
"""simple docstring"""






import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput





class 							snake_case       (     nn.Module		):







   def __init__( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase=1.0)   ->List[str]:
      super().__init__()

      a_						   =     cut_size
      a_						   =     cut_power







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase)   ->int:
      a_   ,			a_						   =     pixel_values.shape[2:4]
      a_						   =     min(__UpperCAmelCase       ,							__UpperCAmelCase)
      a_						   =     min(__UpperCAmelCase       ,							__UpperCAmelCase       ,							self.cut_size)
      a_						   =     []
      for _ in range(__UpperCAmelCase):
         a_						   =     int(torch.rand([]) ** self.cut_power * (max_size - min_size) + min_size)
         a_						   =     torch.randint(0       ,							sideX - size + 1       ,							())
         a_						   =     torch.randint(0       ,							sideY - size + 1       ,							())
         a_						   =     pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
         cutouts.append(F.adaptive_avg_poolad(__UpperCAmelCase       ,							self.cut_size))
      return torch.cat(__UpperCAmelCase)






def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase     )						->Optional[Any]:

   """simple docstring"""



   a_						   =     F.normalize(UpperCAmelCase ,   dim=-1     )
   a_						   =     F.normalize(UpperCAmelCase ,   dim=-1     )
   return (x - y).norm(dim=-1     ).div(2     ).arcsin().pow(2     ).mul(2     )
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase     )						->int:

   """simple docstring"""



   for param in model.parameters():
      a_						   =     value





class 							snake_case       (     SCREAMING_SNAKE_CASE_		):







   def __init__( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							)   ->str:
      super().__init__()
      self.register_modules(
          vae=__UpperCAmelCase       ,							text_encoder=__UpperCAmelCase       ,							clip_model=__UpperCAmelCase       ,							tokenizer=__UpperCAmelCase       ,							unet=__UpperCAmelCase       ,							scheduler=__UpperCAmelCase       ,							feature_extractor=__UpperCAmelCase       ,							)

      a_						   =     transforms.Normalize(mean=feature_extractor.image_mean       ,							std=feature_extractor.image_std)
      a_						   =     (
          feature_extractor.size
          if isinstance(feature_extractor.size       ,							__UpperCAmelCase)
          else feature_extractor.size["shortest_edge"]
      )
      a_						   =     MakeCutouts(self.cut_out_size)

      set_requires_grad(self.text_encoder       ,							__UpperCAmelCase)
      set_requires_grad(self.clip_model       ,							__UpperCAmelCase)







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase = "auto")   ->Any:
      if slice_size == "auto":
         # half the attention head size is usually a good trade-off between
         # speed and memory
         a_						   =     self.unet.config.attention_head_dim // 2
      self.unet.set_attention_slice(__UpperCAmelCase)







   def    UpperCAmelCase__	( self)   ->Any:
      self.enable_attention_slicing(__UpperCAmelCase)







   def    UpperCAmelCase__	( self)   ->str:
      set_requires_grad(self.vae       ,							__UpperCAmelCase)







   def    UpperCAmelCase__	( self)   ->int:
      set_requires_grad(self.vae       ,							__UpperCAmelCase)







   def    UpperCAmelCase__	( self)   ->Dict:
      set_requires_grad(self.unet       ,							__UpperCAmelCase)







   def    UpperCAmelCase__	( self)   ->int:
      set_requires_grad(self.unet       ,							__UpperCAmelCase)







   @torch.enable_grad()
   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase=True       ,							)   ->Dict:
      a_						   =     latents.detach().requires_grad_()

      a_						   =     self.scheduler.scale_model_input(__UpperCAmelCase       ,							__UpperCAmelCase)

