


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


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


class     lowercase__  (					__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE		):






  @register_to_config
  def __init__(					self      :					int		,						_lowercase      :					bool		,						_lowercase      :					Optional[int] = None		,						_lowercase      :					Optional[int] = None ):




         """simple docstring"""







         super().__init__()

         UpperCAmelCase__										=     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__										=     torch.zeros(_lowercase		,						_lowercase )
         else:
                UpperCAmelCase__										=     None

         UpperCAmelCase__										=     torch.nn.Parameter(_lowercase )




class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  42
  A__=  42
  A__=  42
  A__=  42
  A__=  42
  A__=  42






  def __init__(					self      :					Tuple		,						_lowercase      :					VQModel		,						_lowercase      :					CLIPTextModel		,						_lowercase      :					CLIPTokenizer		,						_lowercase      :					TransformeraDModel		,						_lowercase      :					VQDiffusionScheduler		,						_lowercase      :					LearnedClassifierFreeSamplingEmbeddings		,						):




         """simple docstring"""







         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      :					List[Any]		,						_lowercase      :					Tuple		,						_lowercase      :					Optional[int]		,						_lowercase      :					int ):




         """simple docstring"""







         UpperCAmelCase__										=     len(_lowercase ) if isinstance(_lowercase		,						_lowercase ) else 1

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

         if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                UpperCAmelCase__										=     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__										=     text_input_ids[:, : self.tokenizer.model_max_length]
         UpperCAmelCase__										=     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__										=     prompt_embeds / prompt_embeds.norm(dim=-1		,						keepdim=_lowercase )

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

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

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

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

         return prompt_embeds






  @torch.no_grad()
  def __call__(					self      :					Tuple		,						_lowercase      :					Union[str, List[str]]		,						_lowercase      :					int = 1_00		,						_lowercase      :					float = 5.0		,						_lowercase      :					float = 1.0		,						_lowercase      :					int = 1		,						_lowercase      :					Optional[Union[torch.Generator, List[torch.Generator]]] = None		,						_lowercase      :					Optional[torch.FloatTensor] = None		,						_lowercase      :					Optional[str] = "pil"		,						_lowercase      :					bool = True		,						_lowercase      :					Optional[Callable[[int, int, torch.FloatTensor], None]] = None		,						_lowercase      :					int = 1		,						):




         """simple docstring"""







         if isinstance(_lowercase		,						_lowercase ):
                UpperCAmelCase__										=     1
         elif isinstance(_lowercase		,						_lowercase ):
                UpperCAmelCase__										=     len(_lowercase )
         else:
                raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(_lowercase )}""" )

         UpperCAmelCase__										=     batch_size * num_images_per_prompt

         UpperCAmelCase__										=     guidance_scale > 1.0

         UpperCAmelCase__										=     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

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

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

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

         UpperCAmelCase__										=     latents

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

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

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

                UpperCAmelCase__										=     self.truncate(_lowercase		,						_lowercase )

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

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

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

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

         if output_type == "pil":
                UpperCAmelCase__										=     self.numpy_to_pil(_lowercase )

         if not return_dict:
                return (image,)

         return ImagePipelineOutput(images=_lowercase )






  def 			_UpperCAmelCase  (					self      :					List[str]		,						_lowercase      :					torch.FloatTensor		,						_lowercase      :					float ):




         """simple docstring"""







         UpperCAmelCase__						,			UpperCAmelCase__										=     torch.sort(_lowercase		,						1		,						descending=_lowercase )
         UpperCAmelCase__										=     torch.exp(_lowercase )
         UpperCAmelCase__										=     sorted_p_x_0.cumsum(dim=1 ) < truncation_rate

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

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

         UpperCAmelCase__										=     log_p_x_0.clone()

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

         return rv









from __future__ import annotations

import math





def __UpperCAmelCase       (		__A ,						__A			)						->					float:



       '''simple docstring'''





       UpperCAmelCase__										=     u
       for i in range(1 ,						__A			):
              UpperCAmelCase__										=     temp * (u - i)
       return temp





def __UpperCAmelCase       (		)						->					None:



       '''simple docstring'''





       UpperCAmelCase__										=     int(input("enter the numbers of values: "			)			)
       UpperCAmelCase__										=     []
       for _ in range(__A			):
              y.append([]			)
       for i in range(__A			):
              for j in range(__A			):
                     y[i].append(__A			)
                     UpperCAmelCase__										=     0

       print("enter the values of parameters in a list: "			)
       UpperCAmelCase__										=     list(map(__A ,						input().split()			)			)

       print("enter the values of corresponding parameters: "			)
       for i in range(__A			):
              UpperCAmelCase__										=     float(input()			)

       UpperCAmelCase__										=     int(input("enter the value to interpolate: "			)			)
       UpperCAmelCase__										=     (value - x[0]) / (x[1] - x[0])

       # for calculating forward difference table

       for i in range(1 ,						__A			):
              for j in range(n - i			):
                     UpperCAmelCase__										=     y[j + 1][i - 1] - y[j][i - 1]

       UpperCAmelCase__										=     y[0][0]
       for i in range(1 ,						__A			):
              summ += (ucal(__A ,						__A			) * y[0][i]) / math.factorial(__A			)

       print(F"""the value at {value} is {summ}"""			)


if __name__ == "__main__":
    main()





