






'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


UpperCamelCase__			=      logging.get_logger(__name__)  # pylint: disable=invalid-name


class 							_UpperCAmelCase    (		snake_case      ):




    def __init__(      self	:							int	,  a	:							WhisperForConditionalGeneration	,  a	:							WhisperProcessor	,  a	:							AutoencoderKL	,  a	:							CLIPTextModel	,  a	:							CLIPTokenizer	,  a	:							UNetaDConditionModel	,  a	:							Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]	,  a	:							StableDiffusionSafetyChecker	,  a	:							CLIPImageProcessor	,  ):






       '''simple docstring'''






       super().__init__()

       if safety_checker is None:
          logger.warning(
              f"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
              " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
              " results in services or applications open to the public. Both the diffusers team and Hugging Face"
              " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
              " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
              " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."      )

       self.register_modules(
           speech_model=a	,  speech_processor=a	,  vae=a	,  text_encoder=a	,  tokenizer=a	,  unet=a	,  scheduler=a	,  feature_extractor=a	,  )




    def 					lowerCAmelCase__					(      self	:							Dict	,  a	:							Optional[Union[str, int]] = "auto"      ):






       '''simple docstring'''






       if slice_size == "auto":
          lowercase_						:     Union[str, Any]							     =  self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(a      )




    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       self.enable_attention_slicing(a      )





    @torch.no_grad()
    def __call__(      self	:							Optional[Any]	,  a	:							Tuple	,  a	:							Dict=1_6_0_0_0	,  a	:							int = 5_1_2	,  a	:							int = 5_1_2	,  a	:							int = 5_0	,  a	:							float = 7.5	,  a	:							Optional[Union[str, List[str]]] = None	,  a	:							Optional[int] = 1	,  a	:							float = 0.0	,  a	:							Optional[torch.Generator] = None	,  a	:							Optional[torch.FloatTensor] = None	,  a	:							Optional[str] = "pil"	,  a	:							bool = True	,  a	:							Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,  a	:							int = 1	,  **a	:							List[Any]	,  ):






       '''simple docstring'''






       lowercase_						:     str							     =  self.speech_processor.feature_extractor(
           a	,  return_tensors="pt"	,  sampling_rate=a      ).input_features.to(self.device      )
       lowercase_						:     Dict							     =  self.speech_model.generate(a	,  max_length=4_8_0_0_0_0      )

       lowercase_						:     Dict							     =  self.speech_processor.tokenizer.batch_decode(a	,  skip_special_tokens=a	,  normalize=a      )[
           0
       ]

       if isinstance(a	,  a      ):
          lowercase_						:     List[str]							     =  1
       elif isinstance(a	,  a      ):
          lowercase_						:     Any							     =  len(a      )
       else:
          raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(a      )}"""      )

       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(a	,  a      ) or callback_steps <= 0)
       ):
          raise ValueError(
              f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
              f""" {type(a      )}."""      )

       # get prompt text embeddings
       lowercase_						:     List[Any]							     =  self.tokenizer(
           a	,  padding="max_length"	,  max_length=self.tokenizer.model_max_length	,  return_tensors="pt"	,  )
       lowercase_						:     str							     =  text_inputs.input_ids

       if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
          lowercase_						:     Union[str, Any]							     =  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}"""      )
          lowercase_						:     int							     =  text_input_ids[:, : self.tokenizer.model_max_length]
       lowercase_						:     Optional[int]							     =  self.text_encoder(text_input_ids.to(self.device      )      )[0]

       # duplicate text embeddings for each generation per prompt, using mps friendly method
       lowercase_     ,		lowercase_     ,		lowercase_						:     List[Any]							     =  text_embeddings.shape
       lowercase_						:     Any							     =  text_embeddings.repeat(1	,  a	,  1      )
       lowercase_						:     List[str]							     =  text_embeddings.view(bs_embed * num_images_per_prompt	,  a	,  -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.
       lowercase_						:     Tuple							     =  guidance_scale > 1.0
       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance:
          lowercase_						:     List[str]
          if negative_prompt is None:
             lowercase_						:     Optional[int]							     =  [""] * batch_size
          elif type(a      ) is not type(a      ):
             raise TypeError(
                 f"""`negative_prompt` should be the same type to `prompt`, but got {type(a      )} !="""
                 f""" {type(a      )}."""      )
          elif isinstance(a	,  a      ):
             lowercase_						:     Optional[Any]							     =  [negative_prompt]
          elif batch_size != len(a      ):
             raise ValueError(
                 f"""`negative_prompt`: {negative_prompt} has batch size {len(a      )}, but `prompt`:"""
                 f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
                 " the batch size of `prompt`."      )
          else:
             lowercase_						:     Dict							     =  negative_prompt

