




'''simple docstring'''







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

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


lowerCAmelCase_     						=				logging.get_logger(__name__)  # pylint: disable=invalid-name

class 	_snake_case(			UpperCAmelCase       , UpperCAmelCase      ):






 @register_to_config
 def __init__(self       :  Union[str, Any]    ,		a       :  Any    ,		a       :  List[Any] = None    ,		a       :  int = None    )       ->   Dict:






    """simple docstring"""


    super().__init__()

    A__       =     learnable

    if self.learnable:
       assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
       assert length is not None, "learnable=True requires `length` to be set"

       A__       =     torch.zeros(lowerCamelCase_    ,		lowerCamelCase_    )
    else:
       A__       =     None

    A__       =     torch.nn.Parameter(lowerCamelCase_    )




class 	_snake_case(			UpperCAmelCase      ):
 __snake_case:							Optional[int]						=					42
 __snake_case:							List[Any]						=					42
 __snake_case:							Dict						=					42
 __snake_case:							List[str]						=					42
 __snake_case:							List[str]						=					42
 __snake_case:							Optional[Any]						=					42






 def __init__(self       :  Tuple    ,		a       :  List[Any]    ,		a       :  Any    ,		a       :  Tuple    ,		a       :  Union[str, Any]    ,		a       :  Optional[Any]    ,		a       :  List[Any]    ,		)       ->   List[str]:






    """simple docstring"""


    super().__init__()

    self.register_modules(
        vqvae=lowerCamelCase_    ,		transformer=lowerCamelCase_    ,		text_encoder=lowerCamelCase_    ,		tokenizer=lowerCamelCase_    ,		scheduler=lowerCamelCase_    ,		learned_classifier_free_sampling_embeddings=lowerCamelCase_    ,		)






 def 						_UpperCamelCase (self       :  List[str]    ,		a       :  Dict    ,		a       :  int    ,		a       :  int    )       ->   int:






    """simple docstring"""


    A__       =     len(lowerCamelCase_    ) if isinstance(lowerCamelCase_    ,		lowerCamelCase_    ) else 1

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

    if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
       A__       =     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}"""    )
       A__       =     text_input_ids[:, : self.tokenizer.model_max_length]
    A__       =     self.text_encoder(text_input_ids.to(self.device    )    )[0]

    # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
    # While CLIP does normalize the pooled output of the text transformer when combining
    # the image and text embeddings, CLIP does not directly normalize the last hidden state.
    #
    # CLIP normalizing the pooled output.
    # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
    A__       =     prompt_embeds / prompt_embeds.norm(dim=-1    ,		keepdim=lowerCamelCase_    )

    # duplicate text embeddings for each generation per prompt
    A__       =     prompt_embeds.repeat_interleave(lowerCamelCase_    ,		dim=0    )

    if do_classifier_free_guidance:
       if self.learned_classifier_free_sampling_embeddings.learnable:
          A__       =     self.learned_classifier_free_sampling_embeddings.embeddings
          A__       =     negative_prompt_embeds.unsqueeze(0    ).repeat(lowerCamelCase_    ,		1    ,		1    )
       else:
          A__       =     [''] * batch_size

          A__       =     text_input_ids.shape[-1]
          A__       =     self.tokenizer(
              lowerCamelCase_    ,		padding='max_length'    ,		max_length=lowerCamelCase_    ,		truncation=lowerCamelCase_    ,		return_tensors='pt'    ,		)
          A__       =     self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]
          # See comment for normalizing text embeddings
          A__       =     negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1    ,		keepdim=lowerCamelCase_    )

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

    return prompt_embeds






 @torch.no_grad()
 def __call__(self       :  str    ,		a       :  Optional[Any]    ,		a       :  Union[str, Any] = 1_00    ,		a       :  Optional[int] = 5.0    ,		a       :  Any = 1.0    ,		a       :  Optional[Any] = 1    ,		a       :  Dict = None    ,		a       :  Tuple = None    ,		a       :  Any = "pil"    ,		a       :  Union[str, Any] = True    ,		a       :  str = None    ,		a       :  Any = 1    ,		)       ->   Union[ImagePipelineOutput, Tuple]:






    """simple docstring"""


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

    A__       =     batch_size * num_images_per_prompt

    A__       =     guidance_scale > 1.0

    A__       =     self._encode_prompt(lowerCamelCase_    ,		lowerCamelCase_    ,		lowerCamelCase_    )

