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


class 							a					(		lowerCAmelCase_						,				lowerCAmelCase_ ):


    @register_to_config
    def __init__(       self						:	Dict ,					__lowerCAmelCase						:	bool ,					__lowerCAmelCase						:	Optional[int] = None ,					__lowerCAmelCase						:	Optional[int] = None       ):
      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(__lowerCAmelCase ,					__lowerCAmelCase       )
      else:
        _UpperCAmelCase    			=   None

      _UpperCAmelCase    			=   torch.nn.Parameter(__lowerCAmelCase       )



class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	VQModel
    _snake_case  :	CLIPTextModel
    _snake_case  :	CLIPTokenizer
    _snake_case  :	TransformeraDModel
    _snake_case  :	LearnedClassifierFreeSamplingEmbeddings
    _snake_case  :	VQDiffusionScheduler


    def __init__(       self						:	Dict ,					__lowerCAmelCase						:	VQModel ,					__lowerCAmelCase						:	CLIPTextModel ,					__lowerCAmelCase						:	CLIPTokenizer ,					__lowerCAmelCase						:	TransformeraDModel ,					__lowerCAmelCase						:	VQDiffusionScheduler ,					__lowerCAmelCase						:	LearnedClassifierFreeSamplingEmbeddings ,					):
      super().__init__()

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


    def   lowerCAmelCase_	(       self						:	Optional[int] ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	List[str]       ):
      _UpperCAmelCase    			=   len(__lowerCAmelCase       ) if isinstance(__lowerCAmelCase ,					__lowerCAmelCase       ) else 1

      # get prompt text embeddings
      _UpperCAmelCase    			=   self.tokenizer(
          __lowerCAmelCase ,					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=__lowerCAmelCase       )

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

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

        # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
        _UpperCAmelCase    			=   negative_prompt_embeds.shape[1]
        _UpperCAmelCase    			=   negative_prompt_embeds.repeat(1 ,					__lowerCAmelCase ,					1       )
        _UpperCAmelCase    			=   negative_prompt_embeds.view(batch_size * num_images_per_prompt ,					__lowerCAmelCase ,					-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						:	int ,					__lowerCAmelCase						:	Union[str, List[str]] ,					__lowerCAmelCase						:	int = 100 ,					__lowerCAmelCase						:	float = 5.0 ,					__lowerCAmelCase						:	float = 1.0 ,					__lowerCAmelCase						:	int = 1 ,					__lowerCAmelCase						:	Optional[Union[torch.Generator, List[torch.Generator]]] = None ,					__lowerCAmelCase						:	Optional[torch.FloatTensor] = None ,					__lowerCAmelCase						:	Optional[str] = "pil" ,					__lowerCAmelCase						:	bool = True ,					__lowerCAmelCase						:	Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,					__lowerCAmelCase						:	int = 1 ,					):
      if isinstance(__lowerCAmelCase ,					__lowerCAmelCase       ):
        _UpperCAmelCase    			=   1
      elif isinstance(__lowerCAmelCase ,					__lowerCAmelCase       ):
        _UpperCAmelCase    			=   len(__lowerCAmelCase       )
      else:
        raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(__lowerCAmelCase       )}'''       )

      _UpperCAmelCase    			=   batch_size * num_images_per_prompt

      _UpperCAmelCase    			=   guidance_scale > 1.0

      _UpperCAmelCase    			=   self._encode_prompt(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase       )

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

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

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

      _UpperCAmelCase    			=   latents

      for i, t in enumerate(self.progress_bar(__lowerCAmelCase       )       ):
        # 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(__lowerCAmelCase ,					encoder_hidden_states=__lowerCAmelCase ,					timestep=__lowerCAmelCase       ).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(__lowerCAmelCase ,					dim=1 ,					keepdim=__lowerCAmelCase       )

        _UpperCAmelCase    			=   self.truncate(__lowerCAmelCase ,					__lowerCAmelCase       )

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

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

      _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(__lowerCAmelCase ,					shape=__lowerCAmelCase       )
      _UpperCAmelCase    			=   self.vqvae.decode(__lowerCAmelCase ,					force_not_quantize=__lowerCAmelCase       ).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(__lowerCAmelCase       )

      if not return_dict:
        return (image,)

      return ImagePipelineOutput(images=__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	str ,					__lowerCAmelCase						:	torch.FloatTensor ,					__lowerCAmelCase						:	float       ):
      _UpperCAmelCase			,					_UpperCAmelCase    			=   torch.sort(__lowerCAmelCase ,					1 ,					descending=__lowerCAmelCase       )
      _UpperCAmelCase    			=   torch.exp(__lowerCAmelCase       )
      _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, :] ,					__lowerCAmelCase       )
      _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

"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__			=    logging.get_logger(__name__)


class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	List[Any]  				=				'timm_backbone'


    def __init__(       self						:	List[Any] ,					__lowerCAmelCase						:	str=None ,					__lowerCAmelCase						:	Optional[int]=3 ,					__lowerCAmelCase						:	Any=True ,					__lowerCAmelCase						:	Optional[int]=True ,					__lowerCAmelCase						:	Optional[Any]=None ,					**__lowerCAmelCase						:	Optional[Any] ,					):
      super().__init__(**__lowerCAmelCase       )
      _UpperCAmelCase    			=   backbone
      _UpperCAmelCase    			=   num_channels
      _UpperCAmelCase    			=   features_only
      _UpperCAmelCase    			=   use_pretrained_backbone
      _UpperCAmelCase    			=   True
      _UpperCAmelCase    			=   out_indices if out_indices is not None else (-1,)
