
from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)


class 							lowerCamelCase       (		__snake_case					):



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase = 0.93						,    __UpperCamelCase = True						,    ):
      A_								=					mixing_coeff
      A_								=					leapfrog_steps

      super().__init__()
      self.register_modules(
          vae=__UpperCamelCase						,    text_encoder=__UpperCamelCase						,    tokenizer=__UpperCamelCase						,    unet=__UpperCamelCase						,    scheduler=__UpperCamelCase						,    )

      A_								=					2 ** (len(self.vae.config.block_out_channels					) - 1)
      A_								=					VaeImageProcessor(vae_scale_factor=self.vae_scale_factor					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase = None						,    __UpperCamelCase = False					):
      A_								=					self.tokenizer(
          __UpperCamelCase						,    padding="max_length"						,    max_length=self.tokenizer.model_max_length						,    truncation=__UpperCamelCase						,    return_tensors="pt"						,    )

      A_								=					self.text_encoder(text_inputs.input_ids.to(self.device					)					).last_hidden_state

      A_								=					prompt_embeds.to(dtype=self.text_encoder.dtype						,    device=self.device					)

      if do_classifier_free_guidance:
          A_								=					"" if negative_prompt is None else negative_prompt

          A_								=					self.tokenizer(
              __UpperCamelCase						,    padding="max_length"						,    max_length=self.tokenizer.model_max_length						,    truncation=__UpperCamelCase						,    return_tensors="pt"						,    )

          A_								=					self.text_encoder(uncond_input.input_ids.to(self.device					)					).last_hidden_state

          A_								=					torch.cat([negative_prompt_embeds, prompt_embeds]					)

      return prompt_embeds

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					self.mixing_coeff * x + (1 - self.mixing_coeff) * y
      A_								=					self.mixing_coeff * y + (1 - self.mixing_coeff) * x

      return [x, y]

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
      A_								=					(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

      return [x, y]

  def 					lowercase_						(					self						,    __UpperCamelCase					):
      # as self.alphas_cumprod is always in cpu
      A_								=					int(__UpperCamelCase					)

      A_								=					self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

      return alpha_prod, 1 - alpha_prod

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    ):
      A_								=					timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

      A_	,	A_								=					self._get_alpha_and_beta(__UpperCamelCase					)
      A_	,	A_								=					self._get_alpha_and_beta(__UpperCamelCase					)

      A_								=					(alpha_prod_t_prev / alpha_prod_t) ** 0.5
      A_								=					-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

      A_								=					(base - b_t * model_output) / a_t

      return model_input, next_model_input.to(base.dtype					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    ):
      A_								=					timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

      A_	,	A_								=					self._get_alpha_and_beta(__UpperCamelCase					)
      A_	,	A_								=					self._get_alpha_and_beta(__UpperCamelCase					)

      A_								=					(alpha_prod_t_prev / alpha_prod_t) ** 0.5
      A_								=					-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
      A_								=					a_t * base + b_t * model_output

      return model_input, next_model_input.to(base.dtype					)

  @torch.no_grad()
  def 					lowercase_						(					self						,    __UpperCamelCase					):
      A_								=					1 / self.vae.config.scaling_factor * latents
      A_								=					self.vae.decode(__UpperCamelCase					).sample
      A_								=					(image / 2 + 0.5).clamp(0						,    1					)
      return image

  @torch.no_grad()
  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase = None						,    ):
      A_								=					guidance_scale > 1.0

      A_								=					image.to(device=self.device						,    dtype=text_embeds.dtype					)
      A_								=					self.vae.encode(__UpperCamelCase					).latent_dist.sample(__UpperCamelCase					)

      A_								=					self.vae.config.scaling_factor * latent

      A_								=					[latent.clone(), latent.clone()]

      for i, t in tqdm(enumerate(__UpperCamelCase					)						,    total=len(__UpperCamelCase					)					):
          A_								=					self.noise_mixing_layer(x=coupled_latents[0]						,    y=coupled_latents[1]					)

          # j - model_input index, k - base index
          for j in range(2					):
              A_								=					j ^ 1

              if self.leapfrog_steps:
                  if i % 2 == 0:
                      A_	,	A_								=					j, k

              A_								=					coupled_latents[j]
              A_								=					coupled_latents[k]

              A_								=					torch.cat([model_input] * 2					) if do_classifier_free_guidance else model_input

              A_								=					self.unet(__UpperCamelCase						,    __UpperCamelCase						,    encoder_hidden_states=__UpperCamelCase					).sample

              if do_classifier_free_guidance:
                  A_	,	A_								=					noise_pred.chunk(2					)
                  A_								=					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

              A_	,	A_								=					self.noise_step(
                  base=__UpperCamelCase						,    model_input=__UpperCamelCase						,    model_output=__UpperCamelCase						,    timestep=__UpperCamelCase						,    )

              A_								=					model_input

      return coupled_latents

  @torch.no_grad()
  def __call__(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase = 3.0						,    __UpperCamelCase = 50						,    __UpperCamelCase = 0.8						,    __UpperCamelCase = None						,    __UpperCamelCase = None						,    __UpperCamelCase = "pil"						,    ):
      A_								=					guidance_scale > 1.0