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

    # get the initial completely masked latents unless the user supplied it

    A__       =     (batch_size, self.transformer.num_latent_pixels)
    if latents is None:
       A__       =     self.transformer.num_vector_embeds - 1
       A__       =     torch.full(lowerCamelCase_    ,		lowerCamelCase_    ).to(self.device    )
    else:
       if latents.shape != latents_shape:
          raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""    )
       if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
          raise ValueError(
              'Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'
              f""" {self.transformer.num_vector_embeds - 1} (inclusive)."""    )
       A__       =     latents.to(self.device    )

    # set timesteps
    self.scheduler.set_timesteps(lowerCamelCase_    ,		device=self.device    )

    A__       =     self.scheduler.timesteps.to(self.device    )

    A__       =     latents

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

       # predict the un-noised image
       # model_output == `log_p_x_0`
       A__       =     self.transformer(lowerCamelCase_    ,		encoder_hidden_states=lowerCamelCase_    ,		timestep=lowerCamelCase_    ).sample

       if do_classifier_free_guidance:
          A__  ,			A__       =     model_output.chunk(2    )
          A__       =     model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
          model_output -= torch.logsumexp(lowerCamelCase_    ,		dim=1    ,		keepdim=lowerCamelCase_    )

       A__       =     self.truncate(lowerCamelCase_    ,		lowerCamelCase_    )

       # remove `log(0)`'s (`-inf`s)
       A__       =     model_output.clamp(-70    )

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

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

    A__       =     self.vqvae.config.vq_embed_dim
    A__       =     (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
    A__       =     self.vqvae.quantize.get_codebook_entry(lowerCamelCase_    ,		shape=lowerCamelCase_    )
    A__       =     self.vqvae.decode(lowerCamelCase_    ,		force_not_quantize=lowerCamelCase_    ).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(lowerCamelCase_    )

    if not return_dict:
       return (image,)

    return ImagePipelineOutput(images=lowerCamelCase_    )






 def 						_UpperCamelCase (self       :  Optional[int]    ,		a       :  str    ,		a       :  Optional[Any]    )       ->   torch.FloatTensor:






    """simple docstring"""


    A__  ,			A__       =     torch.sort(lowerCamelCase_    ,		1    ,		descending=lowerCamelCase_    )
    A__       =     torch.exp(lowerCamelCase_    )
    A__       =     sorted_p_x_0.cumsum(dim=1    ) < truncation_rate

    # Ensure that at least the largest probability is not zeroed out
    A__       =     torch.full_like(keep_mask[:, 0:1, :]    ,		lowerCamelCase_    )
    A__       =     torch.cat((all_true, keep_mask)    ,		dim=1    )
    A__       =     keep_mask[:, :-1, :]

    A__       =     keep_mask.gather(1    ,		indices.argsort(1    )    )

    A__       =     log_p_x_0.clone()

    A__       =     -torch.inf  # -inf = log(0)

    return rv





"""simple docstring"""


UpperCAmelCase													=[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]



def   _A					(	_a	:						Dict     ,   _a	:						int     ,   _a	:						Dict     ,   _a	:						List[Any]				):



      """simple docstring"""






      A				  =		[False] * len(_a				)
      A				  =		[s]
      A				  =		True

      while queue:
            A				  =		queue.pop(0				)
            for ind in range(len(graph[u]				)				):
                  if visited[ind] is False and graph[u][ind] > 0:
                        queue.append(_a				)
                        A				  =		True
                        A				  =		u

      return visited[t]



def   _A					(	_a	:						str     ,   _a	:						Optional[Any]     ,   _a	:						Optional[int]				):



      """simple docstring"""






      A				  =		[-1] * (len(_a				))
      A				  =		0
      A				  =		[]
      A				  =		[i[:] for i in graph]  # Record original cut, copy.
      while bfs(_a     ,   _a     ,   _a     ,   _a				):
            A				  =		float("""Inf"""				)
            A				  =		sink

            while s != source:
                  # Find the minimum value in select path
                  A				  =		min(_a     ,   graph[parent[s]][s]				)
                  A				  =		parent[s]

            max_flow += path_flow
            A				  =		sink

            while v != source:
                  A				  =		parent[v]
                  graph[u][v] -= path_flow
                  graph[v][u] += path_flow
                  A				  =		parent[v]

      for i in range(len(_a				)				):
            for j in range(len(graph[0]				)				):
                  if graph[i][j] == 0 and temp[i][j] > 0:
                        res.append((i, j)				)

      return res


if __name__ == "__main__":
  print(mincut(test_graph, source=0, sink=5))




