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


_SCREAMING_SNAKE_CASE:    Tuple     						=							logging.get_logger(__name__)

class 						_SCREAMING_SNAKE_CASE				(  _lowercase				):






		"""simple docstring"""







		A_    			=   ['''input_values''', '''attention_mask''']



		def __init__(      self     :					Any	,       __SCREAMING_SNAKE_CASE     :					Dict=1	,       __SCREAMING_SNAKE_CASE     :					Dict=16_000	,       __SCREAMING_SNAKE_CASE     :					str=0.0	,       __SCREAMING_SNAKE_CASE     :					Optional[int]=False	,       __SCREAMING_SNAKE_CASE     :					Optional[int]=True	,       **__SCREAMING_SNAKE_CASE     :					Optional[int]	,       ):






					"""simple docstring"""
					super().__init__(feature_size=__SCREAMING_SNAKE_CASE	,       sampling_rate=__SCREAMING_SNAKE_CASE	,       padding_value=__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )
					_snake_case														=   return_attention_mask
					_snake_case														=   do_normalize



		@staticmethod
		def 					_a					(      __SCREAMING_SNAKE_CASE     :					List[np.ndarray]	,       __SCREAMING_SNAKE_CASE     :					List[np.ndarray]	,       __SCREAMING_SNAKE_CASE     :					float = 0.0 ):






					"""simple docstring"""
					if attention_mask is not None:
								_snake_case														=   np.array(__SCREAMING_SNAKE_CASE	,       np.intaa )
								_snake_case														=   []

								for vector, length in zip(__SCREAMING_SNAKE_CASE	,       attention_mask.sum(-1 ) ):
											_snake_case														=   (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7 )
											if length < normed_slice.shape[0]:
														_snake_case														=   padding_value

											normed_input_values.append(__SCREAMING_SNAKE_CASE )
					else:
								_snake_case														=   [(x - x.mean()) / np.sqrt(x.var() + 1e-7 ) for x in input_values]

					return normed_input_values







		def __call__(      self     :					str	,       __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     :					Optional[Any]	,       ):






					"""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 `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." )

					_snake_case														=   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}' )
					_snake_case														=   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:
								_snake_case														=   [raw_speech]

					# convert into correct format for padding
					_snake_case														=   BatchFeature({"input_values": raw_speech} )

					_snake_case														=   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
					_snake_case														=   padded_inputs["input_values"]
					if not isinstance(input_values[0]	,       np.ndarray ):
								_snake_case														=   [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 )
					):
								_snake_case														=   [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 ):
								_snake_case														=   input_values.astype(np.floataa )

					# convert attention_mask to correct format
					_snake_case														=   padded_inputs.get("attention_mask" )
					if attention_mask is not None:
								_snake_case														=   [np.asarray(__SCREAMING_SNAKE_CASE	,       dtype=np.intaa ) for array in attention_mask]

					# zero-mean and unit-variance normalization
					if self.do_normalize:
								_snake_case														=   (
								    attention_mask
								    if self._get_padding_strategies(__SCREAMING_SNAKE_CASE	,       max_length=__SCREAMING_SNAKE_CASE ) is not PaddingStrategy.DO_NOT_PAD
								    else None
								)
								_snake_case														=   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:
								_snake_case														=   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,
)


_SCREAMING_SNAKE_CASE:    Optional[Any]     						=							{
    '''configuration_sam''': [
        '''SAM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SamConfig''',
        '''SamMaskDecoderConfig''',
        '''SamPromptEncoderConfig''',
        '''SamVisionConfig''',
    ],
    '''processing_sam''': ['''SamProcessor'''],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_SCREAMING_SNAKE_CASE:    Dict     						=							[
			    '''SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''SamModel''',
			    '''SamPreTrainedModel''',
			]
try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_SCREAMING_SNAKE_CASE:    Optional[Any]     						=							[
			    '''TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFSamModel''',
			    '''TFSamPreTrainedModel''',
			]
try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_SCREAMING_SNAKE_CASE:    List[Any]     						=							['''SamImageProcessor''']


if TYPE_CHECKING:
			from .configuration_sam import (
			    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    SamConfig,
			    SamMaskDecoderConfig,
			    SamPromptEncoderConfig,
			    SamVisionConfig,
			)
			from .processing_sam import SamProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_sam import SamImageProcessor

else:
			import sys

			_SCREAMING_SNAKE_CASE:    Union[str, Any]     						=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

