
'''simple docstring'''




import math
from typing import Callable, List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__=[]						):




    '''simple docstring'''



    A       :   Union[str, Any]					=						size[0] - overlap_pixels * 2
    A       :   str					=						size[1] - overlap_pixels * 2
    for letter in ["l", "r"]:
        if letter in remove_borders:
            size_x += overlap_pixels
    for letter in ["t", "b"]:
        if letter in remove_borders:
            size_y += overlap_pixels
    A       :   str					=						np.ones((size_y, size_x)		,				dtype=np.uinta						) * 255
    A       :   Dict					=						np.pad(snake_case__		,				mode='''linear_ramp'''		,				pad_width=snake_case__		,				end_values=0						)

    if "l" in remove_borders:
        A       :   Any					=						mask[:, overlap_pixels : mask.shape[1]]
    if "r" in remove_borders:
        A       :   Any					=						mask[:, 0 : mask.shape[1] - overlap_pixels]
    if "t" in remove_borders:
        A       :   Any					=						mask[overlap_pixels : mask.shape[0], :]
    if "b" in remove_borders:
        A       :   Union[str, Any]					=						mask[0 : mask.shape[0] - overlap_pixels, :]
    return mask







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    return max(snake_case__		,				min(snake_case__		,				snake_case__						)						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    return (
        clamp(rect[0]		,				min[0]		,				max[0]						),
        clamp(rect[1]		,				min[1]		,				max[1]						),
        clamp(rect[2]		,				min[0]		,				max[0]						),
        clamp(rect[3]		,				min[1]		,				max[1]						),
    )







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   List[Any]					=						list(snake_case__						)
    rect[0] -= overlap
    rect[1] -= overlap
    rect[2] += overlap
    rect[3] += overlap
    A       :   str					=						clamp_rect(snake_case__		,				[0, 0]		,				[image_size[0], image_size[1]]						)
    return rect







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   int					=						Image.new('''RGB'''		,				(tile.size[0] + original_slice, tile.size[1])						)
    result.paste(
        original_image.resize((tile.size[0], tile.size[1])		,				Image.BICUBIC						).crop(
            (slice_x, 0, slice_x + original_slice, tile.size[1])						)		,				(0, 0)		,				)
    result.paste(snake_case__		,				(original_slice, 0)						)
    return result







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   Dict					=						(original_image_slice * 4, 0, tile.size[0], tile.size[1])
    A       :   Union[str, Any]					=						tile.crop(snake_case__						)
    return tile







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   Union[str, Any]					=						n % d
    return n - divisor





class  A				(							__snake_case						):





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = 350						,			) ->				List[Any]:






         """simple docstring"""






         super().__init__(
             vae=SCREAMING_SNAKE_CASE						,			text_encoder=SCREAMING_SNAKE_CASE						,			tokenizer=SCREAMING_SNAKE_CASE						,			unet=SCREAMING_SNAKE_CASE						,			low_res_scheduler=SCREAMING_SNAKE_CASE						,			scheduler=SCREAMING_SNAKE_CASE						,			max_noise_level=SCREAMING_SNAKE_CASE						,			)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	) ->				Optional[int]:






         """simple docstring"""






