


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   _lowerCamelCase (					UpperCAmelCase__					,							UpperCAmelCase__			):




       """simple docstring"""



       @register_to_config
       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None		)->Optional[int]:




         '''simple docstring'''





         super().__init__()

         A_      :				Optional[int]		     =					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_      :				Tuple		     =					torch.zeros(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__		)
         else:
           A_      :				Tuple		     =					None

         A_      :				Optional[Any]		     =					torch.nn.Parameter(SCREAMING_SNAKE_CASE__		)






class   _lowerCamelCase (					UpperCAmelCase__			):




       """simple docstring"""
       snake_case      =    42
       snake_case      =    42
       snake_case      =    42
       snake_case      =    42
       snake_case      =    42
       snake_case      =    42



       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       )->str:




         '''simple docstring'''





         super().__init__()

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



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->Tuple:




         '''simple docstring'''





         A_      :				Optional[int]		     =					len(SCREAMING_SNAKE_CASE__		) if isinstance(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__		) else 1

         # get prompt text embeddings
         A_      :				List[str]		     =					self.tokenizer(
             SCREAMING_SNAKE_CASE__				,       padding='''max_length'''				,       max_length=self.tokenizer.model_max_length				,       return_tensors='''pt'''				,       )
         A_      :				str		     =					text_inputs.input_ids

         if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
           A_      :				int		     =					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_      :				Dict		     =					text_input_ids[:, : self.tokenizer.model_max_length]
         A_      :				Optional[int]		     =					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_      :				Any		     =					prompt_embeds / prompt_embeds.norm(dim=-1				,       keepdim=SCREAMING_SNAKE_CASE__		)

         # duplicate text embeddings for each generation per prompt
         A_      :				Union[str, Any]		     =					prompt_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__				,       dim=0		)

         if do_classifier_free_guidance:
           if self.learned_classifier_free_sampling_embeddings.learnable:
             A_      :				Tuple		     =					self.learned_classifier_free_sampling_embeddings.embeddings
             A_      :				Any		     =					negative_prompt_embeds.unsqueeze(0		).repeat(SCREAMING_SNAKE_CASE__				,       1				,       1		)
           else:
             A_      :				str		     =					[''''''] * batch_size

             A_      :				Dict		     =					text_input_ids.shape[-1]
             A_      :				List[str]		     =					self.tokenizer(
                 SCREAMING_SNAKE_CASE__				,       padding='''max_length'''				,       max_length=SCREAMING_SNAKE_CASE__				,       truncation=SCREAMING_SNAKE_CASE__				,       return_tensors='''pt'''				,       )
             A_      :				Optional[Any]		     =					self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]
             # See comment for normalizing text embeddings
             A_      :				Any		     =					negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1				,       keepdim=SCREAMING_SNAKE_CASE__		)

           # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
           A_      :				Any		     =					negative_prompt_embeds.shape[1]
           A_      :				List[Any]		     =					negative_prompt_embeds.repeat(1				,       SCREAMING_SNAKE_CASE__				,       1		)
           A_      :				Tuple		     =					negative_prompt_embeds.view(batch_size * num_images_per_prompt				,       SCREAMING_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_      :				str		     =					torch.cat([negative_prompt_embeds, prompt_embeds]		)

         return prompt_embeds



       @torch.no_grad()
       def __call__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = 100				,       _SCREAMING_SNAKE_CASE = 5.0				,       _SCREAMING_SNAKE_CASE = 1.0				,       _SCREAMING_SNAKE_CASE = 1				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = "pil"				,       _SCREAMING_SNAKE_CASE = True				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = 1				,       )->Union[ImagePipelineOutput, Tuple]:




         '''simple docstring'''





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

         A_      :				Optional[int]		     =					batch_size * num_images_per_prompt

         A_      :				Dict		     =					guidance_scale > 1.0

         A_      :				List[str]		     =					self._encode_prompt(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__		)

         if (callback_steps is None) or (
             callback_steps is not None and (not isinstance(SCREAMING_SNAKE_CASE__				,       SCREAMING_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(SCREAMING_SNAKE_CASE__		)}.'''		)

