
class        lowercase      :



		def __init__(				self       ,   A_       ,   A_       ,   A_ )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  None
				UpperCamelCase						=  None
				UpperCamelCase						=  graph

				self._normalize_graph(A_       ,   A_ )
				UpperCamelCase						=  len(A_ )
				UpperCamelCase						=  None



		def __UpperCamelCase				(				self       ,   A_       ,   A_ )						->      str:
				"""simple docstring"""
				if sources is int:
						UpperCamelCase						=  [sources]
				if sinks is int:
						UpperCamelCase						=  [sinks]

				if len(A_ ) == 0 or len(A_ ) == 0:
						return

				UpperCamelCase						=  sources[0]
				UpperCamelCase						=  sinks[0]

				# make fake vertex if there are more
				# than one source or sink
				if len(A_ ) > 1 or len(A_ ) > 1:
						UpperCamelCase						=  0
						for i in sources:
								max_input_flow += sum(self.graph[i] )

						UpperCamelCase						=  len(self.graph ) + 1
						for room in self.graph:
								room.insert(0       ,   0 )
						self.graph.insert(0       ,   [0] * size )
						for i in sources:
								UpperCamelCase						=  max_input_flow
						UpperCamelCase						=  0

						UpperCamelCase						=  len(self.graph ) + 1
						for room in self.graph:
								room.append(0 )
						self.graph.append([0] * size )
						for i in sinks:
								UpperCamelCase						=  max_input_flow
						UpperCamelCase						=  size - 1



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				if self.maximum_flow_algorithm is None:
						raise Exception('You need to set maximum flow algorithm before.' )
				if self.source_index is None or self.sink_index is None:
						return 0

				self.maximum_flow_algorithm.execute()
				return self.maximum_flow_algorithm.getMaximumFlow()



		def __UpperCamelCase				(				self       ,   A_ )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  algorithm(self )






class        lowercase      :



		def __init__(				self       ,   A_ )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  flow_network
				UpperCamelCase						=  flow_network.verticesCount
				UpperCamelCase						=  flow_network.sourceIndex
				UpperCamelCase						=  flow_network.sinkIndex
				# it's just a reference, so you shouldn't change
				# it in your algorithms, use deep copy before doing that
				UpperCamelCase						=  flow_network.graph
				UpperCamelCase						=  False



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				if not self.executed:
						self._algorithm()
						UpperCamelCase						=  True



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				pass






class        lowercase      (	_SCREAMING_SNAKE_CASE     ):



		def __init__(				self       ,   A_ )						->      List[Any]:
				"""simple docstring"""
				super().__init__(A_ )
				# use this to save your result
				UpperCamelCase						=  -1



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				if not self.executed:
						raise Exception('You should execute algorithm before using its result!' )

				return self.maximum_flow






class        lowercase      (	_SCREAMING_SNAKE_CASE     ):



		def __init__(				self       ,   A_ )						->      List[str]:
				"""simple docstring"""
				super().__init__(A_ )

				UpperCamelCase						=  [[0] * self.verticies_count for i in range(self.verticies_count )]

				UpperCamelCase						=  [0] * self.verticies_count
				UpperCamelCase						=  [0] * self.verticies_count



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  self.verticies_count

				# push some substance to graph
				for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index] ):
						self.preflow[self.source_index][nextvertex_index] += bandwidth
						self.preflow[nextvertex_index][self.source_index] -= bandwidth
						self.excesses[nextvertex_index] += bandwidth

				# Relabel-to-front selection rule
				UpperCamelCase						=  [
				    i
				    for i in range(self.verticies_count )
				    if i != self.source_index and i != self.sink_index
				]

				# move through list
				UpperCamelCase						=  0
				while i < len(A_ ):
						UpperCamelCase						=  vertices_list[i]
						UpperCamelCase						=  self.heights[vertex_index]
						self.process_vertex(A_ )
						if self.heights[vertex_index] > previous_height:
								# if it was relabeled, swap elements
								# and start from 0 index
								vertices_list.insert(0       ,   vertices_list.pop(A_ ) )
								UpperCamelCase						=  0
						else:
								i += 1

				UpperCamelCase						=  sum(self.preflow[self.source_index] )



		def __UpperCamelCase				(				self       ,   A_ )						->      List[Any]:
				"""simple docstring"""
				while self.excesses[vertex_index] > 0:
						for neighbour_index in range(self.verticies_count ):
								# if it's neighbour and current vertex is higher
								if (
								    self.graph[vertex_index][neighbour_index]
								    - self.preflow[vertex_index][neighbour_index]
								    > 0
								    and self.heights[vertex_index] > self.heights[neighbour_index]
								):
										self.push(A_       ,   A_ )

						self.relabel(A_ )



		def __UpperCamelCase				(				self       ,   A_       ,   A_ )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  min(
				    self.excesses[from_index]       ,   self.graph[from_index][to_index] - self.preflow[from_index][to_index]       ,   )
				self.preflow[from_index][to_index] += preflow_delta
				self.preflow[to_index][from_index] -= preflow_delta
				self.excesses[from_index] -= preflow_delta
				self.excesses[to_index] += preflow_delta



		def __UpperCamelCase				(				self       ,   A_ )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  None
				for to_index in range(self.verticies_count ):
						if (
						    self.graph[vertex_index][to_index]
						    - self.preflow[vertex_index][to_index]
						    > 0
						) and (min_height is None or self.heights[to_index] < min_height):
								UpperCamelCase						=  self.heights[to_index]

				if min_height is not None:
						UpperCamelCase						=  min_height + 1


if __name__ == "__main__":
					_UpperCAmelCase      :				str          = [0]
					_UpperCAmelCase      :				List[str]          = [3]
					# graph = [
					#     [0, 0, 4, 6, 0, 0],
					#     [0, 0, 5, 2, 0, 0],
					#     [0, 0, 0, 0, 4, 4],
					#     [0, 0, 0, 0, 6, 6],
					#     [0, 0, 0, 0, 0, 0],
					#     [0, 0, 0, 0, 0, 0],
					# ]
					_UpperCAmelCase      :				Optional[Any]          = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

