
'''simple docstring'''







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


_lowercase :  Any    							=       logging.get_logger(__name__)

_lowercase :  int    							=       {
    "naver-clova-ix/donut-base": "https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}






class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     '''donut-swin'''

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






					def __init__(   self				,							__SCREAMING_SNAKE_CASE=2_24				,							__SCREAMING_SNAKE_CASE=4				,							__SCREAMING_SNAKE_CASE=3				,							__SCREAMING_SNAKE_CASE=96				,							__SCREAMING_SNAKE_CASE=[2, 2, 6, 2]				,							__SCREAMING_SNAKE_CASE=[3, 6, 12, 24]				,							__SCREAMING_SNAKE_CASE=7				,							__SCREAMING_SNAKE_CASE=4.0				,							__SCREAMING_SNAKE_CASE=True				,							__SCREAMING_SNAKE_CASE=0.0				,							__SCREAMING_SNAKE_CASE=0.0				,							__SCREAMING_SNAKE_CASE=0.1				,							__SCREAMING_SNAKE_CASE="gelu"				,							__SCREAMING_SNAKE_CASE=False				,							__SCREAMING_SNAKE_CASE=0.02				,							__SCREAMING_SNAKE_CASE=1E-5				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											super().__init__(**__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Dict													=       image_size
											lowercase_	:     List[str]													=       patch_size
											lowercase_	:     Union[str, Any]													=       num_channels
											lowercase_	:     List[Any]													=       embed_dim
											lowercase_	:     List[str]													=       depths
											lowercase_	:     List[str]													=       len(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Any													=       num_heads
											lowercase_	:     int													=       window_size
											lowercase_	:     List[str]													=       mlp_ratio
											lowercase_	:     List[Any]													=       qkv_bias
											lowercase_	:     Optional[Any]													=       hidden_dropout_prob
											lowercase_	:     Optional[Any]													=       attention_probs_dropout_prob
											lowercase_	:     Optional[Any]													=       drop_path_rate
											lowercase_	:     Tuple													=       hidden_act
											lowercase_	:     Any													=       use_absolute_embeddings
											lowercase_	:     Optional[int]													=       layer_norm_eps
											lowercase_	:     List[str]													=       initializer_range
											# 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
											lowercase_	:     str													=       int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE		) - 1)		)







from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    def _a					(			self					,   A_    )  ->       float:
         return 0.0

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					np.ndarray			,     SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=min([-20, np.min(fft_results[1 : samplerate // 2 - 1]				)]				)
     __UpperCamelCase    							=max([20, np.max(fft_results[1 : samplerate // 2 - 1]				)]				)
     return lowest, highest

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					FilterType			,     SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=5_12
     __UpperCamelCase    							=[1] + [0] * (size - 1)
     __UpperCamelCase    							=[filter_type.process(SCREAMING_SNAKE_CASE__				) for item in inputs]

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

     # 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
     __UpperCamelCase    							=get_bounds(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
     plt.ylim(max([-80, bounds[0]]				)			,     min([80, bounds[1]]				)				)
     plt.ylabel('Gain (dB)'				)

     plt.plot(SCREAMING_SNAKE_CASE__				)
     plt.show()





def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					FilterType			,     SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=5_12
     __UpperCamelCase    							=[1] + [0] * (size - 1)
     __UpperCamelCase    							=[filter_type.process(SCREAMING_SNAKE_CASE__				) for item in inputs]

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

     # 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(SCREAMING_SNAKE_CASE__			,     -2 * pi				)				)
     plt.show()





