






from json import JSONDecodeError  # Workaround for requests.exceptions.JSONDecodeError

import requests




def 					lowercase__				(					__snake_case   :							str = "isbn/0140328726"  ):


					'''simple docstring'''


					UpperCAmelCase_   :				str						=						olid.strip().strip('/'  )  # Remove leading/trailing whitespace & slashes
					if new_olid.count('/'  ) != 1:
										UpperCAmelCase_   :				List[str]						=						F"{olid} is not a valid Open Library olid"
										raise ValueError(__snake_case  )
					return requests.get(F"https://openlibrary.org/{new_olid}.json"  ).json()




def 					lowercase__				(					__snake_case   :							dict  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[Any]						=						{
					    'title': 'Title',
					    'publish_date': 'Publish date',
					    'authors': 'Authors',
					    'number_of_pages': 'Number of pages:',
					    'first_sentence': 'First sentence',
					    'isbn_10': 'ISBN (10)',
					    'isbn_13': 'ISBN (13)',
					}
					UpperCAmelCase_   :				Optional[Any]						=						{better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
					UpperCAmelCase_   :				Optional[Any]						=						[
					    get_openlibrary_data(author['key']  )['name'] for author in data['Authors']
					]
					UpperCAmelCase_   :				Optional[int]						=						data['First sentence']['value']
					for key, value in data.items():
										if isinstance(__snake_case					,	__snake_case  ):
															UpperCAmelCase_   :				Dict						=						', '.join(__snake_case  )
					return data


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		while True:
				__UpperCAmelCase     							=						input('\nEnter the ISBN code to search (or \'quit\' to stop): ').strip()
				if isbn.lower() in ("", "q", "quit", "exit", "stop"):
						break

				if len(isbn) not in (10, 13) or not isbn.isdigit():
						print(F'Sorry, {isbn} is not a valid ISBN.  Please, input a valid ISBN.')
						continue

				print(F'\nSearching Open Library for ISBN: {isbn}...\n')

				try:
						__UpperCAmelCase     							=						summarize_book(get_openlibrary_data(F'isbn/{isbn}'))
						print('\n'.join(F'{key}: {value}' for key, value in book_summary.items()))
				except JSONDecodeError:  # Workaround for requests.exceptions.RequestException:
						print(F'Sorry, there are no results for ISBN: {isbn}.')














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



class   lowerCamelCase				(_snake_case			,  _snake_case      ):







		'''simple docstring'''



		@register_to_config
		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None       )     ->							int:
							super().__init__()

							UpperCAmelCase_   :				str						=						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"

												UpperCAmelCase_   :				Optional[Any]						=						torch.zeros(_UpperCamelCase    ,		_UpperCamelCase       )
							else:
												UpperCAmelCase_   :				Any						=						None

							UpperCAmelCase_   :				Any						=						torch.nn.Parameter(_UpperCamelCase       )





class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   VQModel
		_snake_case   :   CLIPTextModel
		_snake_case   :   CLIPTokenizer
		_snake_case   :   TransformeraDModel
		_snake_case   :   LearnedClassifierFreeSamplingEmbeddings
		_snake_case   :   VQDiffusionScheduler



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		)     ->							List[Any]:
							super().__init__()

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



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							List[str]:
							UpperCAmelCase_   :				Union[str, Any]						=						len(_UpperCamelCase       ) if isinstance(_UpperCamelCase    ,		_UpperCamelCase       ) else 1

							# get prompt text embeddings
							UpperCAmelCase_   :				str						=						self.tokenizer(
							    _UpperCamelCase    ,		padding='max_length'    ,		max_length=self.tokenizer.model_max_length    ,		return_tensors='pt'    ,		)
							UpperCAmelCase_   :				Optional[Any]						=						text_inputs.input_ids

							if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
												UpperCAmelCase_   :				List[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}"       )
												UpperCAmelCase_   :				str						=						text_input_ids[:, : self.tokenizer.model_max_length]
							UpperCAmelCase_   :				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
							UpperCAmelCase_   :				Dict						=						prompt_embeds / prompt_embeds.norm(dim=-1    ,		keepdim=_UpperCamelCase       )

