






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



class   lowerCamelCase      (_snake_case ,  _snake_case			):







		'''simple docstring'''



		@register_to_config
		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase = None				,		_UpperCamelCase = None   )	->							Optional[int]:
							super().__init__()

							UpperCAmelCase_    :      str            =						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_    :      List[str]            =						torch.zeros(_UpperCamelCase				,		_UpperCamelCase   )
							else:
												UpperCAmelCase_    :      Optional[Any]            =						None

							UpperCAmelCase_    :      int            =						torch.nn.Parameter(_UpperCamelCase   )





class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    VQModel
		_snake_case    :    CLIPTextModel
		_snake_case    :    CLIPTokenizer
		_snake_case    :    TransformeraDModel
		_snake_case    :    LearnedClassifierFreeSamplingEmbeddings
		_snake_case    :    VQDiffusionScheduler



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		)	->							Optional[int]:
							super().__init__()

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



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							List[Any]:
							UpperCAmelCase_    :      Tuple            =						len(_UpperCamelCase   ) if isinstance(_UpperCamelCase				,		_UpperCamelCase   ) else 1

							# get prompt text embeddings
							UpperCAmelCase_    :      Optional[Any]            =						self.tokenizer(
							    _UpperCamelCase				,		padding='max_length'				,		max_length=self.tokenizer.model_max_length				,		return_tensors='pt'				,		)
							UpperCAmelCase_    :      Optional[Any]            =						text_inputs.input_ids

							if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
												UpperCAmelCase_    :      List[str]            =						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_    :      Tuple            =						text_input_ids[:, : self.tokenizer.model_max_length]
							UpperCAmelCase_    :      str            =						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_    :      Any            =						prompt_embeds / prompt_embeds.norm(dim=-1				,		keepdim=_UpperCamelCase   )

							# duplicate text embeddings for each generation per prompt
							UpperCAmelCase_    :      Dict            =						prompt_embeds.repeat_interleave(_UpperCamelCase				,		dim=0   )

							if do_classifier_free_guidance:
												if self.learned_classifier_free_sampling_embeddings.learnable:
																	UpperCAmelCase_    :      Optional[Any]            =						self.learned_classifier_free_sampling_embeddings.embeddings
																	UpperCAmelCase_    :      List[str]            =						negative_prompt_embeds.unsqueeze(0   ).repeat(_UpperCamelCase				,		1				,		1   )
												else:
																	UpperCAmelCase_    :      Tuple            =						[''] * batch_size

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

												# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
												UpperCAmelCase_    :      Dict            =						negative_prompt_embeds.shape[1]
												UpperCAmelCase_    :      Optional[Any]            =						negative_prompt_embeds.repeat(1				,		_UpperCamelCase				,		1   )
												UpperCAmelCase_    :      Union[str, 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
												UpperCAmelCase_    :      str            =						torch.cat([negative_prompt_embeds, prompt_embeds]   )

							return prompt_embeds



		@torch.no_grad()
		def __call__(							self				,		_UpperCamelCase				,		_UpperCamelCase = 1_0_0				,		_UpperCamelCase = 5.0				,		_UpperCamelCase = 1.0				,		_UpperCamelCase = 1				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = "pil"				,		_UpperCamelCase = True				,		_UpperCamelCase = None				,		_UpperCamelCase = 1				,		)	->							Union[ImagePipelineOutput, Tuple]:
							if isinstance(_UpperCamelCase				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      Any            =						1
							elif isinstance(_UpperCamelCase				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      Optional[int]            =						len(_UpperCamelCase   )
							else:
												raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(_UpperCamelCase   )}"   )

							UpperCAmelCase_    :      List[str]            =						batch_size * num_images_per_prompt

							UpperCAmelCase_    :      int            =						guidance_scale > 1.0

							UpperCAmelCase_    :      Tuple            =						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

							UpperCAmelCase_    :      Tuple            =						(batch_size, self.transformer.num_latent_pixels)
							if latents is None:
												UpperCAmelCase_    :      Optional[Any]            =						self.transformer.num_vector_embeds - 1
												UpperCAmelCase_    :      Any            =						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)."   )
												UpperCAmelCase_    :      Dict            =						latents.to(self.device   )

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

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

							UpperCAmelCase_    :      Optional[int]            =						latents

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

												# predict the un-noised image
												# model_output == `log_p_x_0`
												UpperCAmelCase_    :      Optional[int]            =						self.transformer(_UpperCamelCase				,		encoder_hidden_states=_UpperCamelCase				,		timestep=_UpperCamelCase   ).sample

												if do_classifier_free_guidance:
																	UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[Any]            =						model_output.chunk(2   )
																	UpperCAmelCase_    :      Optional[int]            =						model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
																	model_output -= torch.logsumexp(_UpperCamelCase				,		dim=1				,		keepdim=_UpperCamelCase   )

