





'''simple docstring'''

from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class     __lowerCAmelCase					(   _lowerCAmelCase			):






							"""simple docstring"""
							_snake_case     :					Optional[Any]						    =   "EncodecFeatureExtractor"
							_snake_case     :					List[Any]						    =   ("T5Tokenizer", "T5TokenizerFast")





							def __init__(							self       :		Tuple	,			lowerCAmelCase__       :		str	,			lowerCAmelCase__       :		Tuple					)   ->					int:

												'''simple docstring'''




												super().__init__(_lowercase	,			_lowercase					)
												_UpperCamelCase       =	self.feature_extractor
												_UpperCamelCase       =	False





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

												'''simple docstring'''




												return self.tokenizer.get_decoder_prompt_ids(task=_lowercase	,			language=_lowercase	,			no_timestamps=_lowercase					)





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

												'''simple docstring'''




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

												_UpperCamelCase       =	kwargs.pop('''audio'''	,			_lowercase					)
												_UpperCamelCase       =	kwargs.pop('''sampling_rate'''	,			_lowercase					)
												_UpperCamelCase       =	kwargs.pop('''text'''	,			_lowercase					)
												if len(_lowercase					) > 0:
																	_UpperCamelCase       =	args[0]
																	_UpperCamelCase       =	args[1:]

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

												if text is not None:
																	_UpperCamelCase       =	self.tokenizer(_lowercase	,			**_lowercase					)

												if audio is not None:
																	_UpperCamelCase       =	self.feature_extractor(_lowercase	,			*_lowercase	,			sampling_rate=_lowercase	,			**_lowercase					)

												if audio is None:
																	return inputs

												elif text is None:
																	return audio_inputs

												else:
																	_UpperCamelCase       =	audio_inputs['''input_values''']
																	if "padding_mask" in audio_inputs:
																						_UpperCamelCase       =	audio_inputs['''padding_mask''']
																	return inputs





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

												'''simple docstring'''




												_UpperCamelCase       =	kwargs.pop('''audio'''	,			_lowercase					)
												_UpperCamelCase       =	kwargs.pop('''padding_mask'''	,			_lowercase					)

												if len(_lowercase					) > 0:
																	_UpperCamelCase       =	args[0]
																	_UpperCamelCase       =	args[1:]

												if audio_values is not None:
																	return self._decode_audio(_lowercase	,			padding_mask=_lowercase					)
												else:
																	return self.tokenizer.batch_decode(*_lowercase	,			**_lowercase					)





							def snake_case__						(							self       :		Dict	,			*lowerCAmelCase__       :		Dict	,			**lowerCAmelCase__       :		Union[str, Any]					)   ->					int:

												'''simple docstring'''




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







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

												'''simple docstring'''




												_UpperCamelCase       =	to_numpy(_lowercase					)
												_UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	audio_values.shape

												if padding_mask is None:
																	return list(_lowercase					)

												_UpperCamelCase       =	to_numpy(_lowercase					)

												# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
												# token (so that the generated audio values are **not** treated as padded tokens)
												_UpperCamelCase       =	seq_len - padding_mask.shape[-1]
												_UpperCamelCase       =	1 - self.feature_extractor.padding_value
												_UpperCamelCase       =	np.pad(_lowercase	,			((0, 0), (0, difference))	,			'''constant'''	,			constant_values=_lowercase					)

												_UpperCamelCase       =	audio_values.tolist()
												for i in range(_lowercase					):
																	_UpperCamelCase       =	np.asarray(audio_values[i]					)[
																	    padding_mask[i][None, :] != self.feature_extractor.padding_value
																	]
																	_UpperCamelCase       =	sliced_audio.reshape(_lowercase	,			-1					)

												return audio_values










'''simple docstring'''

import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       def snake_case__						(							self       :		Tuple					)   ->					int:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''safety_checker/pytorch_model.bin''',
                '''safety_checker/model.safetensors''',
                '''vae/diffusion_pytorch_model.bin''',
                '''vae/diffusion_pytorch_model.safetensors''',
                '''text_encoder/pytorch_model.bin''',
                '''text_encoder/model.safetensors''',
                '''unet/diffusion_pytorch_model.bin''',
                '''unet/diffusion_pytorch_model.safetensors''',
            ]
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__					)					)





