
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        _SCREAMING_SNAKE_CASE	( snake_case_		):
	lowerCAmelCase__   =					['input_values', 'padding_mask']



	def __init__(       self		,    lowercase = 1		,    lowercase = 24000		,    lowercase = 0.0		,    lowercase = None		,    lowercase = None		,    **lowercase		,    )							->			str:
					super().__init__(feature_size=lowercase		,    sampling_rate=lowercase		,    padding_value=lowercase		,    **lowercase					)
					lowerCamelCase_         =     chunk_length_s
					lowerCamelCase_         =     overlap



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[int]:
					if self.chunk_length_s is None:
									return None
					else:
									return int(self.chunk_length_s * self.sampling_rate					)



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[int]:
					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		,    )							->			BatchFeature:
					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




import enum
import shutil
import sys


__A,		__A							=shutil.get_terminal_size()

__A							={'''UP''': '''A''', '''DOWN''': '''B''', '''RIGHT''': '''C''', '''LEFT''': '''D'''}




class        _SCREAMING_SNAKE_CASE	( enum.Enum		):
	lowerCAmelCase__   =					0
	lowerCAmelCase__   =					1





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__=""       ):
				sys.stdout.write(str(lowerCamelCase__       ) + end       )
				sys.stdout.flush()





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__=""       ):
				forceWrite(F'\u001b[{color}m{content}\u001b[0m'      ,					lowerCamelCase__       )





def      lowerCamelCase_					(							):
				forceWrite("\r"       )





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				forceWrite(F'\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}'       )





def      lowerCamelCase_					(							):
				forceWrite(" " * TERMINAL_WIDTH       )
				reset_cursor()





def      lowerCamelCase_					(							):
				reset_cursor()
				forceWrite("-" * TERMINAL_WIDTH       )