												UpperCAmelCase_    :      Optional[Any]            =						self.truncate(_UpperCamelCase				,		_UpperCamelCase   )

												# remove `log(0)`'s (`-inf`s)
												UpperCAmelCase_    :      Optional[Any]            =						model_output.clamp(-7_0   )

												# compute the previous noisy sample x_t -> x_t-1
												UpperCAmelCase_    :      Optional[Any]            =						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   )

							UpperCAmelCase_    :      List[str]            =						self.vqvae.config.vq_embed_dim
							UpperCAmelCase_    :      Union[str, Any]            =						(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
							UpperCAmelCase_    :      List[Any]            =						self.vqvae.quantize.get_codebook_entry(_UpperCamelCase				,		shape=_UpperCamelCase   )
							UpperCAmelCase_    :      Dict            =						self.vqvae.decode(_UpperCamelCase				,		force_not_quantize=_UpperCamelCase   ).sample

							UpperCAmelCase_    :      str            =						(image / 2 + 0.5).clamp(0				,		1   )
							UpperCAmelCase_    :      Tuple            =						image.cpu().permute(0				,		2				,		3				,		1   ).numpy()

							if output_type == "pil":
												UpperCAmelCase_    :      List[str]            =						self.numpy_to_pil(_UpperCamelCase   )

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase   )	->							torch.FloatTensor:
							UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[int]            =						torch.sort(_UpperCamelCase				,		1				,		descending=_UpperCamelCase   )
							UpperCAmelCase_    :      Tuple            =						torch.exp(_UpperCamelCase   )
							UpperCAmelCase_    :      Any            =						sorted_p_x_0.cumsum(dim=1   ) < truncation_rate

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

							UpperCAmelCase_    :      List[str]            =						keep_mask.gather(1				,		indices.argsort(1   )   )

							UpperCAmelCase_    :      str            =						log_p_x_0.clone()

							UpperCAmelCase_    :      Optional[int]            =						-torch.inf  # -inf = log(0)

							return rv














from __future__ import annotations




def      lowercase__      (     __snake_case    :							tuple[int, int]     ,       __snake_case    :							int  ):


					'''simple docstring'''


					UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						position
					UpperCAmelCase_    :      str            =						[
					    (y + 1, x + 2),
					    (y - 1, x + 2),
					    (y + 1, x - 2),
					    (y - 1, x - 2),
					    (y + 2, x + 1),
					    (y + 2, x - 1),
					    (y - 2, x + 1),
					    (y - 2, x - 1),
					]
					UpperCAmelCase_    :      Optional[Any]            =						[]

					for position in positions:
										UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						position
										if 0 <= y_test < n and 0 <= x_test < n:
															permissible_positions.append(__snake_case  )

					return permissible_positions




def      lowercase__      (     __snake_case    :							list[list[int]]  ):


					'''simple docstring'''


					return not any(elem == 0 for row in board for elem in row  )




def      lowercase__      (     __snake_case    :							list[list[int]]     ,       __snake_case    :							tuple[int, int]     ,       __snake_case    :							int  ):


					'''simple docstring'''


					if is_complete(__snake_case  ):
										return True

					for position in get_valid_pos(__snake_case     ,       len(__snake_case  )  ):
										UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						position

										if board[y][x] == 0:
															UpperCAmelCase_    :      Optional[Any]            =						curr + 1
															if open_knight_tour_helper(__snake_case     ,       __snake_case     ,       curr + 1  ):
																				return True
															UpperCAmelCase_    :      List[Any]            =						0

					return False




def      lowercase__      (     __snake_case    :							int  ):


					'''simple docstring'''


					UpperCAmelCase_    :      str            =						[[0 for i in range(__snake_case  )] for j in range(__snake_case  )]

					for i in range(__snake_case  ):
										for j in range(__snake_case  ):
															UpperCAmelCase_    :      Optional[Any]            =						1
															if open_knight_tour_helper(__snake_case     ,       (i, j)     ,       1  ):
																				return board
															UpperCAmelCase_    :      List[Any]            =						0

					UpperCAmelCase_    :      List[str]            =						F"Open Kight Tour cannot be performed on a board of size {n}"
					raise ValueError(__snake_case  )


if __name__ == "__main__":
		import doctest

		doctest.testmod()






