



'''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


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



class 		lowerCamelCase__      (						SCREAMING_SNAKE_CASE__	,   SCREAMING_SNAKE_CASE__     ):




	'''simple docstring'''



	@register_to_config
	def __init__(  self				:	Optional[Any]     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	List[str] = None     ,  UpperCamelCase_				:	Dict = None					)  ->       List[str]:



		'''simple docstring'''





		super().__init__()

		_lowercase					:       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"

			_lowercase					:       Union[str, Any]       =		torch.zeros(_lowercase     ,  _lowercase					)
		else:
			_lowercase					:       Tuple       =		None

		_lowercase					:       Optional[Any]       =		torch.nn.Parameter(_lowercase					)






class 		lowerCamelCase__      (						SCREAMING_SNAKE_CASE__     ):




	'''simple docstring'''



	A_						    =						42
	A_						    =						42
	A_						    =						42
	A_						    =						42
	A_						    =						42
	A_						    =						42




	def __init__(  self				:	Tuple     ,  UpperCamelCase_				:	Any     ,  UpperCamelCase_				:	Optional[Any]     ,  UpperCamelCase_				:	int     ,  UpperCamelCase_				:	int     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	Optional[Any]     ,  )  ->       Optional[Any]:



		'''simple docstring'''





		super().__init__()

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




	def 						__UpperCAmelCase     (  self				:	str     ,  UpperCamelCase_				:	int     ,  UpperCamelCase_				:	List[str]     ,  UpperCamelCase_				:	str					)  ->       int:



		'''simple docstring'''





		_lowercase					:       Union[str, Any]       =		len(_lowercase					) if isinstance(_lowercase     ,  _lowercase					) else 1

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

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

		# duplicate text embeddings for each generation per prompt
		_lowercase					:       Tuple       =		prompt_embeds.repeat_interleave(_lowercase     ,  dim=0					)

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

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

			# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
			_lowercase					:       List[Any]       =		negative_prompt_embeds.shape[1]
			_lowercase					:       int       =		negative_prompt_embeds.repeat(1     ,  _lowercase     ,  1					)
			_lowercase					:       int       =		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
			_lowercase					:       Tuple       =		torch.cat([negative_prompt_embeds, prompt_embeds]					)

		return prompt_embeds




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



		'''simple docstring'''





		if isinstance(_lowercase     ,  _lowercase					):
			_lowercase					:       Optional[Any]       =		1
		elif isinstance(_lowercase     ,  _lowercase					):
			_lowercase					:       List[str]       =		len(_lowercase					)
		else:
			raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(_lowercase					)}'''					)

		_lowercase					:       List[str]       =		batch_size * num_images_per_prompt

		_lowercase					:       List[str]       =		guidance_scale > 1.0

		_lowercase					:       Tuple       =		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

		_lowercase					:       Optional[Any]       =		(batch_size, self.transformer.num_latent_pixels)
		if latents is None:
			_lowercase					:       Optional[Any]       =		self.transformer.num_vector_embeds - 1
			_lowercase					:       List[str]       =		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).'''					)
			_lowercase					:       Optional[int]       =		latents.to(self.device					)

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

		_lowercase					:       Optional[Any]       =		self.scheduler.timesteps.to(self.device					)

		_lowercase					:       int       =		latents

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

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

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

			_lowercase					:       str       =		self.truncate(_lowercase     ,  _lowercase					)

			# remove `log(0)`'s (`-inf`s)
			_lowercase					:       Union[str, Any]       =		model_output.clamp(-70					)

			# compute the previous noisy sample x_t -> x_t-1
			_lowercase					:       List[str]       =		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					)

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

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

		if output_type == "pil":
			_lowercase					:       Any       =		self.numpy_to_pil(_lowercase					)

		if not return_dict:
			return (image,)

		return ImagePipelineOutput(images=_lowercase					)




	def 						__UpperCAmelCase     (  self				:	Optional[int]     ,  UpperCamelCase_				:	int     ,  UpperCamelCase_				:	Dict					)  ->       torch.FloatTensor:



		'''simple docstring'''





		_lowercase					:       List[Any]       =		torch.sort(_lowercase     ,  1     ,  descending=_lowercase					)
		_lowercase					:       Optional[Any]       =		torch.exp(_lowercase					)
		_lowercase					:       Optional[Any]       =		sorted_p_x_0.cumsum(dim=1					) < truncation_rate

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

		_lowercase					:       List[Any]       =		keep_mask.gather(1     ,  indices.argsort(1					)					)

		_lowercase					:       int       =		log_p_x_0.clone()

		_lowercase					:       Any       =		-torch.inf  # -inf = log(0)

		return rv





'''simple docstring'''

def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			list:
	_lowercase					:       List[str]       =		word.split()

	def justify(_lowercase,				_lowercase,				_lowercase					) -> str:
		_lowercase					:       Dict       =		max_width - width
		_lowercase					:       Tuple       =		len(_lowercase					)
		if len(_lowercase					) == 1:
			# if there is only word in line
			# just insert overall_spaces_count for the remainder of line
			return line[0] + " " * overall_spaces_count
		else:
			_lowercase					:       Tuple       =		words_count - 1
			# num_spaces_between_words_list[i] : tells you to insert
			# num_spaces_between_words_list[i] spaces
			# after word on line[i]
			_lowercase					:       str       =		spaces_to_insert_between_words * [
			    overall_spaces_count // spaces_to_insert_between_words
			]
			_lowercase					:       Optional[int]       =		(
			    overall_spaces_count % spaces_to_insert_between_words
			)
			# distribute spaces via round robin to the left words
			for i in range(_lowercase					):
				num_spaces_between_words_list[i] += 1
			_lowercase					:       Union[str, Any]       =		[]
			for i in range(_lowercase					):
				# add the word
				aligned_words_list.append(line[i]					)
				# add the spaces to insert
				aligned_words_list.append(num_spaces_between_words_list[i] * ' '					)
			# just add the last word to the sentence
			aligned_words_list.append(line[-1]					)
			# join the aligned words list to form a justified line
			return "".join(_lowercase					)

	_lowercase					:       str       =		[]
	_lowercase					:       list[str]       =		[]
	_lowercase					:       Union[str, Any]       =		0
	for word in words:
		if width + len(_lowercase					) + len(_lowercase					) <= max_width:
			# keep adding words until we can fill out max_width
			# width = sum of length of all words (without overall_spaces_count)
			# len(word) = length of current word
			# len(line) = number of overall_spaces_count to insert between words
			line.append(_lowercase					)
			width += len(_lowercase					)
		else:
			# justify the line and add it to result
			answer.append(justify(_lowercase,				_lowercase,				_lowercase					)					)
			# reset new line and new width
			_lowercase  ,    _lowercase					:       Optional[Any]       =		[word], len(_lowercase					)
	_lowercase					:       Optional[int]       =		max_width - width - len(_lowercase					)
	answer.append(' '.join(_lowercase					) + (remaining_spaces + 1) * ' '					)
	return answer


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

		testmod()
