





'''simple docstring'''







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

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


_lowercase       						=			logging.get_logger(__name__)  # pylint: disable=invalid-name
class 			_lowercase						(       __a					):







     def __init__( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   ) ->	List[Any]:
      super().__init__()
      self.register_modules(
          vae=A__    ,   text_encoder=A__    ,   tokenizer=A__    ,   unet=A__    ,   scheduler=A__    ,   safety_checker=A__    ,   feature_extractor=A__    ,   )







     def     UpperCamelCase			( self    ,   A__ = "auto"    ) ->	Optional[Any]:
      if slice_size == "auto":
       # half the attention head size is usually a good trade-off between
       # speed and memory
       snake_case				=			self.unet.config.attention_head_dim // 2
      self.unet.set_attention_slice(A__    )







     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      self.enable_attention_slicing(A__    )







     @torch.no_grad()
     def __call__( self    ,   A__    ,   A__ = 5_12    ,   A__ = 5_12    ,   A__ = 50    ,   A__ = 7.5    ,   A__ = None    ,   A__ = 1    ,   A__ = 0.0    ,   A__ = None    ,   A__ = None    ,   A__ = "pil"    ,   A__ = True    ,   A__ = None    ,   A__ = 1    ,   A__ = None    ,   **A__    ,   ) ->	Optional[Any]:

      if isinstance(A__    ,   A__    ):
       snake_case				=			1
      elif isinstance(A__    ,   A__    ):
       snake_case				=			len(A__    )
      else:
       raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(A__    )}"""    )

      if height % 8 != 0 or width % 8 != 0:
       raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""    )

      if (callback_steps is None) or (
          callback_steps is not None and (not isinstance(A__    ,   A__    ) or callback_steps <= 0)
      ):
       raise ValueError(
           F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
           F""" {type(A__    )}."""    )

      # get prompt text embeddings
      snake_case				=			self.tokenizer(
          A__    ,   padding='''max_length'''    ,   max_length=self.tokenizer.model_max_length    ,   return_tensors='''pt'''    ,   )
      snake_case				=			text_inputs.input_ids

      if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
       snake_case				=			self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]    )
       logger.warning(
           '''The following part of your input was truncated because CLIP can only handle sequences up to'''
           F""" {self.tokenizer.model_max_length} tokens: {removed_text}"""    )
       snake_case				=			text_input_ids[:, : self.tokenizer.model_max_length]

      if text_embeddings is None:
       snake_case				=			self.text_encoder(text_input_ids.to(self.device    )    )[0]

      # duplicate text embeddings for each generation per prompt, using mps friendly method
      snake_case				,		snake_case				,		snake_case				=			text_embeddings.shape
      snake_case				=			text_embeddings.repeat(1    ,   A__    ,   1    )
      snake_case				=			text_embeddings.view(bs_embed * num_images_per_prompt    ,   A__    ,   -1    )

      # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
      # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
      # corresponds to doing no classifier free guidance.
      snake_case				=			guidance_scale > 1.0
      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance:
       snake_case				=			42
       if negative_prompt is None:
        snake_case				=			['''''']
       elif type(A__    ) is not type(A__    ):
        raise TypeError(
            F"""`negative_prompt` should be the same type to `prompt`, but got {type(A__    )} !="""
            F""" {type(A__    )}."""    )
       elif isinstance(A__    ,   A__    ):
        snake_case				=			[negative_prompt]
       elif batch_size != len(A__    ):
        raise ValueError(
            F"""`negative_prompt`: {negative_prompt} has batch size {len(A__    )}, but `prompt`:"""
            F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
            ''' the batch size of `prompt`.'''    )
       else:
        snake_case				=			negative_prompt

       snake_case				=			text_input_ids.shape[-1]
       snake_case				=			self.tokenizer(
           A__    ,   padding='''max_length'''    ,   max_length=A__    ,   truncation=A__    ,   return_tensors='''pt'''    ,   )
       snake_case				=			self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]

       # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
       snake_case				=			uncond_embeddings.shape[1]
       snake_case				=			uncond_embeddings.repeat(A__    ,   A__    ,   1    )
       snake_case				=			uncond_embeddings.view(batch_size * num_images_per_prompt    ,   A__    ,   -1    )

       # For classifier free guidance, we need to do two forward passes.
       # Here we concatenate the unconditional and text embeddings into a single batch
       # to avoid doing two forward passes
       snake_case				=			torch.cat([uncond_embeddings, text_embeddings]    )

