


'''simple docstring'''






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




class 						a__							(				nn.Module    ):





							'''simple docstring'''






							lowercase__     :  int
							lowercase__     :  jnp.dtype      		= jnp.floataa







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      nn.Conv(
														    self.out_channels   ,       kernel_size=(3, 3)   ,       strides=(1, 1)   ,       padding=((1, 1), (1, 1))   ,       dtype=self.dtype   ,       )







							def __call__(						self   ,       lowerCamelCase_  )			->				List[str]:
														lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__			  =      hidden_states.shape
														lowerCAmelCase__			  =      jax.image.resize(
														    lowerCamelCase_   ,       shape=(batch, height * 2, width * 2, channels)   ,       method='''nearest'''   ,       )
														lowerCAmelCase__			  =      self.conv(lowerCamelCase_  )
														return hidden_states




class 						a__							(				nn.Module    ):





							'''simple docstring'''






							lowercase__     :  int
							lowercase__     :  jnp.dtype      		= jnp.floataa







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__			  =      nn.Conv(
														    self.out_channels   ,       kernel_size=(3, 3)   ,       strides=(2, 2)   ,       padding=((1, 1), (1, 1))   ,       dtype=self.dtype   ,       )







							def __call__(						self   ,       lowerCamelCase_  )			->				List[Any]:
														# pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
														# hidden_states = jnp.pad(hidden_states, pad_width=pad)
														lowerCAmelCase__			  =      self.conv(lowerCamelCase_  )
														return hidden_states




class 						a__							(				nn.Module    ):





							'''simple docstring'''






							lowercase__     :  int
							lowercase__     :  int      		= None
							lowercase__     :  float      		= 0.0
							lowercase__     :  bool      		= None
							lowercase__     :  jnp.dtype      		= jnp.floataa







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__			  =      self.in_channels if self.out_channels is None else self.out_channels

														lowerCAmelCase__			  =      nn.GroupNorm(num_groups=32   ,       epsilon=1e-5  )
														lowerCAmelCase__			  =      nn.Conv(
														    lowerCamelCase_   ,       kernel_size=(3, 3)   ,       strides=(1, 1)   ,       padding=((1, 1), (1, 1))   ,       dtype=self.dtype   ,       )

														lowerCAmelCase__			  =      nn.Dense(lowerCamelCase_   ,       dtype=self.dtype  )

														lowerCAmelCase__			  =      nn.GroupNorm(num_groups=32   ,       epsilon=1e-5  )
														lowerCAmelCase__			  =      nn.Dropout(self.dropout_prob  )
														lowerCAmelCase__			  =      nn.Conv(
														    lowerCamelCase_   ,       kernel_size=(3, 3)   ,       strides=(1, 1)   ,       padding=((1, 1), (1, 1))   ,       dtype=self.dtype   ,       )

														lowerCAmelCase__			  =      self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

														lowerCAmelCase__			  =      None
														if use_nin_shortcut:
																					lowerCAmelCase__			  =      nn.Conv(
																					    lowerCamelCase_   ,       kernel_size=(1, 1)   ,       strides=(1, 1)   ,       padding='''VALID'''   ,       dtype=self.dtype   ,       )







							def __call__(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_=True  )			->				Any:
														lowerCAmelCase__			  =      hidden_states
														lowerCAmelCase__			  =      self.norma(lowerCamelCase_  )
														lowerCAmelCase__			  =      nn.swish(lowerCamelCase_  )
														lowerCAmelCase__			  =      self.conva(lowerCamelCase_  )

														lowerCAmelCase__			  =      self.time_emb_proj(nn.swish(lowerCamelCase_  )  )
														lowerCAmelCase__			  =      jnp.expand_dims(jnp.expand_dims(lowerCamelCase_   ,       1  )   ,       1  )
														lowerCAmelCase__			  =      hidden_states + temb

														lowerCAmelCase__			  =      self.norma(lowerCamelCase_  )
														lowerCAmelCase__			  =      nn.swish(lowerCamelCase_  )
														lowerCAmelCase__			  =      self.dropout(lowerCamelCase_   ,       lowerCamelCase_  )
														lowerCAmelCase__			  =      self.conva(lowerCamelCase_  )

														if self.conv_shortcut is not None:
																					lowerCAmelCase__			  =      self.conv_shortcut(lowerCamelCase_  )

														return hidden_states + residual


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,
   )


