






"""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


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






class      a						(					__lowerCamelCase  ,      __lowerCamelCase		):






    @register_to_config
    def __init__(       self     :							str       ,						__SCREAMING_SNAKE_CASE     :							bool       ,						__SCREAMING_SNAKE_CASE     :							Optional[int] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[int] = None					)     ->		int:
      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						(					__lowerCamelCase		):
    SCREAMING_SNAKE_CASE						:     VQModel
    SCREAMING_SNAKE_CASE						:     CLIPTextModel
    SCREAMING_SNAKE_CASE						:     CLIPTokenizer
    SCREAMING_SNAKE_CASE						:     TransformeraDModel
    SCREAMING_SNAKE_CASE						:     LearnedClassifierFreeSamplingEmbeddings
    SCREAMING_SNAKE_CASE						:     VQDiffusionScheduler






    def __init__(       self     :							int       ,						__SCREAMING_SNAKE_CASE     :							VQModel       ,						__SCREAMING_SNAKE_CASE     :							CLIPTextModel       ,						__SCREAMING_SNAKE_CASE     :							CLIPTokenizer       ,						__SCREAMING_SNAKE_CASE     :							TransformeraDModel       ,						__SCREAMING_SNAKE_CASE     :							VQDiffusionScheduler       ,						__SCREAMING_SNAKE_CASE     :							LearnedClassifierFreeSamplingEmbeddings       ,						)     ->		Tuple:
      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     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							int       ,						__SCREAMING_SNAKE_CASE     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]					)     ->		Dict:
      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     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Union[str, List[str]]       ,						__SCREAMING_SNAKE_CASE     :							int = 100       ,						__SCREAMING_SNAKE_CASE     :							float = 5.0       ,						__SCREAMING_SNAKE_CASE     :							float = 1.0       ,						__SCREAMING_SNAKE_CASE     :							int = 1       ,						__SCREAMING_SNAKE_CASE     :							Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[torch.FloatTensor] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[str] = "pil"       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						__SCREAMING_SNAKE_CASE     :							Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,						__SCREAMING_SNAKE_CASE     :							int = 1       ,						)     ->		List[Any]:
      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_         =						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    UpperCamelCase       (       self     :							int       ,						__SCREAMING_SNAKE_CASE     :							torch.FloatTensor       ,						__SCREAMING_SNAKE_CASE     :							float					)     ->		Any:
      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









import random

from .binary_exp_mod import bin_exp_mod



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase=1000      )   ->					str:


				"""simple docstring"""

				if n < 2:
								return False

				if n % 2 == 0:
								return n == 2

				# this means n is odd
				snake_case__	:      Tuple     		=	n - 1
				snake_case__	:      Tuple     		=	0
				while d % 2 == 0:
								d /= 2
								exp += 1

				# n - 1=d*(2**exp)
				snake_case__	:      List[str]     		=	0
				while count < prec:
								snake_case__	:      List[str]     		=	random.randint(2  ,  n - 1      )
								snake_case__	:      Optional[Any]     		=	bin_exp_mod(__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )
								if b != 1:
												snake_case__	:      List[Any]     		=	True
												for _ in range(__lowerCAmelCase      ):
																if b == n - 1:
																				snake_case__	:      List[str]     		=	False
																				break
																snake_case__	:      str     		=	b * b
																b %= n
												if flag:
																return False
												count += 1
				return True


if __name__ == "__main__":
				A__								=						abs(int(input('''Enter bound : ''').strip()))
				print('''Here\'s the list of primes:''')
				print(''', '''.join(str(i) for i in range(n + 1) if is_prime_big(i)))