      # get the initial random noise unless the user supplied it

      # Unlike in other pipelines, latents need to be generated in the target device
      # for 1-to-1 results reproducibility with the CompVis implementation.
      # However this currently doesn't work in `mps`.
      snake_case				=			(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
      snake_case				=			(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
      snake_case				=			text_embeddings.dtype
      if latents is None:
       if self.device.type == "mps":
        # randn does not exist on mps
        snake_case				=			torch.randn(
            A__    ,   generator=A__    ,   device='''cpu'''    ,   dtype=A__    ).to(self.device    )
        snake_case				=			torch.randn(A__    ,   generator=A__    ,   device='''cpu'''    ,   dtype=A__    ).to(
            self.device    )
       else:
        snake_case				=			torch.randn(
            A__    ,   generator=A__    ,   device=self.device    ,   dtype=A__    )
        snake_case				=			torch.randn(A__    ,   generator=A__    ,   device=self.device    ,   dtype=A__    )
      else:
       if latents_reference.shape != latents_shape:
        raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""    )
       snake_case				=			latents_reference.to(self.device    )
       snake_case				=			latents.to(self.device    )

      # This is the key part of the pipeline where we
      # try to ensure that the generated images w/ the same seed
      # but different sizes actually result in similar images
      snake_case				=			(latents_shape[3] - latents_shape_reference[3]) // 2
      snake_case				=			(latents_shape[2] - latents_shape_reference[2]) // 2
      snake_case				=			latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
      snake_case				=			latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
      snake_case				=			0 if dx < 0 else dx
      snake_case				=			0 if dy < 0 else dy
      snake_case				=			max(-dx    ,   0    )
      snake_case				=			max(-dy    ,   0    )
      # import pdb
      # pdb.set_trace()
      snake_case				=			latents_reference[:, :, dy : dy + h, dx : dx + w]

      # set timesteps
      self.scheduler.set_timesteps(A__    )

      # Some schedulers like PNDM have timesteps as arrays
      # It's more optimized to move all timesteps to correct device beforehand
      snake_case				=			self.scheduler.timesteps.to(self.device    )

      # scale the initial noise by the standard deviation required by the scheduler
      snake_case				=			latents * self.scheduler.init_noise_sigma

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
      # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
      # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
      # and should be between [0, 1]
      snake_case				=			'''eta''' in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
      snake_case				=			{}
      if accepts_eta:
       snake_case				=			eta

      for i, t in enumerate(self.progress_bar(A__    )    ):
       # expand the latents if we are doing classifier free guidance
       snake_case				=			torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents
       snake_case				=			self.scheduler.scale_model_input(A__    ,   A__    )

       # predict the noise residual
       snake_case				=			self.unet(A__    ,   A__    ,   encoder_hidden_states=A__    ).sample

       # perform guidance
       if do_classifier_free_guidance:
        snake_case				,		snake_case				=			noise_pred.chunk(2    )
        snake_case				=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

       # compute the previous noisy sample x_t -> x_t-1
       snake_case				=			self.scheduler.step(A__    ,   A__    ,   A__    ,   **A__    ).prev_sample

       # call the callback, if provided
       if callback is not None and i % callback_steps == 0:
        callback(A__    ,   A__    ,   A__    )

      snake_case				=			1 / 0.1_8_2_1_5 * latents
      snake_case				=			self.vae.decode(A__    ).sample

      snake_case				=			(image / 2 + 0.5).clamp(0    ,   1    )

      # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
      snake_case				=			image.cpu().permute(0    ,   2    ,   3    ,   1    ).float().numpy()

      if self.safety_checker is not None:
       snake_case				=			self.feature_extractor(self.numpy_to_pil(A__    )    ,   return_tensors='''pt'''    ).to(
           self.device    )
       snake_case				,		snake_case				=			self.safety_checker(
           images=A__    ,   clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype    )    )
      else:
       snake_case				=			None

      if output_type == "pil":
       snake_case				=			self.numpy_to_pil(A__    )

      if not return_dict:
       return (image, has_nsfw_concept)

      return StableDiffusionPipelineOutput(images=A__    ,   nsfw_content_detected=A__    )








'''simple docstring'''







import argparse
import copy

def    __UpperCamelCase	(	a    : Union[str, Any]     ) ->Tuple:
 snake_case				=			{}

 with open(a     ) as f:
  for line in f:
   if line.split()[0] not in dict_of_neighbours:
    snake_case				=			[]
    _list.append([line.split()[1], line.split()[2]]     )
    snake_case				=			_list
   else:
    dict_of_neighbours[line.split()[0]].append(
        [line.split()[1], line.split()[2]]     )
   if line.split()[1] not in dict_of_neighbours:
    snake_case				=			[]
    _list.append([line.split()[0], line.split()[2]]     )
    snake_case				=			_list
   else:
    dict_of_neighbours[line.split()[1]].append(
        [line.split()[0], line.split()[2]]     )

