





from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)



class       A__     ( __snake_case    ):





      def __init__(				self ,							A_ ,							A_=None ,							A_=None ,							A_=0     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     1.0 if scale is None else scale
       UpperCamelCase			:      int 			=     0.0 if loc is None else loc

       super().__init__(A_ ,							[AffineTransform(loc=self.loc ,							scale=self.scale ,							event_dim=A_     )]     )





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self.base_dist.mean * self.scale + self.loc





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self.base_dist.variance * self.scale**2





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self.variance.sqrt()



class       A__     ( nn.Module    ):





      def __init__(				self ,							A_ ,							A_ ,							A_ ,							**A_     ):
       '''simple docstring'''




       super().__init__(**A_     )
       UpperCamelCase			:      Any 			=     args_dim
       UpperCamelCase			:      List[Any] 			=     nn.ModuleList([nn.Linear(A_ ,							A_     ) for dim in args_dim.values()]     )
       UpperCamelCase			:      Tuple 			=     domain_map





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     [proj(A_     ) for proj in self.proj]

       return self.domain_map(*A_     )



class       A__     ( nn.Module    ):





      def __init__(				self ,							A_     ):
       '''simple docstring'''




       super().__init__()
       UpperCamelCase			:      int 			=     function





      def  __UpperCamelCase(				self ,							A_ ,							*A_     ):
       '''simple docstring'''




       return self.function(A_ ,							*A_     )



class       A__     :
      _UpperCAmelCase			:type
      _UpperCAmelCase			:int
      _UpperCAmelCase			:Dict[str, int]





      def __init__(				self ,							A_ = 1     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     dim
       UpperCamelCase			:      Optional[int] 			=     {k: dim * self.args_dim[k] for k in self.args_dim}





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       if self.dim == 1:
        return self.distribution_class(*A_     )
       else:
        return Independent(self.distribution_class(*A_     ) ,							1     )





      def  __UpperCamelCase(				self ,							A_ ,							A_ = None ,							A_ = None ,							):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     self._base_distribution(A_     )
       if loc is None and scale is None:
        return distr
       else:
        return AffineTransformed(A_ ,							loc=A_ ,							scale=A_ ,							event_dim=self.event_dim     )





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return () if self.dim == 1 else (self.dim,)





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return len(self.event_shape     )





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return 0.0





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       return ParameterProjection(
           in_features=A_ ,							args_dim=self.args_dim ,							domain_map=LambdaLayer(self.domain_map     ) ,							)





      def  __UpperCamelCase(				self ,							*A_     ):
       '''simple docstring'''




       raise NotImplementedError()





      @staticmethod
      def  __UpperCamelCase(				A_     ):
       '''simple docstring'''




       return (x + torch.sqrt(torch.square(A_     ) + 4.0     )) / 2.0



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:Dict[str, int]							=     {"df": 1, "loc": 1, "scale": 1}
      _UpperCAmelCase			:type							=     StudentT





      @classmethod
      def  __UpperCamelCase(				cls ,							A_ ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Any 			=     cls.squareplus(A_     ).clamp_min(torch.finfo(scale.dtype     ).eps     )
       UpperCamelCase			:      Dict 			=     2.0 + cls.squareplus(A_     )
       return df.squeeze(-1     ), loc.squeeze(-1     ), scale.squeeze(-1     )



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:Dict[str, int]							=     {"loc": 1, "scale": 1}
      _UpperCAmelCase			:type							=     Normal





      @classmethod
      def  __UpperCamelCase(				cls ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     cls.squareplus(A_     ).clamp_min(torch.finfo(scale.dtype     ).eps     )
       return loc.squeeze(-1     ), scale.squeeze(-1     )



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:Dict[str, int]							=     {"total_count": 1, "logits": 1}
      _UpperCAmelCase			:type							=     NegativeBinomial





      @classmethod
      def  __UpperCamelCase(				cls ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     cls.squareplus(A_     )
       return total_count.squeeze(-1     ), logits.squeeze(-1     )





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			,					UpperCamelCase			:      Any 			=     distr_args
       if self.dim == 1:
        return self.distribution_class(total_count=A_ ,							logits=A_     )
       else:
        return Independent(self.distribution_class(total_count=A_ ,							logits=A_     ) ,							1     )





      def  __UpperCamelCase(				self ,							A_ ,							A_ = None ,							A_ = None     ):
       '''simple docstring'''




       UpperCamelCase			,					UpperCamelCase			:      Optional[int] 			=     distr_args

       if scale is not None:
        # See scaling property of Gamma.
        logits += scale.log()

       return self._base_distribution((total_count, logits)     )







import argparse
from pathlib import Path

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



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase = None							,     _lowerCAmelCase = None							,     _lowerCAmelCase = None							,     )		->				str:
 if config_name_or_path is None:
  UpperCamelCase			:      Dict 			=     "facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

 if generator_tokenizer_name_or_path is None:
  UpperCamelCase			:      Tuple 			=     generator_name_or_path

 if question_encoder_tokenizer_name_or_path is None:
  UpperCamelCase			:      Tuple 			=     question_encoder_name_or_path

 UpperCamelCase			:      Any 			=     RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration

 # Save model.
 UpperCamelCase			:      Optional[Any] 			=     RagConfig.from_pretrained(_lowerCAmelCase  )
 UpperCamelCase			:      Union[str, Any] 			=     AutoConfig.from_pretrained(_lowerCAmelCase  )
 UpperCamelCase			:      Tuple 			=     AutoConfig.from_pretrained(_lowerCAmelCase  )

 UpperCamelCase			:      int 			=     gen_config
 UpperCamelCase			:      Dict 			=     question_encoder_config

 UpperCamelCase			:      Tuple 			=     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			:      Optional[Any] 			=     AutoTokenizer.from_pretrained(_lowerCAmelCase  )
 gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"  )
 UpperCamelCase			:      Union[str, Any] 			=     AutoTokenizer.from_pretrained(_lowerCAmelCase  )
 question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"  )


if __name__ == "__main__":
    __lowerCamelCase :  Tuple										=  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 :  Dict										=  parser.parse_args()

    __lowerCamelCase :  Dict										=  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,
    )
