
"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

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


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




class 		a_						( _UpperCAmelCase	):







    def __init__(			self			:       List[str]     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       int     )		->Optional[int]:






        '''simple docstring'''






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







    @torch.no_grad()
    def __call__(			self			:       int     ,					__UpperCamelCase			:       int = 1     ,					__UpperCamelCase			:       int = 1_00     ,					__UpperCamelCase			:       Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,					__UpperCamelCase			:       Optional[float] = None     ,					__UpperCamelCase			:       bool = True     ,					)		->Union[AudioPipelineOutput, Tuple]:






        '''simple docstring'''






        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(__UpperCamelCase     )
        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(__UpperCamelCase     )

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

        _UpperCAmelCase          =   randn_tensor(__UpperCamelCase     ,					generator=__UpperCamelCase     ,					device=self.device     ,					dtype=__UpperCamelCase     )

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

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

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

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

        _UpperCAmelCase          =   audio[:, :, :original_sample_size]

        if not return_dict:
            return (audio,)

        return AudioPipelineOutput(audios=__UpperCamelCase     )

"""simple docstring"""



import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class 		a_						( _UpperCAmelCase	):
    a      :				Any     =				['image_processor', 'tokenizer']
    a      :				Optional[int]     =				'AutoImageProcessor'
    a      :				Any     =				'AutoTokenizer'







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






        '''simple docstring'''






        _UpperCAmelCase          =   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     ,					)
            _UpperCAmelCase          =   kwargs.pop("""feature_extractor"""     )

        _UpperCAmelCase          =   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     )
        _UpperCAmelCase          =   self.image_processor
        _UpperCAmelCase          =   False







    def __call__(			self			:       Union[str, Any]     ,					*__UpperCamelCase			:       str     ,					**__UpperCamelCase			:       Optional[Any]     )		->List[str]:






        '''simple docstring'''






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

        _UpperCAmelCase          =   kwargs.pop("""images"""     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   kwargs.pop("""text"""     ,					__UpperCamelCase     )
        if len(__UpperCamelCase     ) > 0:
            _UpperCAmelCase          =   args[0]
            _UpperCAmelCase          =   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:
            _UpperCAmelCase          =   self.image_processor(__UpperCamelCase     ,					*__UpperCamelCase     ,					**__UpperCamelCase     )
        if text is not None:
            _UpperCAmelCase          =   self.tokenizer(__UpperCamelCase     ,					**__UpperCamelCase     )

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







    def      _snake_case					(			self			:       Union[str, Any]     ,					*__UpperCamelCase			:       int     ,					**__UpperCamelCase			:       Tuple     )		->Tuple:






        '''simple docstring'''






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







    def      _snake_case					(			self			:       Tuple     ,					*__UpperCamelCase			:       int     ,					**__UpperCamelCase			:       Union[str, Any]     )		->int:






        '''simple docstring'''






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







    @contextmanager
    def      _snake_case					(			self			:       Tuple     )		->Union[str, Any]:






        '''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."""     )
        _UpperCAmelCase          =   True
        _UpperCAmelCase          =   self.tokenizer
        yield
        _UpperCAmelCase          =   self.image_processor
        _UpperCAmelCase          =   False







    def      _snake_case					(			self			:       str     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Optional[Any]=False     ,					__UpperCamelCase			:       Union[str, Any]=None     )		->List[str]:






        '''simple docstring'''






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

        _UpperCAmelCase          =   {}

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

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

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







    @property
    def      _snake_case					(			self			:       Tuple     )		->Optional[int]:






        '''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







    @property
    def      _snake_case					(			self			:       List[str]     )		->Dict:






        '''simple docstring'''






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