

'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case_       =       logging.get_logger(__name__)

snake_case_       =       {
    'apple/mobilevit-small': 'https://huggingface.co/apple/mobilevit-small/resolve/main/config.json',
    'apple/mobilevit-x-small': 'https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json',
    'apple/mobilevit-xx-small': 'https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json',
    'apple/deeplabv3-mobilevit-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-x-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-xx-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}
class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							"mobilevit"




    def __init__(					self				,     lowercase__=3				,     lowercase__=256				,     lowercase__=2				,     lowercase__=[144, 192, 240]				,     lowercase__=[16, 32, 64, 96, 128, 160, 640]				,     lowercase__=4				,     lowercase__=2.0				,     lowercase__=4.0				,     lowercase__="silu"				,     lowercase__=3				,     lowercase__=32				,     lowercase__=0.1				,     lowercase__=0.0				,     lowercase__=0.1				,     lowercase__=0.02				,     lowercase__=1e-5				,     lowercase__=True				,     lowercase__=256				,     lowercase__=[6, 12, 18]				,     lowercase__=0.1				,     lowercase__=255				,     **lowercase__				,     ):






          """simple docstring"""






          super().__init__(**lowercase__			)

          SCREAMING_SNAKE_CASE_      :			int     =       num_channels
          SCREAMING_SNAKE_CASE_      :			List[str]     =       image_size
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       patch_size
          SCREAMING_SNAKE_CASE_      :			List[str]     =       hidden_sizes
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       neck_hidden_sizes
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       num_attention_heads
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       mlp_ratio
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       expand_ratio
          SCREAMING_SNAKE_CASE_      :			str     =       hidden_act
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       conv_kernel_size
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       output_stride
          SCREAMING_SNAKE_CASE_      :			Tuple     =       hidden_dropout_prob
          SCREAMING_SNAKE_CASE_      :			int     =       attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_      :			Tuple     =       classifier_dropout_prob
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       initializer_range
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       layer_norm_eps
          SCREAMING_SNAKE_CASE_      :			str     =       qkv_bias

          # decode head attributes for semantic segmentation
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       aspp_out_channels
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       atrous_rates
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       aspp_dropout_prob
          SCREAMING_SNAKE_CASE_      :			Any     =       semantic_loss_ignore_index






class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							version.parse("1.11"      )




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})]			)




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          if self.task == "image-classification":
                return OrderedDict([("logits", {0: "batch"})]			)
          else:
                return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]			)







    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          return 1e-4







import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
	import librosa  # noqa: E402

	_lowerCamelCase					:     Union[str, Any]					  =  True
	_lowerCamelCase					:     Optional[Any]					  =  ""
except Exception as e:
	_lowerCamelCase					:     int					  =  False
	_lowerCamelCase					:     List[str]					  =  (
	    F'''Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it.'''
	)


from PIL import Image  # noqa: E402
class 				__snake_case     (_a  ,		_a						):
				lowerCAmelCase__			=   "mel_config.json"



				@register_to_config
				def __init__(					self					:						Dict						,		_UpperCAmelCase					:						int = 256						,		_UpperCAmelCase					:						int = 256						,		_UpperCAmelCase					:						int = 2_2050						,		_UpperCAmelCase					:						int = 2048						,		_UpperCAmelCase					:						int = 512						,		_UpperCAmelCase					:						int = 80						,		_UpperCAmelCase					:						int = 32						,		)     ->			List[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			List[str]        =					hop_length
								_lowerCAmelCase			:			Optional[Any]        =					sample_rate
								_lowerCAmelCase			:			Optional[Any]        =					n_fft
								_lowerCAmelCase			:			Dict        =					top_db
								_lowerCAmelCase			:			List[Any]        =					n_iter
								self.set_resolution(_UpperCAmelCase						,		_UpperCAmelCase					)
								_lowerCAmelCase			:			Optional[int]        =					None

								if not _librosa_can_be_imported:
												raise ValueError(_import_error					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]						,		_UpperCAmelCase					:						int						,		_UpperCAmelCase					:						int					)     ->			List[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			Dict        =					x_res
								_lowerCAmelCase			:			Union[str, Any]        =					y_res
								_lowerCAmelCase			:			Any        =					self.y_res
								_lowerCAmelCase			:			Optional[Any]        =					self.x_res * self.hop_length - 1



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict						,		_UpperCAmelCase					:						str = None						,		_UpperCAmelCase					:						np.ndarray = None					)     ->			List[str]:





								'''simple docstring'''




								if audio_file is not None:
												_lowerCAmelCase    ,			_lowerCAmelCase			:			Dict        =					librosa.load(_UpperCAmelCase						,		mono=_UpperCAmelCase						,		sr=self.sr					)
								else:
												_lowerCAmelCase			:			Any        =					raw_audio

								# Pad with silence if necessary.
								if len(self.audio					) < self.x_res * self.hop_length:
												_lowerCAmelCase			:			Optional[Any]        =					np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio					),)					)]					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Any					)     ->			int:





								'''simple docstring'''




								return len(self.audio					) // self.slice_size



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[str]						,		_UpperCAmelCase					:						int = 0					)     ->			np.ndarray:





								'''simple docstring'''




								return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]					)     ->			int:





								'''simple docstring'''




								return self.sr



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict						,		_UpperCAmelCase					:						int					)     ->			Image.Image:





								'''simple docstring'''




								_lowerCAmelCase			:			Union[str, Any]        =					librosa.feature.melspectrogram(
								    y=self.get_audio_slice(_UpperCAmelCase					)						,		sr=self.sr						,		n_fft=self.n_fft						,		hop_length=self.hop_length						,		n_mels=self.n_mels					)
								_lowerCAmelCase			:			List[Any]        =					librosa.power_to_db(_UpperCAmelCase						,		ref=np.max						,		top_db=self.top_db					)
								_lowerCAmelCase			:			List[Any]        =					(((log_S + self.top_db) * 255 / self.top_db).clip(0						,		255					) + 0.5).astype(np.uinta					)
								_lowerCAmelCase			:			Optional[Any]        =					Image.fromarray(_UpperCAmelCase					)
								return image



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]						,		_UpperCAmelCase					:						Image.Image					)     ->			np.ndarray:





								'''simple docstring'''




								_lowerCAmelCase			:			Optional[int]        =					np.frombuffer(image.tobytes()						,		dtype="""uint8"""					).reshape((image.height, image.width)					)
								_lowerCAmelCase			:			Optional[int]        =					bytedata.astype("""float"""					) * self.top_db / 255 - self.top_db
								_lowerCAmelCase			:			List[Any]        =					librosa.db_to_power(_UpperCAmelCase					)
								_lowerCAmelCase			:			Optional[int]        =					librosa.feature.inverse.mel_to_audio(
								    _UpperCAmelCase						,		sr=self.sr						,		n_fft=self.n_fft						,		hop_length=self.hop_length						,		n_iter=self.n_iter					)
								return audio

