


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


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






class       _UpperCamelCase    (					__snake_case    ,		__snake_case				):

				"""simple docstring"""







				@register_to_config
				def __init__(						self				,   a__				,   a__ = None				,   a__ = None   )	->				Optional[Any]:
					super().__init__()

					A								=				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"

						A								=				torch.zeros(a__				,   a__   )
					else:
						A								=				None

					A								=				torch.nn.Parameter(a__   )






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= 42
				lowerCAmelCase 		= 42
				lowerCAmelCase 		= 42
				lowerCAmelCase 		= 42
				lowerCAmelCase 		= 42
				lowerCAmelCase 		= 42

				def __init__(						self				,   a__				,   a__				,   a__				,   a__				,   a__				,   a__				,   )	->				Any:
					super().__init__()

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

				def      _UpperCAmelCase					(						self				,   a__				,   a__				,   a__   )	->				Tuple:
					A								=				len(a__   ) if isinstance(a__				,   a__   ) else 1

					# get prompt text embeddings
					A								=				self.tokenizer(
					    a__				,   padding="""max_length"""				,   max_length=self.tokenizer.model_max_length				,   return_tensors="""pt"""				,   )
					A								=				text_inputs.input_ids

					if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
						A								=				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}'   )
						A								=				text_input_ids[:, : self.tokenizer.model_max_length]
					A								=				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
					A								=				prompt_embeds / prompt_embeds.norm(dim=-1				,   keepdim=a__   )

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

					if do_classifier_free_guidance:
						if self.learned_classifier_free_sampling_embeddings.learnable:
							A								=				self.learned_classifier_free_sampling_embeddings.embeddings
							A								=				negative_prompt_embeds.unsqueeze(0   ).repeat(a__				,   1				,   1   )
						else:
							A								=				[""""""] * batch_size

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

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

					return prompt_embeds

				@torch.no_grad()
				def __call__(						self				,   a__				,   a__ = 100				,   a__ = 5.0				,   a__ = 1.0				,   a__ = 1				,   a__ = None				,   a__ = None				,   a__ = "pil"				,   a__ = True				,   a__ = None				,   a__ = 1				,   )	->				Union[ImagePipelineOutput, Tuple]:
					if isinstance(a__				,   a__   ):
						A								=				1
					elif isinstance(a__				,   a__   ):
						A								=				len(a__   )
					else:
						raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(a__   )}'   )

					A								=				batch_size * num_images_per_prompt

					A								=				guidance_scale > 1.0

					A								=				self._encode_prompt(a__				,   a__				,   a__   )

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

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

					A								=				(batch_size, self.transformer.num_latent_pixels)
					if latents is None:
						A								=				self.transformer.num_vector_embeds - 1
						A								=				torch.full(a__				,   a__   ).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).'   )
						A								=				latents.to(self.device   )

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

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

					A								=				latents

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

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

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

						A								=				self.truncate(a__				,   a__   )

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

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

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

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

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

					if output_type == "pil":
						A								=				self.numpy_to_pil(a__   )

					if not return_dict:
						return (image,)

					return ImagePipelineOutput(images=a__   )

				def      _UpperCAmelCase					(						self				,   a__				,   a__   )	->				torch.FloatTensor:
					A					,			A								=				torch.sort(a__				,   1				,   descending=a__   )
					A								=				torch.exp(a__   )
					A								=				sorted_p_x_0.cumsum(dim=1   ) < truncation_rate

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

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

					A								=				log_p_x_0.clone()

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

					return rv







def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   list[int]:





	"""simple docstring"""







	if num <= 0:
		raise ValueError("""Input must be a positive integer"""						)

	A								=				[True] * (num + 1)

	A								=				2
	while p * p <= num:
		if primes[p]:
			for i in range(p * p							,					num + 1							,					UpperCamelCase__						):
				A								=				False
		p += 1

	return [prime for prime in range(2							,					num + 1						) if primes[prime]]


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_lowercase       :      Tuple	=	int(input("Enter a positive integer: ").strip())
				print(prime_sieve_eratosthenes(user_num))