      # predict the noise residual
      a_						   =     self.unet(__UpperCAmelCase       ,							__UpperCAmelCase       ,							encoder_hidden_states=__UpperCAmelCase).sample

      if isinstance(self.scheduler       ,							(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)):
         a_						   =     self.scheduler.alphas_cumprod[timestep]
         a_						   =     1 - alpha_prod_t
         # compute predicted original sample from predicted noise also called
         # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         a_						   =     (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

         a_						   =     torch.sqrt(__UpperCAmelCase)
         a_						   =     pred_original_sample * (fac) + latents * (1 - fac)
      elif isinstance(self.scheduler       ,							__UpperCAmelCase):
         a_						   =     self.scheduler.sigmas[index]
         a_						   =     latents - sigma * noise_pred
      else:
         raise ValueError(F'''scheduler type {type(self.scheduler)} not supported''')

      a_						   =     1 / self.vae.config.scaling_factor * sample
      a_						   =     self.vae.decode(__UpperCAmelCase).sample
      a_						   =     (image / 2 + 0.5).clamp(0       ,							1)

      if use_cutouts:
         a_						   =     self.make_cutouts(__UpperCAmelCase       ,							__UpperCAmelCase)
      else:
         a_						   =     transforms.Resize(self.cut_out_size)(__UpperCAmelCase)
      a_						   =     self.normalize(__UpperCAmelCase).to(latents.dtype)

      a_						   =     self.clip_model.get_image_features(__UpperCAmelCase)
      a_						   =     image_embeddings_clip / image_embeddings_clip.norm(p=2       ,							dim=-1       ,							keepdim=__UpperCAmelCase)

      if use_cutouts:
         a_						   =     spherical_dist_loss(__UpperCAmelCase       ,							__UpperCAmelCase)
         a_						   =     dists.view([num_cutouts, sample.shape[0], -1])
         a_						   =     dists.sum(2).mean(0).sum() * clip_guidance_scale
      else:
         a_						   =     spherical_dist_loss(__UpperCAmelCase       ,							__UpperCAmelCase).mean() * clip_guidance_scale

      a_						   =     -torch.autograd.grad(__UpperCAmelCase       ,							__UpperCAmelCase)[0]

      if isinstance(self.scheduler       ,							__UpperCAmelCase):
         a_						   =     latents.detach() + grads * (sigma**2)
         a_						   =     noise_pred_original
      else:
         a_						   =     noise_pred_original - torch.sqrt(__UpperCAmelCase) * grads
      return noise_pred, latents







   @torch.no_grad()
   def __call__( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase = 5_12       ,							__UpperCAmelCase = 5_12       ,							__UpperCAmelCase = 50       ,							__UpperCAmelCase = 7.5       ,							__UpperCAmelCase = 1       ,							__UpperCAmelCase = 0.0       ,							__UpperCAmelCase = 1_00       ,							__UpperCAmelCase = None       ,							__UpperCAmelCase = 4       ,							__UpperCAmelCase = True       ,							__UpperCAmelCase = None       ,							__UpperCAmelCase = None       ,							__UpperCAmelCase = "pil"       ,							__UpperCAmelCase = True       ,							)   ->List[str]:
      if isinstance(__UpperCAmelCase       ,							__UpperCAmelCase):
         a_						   =     1
      elif isinstance(__UpperCAmelCase       ,							__UpperCAmelCase):
         a_						   =     len(__UpperCAmelCase)
      else:
         raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(__UpperCAmelCase)}''')

      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}.''')

      # get prompt text embeddings
      a_						   =     self.tokenizer(
          __UpperCAmelCase       ,							padding="max_length"       ,							max_length=self.tokenizer.model_max_length       ,							truncation=__UpperCAmelCase       ,							return_tensors="pt"       ,							)
      a_						   =     self.text_encoder(text_input.input_ids.to(self.device))[0]
      # duplicate text embeddings for each generation per prompt
      a_						   =     text_embeddings.repeat_interleave(__UpperCAmelCase       ,							dim=0)

      if clip_guidance_scale > 0:
         if clip_prompt is not None:
            a_						   =     self.tokenizer(
                __UpperCAmelCase       ,							padding="max_length"       ,							max_length=self.tokenizer.model_max_length       ,							truncation=__UpperCAmelCase       ,							return_tensors="pt"       ,							).input_ids.to(self.device)
         else:
            a_						   =     text_input.input_ids.to(self.device)
         a_						   =     self.clip_model.get_text_features(__UpperCAmelCase)
         a_						   =     text_embeddings_clip / text_embeddings_clip.norm(p=2       ,							dim=-1       ,							keepdim=__UpperCAmelCase)
         # duplicate text embeddings clip for each generation per prompt
         a_						   =     text_embeddings_clip.repeat_interleave(__UpperCAmelCase       ,							dim=0)