							# duplicate text embeddings for each generation per prompt
							UpperCAmelCase_   :				Dict						=						prompt_embeds.repeat_interleave(_UpperCamelCase    ,		dim=0       )

							if do_classifier_free_guidance:
												if self.learned_classifier_free_sampling_embeddings.learnable:
																	UpperCAmelCase_   :				List[str]						=						self.learned_classifier_free_sampling_embeddings.embeddings
																	UpperCAmelCase_   :				List[str]						=						negative_prompt_embeds.unsqueeze(0       ).repeat(_UpperCamelCase    ,		1    ,		1       )
												else:
																	UpperCAmelCase_   :				List[Any]						=						[''] * batch_size

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

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

							return prompt_embeds



		@torch.no_grad()
		def __call__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase = 1_0_0    ,		_UpperCamelCase = 5.0    ,		_UpperCamelCase = 1.0    ,		_UpperCamelCase = 1    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = "pil"    ,		_UpperCamelCase = True    ,		_UpperCamelCase = None    ,		_UpperCamelCase = 1    ,		)     ->							Union[ImagePipelineOutput, Tuple]:
							if isinstance(_UpperCamelCase    ,		_UpperCamelCase       ):
												UpperCAmelCase_   :				Any						=						1
							elif isinstance(_UpperCamelCase    ,		_UpperCamelCase       ):
												UpperCAmelCase_   :				Tuple						=						len(_UpperCamelCase       )
							else:
												raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(_UpperCamelCase       )}"       )

							UpperCAmelCase_   :				Union[str, Any]						=						batch_size * num_images_per_prompt

							UpperCAmelCase_   :				Optional[int]						=						guidance_scale > 1.0

							UpperCAmelCase_   :				Any						=						self._encode_prompt(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )

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

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

							UpperCAmelCase_   :				Optional[int]						=						(batch_size, self.transformer.num_latent_pixels)
							if latents is None:
												UpperCAmelCase_   :				Tuple						=						self.transformer.num_vector_embeds - 1
												UpperCAmelCase_   :				List[Any]						=						torch.full(_UpperCamelCase    ,		_UpperCamelCase       ).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)."       )
												UpperCAmelCase_   :				Any						=						latents.to(self.device       )

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

							UpperCAmelCase_   :				List[str]						=						self.scheduler.timesteps.to(self.device       )

							UpperCAmelCase_   :				Union[str, Any]						=						latents

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

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

												if do_classifier_free_guidance:
																	UpperCAmelCase_	,  UpperCAmelCase_   :				Optional[Any]						=						model_output.chunk(2       )
																	UpperCAmelCase_   :				Optional[int]						=						model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
																	model_output -= torch.logsumexp(_UpperCamelCase    ,		dim=1    ,		keepdim=_UpperCamelCase       )

												UpperCAmelCase_   :				str						=						self.truncate(_UpperCamelCase    ,		_UpperCamelCase       )

												# remove `log(0)`'s (`-inf`s)
												UpperCAmelCase_   :				Optional[int]						=						model_output.clamp(-7_0       )

												# compute the previous noisy sample x_t -> x_t-1
												UpperCAmelCase_   :				Union[str, Any]						=						self.scheduler.step(_UpperCamelCase    ,		timestep=_UpperCamelCase    ,		sample=_UpperCamelCase    ,		generator=_UpperCamelCase       ).prev_sample

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

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

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

							if output_type == "pil":
												UpperCAmelCase_   :				int						=						self.numpy_to_pil(_UpperCamelCase       )

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							torch.FloatTensor:
							UpperCAmelCase_	,  UpperCAmelCase_   :				int						=						torch.sort(_UpperCamelCase    ,		1    ,		descending=_UpperCamelCase       )
							UpperCAmelCase_   :				Dict						=						torch.exp(_UpperCamelCase       )
							UpperCAmelCase_   :				int						=						sorted_p_x_0.cumsum(dim=1       ) < truncation_rate

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

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

							UpperCAmelCase_   :				str						=						log_p_x_0.clone()

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

							return rv






