





'''simple docstring'''

import argparse
from pathlib import Path

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





def 		UpperCamelCase							(				_lowerCamelCase   :   Union[str, Any]		,			_lowerCamelCase   :   str		,			_lowerCamelCase   :   str		,			_lowerCamelCase   :   Path		,			_lowerCamelCase   :   str = None		,			_lowerCamelCase   :   str = None		,			_lowerCamelCase   :   str = None		,			):
    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(_lowerCamelCase			)
    A__							=     AutoConfig.from_pretrained(_lowerCamelCase			)
    A__							=     AutoConfig.from_pretrained(_lowerCamelCase			)

    A__							=     gen_config
    A__							=     question_encoder_config

    A__							=     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.
    A__							=     AutoTokenizer.from_pretrained(_lowerCamelCase			)
    gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"			)
    A__							=     AutoTokenizer.from_pretrained(_lowerCamelCase			)
    question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"			)


if __name__ == "__main__":
  __lowerCAmelCase				:      int						      =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				:      Union[str, Any]						      =parser.parse_args()

  __lowerCAmelCase				:      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,
  )










'''simple docstring'''

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer






@dataclass
class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							42






class   UpperCAmelCase      (			UpperCamelCase__     ,			UpperCamelCase__							):
 @register_to_config
 def __init__(							self :Tuple		,			lowercase_ :int = 3		,			lowercase_ :int = 3		,			lowercase_ :Tuple[str] = ("DownEncoderBlock2D",)		,			lowercase_ :Tuple[str] = ("UpDecoderBlock2D",)		,			lowercase_ :Tuple[int] = (64,)		,			lowercase_ :int = 1		,			lowercase_ :str = "silu"		,			lowercase_ :int = 3		,			lowercase_ :int = 32		,			lowercase_ :int = 2_56		,			lowercase_ :int = 32		,			lowercase_ :Optional[int] = None		,			lowercase_ :float = 0.1_8_2_1_5		,			lowercase_ :str = "group"		,			)->				Optional[int]:
     super().__init__()

     # pass init params to Encoder
     A__							=     Encoder(
         in_channels=lowercase_		,			out_channels=lowercase_		,			down_block_types=lowercase_		,			block_out_channels=lowercase_		,			layers_per_block=lowercase_		,			act_fn=lowercase_		,			norm_num_groups=lowercase_		,			double_z=lowercase_		,			)

     A__							=     vq_embed_dim if vq_embed_dim is not None else latent_channels

     A__							=     nn.Convad(lowercase_		,			lowercase_		,			1				)
     A__							=     VectorQuantizer(lowercase_		,			lowercase_		,			beta=0.2_5		,			remap=lowercase_		,			sane_index_shape=lowercase_				)
     A__							=     nn.Convad(lowercase_		,			lowercase_		,			1				)

     # pass init params to Decoder
     A__							=     Decoder(
         in_channels=lowercase_		,			out_channels=lowercase_		,			up_block_types=lowercase_		,			block_out_channels=lowercase_		,			layers_per_block=lowercase_		,			act_fn=lowercase_		,			norm_num_groups=lowercase_		,			norm_type=lowercase_		,			)
 @apply_forward_hook
 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :torch.FloatTensor		,			lowercase_ :bool = True				)->				VQEncoderOutput:
     A__							=     self.encoder(lowercase_				)
     A__							=     self.quant_conv(lowercase_				)

     if not return_dict:
         return (h,)

     return VQEncoderOutput(latents=lowercase_				)
 @apply_forward_hook
 def 	UpperCAmelCase_				(							self :Dict		,			lowercase_ :torch.FloatTensor		,			lowercase_ :bool = False		,			lowercase_ :bool = True				)->				Union[DecoderOutput, torch.FloatTensor]:
     # also go through quantization layer
     if not force_not_quantize:
         A__,      A__,      A__							=     self.quantize(lowercase_				)
     else:
         A__							=     h
     A__							=     self.post_quant_conv(lowercase_				)
     A__							=     self.decoder(lowercase_		,			quant if self.config.norm_type == "spatial" else None				)

     if not return_dict:
         return (dec,)

     return DecoderOutput(sample=lowercase_				)



 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :torch.FloatTensor		,			lowercase_ :bool = True				)->				Union[DecoderOutput, torch.FloatTensor]:
     A__							=     sample
     A__							=     self.encode(lowercase_				).latents
     A__							=     self.decode(lowercase_				).sample

     if not return_dict:
         return (dec,)

     return DecoderOutput(sample=lowercase_				)