					# prepare our network
					_UpperCAmelCase      :				Any          = FlowNetwork(graph, entrances, exits)
					# set algorithm
					flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
					# and calculate
					_UpperCAmelCase      :				str          = flow_network.find_maximum_flow()

					print(F'''maximum flow is {maximum_flow}''')


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


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


class        lowercase      (	_SCREAMING_SNAKE_CASE						,     _SCREAMING_SNAKE_CASE     ):



		@register_to_config
		def __init__(				self       ,   A_       ,   A_ = None       ,   A_ = None )						->      Any:
				"""simple docstring"""
				super().__init__()

				UpperCamelCase						=  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"

						UpperCamelCase						=  torch.zeros(A_       ,   A_ )
				else:
						UpperCamelCase						=  None

				UpperCamelCase						=  torch.nn.Parameter(A_ )






class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : VQModel
		__lowercase : CLIPTextModel
		__lowercase : CLIPTokenizer
		__lowercase : TransformeraDModel
		__lowercase : LearnedClassifierFreeSamplingEmbeddings
		__lowercase : VQDiffusionScheduler



		def __init__(				self       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   )						->      Optional[Any]:
				"""simple docstring"""
				super().__init__()

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



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_ )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  len(A_ ) if isinstance(A_       ,   A_ ) else 1

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

				if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
						UpperCamelCase						=  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}''' )
						UpperCamelCase						=  text_input_ids[:, : self.tokenizer.model_max_length]
				UpperCamelCase						=  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
				UpperCamelCase						=  prompt_embeds / prompt_embeds.norm(dim=-1       ,   keepdim=A_ )

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

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

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

						# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
						UpperCamelCase						=  negative_prompt_embeds.shape[1]
						UpperCamelCase						=  negative_prompt_embeds.repeat(1       ,   A_       ,   1 )
						UpperCamelCase						=  negative_prompt_embeds.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
						UpperCamelCase						=  torch.cat([negative_prompt_embeds, prompt_embeds] )

				return prompt_embeds



		@torch.no_grad()
		def __call__(				self       ,   A_       ,   A_ = 100       ,   A_ = 5.0       ,   A_ = 1.0       ,   A_ = 1       ,   A_ = None       ,   A_ = None       ,   A_ = "pil"       ,   A_ = True       ,   A_ = None       ,   A_ = 1       ,   )						->      Union[ImagePipelineOutput, Tuple]:
				"""simple docstring"""
				if isinstance(A_       ,   A_ ):
						UpperCamelCase						=  1
				elif isinstance(A_       ,   A_ ):
						UpperCamelCase						=  len(A_ )
				else:
						raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(A_ )}''' )

				UpperCamelCase						=  batch_size * num_images_per_prompt

				UpperCamelCase						=  guidance_scale > 1.0

				UpperCamelCase						=  self._encode_prompt(A_       ,   A_       ,   A_ )

				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 the initial completely masked latents unless the user supplied it

				UpperCamelCase						=  (batch_size, self.transformer.num_latent_pixels)
				if latents is None:
						UpperCamelCase						=  self.transformer.num_vector_embeds - 1
						UpperCamelCase						=  torch.full(A_       ,   A_ ).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).''' )
						UpperCamelCase						=  latents.to(self.device )

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

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

				UpperCamelCase						=  latents

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

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

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

						UpperCamelCase						=  self.truncate(A_       ,   A_ )

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

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

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

				UpperCamelCase						=  self.vqvae.config.vq_embed_dim
				UpperCamelCase						=  (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
				UpperCamelCase						=  self.vqvae.quantize.get_codebook_entry(A_       ,   shape=A_ )
				UpperCamelCase						=  self.vqvae.decode(A_       ,   force_not_quantize=A_ ).sample

				UpperCamelCase						=  (image / 2 + 0.5).clamp(0       ,   1 )
				UpperCamelCase						=  image.cpu().permute(0       ,   2       ,   3       ,   1 ).numpy()

				if output_type == "pil":
						UpperCamelCase						=  self.numpy_to_pil(A_ )

				if not return_dict:
						return (image,)

				return ImagePipelineOutput(images=A_ )



		def __UpperCamelCase				(				self       ,   A_       ,   A_ )						->      torch.FloatTensor:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase						=  torch.sort(A_       ,   1       ,   descending=A_ )
				UpperCamelCase						=  torch.exp(A_ )
				UpperCamelCase						=  sorted_p_x_0.cumsum(dim=1 ) < truncation_rate

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

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

				UpperCamelCase						=  log_p_x_0.clone()

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

				return rv
