




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				:		List[Any]     =   transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       List[Any]     ):


      '''simple docstring'''



      if isinstance(__a	,							torch.Tensor     ):
            return image
      elif isinstance(__a	,							PIL.Image.Image     ):
            lowerCamelCase_		     =    [image]

      lowerCamelCase_		     =    [trans(img.convert('RGB'     )     ) for img in image]
      lowerCamelCase_		     =    torch.stack(__a     )
      return image







class   A(						__SCREAMING_SNAKE_CASE   ):



 '''simple docstring'''


 def __init__(				self					:  Any						,							A_					:  Optional[Any]						,							A_					:  str  )   ->	Any:

       """simple docstring"""






       super().__init__()

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

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

 def 				a__ (				self					:  str						,							A_					:  Tuple  )   ->	Any:

       """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 				a__ (				self					:  List[Any]						,							A_					:  int						,							A_					:  Optional[Any]						,							A_					:  Dict  )   ->	Tuple:

       """simple docstring"""






       lowerCamelCase_		     =    min(int(num_inference_steps * strength  )						,							UpperCAmelCase_  )

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

       return timesteps, num_inference_steps - t_start

 def 				a__ (				self					:  Optional[Any]						,							A_					:  Optional[int]						,							A_					:  Any						,							A_					:  List[Any]						,							A_					:  Dict						,							A_					:  Union[str, Any]						,							A_					:  int=None  )   ->	str:

       """simple docstring"""






       if not isinstance(UpperCAmelCase_						,							(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(UpperCAmelCase_  )}"""  )

       lowerCamelCase_		     =    image.to(device=UpperCAmelCase_						,							dtype=UpperCAmelCase_  )

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

       lowerCamelCase_		     =    init_latents.shape
       lowerCamelCase_		     =    randn_tensor(UpperCAmelCase_						,							generator=UpperCAmelCase_						,							device=UpperCAmelCase_						,							dtype=UpperCAmelCase_  )

       # get latents
       print('add noise to latents at timestep'						,							UpperCAmelCase_  )
       lowerCamelCase_		     =    self.scheduler.add_noise(UpperCAmelCase_						,							UpperCAmelCase_						,							UpperCAmelCase_  )
       lowerCamelCase_		     =    init_latents

       return latents





 @torch.no_grad()
 def __call__(				self					:  str						,							A_					:  Union[torch.FloatTensor, PIL.Image.Image] = None						,							A_					:  float = 0.8						,							A_					:  int = 1						,							A_					:  Optional[Union[torch.Generator, List[torch.Generator]]] = None						,							A_					:  float = 0.0						,							A_					:  int = 50						,							A_					:  Optional[bool] = None						,							A_					:  Optional[str] = "pil"						,							A_					:  bool = True						,							)   ->	Union[ImagePipelineOutput, Tuple]:

       """simple docstring"""






       self.check_inputs(UpperCAmelCase_  )

       # 2. Preprocess image
       lowerCamelCase_		     =    preprocess(UpperCAmelCase_  )

       # 3. set timesteps
       self.scheduler.set_timesteps(UpperCAmelCase_						,							device=self.device  )
       lowerCamelCase_		     =    self.get_timesteps(UpperCAmelCase_						,							UpperCAmelCase_						,							self.device  )
       lowerCamelCase_		     =    timesteps[:1].repeat(UpperCAmelCase_  )

       # 4. Prepare latent variables
       lowerCamelCase_		     =    self.prepare_latents(UpperCAmelCase_						,							UpperCAmelCase_						,							UpperCAmelCase_						,							self.unet.dtype						,							self.device						,							UpperCAmelCase_  )
       lowerCamelCase_		     =    latents

       # 5. Denoising loop
       for t in self.progress_bar(UpperCAmelCase_  ):
             # 1. predict noise model_output
             lowerCamelCase_		     =    self.unet(UpperCAmelCase_						,							UpperCAmelCase_  ).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
             lowerCamelCase_		     =    self.scheduler.step(
                 UpperCAmelCase_						,							UpperCAmelCase_						,							UpperCAmelCase_						,							eta=UpperCAmelCase_						,							use_clipped_model_output=UpperCAmelCase_						,							generator=UpperCAmelCase_						,							).prev_sample

       lowerCamelCase_		     =    (image / 2 + 0.5).clamp(0						,							1  )
       lowerCamelCase_		     =    image.cpu().permute(0						,							2						,							3						,							1  ).numpy()
       if output_type == "pil":
             lowerCamelCase_		     =    self.numpy_to_pil(UpperCAmelCase_  )

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

       return ImagePipelineOutput(images=UpperCAmelCase_  )










from typing import Any





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a	,    __a	,    )     ->		list:







  """simple docstring"""





