





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''
 lowercase_						=     ["image_processor", "tokenizer"]
 lowercase_						=     "ChineseCLIPImageProcessor"
 lowercase_						=     ("BertTokenizer", "BertTokenizerFast")

 def __init__(self							:  Any			,					UpperCAmelCase_							:  List[str]=None			,					UpperCAmelCase_							:  Union[str, Any]=None			,					**UpperCAmelCase_							:  str)			->Dict:



   '''simple docstring'''



   lowerCamelCase__: str         =None
   if "feature_extractor" in kwargs:
     warnings.warn(
         "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
         " instead."			,					UpperCAmelCase_			,					)
     lowerCamelCase__: Tuple         =kwargs.pop("feature_extractor")

   lowerCamelCase__: 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__(UpperCAmelCase_			,					UpperCAmelCase_)
   lowerCamelCase__: Optional[int]         =self.image_processor

 def __call__(self							:  int			,					UpperCAmelCase_							:  Optional[Any]=None			,					UpperCAmelCase_							:  List[Any]=None			,					UpperCAmelCase_							:  List[Any]=None			,					**UpperCAmelCase_							:  Dict)			->Optional[int]:



   '''simple docstring'''



   if text is None and images is None:
     raise ValueError("You have to specify either text or images. Both cannot be none.")

   if text is not None:
     lowerCamelCase__: Dict         =self.tokenizer(UpperCAmelCase_			,					return_tensors=UpperCAmelCase_			,					**UpperCAmelCase_)

   if images is not None:
     lowerCamelCase__: List[str]         =self.image_processor(UpperCAmelCase_			,					return_tensors=UpperCAmelCase_			,					**UpperCAmelCase_)

   if text is not None and images is not None:
     lowerCamelCase__: Union[str, Any]         =image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   else:
     return BatchEncoding(data=dict(**UpperCAmelCase_)			,					tensor_type=UpperCAmelCase_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any]			,					*UpperCAmelCase_							:  Dict			,					**UpperCAmelCase_							:  int)			->str:



   '''simple docstring'''



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

 def 				SCREAMING_SNAKE_CASE_   (self							:  Any			,					*UpperCAmelCase_							:  Optional[Any]			,					**UpperCAmelCase_							:  List[Any])			->Dict:



   '''simple docstring'''



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

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  int)			->List[str]:



   '''simple docstring'''



   lowerCamelCase__: str         =self.tokenizer.model_input_names
   lowerCamelCase__: Union[str, Any]         =self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))







 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int])			->str:



   '''simple docstring'''



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


'''simple docstring'''


from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


__lowerCAmelCase						:							List[Any]    							=						logging.get_logger(__name__)  # pylint: disable=invalid-name






class     A ( UpperCAmelCase ):





							def __init__(   self					:				Dict  ,		__a					:				List[Any]  ,		__a					:				Optional[Any]						)				->						Dict:
								super().__init__()
								self.register_modules(unet=__a  ,		scheduler=__a						)






							@torch.no_grad()
							def __call__(   self					:				Tuple  ,		__a					:				int = 1  ,		__a					:				int = 1_0_0  ,		__a					:				Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,		__a					:				Optional[float] = None  ,		__a					:				bool = True  ,		)				->						Union[AudioPipelineOutput, Tuple]:

								if audio_length_in_s is None:
									__UpperCAmelCase      					=      self.unet.config.sample_size / self.unet.config.sample_rate

								__UpperCAmelCase      					=      audio_length_in_s * self.unet.config.sample_rate

								__UpperCAmelCase      					=      2 ** len(self.unet.up_blocks						)
								if sample_size < 3 * down_scale_factor:
									raise ValueError(
									    f"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
									    f""" {3 * down_scale_factor / self.unet.config.sample_rate}."""						)

								__UpperCAmelCase      					=      int(__a						)
								if sample_size % down_scale_factor != 0:
									__UpperCAmelCase      					=      (
									    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
									) * down_scale_factor
									logger.info(
									    f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
									    f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
									    ''' process.'''						)
								__UpperCAmelCase      					=      int(__a						)

								__UpperCAmelCase      					=      next(iter(self.unet.parameters()						)						).dtype
								__UpperCAmelCase      					=      (batch_size, self.unet.config.in_channels, sample_size)
								if isinstance(__a  ,		__a						) and len(__a						) != batch_size:
									raise ValueError(
									    f"""You have passed a list of generators of length {len(__a						)}, but requested an effective batch"""
									    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""						)

								__UpperCAmelCase      					=      randn_tensor(__a  ,		generator=__a  ,		device=self.device  ,		dtype=__a						)

								# set step values
								self.scheduler.set_timesteps(__a  ,		device=audio.device						)
								__UpperCAmelCase      					=      self.scheduler.timesteps.to(__a						)

								for t in self.progress_bar(self.scheduler.timesteps						):
									# 1. predict noise model_output
									__UpperCAmelCase      					=      self.unet(__a  ,		__a						).sample

									# 2. compute previous image: x_t -> t_t-1
									__UpperCAmelCase      					=      self.scheduler.step(__a  ,		__a  ,		__a						).prev_sample

								__UpperCAmelCase      					=      audio.clamp(-1  ,		1						).float().cpu().numpy()

								__UpperCAmelCase      					=      audio[:, :, :original_sample_size]

								if not return_dict:
									return (audio,)

								return AudioPipelineOutput(audios=__a						)
