


import argparse
from pathlib import Path

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






def       lowerCamelCase					(a_						:Optional[int]				,				a_						:List[Any]				,				a_						:Any				,				a_						:str				,				a_						:List[str] = None				,				a_						:List[Any] = None				,				a_						:List[str] = None				,				)     ->							List[str]:
       if config_name_or_path is None:
              lowercase		:Union[str, Any]       							=				'''facebook/rag-token-base''' if model_type == '''rag_token''' else '''facebook/rag-sequence-base'''

       if generator_tokenizer_name_or_path is None:
              lowercase		:Any       							=				generator_name_or_path

       if question_encoder_tokenizer_name_or_path is None:
              lowercase		:int       							=				question_encoder_name_or_path

       lowercase		:Optional[int]       							=				RagTokenForGeneration if model_type == '''rag_token''' else RagSequenceForGeneration

       # Save model.
       lowercase		:List[str]       							=				RagConfig.from_pretrained(_lowerCAmelCase)
       lowercase		:int       							=				AutoConfig.from_pretrained(_lowerCAmelCase)
       lowercase		:int       							=				AutoConfig.from_pretrained(_lowerCAmelCase)

       lowercase		:Tuple       							=				gen_config
       lowercase		:str       							=				question_encoder_config

       lowercase		: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.
       lowercase		:Optional[Any]       							=				AutoTokenizer.from_pretrained(_lowerCAmelCase)
       gen_tokenizer.save_pretrained(dest_dir / '''generator_tokenizer/''')
       lowercase		:List[Any]       							=				AutoTokenizer.from_pretrained(_lowerCAmelCase)
       question_encoder_tokenizer.save_pretrained(dest_dir / '''question_encoder_tokenizer/''')


if __name__ == "__main__":
 UpperCAmelCase      		=			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``'''
     ),
 )

 UpperCAmelCase      		=			parser.parse_args()

 UpperCAmelCase      		=			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 typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      UNetaDModel
       __A			:      ScoreSdeVeScheduler






       def __init__(   self  :    Optional[Any]  ,     snake_case__  :    UNetaDModel  ,     snake_case__  :    ScoreSdeVeScheduler				):

              '''simple docstring'''






              super().__init__()
              self.register_modules(unet=snake_case__  ,     scheduler=snake_case__				)







       @torch.no_grad()
       def __call__(   self  :    Tuple  ,     snake_case__  :    int = 1  ,     snake_case__  :    int = 2_0_0_0  ,     snake_case__  :    Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,     snake_case__  :    Optional[str] = "pil"  ,     snake_case__  :    bool = True  ,     **snake_case__  :    Optional[Any]  ,     ):

              '''simple docstring'''






              lowercase		:str       							=				self.unet.config.sample_size
              lowercase		:str       							=				(batch_size, 3, img_size, img_size)

              lowercase		:Any       							=				self.unet

              lowercase		:Dict       							=				randn_tensor(snake_case__  ,     generator=snake_case__				) * self.scheduler.init_noise_sigma
              lowercase		:int       							=				sample.to(self.device				)

              self.scheduler.set_timesteps(snake_case__				)
              self.scheduler.set_sigmas(snake_case__				)

              for i, t in enumerate(self.progress_bar(self.scheduler.timesteps				)				):
                     lowercase		:Optional[int]       							=				self.scheduler.sigmas[i] * torch.ones(shape[0]  ,     device=self.device				)

                     # correction step
                     for _ in range(self.scheduler.config.correct_steps				):
                            lowercase		:str       							=				self.unet(snake_case__  ,     snake_case__				).sample
                            lowercase		:Optional[Any]       							=				self.scheduler.step_correct(snake_case__  ,     snake_case__  ,     generator=snake_case__				).prev_sample

                     # prediction step
                     lowercase		:Optional[Any]       							=				model(snake_case__  ,     snake_case__				).sample
                     lowercase		:Dict       							=				self.scheduler.step_pred(snake_case__  ,     snake_case__  ,     snake_case__  ,     generator=snake_case__				)

                     lowercase							,  lowercase		:Tuple       							=				output.prev_sample, output.prev_sample_mean

              lowercase		:str       							=				sample_mean.clamp(0  ,     1				)
              lowercase		:int       							=				sample.cpu().permute(0  ,     2  ,     3  ,     1				).numpy()
              if output_type == "pil":
                     lowercase		:str       							=				self.numpy_to_pil(snake_case__				)

              if not return_dict:
                     return (sample,)

              return ImagePipelineOutput(images=snake_case__				)