      # 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.
      a_						   =     guidance_scale > 1.0
      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance:
         a_						   =     text_input.input_ids.shape[-1]
         a_						   =     self.tokenizer([""]       ,							padding="max_length"       ,							max_length=__UpperCAmelCase       ,							return_tensors="pt")
         a_						   =     self.text_encoder(uncond_input.input_ids.to(self.device))[0]
         # duplicate unconditional embeddings for each generation per prompt
         a_						   =     uncond_embeddings.repeat_interleave(__UpperCAmelCase       ,							dim=0)

         # 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
         a_						   =     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`.
      a_						   =     (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
      a_						   =     text_embeddings.dtype
      if latents is None:
         if self.device.type == "mps":
            # randn does not work reproducibly on mps
            a_						   =     torch.randn(__UpperCAmelCase       ,							generator=__UpperCAmelCase       ,							device="cpu"       ,							dtype=__UpperCAmelCase).to(
                self.device)
         else:
            a_						   =     torch.randn(__UpperCAmelCase       ,							generator=__UpperCAmelCase       ,							device=self.device       ,							dtype=__UpperCAmelCase)
      else:
         if latents.shape != latents_shape:
            raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''')
         a_						   =     latents.to(self.device)

      # set timesteps
      a_						   =     "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
      a_						   =     {}
      if accepts_offset:
         a_						   =     1

      self.scheduler.set_timesteps(__UpperCAmelCase       ,							**__UpperCAmelCase)

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

      # scale the initial noise by the standard deviation required by the scheduler
      a_						   =     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]
      a_						   =     "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
      a_						   =     {}
      if accepts_eta:
         a_						   =     eta

      # check if the scheduler accepts generator
      a_						   =     "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
      if accepts_generator:
         a_						   =     generator

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

         # predict the noise residual
         a_						   =     self.unet(__UpperCAmelCase       ,							__UpperCAmelCase       ,							encoder_hidden_states=__UpperCAmelCase).sample

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

         # perform clip guidance
         if clip_guidance_scale > 0:
            a_						   =     (
                text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings
            )
            a_   ,			a_						   =     self.cond_fn(
                __UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							)

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

      # scale and decode the image latents with vae
      a_						   =     1 / self.vae.config.scaling_factor * latents
      a_						   =     self.vae.decode(__UpperCAmelCase).sample

      a_						   =     (image / 2 + 0.5).clamp(0       ,							1)
      a_						   =     image.cpu().permute(0       ,							2       ,							3       ,							1).numpy()

      if output_type == "pil":
         a_						   =     self.numpy_to_pil(__UpperCAmelCase)

      if not return_dict:
         return (image, None)

      return StableDiffusionPipelineOutput(images=__UpperCAmelCase       ,							nsfw_content_detected=__UpperCAmelCase)

"""simple docstring"""






import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )						->Dict:

   """simple docstring"""



   a_						   =     RoFormerConfig.from_json_file(UpperCAmelCase     )
   print(F'''Building PyTorch model from configuration: {config}'''     )
   a_						   =     RoFormerForMaskedLM(UpperCAmelCase     )

   # Load weights from tf checkpoint
   load_tf_weights_in_roformer(UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )

   # Save pytorch-model
   print(F'''Save PyTorch model to {pytorch_dump_path}'''     )
   torch.save(model.state_dict() ,   UpperCAmelCase ,   _use_new_zipfile_serialization=UpperCAmelCase     )


if __name__ == "__main__":
      UpperCamelCase_						   =   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
      )
      parser.add_argument(
          '--bert_config_file',
          default=None,
          type=str,
          required=True,
          help=(
              'The config json file corresponding to the pre-trained BERT model. \n'
              'This specifies the model architecture.'
          ),
      )
      parser.add_argument(
          '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
      )
      UpperCamelCase_						   =   parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)