



from typing import List, Optional, Tuple, Union

import torch

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


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






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__		):
        super().__init__()
        self.register_modules(unet=lowerCamelCase__,		scheduler=lowerCamelCase__		)






    @torch.no_grad()
    def __call__(	self,		lowerCamelCase__ = 1,		lowerCamelCase__ = 100,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = True,		):

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

        A			:	List[str]										=       audio_length_in_s * self.unet.config.sample_rate

        A			:	Union[str, Any]										=       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}.'''		)

        A			:	Tuple										=       int(lowerCamelCase__		)
        if sample_size % down_scale_factor != 0:
            A			:	Dict										=       (
                (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."""		)
        A			:	Dict										=       int(lowerCamelCase__		)

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

        A			:	int										=       randn_tensor(lowerCamelCase__,		generator=lowerCamelCase__,		device=self.device,		dtype=lowerCamelCase__		)

        # set step values
        self.scheduler.set_timesteps(lowerCamelCase__,		device=audio.device		)
        A			:	Tuple										=       self.scheduler.timesteps.to(lowerCamelCase__		)

        for t in self.progress_bar(self.scheduler.timesteps		):
            # 1. predict noise model_output
            A			:	List[Any]										=       self.unet(lowerCamelCase__,		lowerCamelCase__		).sample

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

        A			:	List[str]										=       audio.clamp(-1,		1		).float().cpu().numpy()

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

        if not return_dict:
            return (audio,)

        return AudioPipelineOutput(audios=lowerCamelCase__		)







import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  str:



    """simple docstring"""






    A			:	int										=       []
    for line in lines:
        A			:	int										=       re.sub(R"""#.*"""				,					""""""				,					_lowerCAmelCase		)  # remove comments
        if line:
            filtered_lines.append(_lowerCAmelCase		)
    A			:	Tuple										=       """\n""".join(_lowerCAmelCase		)

    # Make a hash from all this code
    A			:	Union[str, Any]										=       full_str.encode("""utf-8"""		)
    return shaaaa(_lowerCAmelCase		).hexdigest()


# get importable module names and hash for caching
SCREAMING_SNAKE_CASE_:List[Any]					=						{
    """csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    """json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    """pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    """parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    """arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    """text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    """imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    """audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
SCREAMING_SNAKE_CASE_:Optional[Any]					=						{
    """.csv""": ("""csv""", {}),
    """.tsv""": ("""csv""", {"""sep""": """\t"""}),
    """.json""": ("""json""", {}),
    """.jsonl""": ("""json""", {}),
    """.parquet""": ("""parquet""", {}),
    """.arrow""": ("""arrow""", {}),
    """.txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
SCREAMING_SNAKE_CASE_:Optional[int]					=						{"""imagefolder""", """audiofolder"""}

# Used to filter data files based on extensions given a module name
SCREAMING_SNAKE_CASE_:Dict[str, List[str]]					=						{}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
     _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")




_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")


