
import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class   UpperCAmelCase		(						A_ ):
   A__					:		str						     =     ["image_processor", "tokenizer"]
   A__					:		Optional[int]						     =     "AutoImageProcessor"
   A__					:		Dict						     =     "AutoTokenizer"



   def __init__(self		:					Dict    ,						snake_case__		:					Optional[Any]=None    ,						snake_case__		:					Any=None    ,						**snake_case__		:					Dict )		->	str:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      None
          if "feature_extractor" in kwargs:
                 warnings.warn(
                     "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                     " instead."    ,						A_    ,						)
                 snake_case       :   Optional[Any]							=      kwargs.pop("feature_extractor" )

          snake_case       :   Optional[int]							=      image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
                 raise ValueError("You need to specify an `image_processor`." )
          if tokenizer is None:
                 raise ValueError("You need to specify a `tokenizer`." )

          super().__init__(A_    ,						A_ )
          snake_case       :   int							=      self.image_processor
          snake_case       :   Tuple							=      False



   def __call__(self		:					Tuple    ,						*snake_case__		:					List[str]    ,						**snake_case__		:					Optional[Any] )		->	Optional[int]:


          '''simple docstring'''


          if self._in_target_context_manager:
                 return self.current_processor(*A_    ,						**A_ )

          snake_case       :   Union[str, Any]							=      kwargs.pop("images"    ,						A_ )
          snake_case       :   Optional[Any]							=      kwargs.pop("text"    ,						A_ )
          if len(A_ ) > 0:
                 snake_case       :   int							=      args[0]
                 snake_case       :   Any							=      args[1:]

          if images is None and text is None:
                 raise ValueError("You need to specify either an `images` or `text` input to process." )

          if images is not None:
                 snake_case       :   int							=      self.image_processor(A_    ,						*A_    ,						**A_ )
          if text is not None:
                 snake_case       :   Tuple							=      self.tokenizer(A_    ,						**A_ )

          if text is None:
                 return inputs
          elif images is None:
                 return encodings
          else:
                 snake_case       :   Any							=      encodings["input_ids"]
                 return inputs



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						*snake_case__		:					Optional[Any]    ,						**snake_case__		:					Optional[int] )		->	Optional[int]:


          '''simple docstring'''


          return self.tokenizer.batch_decode(*A_    ,						**A_ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						*snake_case__		:					Any    ,						**snake_case__		:					Optional[int] )		->	List[str]:


          '''simple docstring'''


          return self.tokenizer.decode(*A_    ,						**A_ )



   @contextmanager
   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	Optional[int]:


          '''simple docstring'''


          warnings.warn(
              "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
              "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
              "your images inputs, or in a separate call." )
          snake_case       :   Optional[int]							=      True
          snake_case       :   List[Any]							=      self.tokenizer
          yield
          snake_case       :   int							=      self.image_processor
          snake_case       :   Optional[int]							=      False



   def 				_SCREAMING_SNAKE_CASE   (self		:					int    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					Any=False    ,						snake_case__		:					int=None )		->	Union[str, Any]:


          '''simple docstring'''


          if added_vocab is None:
                 snake_case       :   int							=      self.tokenizer.get_added_vocab()

          snake_case       :   Optional[int]							=      {}

          while tokens:
                 snake_case       :   str							=      re.search(R"<s_(.*?)>"    ,						A_    ,						re.IGNORECASE )
                 if start_token is None:
                        break
                 snake_case       :   int							=      start_token.group(1 )
                 snake_case       :   Tuple							=      re.search(Rf"""</s_{key}>"""    ,						A_    ,						re.IGNORECASE )
                 snake_case       :   Optional[int]							=      start_token.group()
                 if end_token is None:
                        snake_case       :   Optional[Any]							=      tokens.replace(A_    ,						"" )
                 else:
                        snake_case       :   Union[str, Any]							=      end_token.group()
                        snake_case       :   Optional[Any]							=      re.escape(A_ )
                        snake_case       :   str							=      re.escape(A_ )
                        snake_case       :   Tuple							=      re.search(f"""{start_token_escaped}(.*?){end_token_escaped}"""    ,						A_    ,						re.IGNORECASE )
                        if content is not None:
                               snake_case       :   List[str]							=      content.group(1 ).strip()
                               if r"<s_" in content and r"</s_" in content:  # non-leaf node
                                      snake_case       :   Optional[Any]							=      self.tokenajson(A_    ,						is_inner_value=A_    ,						added_vocab=A_ )
                                      if value:
                                             if len(A_ ) == 1:
                                                    snake_case       :   Union[str, Any]							=      value[0]
                                             snake_case       :   Dict							=      value
                               else:  # leaf nodes
                                      snake_case       :   Any							=      []
                                      for leaf in content.split(R"<sep/>" ):
                                             snake_case       :   Optional[Any]							=      leaf.strip()
                                             if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                                                    snake_case       :   Optional[int]							=      leaf[1:-2]  # for categorical special tokens
                                             output[key].append(A_ )
                                      if len(output[key] ) == 1:
                                             snake_case       :   Tuple							=      output[key][0]

                        snake_case       :   Tuple							=      tokens[tokens.find(A_ ) + len(A_ ) :].strip()
                        if tokens[:6] == r"<sep/>":  # non-leaf nodes
                               return [output] + self.tokenajson(tokens[6:]    ,						is_inner_value=A_    ,						added_vocab=A_ )

          if len(A_ ):
                 return [output] if is_inner_value else output
          else:
                 return [] if is_inner_value else {"text_sequence": tokens}



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Dict:


          '''simple docstring'''


          warnings.warn(
              "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."    ,						A_    ,						)
          return self.image_processor_class