      A_								=					self.image_processor.preprocess(__UpperCamelCase					)

      A_								=					self._encode_prompt(__UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					)
      A_								=					self._encode_prompt(__UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					)

      self.scheduler.set_timesteps(__UpperCamelCase						,    self.device					)

      A_								=					num_inference_steps - int(num_inference_steps * strength					)
      A_								=					self.scheduler.timesteps[t_limit:]
      A_								=					fwd_timesteps.flip(0					)

      A_								=					self.prepare_latents(__UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					)

      for i, t in tqdm(enumerate(__UpperCamelCase					)						,    total=len(__UpperCamelCase					)					):
          # j - model_input index, k - base index
          for k in range(2					):
              A_								=					k ^ 1

              if self.leapfrog_steps:
                  if i % 2 == 1:
                      A_	,	A_								=					j, k

              A_								=					coupled_latents[j]
              A_								=					coupled_latents[k]

              A_								=					torch.cat([model_input] * 2					) if do_classifier_free_guidance else model_input

              A_								=					self.unet(__UpperCamelCase						,    __UpperCamelCase						,    encoder_hidden_states=__UpperCamelCase					).sample

              if do_classifier_free_guidance:
                  A_	,	A_								=					noise_pred.chunk(2					)
                  A_								=					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

              A_	,	A_								=					self.denoise_step(
                  base=__UpperCamelCase						,    model_input=__UpperCamelCase						,    model_output=__UpperCamelCase						,    timestep=__UpperCamelCase						,    )

              A_								=					model_input

          A_								=					self.denoise_mixing_layer(x=coupled_latents[0]						,    y=coupled_latents[1]					)

      # either one is fine
      A_								=					coupled_latents[0]

      if output_type not in ["latent", "pt", "np", "pil"]:
          A_								=					(
              f'the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '
              "`pil`, `np`, `pt`, `latent`"
          )
          deprecate("Unsupported output_type"						,    "1.0.0"						,    __UpperCamelCase						,    standard_warn=__UpperCamelCase					)
          A_								=					"np"

      if output_type == "latent":
          A_								=					final_latent
      else:
          A_								=					self.decode_latents(__UpperCamelCase					)
          A_								=					self.image_processor.postprocess(__UpperCamelCase						,    output_type=__UpperCamelCase					)

      return image








import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification





def        lowerCAmelCase       (				snake_case__   :      Union[str, Any] )->			List[str]:
    A_								=					[
        "encoder.version",
        "decoder.version",
        "model.encoder.version",
        "model.decoder.version",
        "_float_tensor",
        "decoder.output_projection.weight",
    ]
    for k in ignore_keys:
        state_dict.pop(snake_case__				,					snake_case__ )





def        lowerCAmelCase       (				snake_case__   :      Dict )->			str:
    A_	,	A_								=					emb.weight.shape
    A_								=					nn.Linear(snake_case__				,					snake_case__				,					bias=snake_case__ )
    A_								=					emb.weight.data
    return lin_layer





def        lowerCAmelCase       (				snake_case__   :      Any				,					snake_case__   :      Optional[int]="uclanlp/plbart-base"				,					snake_case__   :      Any=False				,					snake_case__   :      str=False )->			str:
    A_								=					torch.load(snake_case__				,					map_location="cpu" )["model"]
    remove_ignore_keys_(snake_case__ )
    A_								=					state_dict["encoder.embed_tokens.weight"].shape[0]

    A_								=					PLBartConfig.from_pretrained(snake_case__				,					vocab_size=snake_case__ )

    A_								=					state_dict["decoder.embed_tokens.weight"]
    if not classification:
        A_								=					PLBartForConditionalGeneration(snake_case__ )
        model.model.load_state_dict(snake_case__ )
        if finetuned:
            A_								=					make_linear_from_emb(model.model.shared )

    else:
        A_								=					{}
        for key, value in state_dict.copy().items():
            if key.startswith("classification_heads.sentence_classification_head" ):
                A_								=					value
                state_dict.pop(snake_case__ )
        A_								=					PLBartForSequenceClassification(snake_case__ )
        model.model.load_state_dict(snake_case__ )
        model.classification_head.load_state_dict(snake_case__ )

    return model


if __name__ == "__main__":
     __magic_name__  :  int     			=   argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('fairseq_path', type=str, help='model.pt on local filesystem.')
     parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
     parser.add_argument(
         '--hf_config',
         default='uclanlp/plbart-base',
         type=str,
         help='Which huggingface architecture to use: plbart-base',
     )
     parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
     parser.add_argument(
         '--classification', action='store_true', help='whether the model is a classification checkpoint'
     )
     __magic_name__  :  Union[str, Any]     			=   parser.parse_args()
     __magic_name__  :  List[str]     			=   convert_fairseq_plbart_checkpoint_from_disk(
         args.fairseq_path,
         hf_config_path=args.hf_config,
         finetuned=args.finetuned,
         classification=args.classification,
     )
     model.save_pretrained(args.pytorch_dump_folder_path)






