



'''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							:Optional[Any]       = logging.get_logger(__name__)  # pylint: disable=invalid-name







class      _lowerCAmelCase							(      __UpperCAmelCase				,      __UpperCAmelCase			):


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

				A_     :						Dict     =					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_     :						Optional[int]     =					torch.zeros(lowercase	,				lowercase							)
				else:
							A_     :						List[Any]     =					None

				A_     :						str     =					torch.nn.Parameter(lowercase							)







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__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	,				lowercase	,				lowercase	,				lowercase	,				lowercase	,				lowercase	,				lowercase	,				):
				super().__init__()

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


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase							):
				A_     :						Tuple     =					len(lowercase							) if isinstance(lowercase	,				lowercase							) else 1

				# get prompt text embeddings
				A_     :						List[Any]     =					self.tokenizer(
				    lowercase	,				padding="""max_length"""	,				max_length=self.tokenizer.model_max_length	,				return_tensors="""pt"""	,				)
				A_     :						List[str]     =					text_inputs.input_ids

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

				# duplicate text embeddings for each generation per prompt
				A_     :						Union[str, Any]     =					prompt_embeds.repeat_interleave(lowercase	,				dim=0							)

				if do_classifier_free_guidance:
							if self.learned_classifier_free_sampling_embeddings.learnable:
										A_     :						Optional[int]     =					self.learned_classifier_free_sampling_embeddings.embeddings
										A_     :						Optional[Any]     =					negative_prompt_embeds.unsqueeze(0							).repeat(lowercase	,				1	,				1							)
							else:
										A_     :						int     =					[""""""] * batch_size

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

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

				return prompt_embeds


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

				A_     :						Any     =					batch_size * num_images_per_prompt

				A_     :						Optional[Any]     =					guidance_scale > 1.0

				A_     :						List[Any]     =					self._encode_prompt(lowercase	,				lowercase	,				lowercase							)

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

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

				A_     :						Any     =					(batch_size, self.transformer.num_latent_pixels)
				if latents is None:
							A_     :						Optional[int]     =					self.transformer.num_vector_embeds - 1
							A_     :						Optional[Any]     =					torch.full(lowercase	,				lowercase							).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_     :						Optional[Any]     =					latents.to(self.device							)

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

				A_     :						int     =					self.scheduler.timesteps.to(self.device							)

				A_     :						Any     =					latents

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

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

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

							A_     :						int     =					self.truncate(lowercase	,				lowercase							)

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

							# compute the previous noisy sample x_t -> x_t-1
							A_     :						Optional[Any]     =					self.scheduler.step(lowercase	,				timestep=lowercase	,				sample=lowercase	,				generator=lowercase							).prev_sample

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

				A_     :						List[str]     =					self.vqvae.config.vq_embed_dim
				A_     :						Optional[int]     =					(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
				A_     :						str     =					self.vqvae.quantize.get_codebook_entry(lowercase	,				shape=lowercase							)
				A_     :						Any     =					self.vqvae.decode(lowercase	,				force_not_quantize=lowercase							).sample

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

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

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=lowercase							)


	def    _a     (self	,				lowercase	,				lowercase							):
				A_,   A_     :						List[Any]     =					torch.sort(lowercase	,				1	,				descending=lowercase							)
				A_     :						List[Any]     =					torch.exp(lowercase							)
				A_     :						Optional[Any]     =					sorted_p_x_0.cumsum(dim=1							) < truncation_rate

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

				A_     :						Any     =					keep_mask.gather(1	,				indices.argsort(1							)							)

				A_     :						Tuple     =					log_p_x_0.clone()

				A_     :						Optional[Any]     =					-torch.inf  # -inf = log(0)

				return rv




'''simple docstring'''




def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			if density <= 0:
						raise ValueError("""Impossible fluid density"""   )
			if bulk_modulus <= 0:
						raise ValueError("""Impossible bulk modulus"""   )

			return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
						import doctest

						doctest.testmod()