






# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


UpperCamelCase        =				logging.get_logger(__name__)

UpperCamelCase        =				{}
UpperCamelCase        =				{}
UpperCamelCase        =				{}




def 				A		(					lowercase__  :  type ,		lowercase__  :  Optional[str] ,		lowercase__  :  Optional[List[str]] = None ,		)		-> int:
	UpperCamelCase__ :Union[str, Any]						=      aliases if aliases is not None else []
	if format_type in _FORMAT_TYPES:
		logger.warning(
		    f"""Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"""  )
	UpperCamelCase__ :Union[str, Any]						=      formatter_cls
	for alias in set(aliases + [format_type]  ):
		if alias in _FORMAT_TYPES_ALIASES:
			logger.warning(
			    f"""Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"""  )
		UpperCamelCase__ :List[Any]						=      format_type




def 				A		(					lowercase__  :  Exception ,		lowercase__  :  Optional[str] ,		lowercase__  :  Optional[List[str]] = None  )		-> List[Any]:
	UpperCamelCase__ :Any						=      aliases if aliases is not None else []
	for alias in set(aliases + [format_type]  ):
		UpperCamelCase__ :Optional[int]						=      unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=["python"])
_register_formatter(ArrowFormatter, "arrow", aliases=["pa", "pyarrow"])
_register_formatter(NumpyFormatter, "numpy", aliases=["np"])
_register_formatter(PandasFormatter, "pandas", aliases=["pd"])
_register_formatter(CustomFormatter, "custom")

if config.TORCH_AVAILABLE:
							from .torch_formatter import TorchFormatter

							_register_formatter(TorchFormatter, "torch", aliases=["pt", "pytorch"])
else:
							UpperCamelCase        =				ValueError("PyTorch needs to be installed to be able to return PyTorch tensors.")
							_register_unavailable_formatter(_torch_error, "torch", aliases=["pt", "pytorch"])

if config.TF_AVAILABLE:
							from .tf_formatter import TFFormatter

							_register_formatter(TFFormatter, "tensorflow", aliases=["tf"])
else:
							UpperCamelCase        =				ValueError("Tensorflow needs to be installed to be able to return Tensorflow tensors.")
							_register_unavailable_formatter(_tf_error, "tensorflow", aliases=["tf"])

if config.JAX_AVAILABLE:
							from .jax_formatter import JaxFormatter

							_register_formatter(JaxFormatter, "jax", aliases=[])
else:
							UpperCamelCase        =				ValueError("JAX needs to be installed to be able to return JAX arrays.")
							_register_unavailable_formatter(_jax_error, "jax", aliases=[])




def 				A		(					lowercase__  :  Optional[str]  )		-> Optional[str]:
	if format_type in _FORMAT_TYPES_ALIASES:
		return _FORMAT_TYPES_ALIASES[format_type]
	else:
		return format_type




def 				A		(					lowercase__  :  Optional[str] ,		**lowercase__  :  Optional[Any]  )		-> Formatter:
	UpperCamelCase__ :Union[str, Any]						=      get_format_type_from_alias(lowercase__  )
	if format_type in _FORMAT_TYPES:
		return _FORMAT_TYPES[format_type](**lowercase__  )
	if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
		raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
	else:
		raise ValueError(
		    f"""Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None  )}, but got '{format_type}'"""  )

'''simple docstring'''



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


_lowercase          =						logging.get_logger(__name__)




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: List[str]										=  ['''input_values''', '''padding_mask''']




						def __init__(   self       ,  _lowercase = 1       ,  _lowercase = 24_000       ,  _lowercase = 0.0       ,  _lowercase = None       ,  _lowercase = None       ,  **_lowercase       ,  ):

												"""simple docstring"""



												super().__init__(feature_size=_lowercase       ,  sampling_rate=_lowercase       ,  padding_value=_lowercase       ,  **_lowercase )
												_lowerCAmelCase    		=					chunk_length_s
												_lowerCAmelCase    		=					overlap




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												if self.chunk_length_s is None:
																		return None
												else:
																		return int(self.chunk_length_s * self.sampling_rate )




						@property
						def    _lowercase      (   self ):

												"""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       ,  _lowercase       ,  _lowercase = None       ,  _lowercase = False       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase = None       ,  ):

												"""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
																		_lowerCAmelCase    		=					True

												_lowerCAmelCase    		=					bool(
												    isinstance(_lowercase       ,  (list, tuple) ) and (isinstance(raw_audio[0]       ,  (np.ndarray, tuple, list) )) )

												if is_batched:
																		_lowerCAmelCase    		=					[np.asarray(_lowercase       ,  dtype=np.floataa ).T for audio in raw_audio]
												elif not is_batched and not isinstance(_lowercase       ,  np.ndarray ):
																		_lowerCAmelCase    		=					np.asarray(_lowercase       ,  dtype=np.floataa )
												elif isinstance(_lowercase       ,  np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
																		_lowerCAmelCase    		=					raw_audio.astype(np.floataa )

												# always return batch
												if not is_batched:
																		_lowerCAmelCase    		=					[np.asarray(_lowercase ).T]

												# verify inputs are valid
												for idx, example in enumerate(_lowercase ):
																		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' )

												_lowerCAmelCase    		=					None
												_lowerCAmelCase    		=					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:
																								_lowerCAmelCase    		=					min(array.shape[0] for array in raw_audio )
																								_lowerCAmelCase    		=					int(np.floor(max_length / self.chunk_stride ) )
																								_lowerCAmelCase    		=					(nb_step - 1) * self.chunk_stride + self.chunk_length
																		elif padding:
																								_lowerCAmelCase    		=					max(array.shape[0] for array in raw_audio )
																								_lowerCAmelCase    		=					int(np.ceil(max_length / self.chunk_stride ) )
																								_lowerCAmelCase    		=					(nb_step - 1) * self.chunk_stride + self.chunk_length
																								_lowerCAmelCase    		=					"""max_length"""
																		else:
																								_lowerCAmelCase    		=					input_values

        # normal padding on batch
												if padded_inputs is None:
																		_lowerCAmelCase    		=					self.pad(
																		    _lowercase       ,  max_length=_lowercase       ,  truncation=_lowercase       ,  padding=_lowercase       ,  return_attention_mask=_lowercase       ,  )
																		if padding:
																								_lowerCAmelCase    		=					padded_inputs.pop("""attention_mask""" )

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

												_lowerCAmelCase    		=					input_values
												if return_tensors is not None:
																		_lowerCAmelCase    		=					padded_inputs.convert_to_tensors(_lowercase )

												return padded_inputs
