





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      42
 lowercase_		=      42

 def __init__(self    :       Dict	,			UpperCAmelCase_    :       UNetaDModel	,			UpperCAmelCase_    :       KarrasVeScheduler)	->Any:



   '''simple docstring'''



   super().__init__()
   self.register_modules(unet=UpperCAmelCase_	,			scheduler=UpperCAmelCase_)







 @torch.no_grad()
 def __call__(self    :       str	,			UpperCAmelCase_    :       int = 1	,			UpperCAmelCase_    :       int = 50	,			UpperCAmelCase_    :       Optional[Union[torch.Generator, List[torch.Generator]]] = None	,			UpperCAmelCase_    :       Optional[str] = "pil"	,			UpperCAmelCase_    :       bool = True	,			**UpperCAmelCase_    :       Optional[Any]	,			)	->Union[Tuple, ImagePipelineOutput]:



   '''simple docstring'''



   lowerCamelCase__:				int        =self.unet.config.sample_size
   lowerCamelCase__:				List[Any]        =(batch_size, 3, img_size, img_size)

   lowerCamelCase__:				Optional[int]        =self.unet

   # sample x_0 ~ N(0, sigma_0^2 * I)
   lowerCamelCase__:				List[Any]        =randn_tensor(UpperCAmelCase_	,			generator=UpperCAmelCase_	,			device=self.device) * self.scheduler.init_noise_sigma

   self.scheduler.set_timesteps(UpperCAmelCase_)

   for t in self.progress_bar(self.scheduler.timesteps):
     # here sigma_t == t_i from the paper
     lowerCamelCase__:				Dict        =self.scheduler.schedule[t]
     lowerCamelCase__:				Optional[int]        =self.scheduler.schedule[t - 1] if t > 0 else 0

     # 1. Select temporarily increased noise level sigma_hat
     # 2. Add new noise to move from sample_i to sample_hat
     lowerCamelCase__  , lowerCamelCase__:				List[Any]        =self.scheduler.add_noise_to_input(UpperCAmelCase_	,			UpperCAmelCase_	,			generator=UpperCAmelCase_)

     # 3. Predict the noise residual given the noise magnitude `sigma_hat`
     # The model inputs and output are adjusted by following eq. (213) in [1].
     lowerCamelCase__:				Any        =(sigma_hat / 2) * model((sample_hat + 1) / 2	,			sigma_hat / 2).sample

     # 4. Evaluate dx/dt at sigma_hat
     # 5. Take Euler step from sigma to sigma_prev
     lowerCamelCase__:				List[str]        =self.scheduler.step(UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_)

     if sigma_prev != 0:
       # 6. Apply 2nd order correction
       # The model inputs and output are adjusted by following eq. (213) in [1].
       lowerCamelCase__:				int        =(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2	,			sigma_prev / 2).sample
       lowerCamelCase__:				Any        =self.scheduler.step_correct(
           UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_	,			step_output.prev_sample	,			step_output["derivative"]	,			)
     lowerCamelCase__:				Union[str, Any]        =step_output.prev_sample

   lowerCamelCase__:				Optional[Any]        =(sample / 2 + 0.5).clamp(0	,			1)
   lowerCamelCase__:				int        =sample.cpu().permute(0	,			2	,			3	,			1).numpy()
   if output_type == "pil":
     lowerCamelCase__:				Optional[Any]        =self.numpy_to_pil(UpperCAmelCase_)

   if not return_dict:
     return (image,)

   return ImagePipelineOutput(images=UpperCAmelCase_)








import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a		)					->		List[str]:







  """simple docstring"""





  lowerCamelCase__:				int        =("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

  lowerCamelCase__:				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(__a		):
    os.makedirs(__a		)

  lowerCamelCase__:				Tuple        =model.state_dict()

  def to_tf_var_name(__a		):
    for patt, repl in iter(__a		):
      lowerCamelCase__:				Tuple        =name.replace(__a     ,   __a		)
    return F"""bert/{name}"""

  def create_tf_var(__a     ,   __a     ,   __a		):
    lowerCamelCase__:				Union[str, Any]        =tf.dtypes.as_dtype(tensor.dtype		)
    lowerCamelCase__:				Any        =tf.get_variable(dtype=__a     ,   shape=tensor.shape     ,   name=__a     ,   initializer=tf.zeros_initializer()		)
    session.run(tf.variables_initializer([tf_var]		)		)
    session.run(__a		)
    return tf_var

  tf.reset_default_graph()
  with tf.Session() as session:
    for var_name in state_dict:
      lowerCamelCase__:				Tuple        =to_tf_var_name(__a		)
      lowerCamelCase__:				str        =state_dict[var_name].numpy()
      if any(x in var_name for x in tensors_to_transpose		):
        lowerCamelCase__:				List[Any]        =torch_tensor.T
      lowerCamelCase__:				Optional[int]        =create_tf_var(tensor=__a     ,   name=__a     ,   session=__a		)
      tf.keras.backend.set_value(__a     ,   __a		)
      lowerCamelCase__:				Union[str, Any]        =session.run(__a		)
      print(F"""Successfully created {tf_name}: {np.allclose(__a     ,   __a		)}"""		)

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





def 					lowerCAmelCase_			(	__a=None		)					->		Tuple:







  """simple docstring"""





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

  lowerCamelCase__:				Dict        =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=__a     ,   ckpt_dir=args.tf_cache_dir     ,   model_name=args.model_name		)


if __name__ == "__main__":
     main()

