


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


lowerCamelCase		=	logging.get_logger(__name__)





class       _a							(       _lowercase):
	_a						:						Optional[int]   					=							['''input_values''', '''attention_mask''']




	def __init__(      self		:	Optional[int]	,      _SCREAMING_SNAKE_CASE		:	str=1	,      _SCREAMING_SNAKE_CASE		:	List[Any]=1_6000	,      _SCREAMING_SNAKE_CASE		:	Union[str, Any]=0.0	,      _SCREAMING_SNAKE_CASE		:	Optional[int]=False	,      _SCREAMING_SNAKE_CASE		:	List[str]=True	,      **_SCREAMING_SNAKE_CASE		:	List[str]	,      )-> Optional[Any]:
			super().__init__(feature_size=_SCREAMING_SNAKE_CASE	,      sampling_rate=_SCREAMING_SNAKE_CASE	,      padding_value=_SCREAMING_SNAKE_CASE	,      **_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    Optional[int]					  =      return_attention_mask
			lowerCAmelCase__     :    Optional[int]					  =      do_normalize




	@staticmethod
	def 							UpperCAmelCase__(      _SCREAMING_SNAKE_CASE		:	List[np.ndarray]	,      _SCREAMING_SNAKE_CASE		:	List[np.ndarray]	,      _SCREAMING_SNAKE_CASE		:	float = 0.0	)-> List[np.ndarray]:
			if attention_mask is not None:
					lowerCAmelCase__     :    List[str]					  =      np.array(_SCREAMING_SNAKE_CASE	,      np.intaa	)
					lowerCAmelCase__     :    Dict					  =      []

					for vector, length in zip(_SCREAMING_SNAKE_CASE	,      attention_mask.sum(-1	)	):
							lowerCAmelCase__     :    Union[str, Any]					  =      (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7	)
							if length < normed_slice.shape[0]:
									lowerCAmelCase__     :    int					  =      padding_value

							normed_input_values.append(_SCREAMING_SNAKE_CASE	)
			else:
					lowerCAmelCase__     :    Any					  =      [(x - x.mean()) / np.sqrt(x.var() + 1E-7	) for x in input_values]

			return normed_input_values




	def __call__(      self		:	Union[str, Any]	,      _SCREAMING_SNAKE_CASE		:	Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,      _SCREAMING_SNAKE_CASE		:	Union[bool, str, PaddingStrategy] = False	,      _SCREAMING_SNAKE_CASE		:	Optional[int] = None	,      _SCREAMING_SNAKE_CASE		:	bool = False	,      _SCREAMING_SNAKE_CASE		:	Optional[int] = None	,      _SCREAMING_SNAKE_CASE		:	Optional[bool] = None	,      _SCREAMING_SNAKE_CASE		:	Optional[Union[str, TensorType]] = None	,      _SCREAMING_SNAKE_CASE		:	Optional[int] = None	,      **_SCREAMING_SNAKE_CASE		:	Any	,      )-> 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 `raw_speech` 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.'''	)

			lowerCAmelCase__     :    Union[str, Any]					  =      isinstance(_SCREAMING_SNAKE_CASE	,      np.ndarray	) and len(raw_speech.shape	) > 1
			if is_batched_numpy and len(raw_speech.shape	) > 2:
					raise ValueError(F'Only mono-channel audio is supported for input to {self}'	)
			lowerCAmelCase__     :    Optional[Any]					  =      is_batched_numpy or (
			    isinstance(_SCREAMING_SNAKE_CASE	,      (list, tuple)	) and (isinstance(raw_speech[0]	,      (np.ndarray, tuple, list)	))
			)