          lowercase_						:     List[str]							     =  text_input_ids.shape[-1]
          lowercase_						:     Union[str, Any]							     =  self.tokenizer(
              a	,  padding="max_length"	,  max_length=a	,  truncation=a	,  return_tensors="pt"	,  )
          lowercase_						:     Tuple							     =  self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

          # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
          lowercase_						:     List[str]							     =  uncond_embeddings.shape[1]
          lowercase_						:     Union[str, Any]							     =  uncond_embeddings.repeat(1	,  a	,  1      )
          lowercase_						:     List[Any]							     =  uncond_embeddings.view(batch_size * num_images_per_prompt	,  a	,  -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
          lowercase_						:     Dict							     =  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`.
       lowercase_						:     List[str]							     =  (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
       lowercase_						:     Tuple							     =  text_embeddings.dtype
       if latents is None:
          if self.device.type == "mps":
             # randn does not exist on mps
             lowercase_						:     Any							     =  torch.randn(a	,  generator=a	,  device="cpu"	,  dtype=a      ).to(
                 self.device      )
          else:
             lowercase_						:     int							     =  torch.randn(a	,  generator=a	,  device=self.device	,  dtype=a      )
       else:
          if latents.shape != latents_shape:
             raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""      )
          lowercase_						:     Optional[int]							     =  latents.to(self.device      )

       # set timesteps
       self.scheduler.set_timesteps(a      )

       # Some schedulers like PNDM have timesteps as arrays
       # It's more optimized to move all timesteps to correct device beforehand
       lowercase_						:     Union[str, Any]							     =  self.scheduler.timesteps.to(self.device      )

       # scale the initial noise by the standard deviation required by the scheduler
       lowercase_						:     Any							     =  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]
       lowercase_						:     List[str]							     =  "eta" in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
       lowercase_						:     Optional[int]							     =  {}
       if accepts_eta:
          lowercase_						:     List[str]							     =  eta

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

          # predict the noise residual
          lowercase_						:     Optional[Any]							     =  self.unet(a	,  a	,  encoder_hidden_states=a      ).sample

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

          # compute the previous noisy sample x_t -> x_t-1
          lowercase_						:     str							     =  self.scheduler.step(a	,  a	,  a	,  **a      ).prev_sample

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

       lowercase_						:     Optional[int]							     =  1 / 0.1_8215 * latents
       lowercase_						:     Any							     =  self.vae.decode(a      ).sample

       lowercase_						:     List[Any]							     =  (image / 2 + 0.5).clamp(0	,  1      )

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

       if output_type == "pil":
          lowercase_						:     int							     =  self.numpy_to_pil(a      )

       if not return_dict:
          return image

       return StableDiffusionPipelineOutput(images=a	,  nsfw_content_detected=a      )












'''simple docstring'''
import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


UpperCamelCase__			=      ['small', 'medium', 'large']

UpperCamelCase__			=      'lm_head.decoder.weight'
UpperCamelCase__			=      'lm_head.weight'




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  torch.load(_UpperCamelCase				)
   lowercase_						:     List[Any]							     =  d.pop(_UpperCamelCase				)
   os.makedirs(_UpperCamelCase    ,		exist_ok=_UpperCamelCase				)
   torch.save(_UpperCamelCase    ,		os.path.join(_UpperCamelCase    ,		_UpperCamelCase				)				)


if __name__ == "__main__":
 UpperCamelCase__			=      argparse.ArgumentParser()
 parser.add_argument('--dialogpt_path', default='.', type=str)
 UpperCamelCase__			=      parser.parse_args()
 for MODEL in DIALOGPT_MODELS:
  UpperCamelCase__			=      os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
  UpperCamelCase__			=      f"""./DialoGPT-{MODEL}"""
  convert_dialogpt_checkpoint(
      checkpoint_path,
      pytorch_dump_folder_path,
  )




