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


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



class        lowercase_   (					__snake_case						,			__snake_case		):



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

													_snake_case						:					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"

																			_snake_case						:					int    =   torch.zeros(lowercase_				,    lowercase_    )
													else:
																			_snake_case						:					Tuple    =   None

													_snake_case						:					List[str]    =   torch.nn.Parameter(lowercase_    )




class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		42
							_lowerCamelCase						=		42
							_lowerCamelCase						=		42
							_lowerCamelCase						=		42
							_lowerCamelCase						=		42
							_lowerCamelCase						=		42



							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 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Tuple    =   len(lowercase_    ) if isinstance(lowercase_				,    lowercase_    ) else 1

													# get prompt text embeddings
													_snake_case						:					Tuple    =   self.tokenizer(
													    lowercase_				,    padding="max_length"				,    max_length=self.tokenizer.model_max_length				,    return_tensors="pt"				,    )
													_snake_case						:					Optional[Any]    =   text_inputs.input_ids

													if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																			_snake_case						:					Tuple    =   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						:					Tuple    =   text_input_ids[:, : self.tokenizer.model_max_length]
													_snake_case						:					List[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
													_snake_case						:					Optional[int]    =   prompt_embeds / prompt_embeds.norm(dim=-1				,    keepdim=lowercase_    )

													# duplicate text embeddings for each generation per prompt
													_snake_case						:					Optional[int]    =   prompt_embeds.repeat_interleave(lowercase_				,    dim=0    )

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

																									_snake_case						:					Optional[int]    =   text_input_ids.shape[-1]
																									_snake_case						:					List[Any]    =   self.tokenizer(
																									    lowercase_				,    padding="max_length"				,    max_length=lowercase_				,    truncation=lowercase_				,    return_tensors="pt"				,    )
																									_snake_case						:					Dict    =   self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]
																									# See comment for normalizing text embeddings
																									_snake_case						:					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
																			_snake_case						:					List[str]    =   negative_prompt_embeds.shape[1]
																			_snake_case						:					int    =   negative_prompt_embeds.repeat(1				,    lowercase_				,    1    )
																			_snake_case						:					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
																			_snake_case						:					str    =   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_    ):
																			_snake_case						:					str    =   1
													elif isinstance(lowercase_				,    lowercase_    ):
																			_snake_case						:					List[Any]    =   len(lowercase_    )
													else:
																			raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(lowercase_    )}"""    )

													_snake_case						:					str    =   batch_size * num_images_per_prompt

													_snake_case						:					List[Any]    =   guidance_scale > 1.0

													_snake_case						:					List[str]    =   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

													_snake_case						:					Tuple    =   (batch_size, self.transformer.num_latent_pixels)
													if latents is None:
																			_snake_case						:					Tuple    =   self.transformer.num_vector_embeds - 1
																			_snake_case						:					List[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)."""    )
																			_snake_case						:					List[str]    =   latents.to(self.device    )

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

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

													_snake_case						:					Tuple    =   latents

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

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

																			if do_classifier_free_guidance:
																									_snake_case						:					str    =   model_output.chunk(2    )
																									_snake_case						:					List[Any]    =   model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
																									model_output -= torch.logsumexp(lowercase_				,    dim=1				,    keepdim=lowercase_    )

																			_snake_case						:					Dict    =   self.truncate(lowercase_				,    lowercase_    )

																			# remove `log(0)`'s (`-inf`s)
																			_snake_case						:					Optional[int]    =   model_output.clamp(-70    )

																			# compute the previous noisy sample x_t -> x_t-1
																			_snake_case						:					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_    )

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

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

													if output_type == "pil":
																			_snake_case						:					Union[str, Any]    =   self.numpy_to_pil(lowercase_    )

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=lowercase_    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													_snake_case						:					int    =   torch.sort(lowercase_				,    1				,    descending=lowercase_    )
													_snake_case						:					int    =   torch.exp(lowercase_    )
													_snake_case						:					int    =   sorted_p_x_0.cumsum(dim=1    ) < truncation_rate

													# Ensure that at least the largest probability is not zeroed out
													_snake_case						:					List[str]    =   torch.full_like(keep_mask[:, 0:1, :]				,    lowercase_    )
													_snake_case						:					Dict    =   torch.cat((all_true, keep_mask)				,    dim=1    )
													_snake_case						:					List[Any]    =   keep_mask[:, :-1, :]

													_snake_case						:					str    =   keep_mask.gather(1				,    indices.argsort(1    )    )

													_snake_case						:					List[str]    =   log_p_x_0.clone()

													_snake_case						:					List[str]    =   -torch.inf  # -inf = log(0)

													return rv
def  snake_case						()	->	Dict:

						'''simple docstring'''



						_snake_case						:					List[str]    =   0
						for i in range(1 ,      1_001						):
												total += i**i
						return str(__lowercase						)[-10:]


if __name__ == "__main__":
				print(solution())