  _validation(
      __a	,    __a	,    __a	,    __a	,    __a	,    )
  # Creates data structures and fill initial step
  lowerCamelCase__:      dict			  ={}
  lowerCamelCase__:      dict			  ={}
  for state in states_space:
    lowerCamelCase__:      Optional[Any]			  =observations_space[0]
    lowerCamelCase__:      List[Any]			  =(
        initial_probabilities[state] * emission_probabilities[state][observation]
    )
    lowerCamelCase__:      int			  =None

  # Fills the data structure with the probabilities of
  # different transitions and pointers to previous states
  for o in range(1	,    len(__a		)		):
    lowerCamelCase__:      Tuple			  =observations_space[o]
    lowerCamelCase__:      Optional[Any]			  =observations_space[o - 1]
    for state in states_space:
      # Calculates the argmax for probability function
      lowerCamelCase__:      Tuple			  =""
      lowerCamelCase__:      Optional[Any]			  =-1
      for k_state in states_space:
        lowerCamelCase__:      int			  =(
            probabilities[(k_state, prior_observation)]
            * transition_probabilities[k_state][state]
            * emission_probabilities[state][observation]
        )
        if probability > max_probability:
          lowerCamelCase__:      List[str]			  =probability
          lowerCamelCase__:      int			  =k_state

            # Update probabilities and pointers dicts
      lowerCamelCase__:      Any			  =(
          probabilities[(arg_max, prior_observation)]
          * transition_probabilities[arg_max][state]
          * emission_probabilities[state][observation]
      )

      lowerCamelCase__:      int			  =arg_max

    # The final observation
  lowerCamelCase__:      Any			  =observations_space[len(__a		) - 1]

  # argmax for given final observation
  lowerCamelCase__:      Optional[Any]			  =""
  lowerCamelCase__:      int			  =-1
  for k_state in states_space:
    lowerCamelCase__:      Tuple			  =probabilities[(k_state, final_observation)]
    if probability > max_probability:
      lowerCamelCase__:      List[Any]			  =probability
      lowerCamelCase__:      Dict			  =k_state
  lowerCamelCase__:      str			  =arg_max

  # Process pointers backwards
  lowerCamelCase__:      Union[str, Any]			  =last_state
  lowerCamelCase__:      List[str]			  =[]
  for o in range(len(__a		) - 1	,    -1	,    -1		):
    result.append(__a		)
    lowerCamelCase__:      Union[str, Any]			  =pointers[previous, observations_space[o]]
  result.reverse()

  return result





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a	,    __a	,    )     ->		None:







  """simple docstring"""





  _validate_not_empty(
      __a	,    __a	,    __a	,    __a	,    __a	,    )
  _validate_lists(__a	,    __a		)
  _validate_dicts(
      __a	,    __a	,    __a		)





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a	,    __a	,    )     ->		None:







  """simple docstring"""





  if not all(
      [
          observations_space,
          states_space,
          initial_probabilities,
          transition_probabilities,
          emission_probabilities,
      ]		):
    raise ValueError("There's an empty parameter"		)





def      lowerCAmelCase_ (       __a	,    __a		)     ->		None:







  """simple docstring"""





  _validate_list(__a	,    "observations_space"		)
  _validate_list(__a	,    "states_space"		)





def      lowerCAmelCase_ (       __a	,    __a		)     ->		None:







  """simple docstring"""





  if not isinstance(_object	,    __a		):
    lowerCamelCase__:      Tuple			  =F"""{var_name} must be a list"""
    raise ValueError(__a		)
  else:
    for x in _object:
      if not isinstance(__a	,    __a		):
        lowerCamelCase__:      str			  =F"""{var_name} must be a list of strings"""
        raise ValueError(__a		)





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    )     ->		None:







  """simple docstring"""





  _validate_dict(__a	,    "initial_probabilities"	,    __a		)
  _validate_nested_dict(__a	,    "transition_probabilities"		)
  _validate_nested_dict(__a	,    "emission_probabilities"		)





def      lowerCAmelCase_ (       __a	,    __a		)     ->		None:







  """simple docstring"""





  _validate_dict(_object	,    __a	,    __a		)
  for x in _object.values():
    _validate_dict(__a	,    __a	,    __a	,    __a		)





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a = False		)     ->		None:







  """simple docstring"""





  if not isinstance(_object	,    __a		):
    lowerCamelCase__:      Optional[int]			  =F"""{var_name} must be a dict"""
    raise ValueError(__a		)
  if not all(isinstance(__a	,    __a		) for x in _object		):
    lowerCamelCase__:      Tuple			  =F"""{var_name} all keys must be strings"""
    raise ValueError(__a		)
  if not all(isinstance(__a	,    __a		) for x in _object.values()		):
    lowerCamelCase__:      Dict			  ="nested dictionary " if nested else ""
    lowerCamelCase__:      List[str]			  =F"""{var_name} {nested_text}all values must be {value_type.__name__}"""
    raise ValueError(__a		)


if __name__ == "__main__":
     from doctest import testmod

     testmod()

