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



class 				a							(			_snake_case       ,			_snake_case       ):







 """simple docstring"""






 @register_to_config
 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_ = None		,				lowerCAmelCase_ = None							)  ->					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(__snake_case		,				__snake_case							)
        else:
               _A        =  None

        _A        =  torch.nn.Parameter(__snake_case							)



class 				a							(			_snake_case       ):







 """simple docstring"""






 lowerCamelCase	:VQModel
 lowerCamelCase	:CLIPTextModel
 lowerCamelCase	:CLIPTokenizer
 lowerCamelCase	:TransformeraDModel
 lowerCamelCase	:LearnedClassifierFreeSamplingEmbeddings
 lowerCamelCase	:VQDiffusionScheduler



 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				)  ->					List[str]:
        super().__init__()

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



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					str:
        _A        =  len(__snake_case							) if isinstance(__snake_case		,				__snake_case							) else 1

        # get prompt text embeddings
        _A        =  self.tokenizer(
            __snake_case		,				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=__snake_case							)

        # duplicate text embeddings for each generation per prompt
        _A        =  prompt_embeds.repeat_interleave(__snake_case		,				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(__snake_case		,				1		,				1							)
               else:
                      _A        =  [""""""] * batch_size

                      _A        =  text_input_ids.shape[-1]
                      _A        =  self.tokenizer(
                          __snake_case		,				padding="""max_length"""		,				max_length=__snake_case		,				truncation=__snake_case		,				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=__snake_case							)

               # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
               _A        =  negative_prompt_embeds.shape[1]
               _A        =  negative_prompt_embeds.repeat(1		,				__snake_case		,				1							)
               _A        =  negative_prompt_embeds.view(batch_size * num_images_per_prompt		,				__snake_case		,				-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		,				lowerCAmelCase_		,				lowerCAmelCase_ = 1_00		,				lowerCAmelCase_ = 5.0		,				lowerCAmelCase_ = 1.0		,				lowerCAmelCase_ = 1		,				lowerCAmelCase_ = None		,				lowerCAmelCase_ = None		,				lowerCAmelCase_ = "pil"		,				lowerCAmelCase_ = True		,				lowerCAmelCase_ = None		,				lowerCAmelCase_ = 1		,				)  ->					Optional[Any]:
        if isinstance(__snake_case		,				__snake_case							):
               _A        =  1
        elif isinstance(__snake_case		,				__snake_case							):
               _A        =  len(__snake_case							)
        else:
               raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(__snake_case							)}'''							)

        _A        =  batch_size * num_images_per_prompt

        _A        =  guidance_scale > 1.0

        _A        =  self._encode_prompt(__snake_case		,				__snake_case		,				__snake_case							)

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

        # 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(__snake_case		,				__snake_case							).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(__snake_case		,				device=self.device							)

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

        _A        =  latents

        for i, t in enumerate(self.progress_bar(__snake_case							)							):
               # 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(__snake_case		,				encoder_hidden_states=__snake_case		,				timestep=__snake_case							).sample

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

               _A        =  self.truncate(__snake_case		,				__snake_case							)

               # 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(__snake_case		,				timestep=__snake_case		,				sample=__snake_case		,				generator=__snake_case							).prev_sample

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

        _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(__snake_case		,				shape=__snake_case							)
        _A        =  self.vqvae.decode(__snake_case		,				force_not_quantize=__snake_case							).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(__snake_case							)

        if not return_dict:
               return (image,)

        return ImagePipelineOutput(images=__snake_case							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					List[Any]:
        _A        =  torch.sort(__snake_case		,				1		,				descending=__snake_case							)
        _A        =  torch.exp(__snake_case							)
        _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, :]		,				__snake_case							)
        _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








'''simple docstring'''


def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       int     		=      0
				while len(SCREAMING_SNAKE_CASE__     ) > 1:
								_SCREAMING_SNAKE_CASE :       Any     		=      0
								# Consider two files with minimum cost to be merged
								for _ in range(2     ):
												_SCREAMING_SNAKE_CASE :       Optional[int]     		=      files.index(min(SCREAMING_SNAKE_CASE__     )     )
												temp += files[min_index]
												files.pop(SCREAMING_SNAKE_CASE__     )
								files.append(SCREAMING_SNAKE_CASE__     )
								optimal_merge_cost += temp
				return optimal_merge_cost


if __name__ == "__main__":
				import doctest

				doctest.testmod()




