
from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowerCamelCase__    :		Optional[int]          =   transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)

def 		UpperCamelCase	(      lowercase_   )		->	Tuple:


  '''simple docstring'''






  if isinstance(__A , torch.Tensor   ):
    return image
  elif isinstance(__A , PIL.Image.Image   ):
    lowercase__  :   Optional[int]    		=      [image]

  lowercase__  :   Tuple    		=      [trans(img.convert("""RGB"""   )   ) for img in image]
  lowercase__  :   int    		=      torch.stack(__A   )
  return image



class       _snake_case   (	_snake_case							):


 def __init__(					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   super().__init__()

   # make sure scheduler can always be converted to DDIM
   lowercase__  :   Optional[int]    		=      DDIMScheduler.from_config(scheduler.config)

   self.register_modules(unet=lowerCAmelCase__					,  scheduler=lowerCAmelCase__)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if strength < 0 or strength > 1:
     raise ValueError(f'The value of strength should in [0.0, 1.0] but is {strength}')


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   int    		=      min(int(num_inference_steps * strength)					,  lowerCAmelCase__)

   lowercase__  :   List[str]    		=      max(num_inference_steps - init_timestep					,  0)
   lowercase__  :   List[str]    		=      self.scheduler.timesteps[t_start:]

   return timesteps, num_inference_steps - t_start


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=None):


   '''simple docstring'''




   if not isinstance(lowerCAmelCase__					,  (torch.Tensor, PIL.Image.Image, list)):
     raise ValueError(
         f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowerCAmelCase__)}')

   lowercase__  :   List[str]    		=      image.to(device=lowerCAmelCase__					,  dtype=lowerCAmelCase__)

   if isinstance(lowerCAmelCase__					,  lowerCAmelCase__) and len(lowerCAmelCase__) != batch_size:
     raise ValueError(
         f'You have passed a list of generators of length {len(lowerCAmelCase__)}, but requested an effective batch'
         f' size of {batch_size}. Make sure the batch size matches the length of the generators.')

   lowercase__  :   Optional[Any]    		=      init_latents.shape
   lowercase__  :   List[str]    		=      randn_tensor(lowerCAmelCase__					,  generator=lowerCAmelCase__					,  device=lowerCAmelCase__					,  dtype=lowerCAmelCase__)

   # get latents
   print("""add noise to latents at timestep"""					,  lowerCAmelCase__)
   lowercase__  :   int    		=      self.scheduler.add_noise(lowerCAmelCase__					,  lowerCAmelCase__					,  lowerCAmelCase__)
   lowercase__  :   Tuple    		=      init_latents

   return latents



 @torch.no_grad()
 def __call__(					self					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = 0.8					,  SCREAMING_SNAKE_CASE_ = 1					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = 0.0					,  SCREAMING_SNAKE_CASE_ = 50					,  SCREAMING_SNAKE_CASE_ = None					,  SCREAMING_SNAKE_CASE_ = "pil"					,  SCREAMING_SNAKE_CASE_ = True					,  ):


   '''simple docstring'''




   self.check_inputs(lowerCAmelCase__)

   # 2. Preprocess image
   lowercase__  :   Optional[Any]    		=      preprocess(lowerCAmelCase__)

   # 3. set timesteps
   self.scheduler.set_timesteps(lowerCAmelCase__					,  device=self.device)
   lowercase__		,    lowercase__  :   Any    		=      self.get_timesteps(lowerCAmelCase__					,  lowerCAmelCase__					,  self.device)
   lowercase__  :   List[str]    		=      timesteps[:1].repeat(lowerCAmelCase__)

   # 4. Prepare latent variables
   lowercase__  :   Dict    		=      self.prepare_latents(lowerCAmelCase__					,  lowerCAmelCase__					,  lowerCAmelCase__					,  self.unet.dtype					,  self.device					,  lowerCAmelCase__)
   lowercase__  :   Union[str, Any]    		=      latents

   # 5. Denoising loop
   for t in self.progress_bar(lowerCAmelCase__):
     # 1. predict noise model_output
     lowercase__  :   str    		=      self.unet(lowerCAmelCase__					,  lowerCAmelCase__).sample

