





'''simple docstring'''

import argparse
from pathlib import Path

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







def    a__							(							lowercase   :	Dict,   lowercase   :	str,   lowercase   :	str,   lowercase   :	Path,   lowercase   :	str = None,   lowercase   :	str = None,   lowercase   :	str = None,   )						->						str:





     """simple docstring"""

     if config_name_or_path is None:
          _UpperCamelCase							       =			'''facebook/rag-token-base''' if model_type == '''rag_token''' else '''facebook/rag-sequence-base'''

     if generator_tokenizer_name_or_path is None:
          _UpperCamelCase							       =			generator_name_or_path

     if question_encoder_tokenizer_name_or_path is None:
          _UpperCamelCase							       =			question_encoder_name_or_path

     _UpperCamelCase							       =			RagTokenForGeneration if model_type == '''rag_token''' else RagSequenceForGeneration

     # Save model.
     _UpperCamelCase							       =			RagConfig.from_pretrained(lowercase )
     _UpperCamelCase							       =			AutoConfig.from_pretrained(lowercase )
     _UpperCamelCase							       =			AutoConfig.from_pretrained(lowercase )

     _UpperCamelCase							       =			gen_config
     _UpperCamelCase							       =			question_encoder_config

     _UpperCamelCase							       =			model_class.from_pretrained_question_encoder_generator(
         lowercase,   lowercase,   config=lowercase )
     rag_model.save_pretrained(lowercase )

     # Sanity check.
     model_class.from_pretrained(lowercase )

     # Save tokenizers.
     _UpperCamelCase							       =			AutoTokenizer.from_pretrained(lowercase )
     gen_tokenizer.save_pretrained(dest_dir / '''generator_tokenizer/''' )
     _UpperCamelCase							       =			AutoTokenizer.from_pretrained(lowercase )
     question_encoder_tokenizer.save_pretrained(dest_dir / '''question_encoder_tokenizer/''' )


if __name__ == "__main__":
   lowercase__		:     Optional[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``'
       ),
   )

   lowercase__		:     Optional[int]			=							parser.parse_args()

   lowercase__		:     Union[str, Any]			=							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'''

import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


lowercase__		:     List[str]			=							logging.get_logger(__name__)  # pylint: disable=invalid-name
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""





       def __init__(				self  :						Optional[Any]			,					lowerCAmelCase__  :						AutoencoderKL			,					lowerCAmelCase__  :						CLIPTextModel			,					lowerCAmelCase__  :						CLIPTokenizer			,					lowerCAmelCase__  :						UNetaDConditionModel			,					lowerCAmelCase__  :						Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]			,					lowerCAmelCase__  :						StableDiffusionSafetyChecker			,					lowerCAmelCase__  :						CLIPImageProcessor			,					)       ->	Optional[Any]:

            '''simple docstring'''




            super().__init__()
            self.register_modules(
                vae=lowerCAmelCase__			,					text_encoder=lowerCAmelCase__			,					tokenizer=lowerCAmelCase__			,					unet=lowerCAmelCase__			,					scheduler=lowerCAmelCase__			,					safety_checker=lowerCAmelCase__			,					feature_extractor=lowerCAmelCase__			,					)





       def snake_case__		(				self  :						Union[str, Any]			,					lowerCAmelCase__  :						Optional[Union[str, int]] = "auto"	)       ->	Union[str, Any]:

            '''simple docstring'''




            if slice_size == "auto":
                 # half the attention head size is usually a good trade-off between
                 # speed and memory
                 _UpperCamelCase							       =			self.unet.config.attention_head_dim // 2
            self.unet.set_attention_slice(lowerCAmelCase__	)





       def snake_case__		(				self  :						int	)       ->	Optional[int]:

            '''simple docstring'''




            self.enable_attention_slicing(lowerCAmelCase__	)







       @torch.no_grad()
       def __call__(				self  :						Optional[Any]			,					lowerCAmelCase__  :						Union[str, List[str]]			,					lowerCAmelCase__  :						int = 512			,					lowerCAmelCase__  :						int = 512			,					lowerCAmelCase__  :						int = 50			,					lowerCAmelCase__  :						float = 7.5			,					lowerCAmelCase__  :						Optional[Union[str, List[str]]] = None			,					lowerCAmelCase__  :						Optional[int] = 1			,					lowerCAmelCase__  :						float = 0.0			,					lowerCAmelCase__  :						Optional[torch.Generator] = None			,					lowerCAmelCase__  :						Optional[torch.FloatTensor] = None			,					lowerCAmelCase__  :						Optional[str] = "pil"			,					lowerCAmelCase__  :						bool = True			,					lowerCAmelCase__  :						Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,					lowerCAmelCase__  :						int = 1			,					lowerCAmelCase__  :						Optional[torch.FloatTensor] = None			,					**lowerCAmelCase__  :						Optional[int]			,					)       ->	Any:

