

from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


a_     =			logging.get_logger(__name__)






class _UpperCamelCase							(							__A      ):
			'''simple docstring'''







			lowerCamelCase__							=['input_values', 'padding_mask']
			def __init__(	self							:       str					,					a							:       int = 1					,					a							:       int = 2_4000					,					a							:       float = 0.0					,					a							:       float = None					,					a							:       float = None					,					**a							:       Optional[Any]					,					)							->				List[Any]:

				"""simple docstring"""

				super().__init__(feature_size=a					,					sampling_rate=a					,					padding_value=a					,					**a		)
				SCREAMING_SNAKE_CASE      :					str    =				chunk_length_s
				SCREAMING_SNAKE_CASE      :					List[str]    =				overlap
			@property
			def 							__UpperCamelCase       (	self							:       List[str]		)							->				Optional[int]:

				"""simple docstring"""

				if self.chunk_length_s is None:
					return None
				else:
					return int(self.chunk_length_s * self.sampling_rate		)
			@property
			def 							__UpperCamelCase       (	self							:       Union[str, Any]		)							->				Optional[int]:

				"""simple docstring"""

				if self.chunk_length_s is None or self.overlap is None:
					return None
				else:
					return max(1					,					int((1.0 - self.overlap) * self.chunk_length		)		)






			def __call__(	self							:       Optional[int]					,					a							:       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,					a							:       Optional[Union[bool, str, PaddingStrategy]] = None					,					a							:       Optional[bool] = False					,					a							:       Optional[int] = None					,					a							:       Optional[Union[str, TensorType]] = None					,					a							:       Optional[int] = None					,					)							->				BatchFeature:

				"""simple docstring"""

				if sampling_rate is not None:
					if sampling_rate != self.sampling_rate:
						raise ValueError(
						    F"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
						    F" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"
						    F" {self.sampling_rate} and not {sampling_rate}."		)
				else:
					logger.warning(
					    "It is strongly recommended to pass the `sampling_rate` argument to this function. "
					    "Failing to do so can result in silent errors that might be hard to debug."		)

				if padding and truncation:
					raise ValueError("Both padding and truncation were set. Make sure you only set one."		)
				elif padding is None:
					# by default let's pad the inputs
					SCREAMING_SNAKE_CASE      :					str    =				True

				SCREAMING_SNAKE_CASE      :					List[Any]    =				bool(
				    isinstance(a					,					(list, tuple)		) and (isinstance(raw_audio[0]					,					(np.ndarray, tuple, list)		))		)

				if is_batched:
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				[np.asarray(a					,					dtype=np.floataa		).T for audio in raw_audio]
				elif not is_batched and not isinstance(a					,					np.ndarray		):
					SCREAMING_SNAKE_CASE      :					Any    =				np.asarray(a					,					dtype=np.floataa		)
				elif isinstance(a					,					np.ndarray		) and raw_audio.dtype is np.dtype(np.floataa		):
					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				raw_audio.astype(np.floataa		)

				# always return batch
				if not is_batched:
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				[np.asarray(a		).T]

				# verify inputs are valid
				for idx, example in enumerate(a		):
					if example.ndim > 2:
						raise ValueError(F"Expected input shape (channels, length) but got shape {example.shape}"		)
					if self.feature_size == 1 and example.ndim != 1:
						raise ValueError(F"Expected mono audio but example has {example.shape[-1]} channels"		)
					if self.feature_size == 2 and example.shape[-1] != 2:
						raise ValueError(F"Expected stereo audio but example has {example.shape[-1]} channels"		)

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				None
				SCREAMING_SNAKE_CASE      :					Tuple    =				BatchFeature({"input_values": raw_audio}		)
				if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
					if truncation:
						SCREAMING_SNAKE_CASE      :					Optional[Any]    =				min(array.shape[0] for array in raw_audio		)
						SCREAMING_SNAKE_CASE      :					Optional[int]    =				int(np.floor(max_length / self.chunk_stride		)		)
						SCREAMING_SNAKE_CASE      :					Dict    =				(nb_step - 1) * self.chunk_stride + self.chunk_length
					elif padding:
						SCREAMING_SNAKE_CASE      :					List[Any]    =				max(array.shape[0] for array in raw_audio		)
						SCREAMING_SNAKE_CASE      :					int    =				int(np.ceil(max_length / self.chunk_stride		)		)
						SCREAMING_SNAKE_CASE      :					List[Any]    =				(nb_step - 1) * self.chunk_stride + self.chunk_length
						SCREAMING_SNAKE_CASE      :					str    =				"max_length"
					else:
						SCREAMING_SNAKE_CASE      :					List[Any]    =				input_values

        # normal padding on batch
				if padded_inputs is None:
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				self.pad(
					    a					,					max_length=a					,					truncation=a					,					padding=a					,					return_attention_mask=a					,					)
					if padding:
						SCREAMING_SNAKE_CASE      :					Optional[Any]    =				padded_inputs.pop("attention_mask"		)

				SCREAMING_SNAKE_CASE      :					List[str]    =				[]
				for example in padded_inputs.pop("input_values"		):
					if self.feature_size == 1:
						SCREAMING_SNAKE_CASE      :					Tuple    =				example[..., None]
					input_values.append(example.T		)

				SCREAMING_SNAKE_CASE      :					Dict    =				input_values
				if return_tensors is not None:
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				padded_inputs.convert_to_tensors(a		)

				return padded_inputs






from argparse import ArgumentParser

from .env import EnvironmentCommand



def    __lowercase   (      ) ->   Dict:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						ArgumentParser("""Diffusers CLI tool""" ,						usage="""diffusers-cli <command> [<args>]"""	)
      SCREAMING_SNAKE_CASE		=						parser.add_subparsers(help="""diffusers-cli command helpers"""	)

      # Register commands
      EnvironmentCommand.register_subcommand(_SCREAMING_SNAKE_CASE	)

      # Let's go
      SCREAMING_SNAKE_CASE		=						parser.parse_args()

      if not hasattr(_SCREAMING_SNAKE_CASE ,						"""func"""	):
            parser.print_help()
            exit(1	)

      # Run
      SCREAMING_SNAKE_CASE		=						args.func(_SCREAMING_SNAKE_CASE	)
      service.run()


if __name__ == "__main__":
      main()


