




"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch

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


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






class 					lowercase(     __a ):







 '''simple docstring'''





 def __init__(				self:		List[Any],      a_:		List[Any],      a_:		Tuple		):




   '''simple docstring'''



   super().__init__()
   self.register_modules(unet=a_,      scheduler=a_		)


 @torch.no_grad()
 def __call__(				self:		Optional[Any],      a_:		int = 1,      a_:		int = 100,      a_:		Optional[Union[torch.Generator, List[torch.Generator]]] = None,      a_:		Optional[float] = None,      a_:		bool = True,      ):




   '''simple docstring'''



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

   _snake_case							:		Any								=  audio_length_in_s * self.unet.config.sample_rate

   _snake_case							:		int								=  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}."		)

   _snake_case							:		str								=  int(a_		)
   if sample_size % down_scale_factor != 0:
     _snake_case							:		Optional[Any]								=  (
         (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."""		)
   _snake_case							:		List[Any]								=  int(a_		)

   _snake_case							:		Dict								=  next(iter(self.unet.parameters()		)		).dtype
   _snake_case							:		Any								=  (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."		)

   _snake_case							:		Union[str, Any]								=  randn_tensor(a_,      generator=a_,      device=self.device,      dtype=a_		)

   # set step values
   self.scheduler.set_timesteps(a_,      device=audio.device		)
   _snake_case							:		Union[str, Any]								=  self.scheduler.timesteps.to(a_		)

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

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

   _snake_case							:		Any								=  audio.clamp(-1,      1		).float().cpu().numpy()

   _snake_case							:		str								=  audio[:, :, :original_sample_size]

   if not return_dict:
     return (audio,)

   return AudioPipelineOutput(audios=a_		)








"""simple docstring"""


import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					["image_processor", "tokenizer"]
 lowercase__ 					=					"AutoImageProcessor"
 lowercase__ 					=					"AutoTokenizer"


 def __init__(				self:		List[str],      a_:		List[str]=None,      a_:		Tuple=None,      **a_:		Tuple		):




   '''simple docstring'''



   _snake_case							:		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.""",      a_,      )
     _snake_case							:		str								=  kwargs.pop("""feature_extractor"""		)

   _snake_case							:		Union[str, Any]								=  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							:		Dict								=  self.image_processor
   _snake_case							:		Any								=  False


 def __call__(				self:		Any,      *a_:		Any,      **a_:		Tuple		):




   '''simple docstring'''



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

   _snake_case							:		Dict								=  kwargs.pop("""images""",      a_		)
   _snake_case							:		Optional[Any]								=  kwargs.pop("""text""",      a_		)
   if len(a_		) > 0:
     _snake_case							:		Optional[int]								=  args[0]
     _snake_case							:		Tuple								=  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							:		Tuple								=  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							:		List[str]								=  encodings["""input_ids"""]
     return inputs


 def  UpperCamelCase_   (				self:		Optional[int],      *a_:		Tuple,      **a_:		List[str]		):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		int,      *a_:		List[str],      **a_:		int		):




   '''simple docstring'''



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


 @contextmanager
 def  UpperCamelCase_   (				self:		Dict		):




   '''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							:		Any								=  True
   _snake_case							:		Optional[int]								=  self.tokenizer
   yield
   _snake_case							:		int								=  self.image_processor
   _snake_case							:		Optional[int]								=  False


 def  UpperCamelCase_   (				self:		Dict,      a_:		Optional[Any],      a_:		str=False,      a_:		Optional[Any]=None		):




   '''simple docstring'''



   if added_vocab is None:
     _snake_case							:		Dict								=  self.tokenizer.get_added_vocab()

   _snake_case							:		str								=  {}

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

       _snake_case							:		Any								=  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  UpperCamelCase_   (				self:		Optional[int]		):




   '''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  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



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