            '''simple docstring'''




            if isinstance(lowerCAmelCase__			,					lowerCAmelCase__	):
                 _UpperCamelCase							       =			1
            elif isinstance(lowerCAmelCase__			,					lowerCAmelCase__	):
                 _UpperCamelCase							       =			len(lowerCAmelCase__	)
            else:
                 raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__	)}"""	)

            if height % 8 != 0 or width % 8 != 0:
                 raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""	)

            if (callback_steps is None) or (
                callback_steps is not None and (not isinstance(lowerCAmelCase__			,					lowerCAmelCase__	) or callback_steps <= 0)
            ):
                 raise ValueError(
                     f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
                     f""" {type(lowerCAmelCase__	)}."""	)

            # get prompt text embeddings
            _UpperCamelCase							       =			self.tokenizer(
                lowerCAmelCase__			,					padding='''max_length'''			,					max_length=self.tokenizer.model_max_length			,					return_tensors='''pt'''			,					)
            _UpperCamelCase							       =			text_inputs.input_ids

            if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                 _UpperCamelCase							       =			self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]	)
                 logger.warning(
                     '''The following part of your input was truncated because CLIP can only handle sequences up to'''
                     f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""	)
                 _UpperCamelCase							       =			text_input_ids[:, : self.tokenizer.model_max_length]

            if text_embeddings is None:
                 _UpperCamelCase							       =			self.text_encoder(text_input_ids.to(self.device	)	)[0]

            # duplicate text embeddings for each generation per prompt, using mps friendly method
            _UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase							       =			text_embeddings.shape
            _UpperCamelCase							       =			text_embeddings.repeat(1			,					lowerCAmelCase__			,					1	)
            _UpperCamelCase							       =			text_embeddings.view(bs_embed * num_images_per_prompt			,					lowerCAmelCase__			,					-1	)

            # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
            # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
            # corresponds to doing no classifier free guidance.
            _UpperCamelCase							       =			guidance_scale > 1.0
            # get unconditional embeddings for classifier free guidance
            if do_classifier_free_guidance:
                 _UpperCamelCase							       =			42
                 if negative_prompt is None:
                      _UpperCamelCase							       =			['''''']
                 elif type(lowerCAmelCase__	) is not type(lowerCAmelCase__	):
                      raise TypeError(
                          f"""`negative_prompt` should be the same type to `prompt`, but got {type(lowerCAmelCase__	)} !="""
                          f""" {type(lowerCAmelCase__	)}."""	)
                 elif isinstance(lowerCAmelCase__			,					lowerCAmelCase__	):
                      _UpperCamelCase							       =			[negative_prompt]
                 elif batch_size != len(lowerCAmelCase__	):
                      raise ValueError(
                          f"""`negative_prompt`: {negative_prompt} has batch size {len(lowerCAmelCase__	)}, but `prompt`:"""
                          f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
                          ''' the batch size of `prompt`.'''	)
                 else:
                      _UpperCamelCase							       =			negative_prompt

                 _UpperCamelCase							       =			text_input_ids.shape[-1]
                 _UpperCamelCase							       =			self.tokenizer(
                     lowerCAmelCase__			,					padding='''max_length'''			,					max_length=lowerCAmelCase__			,					truncation=lowerCAmelCase__			,					return_tensors='''pt'''			,					)
                 _UpperCamelCase							       =			self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]

                 # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                 _UpperCamelCase							       =			uncond_embeddings.shape[1]
                 _UpperCamelCase							       =			uncond_embeddings.repeat(lowerCAmelCase__			,					lowerCAmelCase__			,					1	)
                 _UpperCamelCase							       =			uncond_embeddings.view(batch_size * num_images_per_prompt			,					lowerCAmelCase__			,					-1	)

                 # For classifier free guidance, we need to do two forward passes.
                 # Here we concatenate the unconditional and text embeddings into a single batch
                 # to avoid doing two forward passes
                 _UpperCamelCase							       =			torch.cat([uncond_embeddings, text_embeddings]	)

