
from typing import List, Optional, Tuple, Union

import torch

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







class 			__A( a			):




				def __init__(     self		,     _snake_case		,     _snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								super().__init__()

								# make sure scheduler can always be converted to DDIM
								__a										=    DDIMScheduler.from_config(scheduler.config				)

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




				@torch.no_grad()
				def __call__(     self		,     _snake_case = 1		,     _snake_case = None		,     _snake_case = 0.0		,     _snake_case = 50		,     _snake_case = None		,     _snake_case = "pil"		,     _snake_case = True		,     )    ->      Union[ImagePipelineOutput, Tuple]:


								'''simple docstring'''




								if isinstance(self.unet.config.sample_size		,     _snake_case				):
												__a										=    (
												    batch_size,
												    self.unet.config.in_channels,
												    self.unet.config.sample_size,
												    self.unet.config.sample_size,
												)
								else:
												__a										=    (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

								__a										=    randn_tensor(_snake_case		,     generator=_snake_case		,     device=self.device		,     dtype=self.unet.dtype				)

								# set step values
								self.scheduler.set_timesteps(_snake_case				)

								for t in self.progress_bar(self.scheduler.timesteps				):
												# 1. predict noise model_output
												__a										=    self.unet(_snake_case		,     _snake_case				).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
												__a										=    self.scheduler.step(
												    _snake_case		,     _snake_case		,     _snake_case		,     eta=_snake_case		,     use_clipped_model_output=_snake_case		,     generator=_snake_case				).prev_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(_snake_case				)

								if not return_dict:
												return (image,)

								return ImagePipelineOutput(images=_snake_case				)






"""simple docstring"""




import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowerCamelCase_     :						Tuple						=					logging.get_logger(__name__)





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       Optional[Any]										=  R'\w+[.]\d+'
  A_  :       int										=  re.findall(_UpperCAmelCase ,					_UpperCAmelCase					)
  for pat in pats:
    A_  :       Optional[int]										=  key.replace(_UpperCAmelCase ,					'_'.join(pat.split('.'					)					)					)
  return key





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[Any]										=  pt_tuple_key[:-1] + ('scale',)
  if (
      any('norm' in str_ for str_ in pt_tuple_key					)
      and (pt_tuple_key[-1] == "bias")
      and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
      and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
  ):
    A_  :       Union[str, Any]										=  pt_tuple_key[:-1] + ('scale',)
    return renamed_pt_tuple_key, pt_tensor
  elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
    A_  :       List[str]										=  pt_tuple_key[:-1] + ('scale',)
    return renamed_pt_tuple_key, pt_tensor

  # embedding
  if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
    A_  :       Optional[Any]										=  pt_tuple_key[:-1] + ('embedding',)
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  A_  :       int										=  pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
    A_  :       str										=  pt_tensor.transpose(2 ,					3 ,					1 ,					0					)
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  A_  :       Optional[Any]										=  pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight":
    A_  :       Optional[Any]										=  pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm weight
  A_  :       Tuple										=  pt_tuple_key[:-1] + ('weight',)
  if pt_tuple_key[-1] == "gamma":
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm bias
  A_  :       Optional[int]										=  pt_tuple_key[:-1] + ('bias',)
  if pt_tuple_key[-1] == "beta":
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase=42					):






  """simple docstring"""





  A_  :       int										=  {k: v.numpy() for k, v in pt_state_dict.items()}

  # Step 2: Since the model is stateless, get random Flax params
  A_  :       Union[str, Any]										=  flax_model.init_weights(PRNGKey(_UpperCAmelCase					)					)

  A_  :       Optional[Any]										=  flatten_dict(_UpperCAmelCase					)
  A_  :       Tuple										=  {}

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    A_  :       Any										=  rename_key(_UpperCAmelCase					)
    A_  :       List[str]										=  tuple(renamed_pt_key.split('.'					)					)

    # Correctly rename weight parameters
    A_						,       A_  :       Union[str, Any]										=  rename_key_and_reshape_tensor(_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					)

    if flax_key in random_flax_state_dict:
      if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
        raise ValueError(
            f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
            f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""					)

        # also add unexpected weight so that warning is thrown
    A_  :       str										=  jnp.asarray(_UpperCAmelCase					)

  return unflatten_dict(_UpperCAmelCase					)