'''simple docstring'''


import argparse
from pathlib import Path

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


def  _lowerCamelCase   (				lowerCamelCase_ :							Optional[int]     ,					lowerCamelCase_ :							str     ,					lowerCamelCase_ :							str     ,					lowerCamelCase_ :							Path     ,					lowerCamelCase_ :							str = None     ,					lowerCamelCase_ :							str = None     ,					lowerCamelCase_ :							str = None     ,					):



     """simple docstring"""





     if config_name_or_path is None:
          UpperCAmelCase_							:       Optional[int]       =				'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'

     if generator_tokenizer_name_or_path is None:
          UpperCAmelCase_							:       Optional[int]       =				generator_name_or_path

     if question_encoder_tokenizer_name_or_path is None:
          UpperCAmelCase_							:       Dict       =				question_encoder_name_or_path

     UpperCAmelCase_							:       List[str]       =				RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

     # Save model.
     UpperCAmelCase_							:       Dict       =				RagConfig.from_pretrained(lowerCamelCase_       )
     UpperCAmelCase_							:       Union[str, Any]       =				AutoConfig.from_pretrained(lowerCamelCase_       )
     UpperCAmelCase_							:       Any       =				AutoConfig.from_pretrained(lowerCamelCase_       )

     UpperCAmelCase_							:       Optional[int]       =				gen_config
     UpperCAmelCase_							:       Optional[Any]       =				question_encoder_config

     UpperCAmelCase_							:       List[Any]       =				model_class.from_pretrained_question_encoder_generator(
         lowerCamelCase_     ,					lowerCamelCase_     ,					config=lowerCamelCase_       )
     rag_model.save_pretrained(lowerCamelCase_       )

     # Sanity check.
     model_class.from_pretrained(lowerCamelCase_       )

     # Save tokenizers.
     UpperCAmelCase_							:       Union[str, Any]       =				AutoTokenizer.from_pretrained(lowerCamelCase_       )
     gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'       )
     UpperCAmelCase_							:       str       =				AutoTokenizer.from_pretrained(lowerCamelCase_       )
     question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/'       )


if __name__ == "__main__":
 snake_case__					:      Optional[Any]				      =  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``'''
     ),
 )

 snake_case__					:      List[Any]				      =  parser.parse_args()

 snake_case__					:      str				      =  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,
 )







'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


snake_case__					:      Dict				      =  {
    '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
    '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
    '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
    '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
    '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
    '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
    '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
    '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:str						   =		'''albert'''


  def __init__(			self				,		snake_case_=3_0_0_0_0				,		snake_case_=1_2_8				,		snake_case_=4_0_9_6				,		snake_case_=1_2				,		snake_case_=1				,		snake_case_=6_4				,		snake_case_=1_6_3_8_4				,		snake_case_=1				,		snake_case_="gelu_new"				,		snake_case_=0				,		snake_case_=0				,		snake_case_=5_1_2				,		snake_case_=2				,		snake_case_=0.02				,		snake_case_=1E-12				,		snake_case_=0.1				,		snake_case_="absolute"				,		snake_case_=0				,		snake_case_=2				,		snake_case_=3				,		**snake_case_				,		):




       '''simple docstring'''

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

       UpperCAmelCase_							:       List[Any]       =				vocab_size
       UpperCAmelCase_							:       Dict       =				embedding_size
       UpperCAmelCase_							:       str       =				hidden_size
       UpperCAmelCase_							:       Any       =				num_hidden_layers
       UpperCAmelCase_							:       Union[str, Any]       =				num_hidden_groups
       UpperCAmelCase_							:       List[str]       =				num_attention_heads
       UpperCAmelCase_							:       Any       =				inner_group_num
       UpperCAmelCase_							:       Optional[int]       =				hidden_act
       UpperCAmelCase_							:       Tuple       =				intermediate_size
       UpperCAmelCase_							:       Any       =				hidden_dropout_prob
       UpperCAmelCase_							:       List[str]       =				attention_probs_dropout_prob
       UpperCAmelCase_							:       Union[str, Any]       =				max_position_embeddings
       UpperCAmelCase_							:       Dict       =				type_vocab_size
       UpperCAmelCase_							:       Union[str, Any]       =				initializer_range
       UpperCAmelCase_							:       Optional[Any]       =				layer_norm_eps
       UpperCAmelCase_							:       Dict       =				classifier_dropout_prob
       UpperCAmelCase_							:       Tuple       =				position_embedding_type







class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       if self.task == "multiple-choice":
            UpperCAmelCase_							:       Optional[Any]       =				{0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
            UpperCAmelCase_							:       List[Any]       =				{0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
               ('token_type_ids', dynamic_axis),
           ]						)