			# always return batch
			if not is_batched:
					lowerCAmelCase__     :    Union[str, Any]					  =      [raw_speech]

			# convert into correct format for padding
			lowerCAmelCase__     :    Dict					  =      BatchFeature({'''input_values''': raw_speech}	)

			lowerCAmelCase__     :    Union[str, Any]					  =      self.pad(
			    _SCREAMING_SNAKE_CASE	,      padding=_SCREAMING_SNAKE_CASE	,      max_length=_SCREAMING_SNAKE_CASE	,      truncation=_SCREAMING_SNAKE_CASE	,      pad_to_multiple_of=_SCREAMING_SNAKE_CASE	,      return_attention_mask=_SCREAMING_SNAKE_CASE	,      )

			# convert input values to correct format
			lowerCAmelCase__     :    Union[str, Any]					  =      padded_inputs['''input_values''']
			if not isinstance(input_values[0]	,      np.ndarray	):
					lowerCAmelCase__     :    int					  =      [np.asarray(_SCREAMING_SNAKE_CASE	,      dtype=np.floataa	) for array in input_values]
			elif (
			    not isinstance(_SCREAMING_SNAKE_CASE	,      np.ndarray	)
			    and isinstance(input_values[0]	,      np.ndarray	)
			    and input_values[0].dtype is np.dtype(np.floataa	)
			):
					lowerCAmelCase__     :    Union[str, Any]					  =      [array.astype(np.floataa	) for array in input_values]
			elif isinstance(_SCREAMING_SNAKE_CASE	,      np.ndarray	) and input_values.dtype is np.dtype(np.floataa	):
					lowerCAmelCase__     :    str					  =      input_values.astype(np.floataa	)

			# convert attention_mask to correct format
			lowerCAmelCase__     :    List[Any]					  =      padded_inputs.get('''attention_mask'''	)
			if attention_mask is not None:
					lowerCAmelCase__     :    List[str]					  =      [np.asarray(_SCREAMING_SNAKE_CASE	,      dtype=np.intaa	) for array in attention_mask]

			# zero-mean and unit-variance normalization
			if self.do_normalize:
					lowerCAmelCase__     :    Dict					  =      (
					    attention_mask
					    if self._get_padding_strategies(_SCREAMING_SNAKE_CASE	,      max_length=_SCREAMING_SNAKE_CASE	) is not PaddingStrategy.DO_NOT_PAD
					    else None
					)
					lowerCAmelCase__     :    Union[str, Any]					  =      self.zero_mean_unit_var_norm(
					    padded_inputs['''input_values''']	,      attention_mask=_SCREAMING_SNAKE_CASE	,      padding_value=self.padding_value	)

			if return_tensors is not None:
					lowerCAmelCase__     :    Optional[int]					  =      padded_inputs.convert_to_tensors(_SCREAMING_SNAKE_CASE	)

			return padded_inputs








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase		=	{
    '''configuration_segformer''': ['''SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SegformerConfig''', '''SegformerOnnxConfig''']
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase		=	['''SegformerFeatureExtractor''']
		lowerCamelCase		=	['''SegformerImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase		=	[
		    '''SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SegformerDecodeHead''',
		    '''SegformerForImageClassification''',
		    '''SegformerForSemanticSegmentation''',
		    '''SegformerLayer''',
		    '''SegformerModel''',
		    '''SegformerPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase		=	[
		    '''TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFSegformerDecodeHead''',
		    '''TFSegformerForImageClassification''',
		    '''TFSegformerForSemanticSegmentation''',
		    '''TFSegformerModel''',
		    '''TFSegformerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_segformer import SegformerFeatureExtractor
				from .image_processing_segformer import SegformerImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_segformer import (
				    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SegformerDecodeHead,
				    SegformerForImageClassification,
				    SegformerForSemanticSegmentation,
				    SegformerLayer,
				    SegformerModel,
				    SegformerPreTrainedModel,
				)
		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_segformer import (
				    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFSegformerDecodeHead,
				    TFSegformerForImageClassification,
				    TFSegformerForSemanticSegmentation,
				    TFSegformerModel,
				    TFSegformerPreTrainedModel,
				)

else:
		import sys

		lowerCamelCase		=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