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Tuple:


          '''simple docstring'''


          warnings.warn(
              "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."    ,						A_    ,						)
          return self.image_processor











from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 def __init__(				self					:  str						,							A_					:  TransformeraDModel						,							A_					:  AutoencoderKL						,							A_					:  KarrasDiffusionSchedulers						,							A_					:  Optional[Dict[int, str]] = None						,							)   ->	str:

       """simple docstring"""






       super().__init__()
       self.register_modules(transformer=A_						,							vae=A_						,							scheduler=A_  )

       # create a imagenet -> id dictionary for easier use
       lowerCamelCase_		     =    {}
       if idalabel is not None:
             for key, value in idalabel.items():
                   for label in value.split(','  ):
                         lowerCamelCase_		     =    int(A_  )
             lowerCamelCase_		     =    dict(sorted(self.labels.items()  )  )

 def 				a__ (				self					:  Optional[int]						,							A_					:  Union[str, List[str]]  )   ->	List[int]:

       """simple docstring"""






       if not isinstance(A_						,							A_  ):
             lowerCamelCase_		     =    list(A_  )

       for l in label:
             if l not in self.labels:
                   raise ValueError(
                       f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""  )

       return [self.labels[l] for l in label]





 @torch.no_grad()
 def __call__(				self					:  Any						,							A_					:  List[int]						,							A_					:  float = 4.0						,							A_					:  Optional[Union[torch.Generator, List[torch.Generator]]] = None						,							A_					:  int = 50						,							A_					:  Optional[str] = "pil"						,							A_					:  bool = True						,							)   ->	Union[ImagePipelineOutput, Tuple]:

       """simple docstring"""






       lowerCamelCase_		     =    len(A_  )
       lowerCamelCase_		     =    self.transformer.config.sample_size
       lowerCamelCase_		     =    self.transformer.config.in_channels

       lowerCamelCase_		     =    randn_tensor(
           shape=(batch_size, latent_channels, latent_size, latent_size)						,							generator=A_						,							device=self.device						,							dtype=self.transformer.dtype						,							)
       lowerCamelCase_		     =    torch.cat([latents] * 2  ) if guidance_scale > 1 else latents

       lowerCamelCase_		     =    torch.tensor(A_						,							device=self.device  ).reshape(-1  )
       lowerCamelCase_		     =    torch.tensor([1000] * batch_size						,							device=self.device  )
       lowerCamelCase_		     =    torch.cat([class_labels, class_null]						,							0  ) if guidance_scale > 1 else class_labels

       # set step values
       self.scheduler.set_timesteps(A_  )

       for t in self.progress_bar(self.scheduler.timesteps  ):
             if guidance_scale > 1:
                   lowerCamelCase_		     =    latent_model_input[: len(A_  ) // 2]
                   lowerCamelCase_		     =    torch.cat([half, half]						,							dim=0  )
             lowerCamelCase_		     =    self.scheduler.scale_model_input(A_						,							A_  )

             lowerCamelCase_		     =    t
             if not torch.is_tensor(A_  ):
                   # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
                   # This would be a good case for the `match` statement (Python 3.10+)
                   lowerCamelCase_		     =    latent_model_input.device.type == 'mps'
                   if isinstance(A_						,							A_  ):
                         lowerCamelCase_		     =    torch.floataa if is_mps else torch.floataa
                   else:
                         lowerCamelCase_		     =    torch.intaa if is_mps else torch.intaa
                   lowerCamelCase_		     =    torch.tensor([timesteps]						,							dtype=A_						,							device=latent_model_input.device  )
             elif len(timesteps.shape  ) == 0:
                   lowerCamelCase_		     =    timesteps[None].to(latent_model_input.device  )
             # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
             lowerCamelCase_		     =    timesteps.expand(latent_model_input.shape[0]  )
             # predict noise model_output
             lowerCamelCase_		     =    self.transformer(
                 A_						,							timestep=A_						,							class_labels=A_  ).sample

             # perform guidance
             if guidance_scale > 1:
                   lowerCamelCase_     ,				lowerCamelCase_		     =    noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                   lowerCamelCase_     ,				lowerCamelCase_		     =    torch.split(A_						,							len(A_  ) // 2						,							dim=0  )

                   lowerCamelCase_		     =    uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                   lowerCamelCase_		     =    torch.cat([half_eps, half_eps]						,							dim=0  )

                   lowerCamelCase_		     =    torch.cat([eps, rest]						,							dim=1  )

             # learned sigma
             if self.transformer.config.out_channels // 2 == latent_channels:
                   lowerCamelCase_     ,				lowerCamelCase_		     =    torch.split(A_						,							A_						,							dim=1  )
             else:
                   lowerCamelCase_		     =    noise_pred

             # compute previous image: x_t -> x_t-1
             lowerCamelCase_		     =    self.scheduler.step(A_						,							A_						,							A_  ).prev_sample

       if guidance_scale > 1:
             lowerCamelCase_     ,				lowerCamelCase_		     =    latent_model_input.chunk(2						,							dim=0  )
       else:
             lowerCamelCase_		     =    latent_model_input

       lowerCamelCase_		     =    1 / self.vae.config.scaling_factor * latents
       lowerCamelCase_		     =    self.vae.decode(A_  ).sample

       lowerCamelCase_		     =    (samples / 2 + 0.5).clamp(0						,							1  )

       # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
       lowerCamelCase_		     =    samples.cpu().permute(0						,							2						,							3						,							1  ).float().numpy()

       if output_type == "pil":
             lowerCamelCase_		     =    self.numpy_to_pil(A_  )

       if not return_dict:
             return (samples,)

       return ImagePipelineOutput(images=A_  )



