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


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

class       snake_case					(      __snake_case   ,						__snake_case						):




		@register_to_config
		def __init__(      self     :	Dict     , a_     :	Dict     , a_     :	Dict = None     , a_     :	Tuple = None				)->       Optional[Any]:




							"""simple docstring"""
							super().__init__()

							SCREAMING_SNAKE_CASE__				:				int			=		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"

												SCREAMING_SNAKE_CASE__				:				Dict			=		torch.zeros(__UpperCamelCase     , __UpperCamelCase				)
							else:
												SCREAMING_SNAKE_CASE__				:				int			=		None

							SCREAMING_SNAKE_CASE__				:				Dict			=		torch.nn.Parameter(__UpperCamelCase				)




class       snake_case					(      __snake_case						):
		lowercase_			=     42
		lowercase_			=     42
		lowercase_			=     42
		lowercase_			=     42
		lowercase_			=     42
		lowercase_			=     42




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




							"""simple docstring"""
							super().__init__()

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




		def 	__lowercase(      self     :	Any     , a_     :	Dict     , a_     :	int     , a_     :	Tuple				)->       Optional[Any]:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Any			=		len(__UpperCamelCase				) if isinstance(__UpperCamelCase     , __UpperCamelCase				) else 1

							# get prompt text embeddings
							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.tokenizer(
							    __UpperCamelCase     , padding='max_length'     , max_length=self.tokenizer.model_max_length     , return_tensors='pt'     , )
							SCREAMING_SNAKE_CASE__				:				List[str]			=		text_inputs.input_ids

							if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
												SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		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}'''				)
												SCREAMING_SNAKE_CASE__				:				Dict			=		text_input_ids[:, : self.tokenizer.model_max_length]
							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		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
							SCREAMING_SNAKE_CASE__				:				int			=		prompt_embeds / prompt_embeds.norm(dim=-1     , keepdim=__UpperCamelCase				)

							# duplicate text embeddings for each generation per prompt
							SCREAMING_SNAKE_CASE__				:				int			=		prompt_embeds.repeat_interleave(__UpperCamelCase     , dim=0				)

							if do_classifier_free_guidance:
												if self.learned_classifier_free_sampling_embeddings.learnable:
																	SCREAMING_SNAKE_CASE__				:				str			=		self.learned_classifier_free_sampling_embeddings.embeddings
																	SCREAMING_SNAKE_CASE__				:				List[Any]			=		negative_prompt_embeds.unsqueeze(0				).repeat(__UpperCamelCase     , 1     , 1				)
												else:
																	SCREAMING_SNAKE_CASE__				:				Dict			=		[''] * batch_size

																	SCREAMING_SNAKE_CASE__				:				Optional[int]			=		text_input_ids.shape[-1]
																	SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.tokenizer(
																	    __UpperCamelCase     , padding='max_length'     , max_length=__UpperCamelCase     , truncation=__UpperCamelCase     , return_tensors='pt'     , )
																	SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]
																	# See comment for normalizing text embeddings
																	SCREAMING_SNAKE_CASE__				:				Dict			=		negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1     , keepdim=__UpperCamelCase				)

												# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
												SCREAMING_SNAKE_CASE__				:				Dict			=		negative_prompt_embeds.shape[1]
												SCREAMING_SNAKE_CASE__				:				List[Any]			=		negative_prompt_embeds.repeat(1     , __UpperCamelCase     , 1				)
												SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		negative_prompt_embeds.view(batch_size * num_images_per_prompt     , __UpperCamelCase     , -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
												SCREAMING_SNAKE_CASE__				:				str			=		torch.cat([negative_prompt_embeds, prompt_embeds]				)

							return prompt_embeds




		@torch.no_grad()
		def __call__(      self     :	List[Any]     , a_     :	Optional[Any]     , a_     :	str = 100     , a_     :	List[Any] = 5.0     , a_     :	List[str] = 1.0     , a_     :	List[str] = 1     , a_     :	Tuple = None     , a_     :	Union[str, Any] = None     , a_     :	Any = "pil"     , a_     :	str = True     , a_     :	Tuple = None     , a_     :	Tuple = 1     , )->       str:




							"""simple docstring"""


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

							SCREAMING_SNAKE_CASE__				:				Any			=		batch_size * num_images_per_prompt

							SCREAMING_SNAKE_CASE__				:				Dict			=		guidance_scale > 1.0

							SCREAMING_SNAKE_CASE__				:				Dict			=		self._encode_prompt(__UpperCamelCase     , __UpperCamelCase     , __UpperCamelCase				)

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

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

							SCREAMING_SNAKE_CASE__				:				str			=		(batch_size, self.transformer.num_latent_pixels)
							if latents is None:
												SCREAMING_SNAKE_CASE__				:				str			=		self.transformer.num_vector_embeds - 1
												SCREAMING_SNAKE_CASE__				:				Tuple			=		torch.full(__UpperCamelCase     , __UpperCamelCase				).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).'''				)
												SCREAMING_SNAKE_CASE__				:				Optional[int]			=		latents.to(self.device				)

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

							SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.scheduler.timesteps.to(self.device				)

							SCREAMING_SNAKE_CASE__				:				Dict			=		latents

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

												# predict the un-noised image
												# model_output == `log_p_x_0`
												SCREAMING_SNAKE_CASE__				:				Tuple			=		self.transformer(__UpperCamelCase     , encoder_hidden_states=__UpperCamelCase     , timestep=__UpperCamelCase				).sample

												if do_classifier_free_guidance:
																	SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		model_output.chunk(2				)
																	SCREAMING_SNAKE_CASE__				:				Any			=		model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
																	model_output -= torch.logsumexp(__UpperCamelCase     , dim=1     , keepdim=__UpperCamelCase				)