     # 2. predict previous mean of image x_t-1 and add variance depending on eta
     # eta corresponds to η in paper and should be between [0, 1]
     # do x_t -> x_t-1
     lowercase__  :   List[Any]    		=      self.scheduler.step(
         lowerCAmelCase__					,  lowerCAmelCase__					,  lowerCAmelCase__					,  eta=lowerCAmelCase__					,  use_clipped_model_output=lowerCAmelCase__					,  generator=lowerCAmelCase__					,  ).prev_sample

   lowercase__  :   List[Any]    		=      (image / 2 + 0.5).clamp(0					,  1)
   lowercase__  :   int    		=      image.cpu().permute(0					,  2					,  3					,  1).numpy()
   if output_type == "pil":
     lowercase__  :   str    		=      self.numpy_to_pil(lowerCAmelCase__)

   if not return_dict:
     return (image, latent_timestep.item())

   return ImagePipelineOutput(images=lowerCAmelCase__)








import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_   )		->	Dict:


  '''simple docstring'''






  lowercase__  :   Dict    		=      ("""dense.weight""", """attention.self.query""", """attention.self.key""", """attention.self.value""")

  lowercase__  :   Union[str, Any]    		=      (
      ("""layer.""", """layer_"""),
      ("""word_embeddings.weight""", """word_embeddings"""),
      ("""position_embeddings.weight""", """position_embeddings"""),
      ("""token_type_embeddings.weight""", """token_type_embeddings"""),
      (""".""", """/"""),
      ("""LayerNorm/weight""", """LayerNorm/gamma"""),
      ("""LayerNorm/bias""", """LayerNorm/beta"""),
      ("""weight""", """kernel"""),
  )

  if not os.path.isdir(lowercase_   ):
    os.makedirs(lowercase_   )

  lowercase__  :   Optional[Any]    		=      model.state_dict()

  def to_tf_var_name(lowercase_   ):
    for patt, repl in iter(lowercase_   ):
      lowercase__  :   str    		=      name.replace(lowercase_ , lowercase_   )
    return F'bert/{name}'

  def create_tf_var(lowercase_ , lowercase_ , lowercase_   ):
    lowercase__  :   List[str]    		=      tf.dtypes.as_dtype(tensor.dtype   )
    lowercase__  :   Tuple    		=      tf.get_variable(dtype=lowercase_ , shape=tensor.shape , name=lowercase_ , initializer=tf.zeros_initializer()   )
    session.run(tf.variables_initializer([tf_var]   )   )
    session.run(lowercase_   )
    return tf_var

  tf.reset_default_graph()
  with tf.Session() as session:
    for var_name in state_dict:
      lowercase__  :   Any    		=      to_tf_var_name(lowercase_   )
      lowercase__  :   Dict    		=      state_dict[var_name].numpy()
      if any(x in var_name for x in tensors_to_transpose   ):
        lowercase__  :   Optional[int]    		=      torch_tensor.T
      lowercase__  :   Any    		=      create_tf_var(tensor=lowercase_ , name=lowercase_ , session=lowercase_   )
      tf.keras.backend.set_value(lowercase_ , lowercase_   )
      lowercase__  :   Optional[int]    		=      session.run(lowercase_   )
      print(F'Successfully created {tf_name}: {np.allclose(lowercase_ , lowercase_   )}'   )

    lowercase__  :   List[Any]    		=      tf.train.Saver(tf.trainable_variables()   )
    saver.save(lowercase_ , os.path.join(lowercase_ , model_name.replace("""-""" , """_"""   ) + """.ckpt"""   )   )

def 		UpperCamelCase	(      lowercase_=None   )		->	Union[str, Any]:


  '''simple docstring'''






  lowercase__  :   List[str]    		=      argparse.ArgumentParser()
  parser.add_argument("""--model_name""" , type=lowercase_ , required=lowercase_ , help="""model name e.g. bert-base-uncased"""   )
  parser.add_argument(
      """--cache_dir""" , type=lowercase_ , default=lowercase_ , required=lowercase_ , help="""Directory containing pytorch model"""   )
  parser.add_argument("""--pytorch_model_path""" , type=lowercase_ , required=lowercase_ , help="""/path/to/<pytorch-model-name>.bin"""   )
  parser.add_argument("""--tf_cache_dir""" , type=lowercase_ , required=lowercase_ , help="""Directory in which to save tensorflow model"""   )
  lowercase__  :   List[str]    		=      parser.parse_args(lowercase_   )

  lowercase__  :   Any    		=      BertModel.from_pretrained(
      pretrained_model_name_or_path=args.model_name , state_dict=torch.load(args.pytorch_model_path   ) , cache_dir=args.cache_dir , )

  convert_pytorch_checkpoint_to_tf(model=lowercase_ , ckpt_dir=args.tf_cache_dir , model_name=args.model_name   )


if __name__ == "__main__":
     main()






