



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



class    UpperCAmelCase__(				__lowercase  ,				__lowercase						):

   '''simple docstring'''




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




         """simple docstring"""

         super().__init__()

         lowercase__  =	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__  =	torch.zeros(lowerCAmelCase      ,					lowerCAmelCase)
         else:
               lowercase__  =	None

         lowercase__  =	torch.nn.Parameter(lowerCAmelCase)



class    UpperCAmelCase__(				__lowercase						):

   '''simple docstring'''




   A     :	VQModel
   A     :	CLIPTextModel
   A     :	CLIPTokenizer
   A     :	TransformeraDModel
   A     :	LearnedClassifierFreeSamplingEmbeddings
   A     :	VQDiffusionScheduler


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




         """simple docstring"""

         super().__init__()

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


   def       UpperCAmelCase		(    self	:      List[Any]      ,					lowerCAmelCase	:      List[Any]      ,					lowerCAmelCase	:      Dict      ,					lowerCAmelCase	:      Union[str, Any])						->					str:




         """simple docstring"""

         lowercase__  =	len(lowerCAmelCase) if isinstance(lowerCAmelCase      ,					lowerCAmelCase) else 1

         # get prompt text embeddings
         lowercase__  =	self.tokenizer(
             lowerCAmelCase      ,					padding='max_length'      ,					max_length=self.tokenizer.model_max_length      ,					return_tensors='pt'      ,					)
         lowercase__  =	text_inputs.input_ids

         if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
               lowercase__  =	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__  =	text_input_ids[:, : self.tokenizer.model_max_length]
         lowercase__  =	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__  =	prompt_embeds / prompt_embeds.norm(dim=-1      ,					keepdim=lowerCAmelCase)

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

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

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

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

         return prompt_embeds


   @torch.no_grad()
   def __call__(    self	:      Optional[int]      ,					lowerCAmelCase	:      Optional[Any]      ,					lowerCAmelCase	:      str = 1_00      ,					lowerCAmelCase	:      List[str] = 5.0      ,					lowerCAmelCase	:      Dict = 1.0      ,					lowerCAmelCase	:      List[Any] = 1      ,					lowerCAmelCase	:      Dict = None      ,					lowerCAmelCase	:      Optional[Any] = None      ,					lowerCAmelCase	:      Any = "pil"      ,					lowerCAmelCase	:      List[Any] = True      ,					lowerCAmelCase	:      Dict = None      ,					lowerCAmelCase	:      str = 1      ,					)						->					Union[ImagePipelineOutput, Tuple]:




         """simple docstring"""

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

         lowercase__  =	batch_size * num_images_per_prompt

         lowercase__  =	guidance_scale > 1.0

         lowercase__  =	self._encode_prompt(lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)

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

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

         lowercase__  =	(batch_size, self.transformer.num_latent_pixels)
         if latents is None:
               lowercase__  =	self.transformer.num_vector_embeds - 1
               lowercase__  =	torch.full(lowerCAmelCase      ,					lowerCAmelCase).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__  =	latents.to(self.device)

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

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

         lowercase__  =	latents

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

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

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

               lowercase__  =	self.truncate(lowerCAmelCase      ,					lowerCAmelCase)

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

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

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

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

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

         if output_type == "pil":
               lowercase__  =	self.numpy_to_pil(lowerCAmelCase)

         if not return_dict:
               return (image,)

         return ImagePipelineOutput(images=lowerCAmelCase)



   def       UpperCAmelCase		(    self	:      Dict      ,					lowerCAmelCase	:      Dict      ,					lowerCAmelCase	:      List[str])						->					torch.FloatTensor:




         """simple docstring"""

         lowercase__,						lowercase__  =	torch.sort(lowerCAmelCase      ,					1      ,					descending=lowerCAmelCase)
         lowercase__  =	torch.exp(lowerCAmelCase)
         lowercase__  =	sorted_p_x_0.cumsum(dim=1) < truncation_rate

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

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

         lowercase__  =	log_p_x_0.clone()

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

         return rv


def 				__A(lowerCAmelCase			)		->					bool:

		"""simple docstring"""






		if not isinstance(lowerCAmelCase    ,	lowerCAmelCase			):
				raise ValueError("""check_bouncy() accepts only integer arguments"""			)
		_UpperCamelCase         =						str(lowerCAmelCase			)
		_UpperCamelCase         =						"""""".join(sorted(lowerCAmelCase			)			)
		return sorted_str_n != str_n and sorted_str_n[::-1] != str_n


def 				__A(lowerCAmelCase = 9_9			)		->					int:

		"""simple docstring"""






		if not 0 < percent < 1_0_0:
				raise ValueError("""solution() only accepts values from 0 to 100"""			)
		_UpperCamelCase         =						0
		_UpperCamelCase         =						1

		while True:
				if check_bouncy(lowerCAmelCase			):
						bouncy_num += 1
				if (bouncy_num / num) * 1_0_0 >= percent:
						return num
				num += 1


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

		testmod()
		print(F"""{solution(99)}""")



