

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 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Union[str, Any]									=				["input_values", "padding_mask"]







			def __init__(     self :				Any							,   _UpperCamelCase :				int = 1							,   _UpperCamelCase :				int = 2_4_0_0_0							,   _UpperCamelCase :				float = 0.0							,   _UpperCamelCase :				float = None							,   _UpperCamelCase :				float = None							,   **_UpperCamelCase :				List[Any]							,   )      ->List[str]:
									super().__init__(feature_size=_UpperCamelCase							,   sampling_rate=_UpperCamelCase							,   padding_value=_UpperCamelCase							,   **_UpperCamelCase							)
									snake_case_   =	chunk_length_s
									snake_case_   =	overlap







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







			@property
			def 				snake_case__(     self :				int							)      ->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 :				Tuple							,   _UpperCamelCase :				Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]							,   _UpperCamelCase :				Optional[Union[bool, str, PaddingStrategy]] = None							,   _UpperCamelCase :				Optional[bool] = False							,   _UpperCamelCase :				Optional[int] = None							,   _UpperCamelCase :				Optional[Union[str, TensorType]] = None							,   _UpperCamelCase :				Optional[int] = 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
															snake_case_   =	True

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

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

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

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

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

        # normal padding on batch
									if padded_inputs is None:
															snake_case_   =	self.pad(
															    _UpperCamelCase							,   max_length=_UpperCamelCase							,   truncation=_UpperCamelCase							,   padding=_UpperCamelCase							,   return_attention_mask=_UpperCamelCase							,   )
															if padding:
																					snake_case_   =	padded_inputs.pop('''attention_mask'''							)

									snake_case_   =	[]
									for example in padded_inputs.pop('''input_values'''							):
															if self.feature_size == 1:
																					snake_case_   =	example[..., None]
															input_values.append(example.T							)

									snake_case_   =	input_values
									if return_tensors is not None:
															snake_case_   =	padded_inputs.convert_to_tensors(_UpperCamelCase							)

									return padded_inputs


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_       	=	logging.get_logger(__name__)

lowerCAmelCase_       	=	{
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   List[Any]									=				"vit_msn"







			def __init__(     self :				Dict							,   _UpperCamelCase :				Optional[int]=7_6_8							,   _UpperCamelCase :				Optional[Any]=1_2							,   _UpperCamelCase :				Union[str, Any]=1_2							,   _UpperCamelCase :				str=3_0_7_2							,   _UpperCamelCase :				Tuple="gelu"							,   _UpperCamelCase :				Dict=0.0							,   _UpperCamelCase :				Dict=0.0							,   _UpperCamelCase :				List[str]=0.02							,   _UpperCamelCase :				List[Any]=1e-06							,   _UpperCamelCase :				Any=2_2_4							,   _UpperCamelCase :				Optional[Any]=1_6							,   _UpperCamelCase :				Any=3							,   _UpperCamelCase :				str=True							,   **_UpperCamelCase :				Any							,   )      ->int:
									super().__init__(**_UpperCamelCase							)

									snake_case_   =	hidden_size
									snake_case_   =	num_hidden_layers
									snake_case_   =	num_attention_heads
									snake_case_   =	intermediate_size
									snake_case_   =	hidden_act
									snake_case_   =	hidden_dropout_prob
									snake_case_   =	attention_probs_dropout_prob
									snake_case_   =	initializer_range
									snake_case_   =	layer_norm_eps
									snake_case_   =	image_size
									snake_case_   =	patch_size
									snake_case_   =	num_channels
									snake_case_   =	qkv_bias