





from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np

class   lowerCAmelCase				(   __UpperCamelCase     ):
 def 	A_      ( self    : str     ,	UpperCAmelCase    : float      )     ->      float:
     return 0.0


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	tuple[int | float, int | float]:
    lowerCamelCase__       : str   					=						min([-20, np.min(fft_results[1 : samplerate // 2 - 1]					)]					)
    lowerCamelCase__       : Optional[int]   					=						max([20, np.max(fft_results[1 : samplerate // 2 - 1]					)]					)
    return lowest, highest


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	None:
    lowerCamelCase__       : Any   					=						512
    lowerCamelCase__       : int   					=						[1] + [0] * (size - 1)
    lowerCamelCase__       : str   					=						[filter_type.process(_UpperCAmelCase					) for item in inputs]

    lowerCamelCase__       : Dict   					=						[0] * (samplerate - size)  # zero-padding
    outputs += filler
    lowerCamelCase__       : Tuple   					=						np.abs(np.fft.fft(_UpperCAmelCase					)					)
    lowerCamelCase__       : Optional[int]   					=						20 * np.logaa(_UpperCAmelCase					)

    # 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__       : Union[str, Any]   					=						get_bounds(_UpperCAmelCase		,      _UpperCAmelCase					)
    plt.ylim(max([-80, bounds[0]]					)		,      min([80, bounds[1]]					)					)
    plt.ylabel('Gain (dB)'					)

    plt.plot(_UpperCAmelCase					)
    plt.show()


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	None:
    lowerCamelCase__       : Optional[Any]   					=						512
    lowerCamelCase__       : Any   					=						[1] + [0] * (size - 1)
    lowerCamelCase__       : Optional[Any]   					=						[filter_type.process(_UpperCAmelCase					) for item in inputs]

    lowerCamelCase__       : Dict   					=						[0] * (samplerate - size)  # zero-padding
    outputs += filler
    lowerCamelCase__       : Tuple   					=						np.angle(np.fft.fft(_UpperCAmelCase					)					)

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












from typing import List

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


_UpperCAmelCase							:			Any        =							logging.get_logger(__name__)

_UpperCAmelCase							:			Union[str, Any]        =							{
    """snap-research/efficientformer-l1-300""": (
        """https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"""
    ),
}

class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						"""efficientformer"""
 def __init__( self    : Dict     ,	UpperCAmelCase    : List[int] = [3, 2, 6, 4]     ,	UpperCAmelCase    : List[int] = [48, 96, 224, 448]     ,	UpperCAmelCase    : List[bool] = [True, True, True, True]     ,	UpperCAmelCase    : int = 448     ,	UpperCAmelCase    : int = 32     ,	UpperCAmelCase    : int = 4     ,	UpperCAmelCase    : int = 7     ,	UpperCAmelCase    : int = 5     ,	UpperCAmelCase    : int = 8     ,	UpperCAmelCase    : int = 4     ,	UpperCAmelCase    : float = 0.0     ,	UpperCAmelCase    : int = 16     ,	UpperCAmelCase    : int = 3     ,	UpperCAmelCase    : int = 3     ,	UpperCAmelCase    : int = 3     ,	UpperCAmelCase    : int = 2     ,	UpperCAmelCase    : int = 1     ,	UpperCAmelCase    : float = 0.0     ,	UpperCAmelCase    : int = 1     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : float = 1e-5     ,	UpperCAmelCase    : str = "gelu"     ,	UpperCAmelCase    : float = 0.0_2     ,	UpperCAmelCase    : float = 1e-12     ,	UpperCAmelCase    : int = 224     ,	UpperCAmelCase    : float = 1e-05     ,	**UpperCAmelCase    : Tuple     ,	)     ->      None:
     super().__init__(**UpperCAmelCase      )

     lowerCamelCase__       : Optional[Any]   					=						hidden_act
     lowerCamelCase__       : int   					=						hidden_dropout_prob
     lowerCamelCase__       : List[Any]   					=						hidden_sizes
     lowerCamelCase__       : int   					=						num_hidden_layers
     lowerCamelCase__       : int   					=						num_attention_heads
     lowerCamelCase__       : Dict   					=						initializer_range
     lowerCamelCase__       : Optional[Any]   					=						layer_norm_eps
     lowerCamelCase__       : int   					=						patch_size
     lowerCamelCase__       : List[Any]   					=						num_channels
     lowerCamelCase__       : List[str]   					=						depths
     lowerCamelCase__       : Any   					=						mlp_expansion_ratio
     lowerCamelCase__       : Any   					=						downsamples
     lowerCamelCase__       : str   					=						dim
     lowerCamelCase__       : Tuple   					=						key_dim
     lowerCamelCase__       : int   					=						attention_ratio
     lowerCamelCase__       : int   					=						resolution
     lowerCamelCase__       : Dict   					=						pool_size
     lowerCamelCase__       : List[str]   					=						downsample_patch_size
     lowerCamelCase__       : Tuple   					=						downsample_stride
     lowerCamelCase__       : int   					=						downsample_pad
     lowerCamelCase__       : Optional[int]   					=						drop_path_rate
     lowerCamelCase__       : Optional[Any]   					=						num_metaad_blocks
     lowerCamelCase__       : Any   					=						distillation
     lowerCamelCase__       : Optional[int]   					=						use_layer_scale
     lowerCamelCase__       : Union[str, Any]   					=						layer_scale_init_value
     lowerCamelCase__       : Optional[int]   					=						image_size
     lowerCamelCase__       : Optional[Any]   					=						batch_norm_eps





