





import flax.linen as nn
import jax
import jax.numpy as jnp



class       A__     ( nn.Module    ):
      _UpperCAmelCase			:int
      _UpperCAmelCase			:jnp.dtype							=     jnp.floataa



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




       UpperCamelCase			:      str 			=     nn.Conv(
           self.out_channels ,							kernel_size=(3, 3) ,							strides=(1, 1) ,							padding=((1, 1), (1, 1)) ,							dtype=self.dtype ,							)



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




       UpperCamelCase			:      List[str] 			=     hidden_states.shape
       UpperCamelCase			:      Dict 			=     jax.image.resize(
           __a ,							shape=(batch, height * 2, width * 2, channels) ,							method="nearest" ,							)
       UpperCamelCase			:      Tuple 			=     self.conv(__a     )
       return hidden_states



class       A__     ( nn.Module    ):
      _UpperCAmelCase			:int
      _UpperCAmelCase			:jnp.dtype							=     jnp.floataa



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




       UpperCamelCase			:      List[Any] 			=     nn.Conv(
           self.out_channels ,							kernel_size=(3, 3) ,							strides=(2, 2) ,							padding=((1, 1), (1, 1)) ,							dtype=self.dtype ,							)



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




       UpperCamelCase			:      Union[str, Any] 			=     self.conv(__a     )
       return hidden_states



class       A__     ( nn.Module    ):
      _UpperCAmelCase			:int
      _UpperCAmelCase			:int							=     None
      _UpperCAmelCase			:float							=     0.0
      _UpperCAmelCase			:bool							=     None
      _UpperCAmelCase			:jnp.dtype							=     jnp.floataa



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




       UpperCamelCase			:      List[str] 			=     self.in_channels if self.out_channels is None else self.out_channels

       UpperCamelCase			:      int 			=     nn.GroupNorm(num_groups=32 ,							epsilon=1e-5     )
       UpperCamelCase			:      List[str] 			=     nn.Conv(
           __a ,							kernel_size=(3, 3) ,							strides=(1, 1) ,							padding=((1, 1), (1, 1)) ,							dtype=self.dtype ,							)

       UpperCamelCase			:      Union[str, Any] 			=     nn.Dense(__a ,							dtype=self.dtype     )

       UpperCamelCase			:      Union[str, Any] 			=     nn.GroupNorm(num_groups=32 ,							epsilon=1e-5     )
       UpperCamelCase			:      Dict 			=     nn.Dropout(self.dropout_prob     )
       UpperCamelCase			:      List[str] 			=     nn.Conv(
           __a ,							kernel_size=(3, 3) ,							strides=(1, 1) ,							padding=((1, 1), (1, 1)) ,							dtype=self.dtype ,							)

       UpperCamelCase			:      Optional[Any] 			=     self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

       UpperCamelCase			:      List[str] 			=     None
       if use_nin_shortcut:
        UpperCamelCase			:      List[Any] 			=     nn.Conv(
            __a ,							kernel_size=(1, 1) ,							strides=(1, 1) ,							padding="VALID" ,							dtype=self.dtype ,							)



      def __call__(				self ,							A_ ,							A_ ,							A_=True     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     hidden_states
       UpperCamelCase			:      List[Any] 			=     self.norma(__a     )
       UpperCamelCase			:      Any 			=     nn.swish(__a     )
       UpperCamelCase			:      str 			=     self.conva(__a     )

       UpperCamelCase			:      Any 			=     self.time_emb_proj(nn.swish(__a     )     )
       UpperCamelCase			:      Any 			=     jnp.expand_dims(jnp.expand_dims(__a ,							1     ) ,							1     )
       UpperCamelCase			:      str 			=     hidden_states + temb

       UpperCamelCase			:      Any 			=     self.norma(__a     )
       UpperCamelCase			:      Optional[Any] 			=     nn.swish(__a     )
       UpperCamelCase			:      Optional[Any] 			=     self.dropout(__a ,							__a     )
       UpperCamelCase			:      List[str] 			=     self.conva(__a     )

       if self.conv_shortcut is not None:
        UpperCamelCase			:      str 			=     self.conv_shortcut(__a     )

       return hidden_states + residual







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,
    )