         torch.manual_seed(0	)
         A       :   List[Any]					=						(
             min(image.size[0] - (tile_size + original_image_slice)						,			x * tile_size	),
             min(image.size[1] - (tile_size + original_image_slice)						,			y * tile_size	),
             min(image.size[0]						,			(x + 1) * tile_size	),
             min(image.size[1]						,			(y + 1) * tile_size	),
         )
         A       :   List[Any]					=						add_overlap_rect(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			image.size	)
         A       :   Dict					=						image.crop(SCREAMING_SNAKE_CASE	)
         A       :   Tuple					=						((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
         A       :   Any					=						translated_slice_x - (original_image_slice / 2)
         A       :   Optional[Any]					=						max(0						,			SCREAMING_SNAKE_CASE	)
         A       :   List[str]					=						squeeze_tile(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
         A       :   List[str]					=						to_input.size
         A       :   Optional[int]					=						to_input.resize((tile_size, tile_size)						,			Image.BICUBIC	)
         A       :   str					=						super(SCREAMING_SNAKE_CASE						,			self	).__call__(image=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	).images[0]
         A       :   str					=						upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)						,			Image.BICUBIC	)
         A       :   int					=						unsqueeze_tile(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
         A       :   List[Any]					=						upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)						,			Image.BICUBIC	)
         A       :   Optional[int]					=						[]
         if x == 0:
             remove_borders.append('''l'''	)
         elif crop_rect[2] == image.size[0]:
             remove_borders.append('''r'''	)
         if y == 0:
             remove_borders.append('''t'''	)
         elif crop_rect[3] == image.size[1]:
             remove_borders.append('''b'''	)
         A       :   Optional[Any]					=						Image.fromarray(
             make_transparency_mask(
                 (upscaled_tile.size[0], upscaled_tile.size[1])						,			tile_border * 4						,			remove_borders=SCREAMING_SNAKE_CASE	)						,			mode='''L'''						,			)
         final_image.paste(
             SCREAMING_SNAKE_CASE						,			(crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)						,			SCREAMING_SNAKE_CASE	)





     @torch.no_grad()
     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = 75						,			SCREAMING_SNAKE_CASE = 9.0						,			SCREAMING_SNAKE_CASE = 50						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = 1						,			SCREAMING_SNAKE_CASE = 0.0						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = 1						,			SCREAMING_SNAKE_CASE = 128						,			SCREAMING_SNAKE_CASE = 32						,			SCREAMING_SNAKE_CASE = 32						,			) ->				Dict:






         """simple docstring"""






         A       :   str					=						Image.new('''RGB'''						,			(image.size[0] * 4, image.size[1] * 4)	)
         A       :   Tuple					=						math.ceil(image.size[0] / tile_size	)
         A       :   List[Any]					=						math.ceil(image.size[1] / tile_size	)
         A       :   Optional[int]					=						tcx * tcy
         A       :   int					=						0
         for y in range(SCREAMING_SNAKE_CASE	):
             for x in range(SCREAMING_SNAKE_CASE	):
                 self._process_tile(
                     SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			prompt=SCREAMING_SNAKE_CASE						,			num_inference_steps=SCREAMING_SNAKE_CASE						,			guidance_scale=SCREAMING_SNAKE_CASE						,			noise_level=SCREAMING_SNAKE_CASE						,			negative_prompt=SCREAMING_SNAKE_CASE						,			num_images_per_prompt=SCREAMING_SNAKE_CASE						,			eta=SCREAMING_SNAKE_CASE						,			generator=SCREAMING_SNAKE_CASE						,			latents=SCREAMING_SNAKE_CASE						,			)
                 current_count += 1
                 if callback is not None:
                     callback({'''progress''': current_count / total_tile_count, '''image''': final_image}	)
         return final_image







def 		lowerCAmelCase_				(   ):




    '''simple docstring'''



    A       :   Dict					=						'''stabilityai/stable-diffusion-x4-upscaler'''
    A       :   int					=						StableDiffusionTiledUpscalePipeline.from_pretrained(snake_case__		,				revision='''fp16'''		,				torch_dtype=torch.floataa						)
    A       :   Dict					=						pipe.to('''cuda'''						)
    A       :   Tuple					=						Image.open('''../../docs/source/imgs/diffusers_library.jpg'''						)

    def callback(snake_case__						):
        print(F'progress: {obj["progress"]:.4f}'						)
        obj["image"].save('''diffusers_library_progress.jpg'''						)

    A       :   Optional[int]					=						pipe(image=snake_case__		,				prompt='''Black font, white background, vector'''		,				noise_level=40		,				callback=snake_case__						)
    final_image.save('''diffusers_library.jpg'''						)


if __name__ == "__main__":
      main()











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()