												SCREAMING_SNAKE_CASE__				:				Tuple			=		self.truncate(__UpperCamelCase     , __UpperCamelCase				)

												# remove `log(0)`'s (`-inf`s)
												SCREAMING_SNAKE_CASE__				:				List[str]			=		model_output.clamp(-70				)

												# compute the previous noisy sample x_t -> x_t-1
												SCREAMING_SNAKE_CASE__				:				Dict			=		self.scheduler.step(__UpperCamelCase     , timestep=__UpperCamelCase     , sample=__UpperCamelCase     , generator=__UpperCamelCase				).prev_sample

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

							SCREAMING_SNAKE_CASE__				:				Dict			=		self.vqvae.config.vq_embed_dim
							SCREAMING_SNAKE_CASE__				:				Tuple			=		(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
							SCREAMING_SNAKE_CASE__				:				int			=		self.vqvae.quantize.get_codebook_entry(__UpperCamelCase     , shape=__UpperCamelCase				)
							SCREAMING_SNAKE_CASE__				:				List[str]			=		self.vqvae.decode(__UpperCamelCase     , force_not_quantize=__UpperCamelCase				).sample

							SCREAMING_SNAKE_CASE__				:				str			=		(image / 2 + 0.5).clamp(0     , 1				)
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		image.cpu().permute(0     , 2     , 3     , 1				).numpy()

							if output_type == "pil":
												SCREAMING_SNAKE_CASE__				:				int			=		self.numpy_to_pil(__UpperCamelCase				)

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=__UpperCamelCase				)




		def 	__lowercase(      self     :	str     , a_     :	Optional[int]     , a_     :	str				)->       str:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.sort(__UpperCamelCase     , 1     , descending=__UpperCamelCase				)
							SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.exp(__UpperCamelCase				)
							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		sorted_p_x_0.cumsum(dim=1				) < truncation_rate

							# Ensure that at least the largest probability is not zeroed out
							SCREAMING_SNAKE_CASE__				:				Tuple			=		torch.full_like(keep_mask[:, 0:1, :]     , __UpperCamelCase				)
							SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.cat((all_true, keep_mask)     , dim=1				)
							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		keep_mask[:, :-1, :]

							SCREAMING_SNAKE_CASE__				:				Tuple			=		keep_mask.gather(1     , indices.argsort(1				)				)

							SCREAMING_SNAKE_CASE__				:				Tuple			=		log_p_x_0.clone()

							SCREAMING_SNAKE_CASE__				:				List[Any]			=		-torch.inf  # -inf = log(0)

							return rv




import heapq as hq
import math
from collections.abc import Iterator

class       snake_case					:




  def __init__(      self     :	str     , a_     :	str				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		str(id_				)
       SCREAMING_SNAKE_CASE__				:				Any			=		None
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		None
       SCREAMING_SNAKE_CASE__				:				Any			=		[]
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{}  # {vertex:distance}




  def __lt__(      self     :	int     , a_     :	Tuple				)->       Union[str, Any]:




       """simple docstring"""
       return self.key < other.key




  def __repr__(      self     :	Any				)->       Dict:




       """simple docstring"""
       return self.id




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	int				)->       List[str]:




       """simple docstring"""
       self.neighbors.append(a_				)




  def 	__lowercase(      self     :	int     , a_     :	int     , a_     :	Optional[Any]				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		weight







def     _a				(      lowercase__  :			Optional[Any]     ,  lowercase__  :			Union[str, Any]     ,  lowercase__  :			Tuple     ,  lowercase__  :			Dict ):







     '''simple docstring'''


     graph[a - 1].add_neighbor(graph[b - 1] )
     graph[b - 1].add_neighbor(graph[a - 1] )
     # add the edges:
     graph[a - 1].add_edge(graph[b - 1]     ,  lowercase__ )
     graph[b - 1].add_edge(graph[a - 1]     ,  lowercase__ )







def     _a				(      lowercase__  :			list     ,  lowercase__  :			Vertex ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Any			=		[]
     for u in graph:
          SCREAMING_SNAKE_CASE__				:				Dict			=		math.inf
          SCREAMING_SNAKE_CASE__				:				str			=		None
     SCREAMING_SNAKE_CASE__				:				List[str]			=		0
     SCREAMING_SNAKE_CASE__				:				int			=		graph[:]
     while q:
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		min(lowercase__ )
          q.remove(lowercase__ )
          for v in u.neighbors:
               if (v in q) and (u.edges[v.id] < v.key):
                    SCREAMING_SNAKE_CASE__				:				int			=		u
                    SCREAMING_SNAKE_CASE__				:				Any			=		u.edges[v.id]
     for i in range(1     ,  len(lowercase__ ) ):
          a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
     return a







def     _a				(      lowercase__  :			list     ,  lowercase__  :			Vertex ):







     '''simple docstring'''


     for u in graph:
          SCREAMING_SNAKE_CASE__				:				List[str]			=		math.inf
          SCREAMING_SNAKE_CASE__				:				int			=		None
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		0

     SCREAMING_SNAKE_CASE__				:				Tuple			=		list(lowercase__ )
     hq.heapify(lowercase__ )

     while h:
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		hq.heappop(lowercase__ )
          for v in u.neighbors:
               if (v in h) and (u.edges[v.id] < v.key):
                    SCREAMING_SNAKE_CASE__				:				List[str]			=		u
                    SCREAMING_SNAKE_CASE__				:				Dict			=		u.edges[v.id]
                    hq.heapify(lowercase__ )

     for i in range(1     ,  len(lowercase__ ) ):
          yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)







def     _a				(      ):







     '''simple docstring'''




if __name__ == "__main__":
      import doctest

      doctest.testmod()