       def snake_case__						(							self       :		int					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''unet/diffusion_pytorch_model.bin''',
                '''unet/diffusion_pytorch_model.safetensors''',
            ]
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Optional[Any]					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''safety_checker/pytorch_model.bin''',
                '''safety_checker/model.safetensors''',
                '''vae/diffusion_pytorch_model.bin''',
                '''vae/diffusion_pytorch_model.safetensors''',
                '''text_encoder/pytorch_model.bin''',
                '''text_encoder/model.safetensors''',
                '''unet/diffusion_pytorch_model.bin''',
                # Removed: 'unet/diffusion_pytorch_model.safetensors',
            ]
            self.assertFalse(is_safetensors_compatible(lowerCAmelCase__					)					)





       def snake_case__						(							self       :		List[Any]					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''text_encoder/pytorch_model.bin''',
                '''text_encoder/model.safetensors''',
            ]
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Dict					)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''safety_checker/pytorch_model.bin''',
                '''safety_checker/model.safetensors''',
                '''vae/diffusion_pytorch_model.bin''',
                '''vae/diffusion_pytorch_model.safetensors''',
                '''text_encoder/pytorch_model.bin''',
                # Removed: 'text_encoder/model.safetensors',
                '''unet/diffusion_pytorch_model.bin''',
                '''unet/diffusion_pytorch_model.safetensors''',
            ]
            self.assertFalse(is_safetensors_compatible(lowerCAmelCase__					)					)





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

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''safety_checker/pytorch_model.fp16.bin''',
                '''safety_checker/model.fp16.safetensors''',
                '''vae/diffusion_pytorch_model.fp16.bin''',
                '''vae/diffusion_pytorch_model.fp16.safetensors''',
                '''text_encoder/pytorch_model.fp16.bin''',
                '''text_encoder/model.fp16.safetensors''',
                '''unet/diffusion_pytorch_model.fp16.bin''',
                '''unet/diffusion_pytorch_model.fp16.safetensors''',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Optional[Any]					)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''unet/diffusion_pytorch_model.fp16.bin''',
                '''unet/diffusion_pytorch_model.fp16.safetensors''',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Tuple					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''unet/diffusion_pytorch_model.bin''',
                '''unet/diffusion_pytorch_model.safetensors''',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Tuple					)   ->					Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''safety_checker/pytorch_model.fp16.bin''',
                '''safety_checker/model.fp16.safetensors''',
                '''vae/diffusion_pytorch_model.fp16.bin''',
                '''vae/diffusion_pytorch_model.fp16.safetensors''',
                '''text_encoder/pytorch_model.fp16.bin''',
                '''text_encoder/model.fp16.safetensors''',
                '''unet/diffusion_pytorch_model.fp16.bin''',
                # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertFalse(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Optional[int]					)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''text_encoder/pytorch_model.fp16.bin''',
                '''text_encoder/model.fp16.safetensors''',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Optional[Any]					)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''text_encoder/pytorch_model.bin''',
                '''text_encoder/model.safetensors''',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertTrue(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)







       def snake_case__						(							self       :		List[str]					)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       =	[
                '''safety_checker/pytorch_model.fp16.bin''',
                '''safety_checker/model.fp16.safetensors''',
                '''vae/diffusion_pytorch_model.fp16.bin''',
                '''vae/diffusion_pytorch_model.fp16.safetensors''',
                '''text_encoder/pytorch_model.fp16.bin''',
                # 'text_encoder/model.fp16.safetensors',
                '''unet/diffusion_pytorch_model.fp16.bin''',
                '''unet/diffusion_pytorch_model.fp16.safetensors''',
            ]
            _UpperCamelCase       =	'''fp16'''
            self.assertFalse(is_safetensors_compatible(lowerCAmelCase__	,			variant=lowerCAmelCase__					)					)



