






'''simple docstring'''







def  lowerCAmelCase__		(					a_  :     str			,						a_  :     str			,						a_  :     int			,						a_  :     int			,						a_  :     int			,						a_  :     int			,						)   -> tuple[list[list[int]], list[list[str]]]:
       UpperCAmelCase__	:   Union[str, Any]												=	list(a_			)
       UpperCAmelCase__	:   Any												=	list(a_			)
       UpperCAmelCase__	:   str												=	len(a_			)
       UpperCAmelCase__	:   Tuple												=	len(a_			)

       UpperCAmelCase__	:   Union[str, Any]												=	[
           [0 for _ in range(len_destination_seq + 1			)] for _ in range(len_source_seq + 1			)
       ]
       UpperCAmelCase__	:   Optional[int]												=	[
           ['''0''' for _ in range(len_destination_seq + 1			)] for _ in range(len_source_seq + 1			)
       ]

       for i in range(1			,						len_source_seq + 1			):
              UpperCAmelCase__	:   List[Any]												=	i * delete_cost
              UpperCAmelCase__	:   Dict												=	f"""D{source_seq[i - 1]:c}"""

       for i in range(1			,						len_destination_seq + 1			):
              UpperCAmelCase__	:   List[str]												=	i * insert_cost
              UpperCAmelCase__	:   Any												=	f"""I{destination_seq[i - 1]:c}"""

       for i in range(1			,						len_source_seq + 1			):
              for j in range(1			,						len_destination_seq + 1			):
                     if source_seq[i - 1] == destination_seq[j - 1]:
                            UpperCAmelCase__	:   List[str]												=	costs[i - 1][j - 1] + copy_cost
                            UpperCAmelCase__	:   List[Any]												=	f"""C{source_seq[i - 1]:c}"""
                     else:
                            UpperCAmelCase__	:   Union[str, Any]												=	costs[i - 1][j - 1] + replace_cost
                            UpperCAmelCase__	:   List[Any]												=	f"""R{source_seq[i - 1]:c}""" + str(destination_seq[j - 1]			)

                     if costs[i - 1][j] + delete_cost < costs[i][j]:
                            UpperCAmelCase__	:   Union[str, Any]												=	costs[i - 1][j] + delete_cost
                            UpperCAmelCase__	:   Optional[int]												=	f"""D{source_seq[i - 1]:c}"""

                     if costs[i][j - 1] + insert_cost < costs[i][j]:
                            UpperCAmelCase__	:   str												=	costs[i][j - 1] + insert_cost
                            UpperCAmelCase__	:   Union[str, Any]												=	f"""I{destination_seq[j - 1]:c}"""

       return costs, ops






def  lowerCAmelCase__		(					a_  :     list[list[str]]			,						a_  :     int			,						a_  :     int			)   -> list[str]:
       if i == 0 and j == 0:
              return []
       else:
              if ops[i][j][0] == "C" or ops[i][j][0] == "R":
                     UpperCAmelCase__	:   Any												=	assemble_transformation(a_			,						i - 1			,						j - 1			)
                     seq.append(ops[i][j]			)
                     return seq
              elif ops[i][j][0] == "D":
                     UpperCAmelCase__	:   Optional[int]												=	assemble_transformation(a_			,						i - 1			,						a_			)
                     seq.append(ops[i][j]			)
                     return seq
              else:
                     UpperCAmelCase__	:   List[Any]												=	assemble_transformation(a_			,						a_			,						j - 1			)
                     seq.append(ops[i][j]			)
                     return seq


if __name__ == "__main__":
  UpperCamelCase_,     UpperCamelCase_					=				compute_transform_tables("Python", "Algorithms", -1, 1, 2, 2)

  UpperCamelCase_					=				len(operations)
  UpperCamelCase_					=				len(operations[0])
  UpperCamelCase_					=				assemble_transformation(operations, m - 1, n - 1)

  UpperCamelCase_					=				list("Python")
  UpperCamelCase_					=				0
  UpperCamelCase_					=				0

  with open("min_cost.txt", "w") as file:
    for op in sequence:
      print("".join(string))

      if op[0] == "C":
        file.write("%-16s" % "Copy %c" % op[1])
        file.write("\t\t\t" + "".join(string))
        file.write("\r\n")

        cost -= 1
      elif op[0] == "R":
        UpperCamelCase_					=				op[2]

        file.write("%-16s" % ("Replace %c" % op[1] + " with " + str(op[2])))
        file.write("\t\t" + "".join(string))
        file.write("\r\n")

        cost += 1
      elif op[0] == "D":
        string.pop(i)

        file.write("%-16s" % "Delete %c" % op[1])
        file.write("\t\t\t" + "".join(string))
        file.write("\r\n")

        cost += 2
      else:
        string.insert(i, op[1])

        file.write("%-16s" % "Insert %c" % op[1])
        file.write("\t\t\t" + "".join(string))
        file.write("\r\n")

        cost += 2

      i += 1

    print("".join(string))
    print("Cost: ", cost)

    file.write("\r\nMinimum cost: " + str(cost))







'''simple docstring'''







from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)




class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      def __init__(					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		):
             super().__init__()

             self.register_modules(vae=_UpperCAmelCase  ,		text_encoder=_UpperCAmelCase  ,		tokenizer=_UpperCAmelCase  ,		unet=_UpperCAmelCase  ,		scheduler=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             with torch.no_grad():
                    UpperCAmelCase__	:   Dict												=	self.vae.encode(tfms.ToTensor()(_UpperCAmelCase						).unsqueeze(0						).to(self.device						) * 2 - 1						)
                    UpperCAmelCase__	:   int												=	0.1_82_15 * latent.latent_dist.sample()
             return latent




