



'''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


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


class       a__				( __A							,						__A      ):







				"""simple docstring"""



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

										__lowerCAmelCase							       =				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"

																__lowerCAmelCase							       =				torch.zeros(__lowercase       ,			__lowercase		)
										else:
																__lowerCAmelCase							       =				None

										__lowerCAmelCase							       =				torch.nn.Parameter(__lowercase		)


class       a__				( __A      ):







				"""simple docstring"""



				__UpperCamelCase    :		VQModel
				__UpperCamelCase    :		CLIPTextModel
				__UpperCamelCase    :		CLIPTokenizer
				__UpperCamelCase    :		TransformeraDModel
				__UpperCamelCase    :		LearnedClassifierFreeSamplingEmbeddings
				__UpperCamelCase    :		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 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase		):
										__lowerCAmelCase							       =				len(__lowercase		) if isinstance(__lowercase       ,			__lowercase		) else 1

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

										if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																__lowerCAmelCase							       =				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}"""		)
																__lowerCAmelCase							       =				text_input_ids[:, : self.tokenizer.model_max_length]
										__lowerCAmelCase							       =				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
										__lowerCAmelCase							       =				prompt_embeds / prompt_embeds.norm(dim=-1       ,			keepdim=__lowercase		)

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

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

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

																# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																__lowerCAmelCase							       =				negative_prompt_embeds.shape[1]
																__lowerCAmelCase							       =				negative_prompt_embeds.repeat(1       ,			__lowercase       ,			1		)
																__lowerCAmelCase							       =				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
																__lowerCAmelCase							       =				torch.cat([negative_prompt_embeds, prompt_embeds]		)

										return prompt_embeds




				@torch.no_grad()
				def __call__(self       ,			__lowercase       ,			__lowercase = 1_00       ,			__lowercase = 5.0       ,			__lowercase = 1.0       ,			__lowercase = 1       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = "pil"       ,			__lowercase = True       ,			__lowercase = None       ,			__lowercase = 1       ,			):
										if isinstance(__lowercase       ,			__lowercase		):
																__lowerCAmelCase							       =				1
										elif isinstance(__lowercase       ,			__lowercase		):
																__lowerCAmelCase							       =				len(__lowercase		)
										else:
																raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(__lowercase		)}"""		)

										__lowerCAmelCase							       =				batch_size * num_images_per_prompt

										__lowerCAmelCase							       =				guidance_scale > 1.0

										__lowerCAmelCase							       =				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

										__lowerCAmelCase							       =				(batch_size, self.transformer.num_latent_pixels)
										if latents is None:
																__lowerCAmelCase							       =				self.transformer.num_vector_embeds - 1
																__lowerCAmelCase							       =				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)."""		)
																__lowerCAmelCase							       =				latents.to(self.device		)

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

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

										__lowerCAmelCase							       =				latents

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

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

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

																__lowerCAmelCase							       =				self.truncate(__lowercase       ,			__lowercase		)

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

																# compute the previous noisy sample x_t -> x_t-1
																__lowerCAmelCase							       =				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		)

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

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

										if output_type == "pil":
																__lowerCAmelCase							       =				self.numpy_to_pil(__lowercase		)

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=__lowercase		)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase		):
										__lowerCAmelCase       ,					__lowerCAmelCase							       =				torch.sort(__lowercase       ,			1       ,			descending=__lowercase		)
										__lowerCAmelCase							       =				torch.exp(__lowercase		)
										__lowerCAmelCase							       =				sorted_p_x_0.cumsum(dim=1		) < truncation_rate

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

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

										__lowerCAmelCase							       =				log_p_x_0.clone()

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

										return rv





'''simple docstring'''

import requests
from bsa import BeautifulSoup

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				BeautifulSoup(requests.get(lowerCamelCase,				params=lowerCamelCase).content,				'''html.parser''')
						__lowerCAmelCase							       =				soup.find('''div''',				attrs={'''class''': '''gs_ri'''})
						__lowerCAmelCase							       =				div.find('''div''',				attrs={'''class''': '''gs_fl'''}).find_all('''a''')
						return anchors[2].get_text()


if __name__ == "__main__":
	_UpperCAmelCase				: Union[str, Any]							    =				{
	    """title""": (
	        """Precisely geometry controlled microsupercapacitors for ultrahigh areal """
	        """capacitance, volumetric capacitance, and energy density"""
	    ),
	    """journal""": """Chem. Mater.""",
	    """volume""": 3_0,
	    """pages""": """3979-3990""",
	    """year""": 2_0_1_8,
	    """hl""": """en""",
	}
	print(get_citation("""https://scholar.google.com/scholar_lookup""", params=params))