 return dict_of_neighbours

def    __UpperCamelCase	(	a    : Dict					,   a    : Tuple     ) ->int:

 with open(a     ) as f:
  snake_case				=			f.read(1     )
 snake_case				=			start_node

 snake_case				=			[]

 snake_case				=			start_node

 snake_case				=			0
 while visiting not in first_solution:
  snake_case				=			1_0000
  for k in dict_of_neighbours[visiting]:
   if int(k[1]     ) < int(a     ) and k[0] not in first_solution:
    snake_case				=			k[1]
    snake_case				=			k[0]

  first_solution.append(a     )
  snake_case				=			distance_of_first_solution + int(a     )
  snake_case				=			best_node

 first_solution.append(a     )

 snake_case				=			0
 for k in dict_of_neighbours[first_solution[-2]]:
  if k[0] == start_node:
   break
  position += 1

 snake_case				=			(
     distance_of_first_solution
     + int(dict_of_neighbours[first_solution[-2]][position][1]     )
     - 1_0000
 )
 return first_solution, distance_of_first_solution

def    __UpperCamelCase	(	a    : Optional[int]					,   a    : str     ) ->str:
 snake_case				=			[]

 for n in solution[1:-1]:
  snake_case				=			solution.index(a     )
  for kn in solution[1:-1]:
   snake_case				=			solution.index(a     )
   if n == kn:
    continue

   snake_case				=			copy.deepcopy(a     )
   snake_case				=			kn
   snake_case				=			n

   snake_case				=			0

   for k in _tmp[:-1]:
    snake_case				=			_tmp[_tmp.index(a     ) + 1]
    for i in dict_of_neighbours[k]:
     if i[0] == next_node:
      snake_case				=			distance + int(i[1]     )
   _tmp.append(a     )

   if _tmp not in neighborhood_of_solution:
    neighborhood_of_solution.append(_tmp     )

 snake_case				=			len(neighborhood_of_solution[0]     ) - 1

 neighborhood_of_solution.sort(key=lambda a     : x[index_of_last_item_in_the_list]     )
 return neighborhood_of_solution

def    __UpperCamelCase	(	a    : Any					,   a    : Optional[Any]					,   a    : int					,   a    : Optional[int]					,   a    : Union[str, Any]     ) ->List[Any]:
 snake_case				=			1
 snake_case				=			first_solution
 snake_case				=			[]
 snake_case				=			distance_of_first_solution
 snake_case				=			solution

 while count <= iters:
  snake_case				=			find_neighborhood(a					,   a     )
  snake_case				=			0
  snake_case				=			neighborhood[index_of_best_solution]
  snake_case				=			len(a     ) - 1

  snake_case				=			False
  while not found:
   snake_case				=			0
   while i < len(a     ):
    if best_solution[i] != solution[i]:
     snake_case				=			best_solution[i]
     snake_case				=			solution[i]
     break
    snake_case				=			i + 1

   if [first_exchange_node, second_exchange_node] not in tabu_list and [
       second_exchange_node,
       first_exchange_node,
   ] not in tabu_list:
    tabu_list.append([first_exchange_node, second_exchange_node]     )
    snake_case				=			True
    snake_case				=			best_solution[:-1]
    snake_case				=			neighborhood[index_of_best_solution][best_cost_index]
    if cost < best_cost:
     snake_case				=			cost
     snake_case				=			solution
   else:
    snake_case				=			index_of_best_solution + 1
    snake_case				=			neighborhood[index_of_best_solution]

  if len(a     ) >= size:
   tabu_list.pop(0     )

  snake_case				=			count + 1

 return best_solution_ever, best_cost

def    __UpperCamelCase	(	a    : Union[str, Any]=None     ) ->Optional[Any]:
 snake_case				=			generate_neighbours(args.File     )

 snake_case				,		snake_case				=			generate_first_solution(
     args.File					,   a     )

 snake_case				,		snake_case				=			tabu_search(
     a					,   a					,   a					,   args.Iterations					,   args.Size					,   )

 print(f"""Best solution: {best_sol}, with total distance: {best_cost}."""     )


if __name__ == "__main__":
   _lowercase       						=			argparse.ArgumentParser(description='Tabu Search')
   parser.add_argument(
       '-f',
       '--File',
       type=str,
       help='Path to the file containing the data',
       required=True,
   )
   parser.add_argument(
       '-i',
       '--Iterations',
       type=int,
       help='How many iterations the algorithm should perform',
       required=True,
   )
   parser.add_argument(
       '-s', '--Size', type=int, help='Size of the tabu list', required=True
   )

   # Pass the arguments to main method
   main(parser.parse_args())

