

"""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


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







class 			lowercase    (	lowercase_     ,  lowercase_      ):




				@register_to_config
				def __init__( self				,       snake_case				,       snake_case = None				,       snake_case = None	):
											super().__init__()

											snake_case_          =   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"

																		snake_case_          =   torch.zeros(snake_case				,       snake_case	)
											else:
																		snake_case_          =   None

											snake_case_          =   torch.nn.Parameter(snake_case	)







class 			lowercase    (	lowercase_      ):
				__SCREAMING_SNAKE_CASE :			VQModel
				__SCREAMING_SNAKE_CASE :			CLIPTextModel
				__SCREAMING_SNAKE_CASE :			CLIPTokenizer
				__SCREAMING_SNAKE_CASE :			TransformeraDModel
				__SCREAMING_SNAKE_CASE :			LearnedClassifierFreeSamplingEmbeddings
				__SCREAMING_SNAKE_CASE :			VQDiffusionScheduler




				def __init__( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											super().__init__()

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




				def    a							( self				,       snake_case				,       snake_case				,       snake_case	):
											snake_case_          =   len(snake_case	) if isinstance(snake_case				,       snake_case	) else 1

											# get prompt text embeddings
											snake_case_          =   self.tokenizer(
											    snake_case				,       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]
											snake_case_          =   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
											snake_case_          =   prompt_embeds / prompt_embeds.norm(dim=-1				,       keepdim=snake_case	)

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

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

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

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

											return prompt_embeds




				@torch.no_grad()
				def __call__( self				,       snake_case				,       snake_case = 100				,       snake_case = 5.0				,       snake_case = 1.0				,       snake_case = 1				,       snake_case = None				,       snake_case = None				,       snake_case = "pil"				,       snake_case = True				,       snake_case = None				,       snake_case = 1				,       ):
											if isinstance(snake_case				,       snake_case	):
																		snake_case_          =   1
											elif isinstance(snake_case				,       snake_case	):
																		snake_case_          =   len(snake_case	)
											else:
																		raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(snake_case	)}'''	)

											snake_case_          =   batch_size * num_images_per_prompt

											snake_case_          =   guidance_scale > 1.0

											snake_case_          =   self._encode_prompt(snake_case				,       snake_case				,       snake_case	)

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

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

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

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

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

											snake_case_          =   latents

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

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

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

																		snake_case_          =   self.truncate(snake_case				,       snake_case	)

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

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

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

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

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

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

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=snake_case	)




				def    a							( self				,       snake_case				,       snake_case	):
											snake_case_    ,   snake_case_          =   torch.sort(snake_case				,       1				,       descending=snake_case	)
											snake_case_          =   torch.exp(snake_case	)
											snake_case_          =   sorted_p_x_0.cumsum(dim=1	) < truncation_rate

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

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

											snake_case_          =   log_p_x_0.clone()

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

											return rv







from __future__ import annotations





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__ = None       ,							UpperCamelCase__ = None				):







							'''simple docstring'''






							if start is None:
														snake_case_          =   0

							if end is None:
														snake_case_          =   len(UpperCamelCase__				) - 1

							if start >= end:
														return

							snake_case_          =   (start + end) // 2

							slowsort(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)
							slowsort(UpperCamelCase__       ,							mid + 1       ,							UpperCamelCase__				)

							if sequence[end] < sequence[mid]:
														snake_case_    ,   snake_case_          =   sequence[mid], sequence[end]

							slowsort(UpperCamelCase__       ,							UpperCamelCase__       ,							end - 1				)


if __name__ == "__main__":
					from doctest import testmod

					testmod()