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

         A_      :				Tuple		     =					(batch_size, self.transformer.num_latent_pixels)
         if latents is None:
           A_      :				List[Any]		     =					self.transformer.num_vector_embeds - 1
           A_      :				Tuple		     =					torch.full(SCREAMING_SNAKE_CASE__				,       SCREAMING_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_      :				Dict		     =					latents.to(self.device		)

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

         A_      :				Dict		     =					self.scheduler.timesteps.to(self.device		)

         A_      :				Tuple		     =					latents

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

           # predict the un-noised image
           # model_output == `log_p_x_0`
           A_      :				Dict		     =					self.transformer(SCREAMING_SNAKE_CASE__				,       encoder_hidden_states=SCREAMING_SNAKE_CASE__				,       timestep=SCREAMING_SNAKE_CASE__		).sample

           if do_classifier_free_guidance:
             A_     ,       A_      :				str		     =					model_output.chunk(2		)
             A_      :				Union[str, Any]		     =					model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
             model_output -= torch.logsumexp(SCREAMING_SNAKE_CASE__				,       dim=1				,       keepdim=SCREAMING_SNAKE_CASE__		)

           A_      :				Tuple		     =					self.truncate(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__		)

           # remove `log(0)`'s (`-inf`s)
           A_      :				List[Any]		     =					model_output.clamp(-70		)

           # compute the previous noisy sample x_t -> x_t-1
           A_      :				Union[str, Any]		     =					self.scheduler.step(SCREAMING_SNAKE_CASE__				,       timestep=SCREAMING_SNAKE_CASE__				,       sample=SCREAMING_SNAKE_CASE__				,       generator=SCREAMING_SNAKE_CASE__		).prev_sample

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

         A_      :				str		     =					self.vqvae.config.vq_embed_dim
         A_      :				Any		     =					(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
         A_      :				List[Any]		     =					self.vqvae.quantize.get_codebook_entry(SCREAMING_SNAKE_CASE__				,       shape=SCREAMING_SNAKE_CASE__		)
         A_      :				Dict		     =					self.vqvae.decode(SCREAMING_SNAKE_CASE__				,       force_not_quantize=SCREAMING_SNAKE_CASE__		).sample

         A_      :				List[str]		     =					(image / 2 + 0.5).clamp(0				,       1		)
         A_      :				Optional[int]		     =					image.cpu().permute(0				,       2				,       3				,       1		).numpy()

         if output_type == "pil":
           A_      :				List[str]		     =					self.numpy_to_pil(SCREAMING_SNAKE_CASE__		)

         if not return_dict:
           return (image,)

         return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__		)







       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->torch.FloatTensor:




         '''simple docstring'''





         A_     ,       A_      :				str		     =					torch.sort(SCREAMING_SNAKE_CASE__				,       1				,       descending=SCREAMING_SNAKE_CASE__		)
         A_      :				Union[str, Any]		     =					torch.exp(SCREAMING_SNAKE_CASE__		)
         A_      :				Optional[int]		     =					sorted_p_x_0.cumsum(dim=1		) < truncation_rate

         # Ensure that at least the largest probability is not zeroed out
         A_      :				int		     =					torch.full_like(keep_mask[:, 0:1, :]				,       SCREAMING_SNAKE_CASE__		)
         A_      :				int		     =					torch.cat((all_true, keep_mask)				,       dim=1		)
         A_      :				Union[str, Any]		     =					keep_mask[:, :-1, :]

         A_      :				Optional[int]		     =					keep_mask.gather(1				,       indices.argsort(1		)		)

         A_      :				Dict		     =					log_p_x_0.clone()

         A_      :				List[Any]		     =					-torch.inf  # -inf = log(0)

         return rv





'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


_A			:      Optional[int]					      =				logging.get_logger(__name__)







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 def __init__(      self   :	Dict      ,       *SCREAMING_SNAKE_CASE__   :	Optional[int]      ,       **SCREAMING_SNAKE_CASE__   :	List[str]							)  ->    None:
  warnings.warn(
      """The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
      """ Please use SegformerImageProcessor instead."""      ,       SCREAMING_SNAKE_CASE__      ,       )
  super().__init__(*SCREAMING_SNAKE_CASE__      ,       **SCREAMING_SNAKE_CASE__							)