            # get the initial random noise unless the user supplied it

            # Unlike in other pipelines, latents need to be generated in the target device
            # for 1-to-1 results reproducibility with the CompVis implementation.
            # However this currently doesn't work in `mps`.
            _UpperCamelCase							       =			(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
            _UpperCamelCase							       =			(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
            _UpperCamelCase							       =			text_embeddings.dtype
            if latents is None:
                 if self.device.type == "mps":
                      # randn does not exist on mps
                      _UpperCamelCase							       =			torch.randn(
                          lowerCAmelCase__			,					generator=lowerCAmelCase__			,					device='''cpu'''			,					dtype=lowerCAmelCase__	).to(self.device	)
                      _UpperCamelCase							       =			torch.randn(lowerCAmelCase__			,					generator=lowerCAmelCase__			,					device='''cpu'''			,					dtype=lowerCAmelCase__	).to(
                          self.device	)
                 else:
                      _UpperCamelCase							       =			torch.randn(
                          lowerCAmelCase__			,					generator=lowerCAmelCase__			,					device=self.device			,					dtype=lowerCAmelCase__	)
                      _UpperCamelCase							       =			torch.randn(lowerCAmelCase__			,					generator=lowerCAmelCase__			,					device=self.device			,					dtype=lowerCAmelCase__	)
            else:
                 if latents_reference.shape != latents_shape:
                      raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""	)
                 _UpperCamelCase							       =			latents_reference.to(self.device	)
                 _UpperCamelCase							       =			latents.to(self.device	)

            # This is the key part of the pipeline where we
            # try to ensure that the generated images w/ the same seed
            # but different sizes actually result in similar images
            _UpperCamelCase							       =			(latents_shape[3] - latents_shape_reference[3]) // 2
            _UpperCamelCase							       =			(latents_shape[2] - latents_shape_reference[2]) // 2
            _UpperCamelCase							       =			latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
            _UpperCamelCase							       =			latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
            _UpperCamelCase							       =			0 if dx < 0 else dx
            _UpperCamelCase							       =			0 if dy < 0 else dy
            _UpperCamelCase							       =			max(-dx			,					0	)
            _UpperCamelCase							       =			max(-dy			,					0	)
            # import pdb
            # pdb.set_trace()
            _UpperCamelCase							       =			latents_reference[:, :, dy : dy + h, dx : dx + w]

            # set timesteps
            self.scheduler.set_timesteps(lowerCAmelCase__	)

            # Some schedulers like PNDM have timesteps as arrays
            # It's more optimized to move all timesteps to correct device beforehand
            _UpperCamelCase							       =			self.scheduler.timesteps.to(self.device	)

            # scale the initial noise by the standard deviation required by the scheduler
            _UpperCamelCase							       =			latents * self.scheduler.init_noise_sigma

            # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
            # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
            # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
            # and should be between [0, 1]
            _UpperCamelCase							       =			'''eta''' in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
            _UpperCamelCase							       =			{}
            if accepts_eta:
                 _UpperCamelCase							       =			eta

            for i, t in enumerate(self.progress_bar(lowerCAmelCase__	)	):
                 # expand the latents if we are doing classifier free guidance
                 _UpperCamelCase							       =			torch.cat([latents] * 2	) if do_classifier_free_guidance else latents
                 _UpperCamelCase							       =			self.scheduler.scale_model_input(lowerCAmelCase__			,					lowerCAmelCase__	)

                 # predict the noise residual
                 _UpperCamelCase							       =			self.unet(lowerCAmelCase__			,					lowerCAmelCase__			,					encoder_hidden_states=lowerCAmelCase__	).sample

                 # perform guidance
                 if do_classifier_free_guidance:
                      _UpperCamelCase       ,							_UpperCamelCase							       =			noise_pred.chunk(2	)
                      _UpperCamelCase							       =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                 # compute the previous noisy sample x_t -> x_t-1
                 _UpperCamelCase							       =			self.scheduler.step(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__			,					**lowerCAmelCase__	).prev_sample

                 # call the callback, if provided
                 if callback is not None and i % callback_steps == 0:
                      callback(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			1 / 0.18215 * latents
            _UpperCamelCase							       =			self.vae.decode(lowerCAmelCase__	).sample

            _UpperCamelCase							       =			(image / 2 + 0.5).clamp(0			,					1	)

            # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
            _UpperCamelCase							       =			image.cpu().permute(0			,					2			,					3			,					1	).float().numpy()

            if self.safety_checker is not None:
                 _UpperCamelCase							       =			self.feature_extractor(self.numpy_to_pil(lowerCAmelCase__	)			,					return_tensors='''pt'''	).to(
                     self.device	)
                 _UpperCamelCase       ,							_UpperCamelCase							       =			self.safety_checker(
                     images=lowerCAmelCase__			,					clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype	)	)
            else:
                 _UpperCamelCase							       =			None

            if output_type == "pil":
                 _UpperCamelCase							       =			self.numpy_to_pil(lowerCAmelCase__	)

            if not return_dict:
                 return (image, has_nsfw_concept)

            return StableDiffusionPipelineOutput(images=lowerCAmelCase__			,					nsfw_content_detected=lowerCAmelCase__	)