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             UpperCAmelCase__	:   int												=	(1 / 0.1_82_15) * latent
             with torch.no_grad():
                    UpperCAmelCase__	:   Optional[Any]												=	self.vae.decode(_UpperCAmelCase						).sample
             UpperCAmelCase__	:   List[str]												=	(img / 2 + 0.5).clamp(0  ,		1						)
             UpperCAmelCase__	:   List[Any]												=	img.detach().cpu().permute(0  ,		2  ,		3  ,		1						).numpy()
             UpperCAmelCase__	:   Union[str, Any]												=	(img * 255).round().astype('''uint8'''						)
             return Image.fromarray(img[0]						)




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             UpperCAmelCase__	:   Dict												=	self.tokenizer(
                 _UpperCAmelCase  ,		padding='''max_length'''  ,		max_length=self.tokenizer.model_max_length  ,		truncation=_UpperCAmelCase  ,		return_tensors='''pt'''  ,		)

             UpperCAmelCase__	:   Tuple												=	self.text_encoder(text_input.input_ids.to(self.device						)						)[0]

             UpperCAmelCase__	:   Dict												=	self.tokenizer(
                 ''''''  ,		padding='''max_length'''  ,		max_length=self.tokenizer.model_max_length  ,		truncation=_UpperCAmelCase  ,		return_tensors='''pt'''  ,		)

             UpperCAmelCase__	:   List[Any]												=	self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]

             return torch.cat([uncond_embedding, text_embedding]						)




      def __call__(					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase = 0.3  ,		_UpperCAmelCase = 0.6  ,		_UpperCAmelCase = 0.5  ,		_UpperCAmelCase = 42  ,		_UpperCAmelCase = 50  ,		_UpperCAmelCase = 7.5  ,		):
             UpperCAmelCase__	:   Any												=	steps - int(kmin * steps						)
             UpperCAmelCase__	:   Any												=	steps - int(kmax * steps						)

             UpperCAmelCase__	:   List[str]												=	self.prep_text(_UpperCAmelCase						)

             self.scheduler.set_timesteps(_UpperCAmelCase						)

             UpperCAmelCase__							,							UpperCAmelCase__	:   List[str]												=	img.size
             UpperCAmelCase__	:   str												=	self.encode(_UpperCAmelCase						)

             torch.manual_seed(_UpperCAmelCase						)
             UpperCAmelCase__	:   Union[str, Any]												=	torch.randn(
                 (1, self.unet.config.in_channels, height // 8, width // 8)  ,		).to(self.device						)

             UpperCAmelCase__	:   Optional[int]												=	self.scheduler.add_noise(
                 _UpperCAmelCase  ,		_UpperCAmelCase  ,		timesteps=self.scheduler.timesteps[tmax]  ,		)

             UpperCAmelCase__	:   Any												=	torch.cat([latents] * 2						)

             UpperCAmelCase__	:   Dict												=	self.scheduler.scale_model_input(_UpperCAmelCase  ,		self.scheduler.timesteps[tmax]						)

             with torch.no_grad():
                    UpperCAmelCase__	:   Tuple												=	self.unet(
                        _UpperCAmelCase  ,		self.scheduler.timesteps[tmax]  ,		encoder_hidden_states=_UpperCAmelCase  ,		).sample

             UpperCAmelCase__							,							UpperCAmelCase__	:   List[Any]												=	pred.chunk(2						)
             UpperCAmelCase__	:   List[str]												=	pred_uncond + guidance_scale * (pred_text - pred_uncond)

             UpperCAmelCase__	:   Union[str, Any]												=	self.scheduler.step(_UpperCAmelCase  ,		self.scheduler.timesteps[tmax]  ,		_UpperCAmelCase						).prev_sample

             for i, t in enumerate(tqdm(self.scheduler.timesteps						)						):
                    if i > tmax:
                           if i < tmin:  # layout generation phase
                                  UpperCAmelCase__	:   Dict												=	self.scheduler.add_noise(
                                      _UpperCAmelCase  ,		_UpperCAmelCase  ,		timesteps=_UpperCAmelCase  ,		)

                                  UpperCAmelCase__	:   Union[str, Any]												=	(mix_factor * latents) + (
                                      1 - mix_factor
                                  ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                                  UpperCAmelCase__	:   Tuple												=	torch.cat([input] * 2						)

                           else:  # content generation phase
                                  UpperCAmelCase__	:   Any												=	torch.cat([latents] * 2						)

                           UpperCAmelCase__	:   str												=	self.scheduler.scale_model_input(_UpperCAmelCase  ,		_UpperCAmelCase						)

                           with torch.no_grad():
                                  UpperCAmelCase__	:   Any												=	self.unet(
                                      _UpperCAmelCase  ,		_UpperCAmelCase  ,		encoder_hidden_states=_UpperCAmelCase  ,		).sample

                           UpperCAmelCase__							,							UpperCAmelCase__	:   Tuple												=	pred.chunk(2						)
                           UpperCAmelCase__	:   str												=	pred_uncond + guidance_scale * (pred_text - pred_uncond)

                           UpperCAmelCase__	:   Union[str, Any]												=	self.scheduler.step(_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase						).prev_sample

             return self.decode(_UpperCAmelCase						)