




from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np







class     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      List[Any]:



							'''simple docstring'''
							return 0.0







def 			lowerCamelCase_		(       UpperCamelCase__				:      Dict ,     UpperCamelCase__				:      Optional[Any]      )     ->       Optional[int]:





				"""simple docstring"""


				__lowerCamelCase		       =    min([-20, np.min(fft_results[1 : samplerate // 2 - 1]      )]      )
				__lowerCamelCase		       =    max([20, np.max(fft_results[1 : samplerate // 2 - 1]      )]      )
				return lowest, highest







def 			lowerCamelCase_		(       UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      Tuple      )     ->       str:





				"""simple docstring"""


				__lowerCamelCase		       =    512
				__lowerCamelCase		       =    [1] + [0] * (size - 1)
				__lowerCamelCase		       =    [filter_type.process(_lowerCamelCase      ) for item in inputs]

				__lowerCamelCase		       =    [0] * (samplerate - size)  # zero-padding
				outputs += filler
				__lowerCamelCase		       =    np.abs(np.fft.fft(_lowerCamelCase      )      )
				__lowerCamelCase		       =    20 * np.logaa(_lowerCamelCase      )

				# Frequencies on log scale from 24 to nyquist frequency
				plt.xlim(24 ,     samplerate / 2 - 1      )
				plt.xlabel('Frequency (Hz)'      )
				plt.xscale('log'      )

				# Display within reasonable bounds
				__lowerCamelCase		       =    get_bounds(_lowerCamelCase ,     _lowerCamelCase      )
				plt.ylim(max([-80, bounds[0]]      ) ,     min([80, bounds[1]]      )      )
				plt.ylabel('Gain (dB)'      )

				plt.plot(_lowerCamelCase      )
				plt.show()







def 			lowerCamelCase_		(       UpperCamelCase__				:      Tuple ,     UpperCamelCase__				:      int      )     ->       Optional[int]:





				"""simple docstring"""


				__lowerCamelCase		       =    512
				__lowerCamelCase		       =    [1] + [0] * (size - 1)
				__lowerCamelCase		       =    [filter_type.process(_lowerCamelCase      ) for item in inputs]

				__lowerCamelCase		       =    [0] * (samplerate - size)  # zero-padding
				outputs += filler
				__lowerCamelCase		       =    np.angle(np.fft.fft(_lowerCamelCase      )      )

				# Frequencies on log scale from 24 to nyquist frequency
				plt.xlim(24 ,     samplerate / 2 - 1      )
				plt.xlabel('Frequency (Hz)'      )
				plt.xscale('log'      )

				plt.ylim(-2 * pi ,     2 * pi      )
				plt.ylabel('Phase shift (Radians)'      )
				plt.plot(np.unwrap(_lowerCamelCase ,     -2 * pi      )      )
				plt.show()







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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_snake_case		=  logging.get_logger(__name__)

_snake_case		=  {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}



class   UpperCAmelCase_    (       a   ,					a):
							lowerCamelCase__   			=		'swin'

							lowerCamelCase__   			=		{
							    'num_attention_heads': 'num_heads',
							    'num_hidden_layers': 'num_layers',
							}







							def __init__(   self,			__a=224,			__a=4,			__a=3,			__a=96,			__a=[2, 2, 6, 2],			__a=[3, 6, 12, 24],			__a=7,			__a=4.0,			__a=True,			__a=0.0,			__a=0.0,			__a=0.1,			__a="gelu",			__a=False,			__a=0.02,			__a=1E-5,			__a=32,			__a=None,			__a=None,			**__a,			):

														'''simple docstring'''

														super().__init__(**__a)

														_lowerCAmelCase     :							Any    =    image_size
														_lowerCAmelCase     :							Union[str, Any]    =    patch_size
														_lowerCAmelCase     :							Tuple    =    num_channels
														_lowerCAmelCase     :							List[Any]    =    embed_dim
														_lowerCAmelCase     :							Tuple    =    depths
														_lowerCAmelCase     :							Optional[Any]    =    len(__a)
														_lowerCAmelCase     :							int    =    num_heads
														_lowerCAmelCase     :							int    =    window_size
														_lowerCAmelCase     :							int    =    mlp_ratio
														_lowerCAmelCase     :							List[Any]    =    qkv_bias
														_lowerCAmelCase     :							str    =    hidden_dropout_prob
														_lowerCAmelCase     :							Union[str, Any]    =    attention_probs_dropout_prob
														_lowerCAmelCase     :							Any    =    drop_path_rate
														_lowerCAmelCase     :							int    =    hidden_act
														_lowerCAmelCase     :							Tuple    =    use_absolute_embeddings
														_lowerCAmelCase     :							Optional[int]    =    layer_norm_eps
														_lowerCAmelCase     :							Tuple    =    initializer_range
														_lowerCAmelCase     :							Tuple    =    encoder_stride
														# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
														# this indicates the channel dimension after the last stage of the model
														_lowerCAmelCase     :							List[str]    =    int(embed_dim * 2 ** (len(__a) - 1))
														_lowerCAmelCase     :							List[Any]    =    ["stem"] + [f"stage{idx}" for idx in range(1,			len(__a) + 1)]
														_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[int]    =    get_aligned_output_features_output_indices(
														    out_features=__a,			out_indices=__a,			stage_names=self.stage_names)



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		version.parse('1.11')







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

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







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return 1E-4
