_type
             return output





import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration





def      A__					(			__A  :						Any     ,				__A  :						str     ,				__A  :						str     ,				__A  :						Path     ,				__A  :						str = None     ,				__A  :						str = None     ,				__A  :						str = None     ,				)					->Optional[Any]:
      if config_name_or_path is None:
            __A    ='''facebook/rag-token-base''' if model_type == '''rag_token''' else '''facebook/rag-sequence-base'''

      if generator_tokenizer_name_or_path is None:
            __A    =generator_name_or_path

      if question_encoder_tokenizer_name_or_path is None:
            __A    =question_encoder_name_or_path

      __A    =RagTokenForGeneration if model_type == '''rag_token''' else RagSequenceForGeneration

      # Save model.
      __A    =RagConfig.from_pretrained(__A			)
      __A    =AutoConfig.from_pretrained(__A			)
      __A    =AutoConfig.from_pretrained(__A			)

      __A    =gen_config
      __A    =question_encoder_config

      __A    =model_class.from_pretrained_question_encoder_generator(
          __A     ,				__A     ,				config=__A			)
      rag_model.save_pretrained(__A			)

      # Sanity check.
      model_class.from_pretrained(__A			)

      # Save tokenizers.
      __A    =AutoTokenizer.from_pretrained(__A			)
      gen_tokenizer.save_pretrained(dest_dir / '''generator_tokenizer/'''			)
      __A    =AutoTokenizer.from_pretrained(__A			)
      question_encoder_tokenizer.save_pretrained(dest_dir / '''question_encoder_tokenizer/'''			)


if __name__ == "__main__":
   _lowerCamelCase      :					List[str]					    =      argparse.ArgumentParser()
   parser.add_argument(
       '''--model_type''',
       choices=['''rag_sequence''', '''rag_token'''],
       required=True,
       type=str,
       help='''RAG model type: rag_sequence, rag_token''',
   )
   parser.add_argument('''--dest''', type=str, required=True, help='''Path to the output checkpoint directory.''')
   parser.add_argument('''--generator_name_or_path''', type=str, required=True, help='''Generator model identifier''')
   parser.add_argument(
       '''--question_encoder_name_or_path''', type=str, required=True, help='''Question encoder model identifier'''
   )

   parser.add_argument(
       '''--generator_tokenizer_name_or_path''',
       type=str,
       help='''Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``''',
   )
   parser.add_argument(
       '''--question_encoder_tokenizer_name_or_path''',
       type=str,
       help='''Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``''',
   )
   parser.add_argument(
       '''--config_name_or_path''',
       type=str,
       help=(
           '''Identifier of the model config to use, if not provided, resolves to a base config for a given'''
           ''' ``model_type``'''
       ),
   )

   _lowerCamelCase      :					str					    =      parser.parse_args()

   _lowerCamelCase      :					Optional[int]					    =      Path(args.dest)
   dest_dir.mkdir(exist_ok=True)

   consolidate(
       args.model_type,
       args.generator_name_or_path,
       args.question_encoder_name_or_path,
       dest_dir,
       args.config_name_or_path,
       args.generator_tokenizer_name_or_path,
       args.question_encoder_tokenizer_name_or_path,
   )


