




from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
  import torch


_lowerCamelCase     :		Tuple								=       logging.get_logger(__name__)


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				['''pixel_values''']


    def __init__(      self				:     Dict ,			UpperCAmelCase__				:     bool = True ,			UpperCAmelCase__				:     Optional[Dict[str, int]] = None ,			UpperCAmelCase__				:     PILImageResampling = PILImageResampling.BILINEAR ,			UpperCAmelCase__				:     bool = True ,			UpperCAmelCase__				:     Dict[str, int] = None ,			UpperCAmelCase__				:     bool = True ,			UpperCAmelCase__				:     Union[int, float] = 1 / 255 ,			UpperCAmelCase__				:     bool = True ,			UpperCAmelCase__				:     Optional[Union[float, List[float]]] = None ,			UpperCAmelCase__				:     Optional[Union[float, List[float]]] = None ,			**UpperCAmelCase__				:     List[str] ,			)						->None:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)
           A__							=   size if size is not None else {'''shortest_edge''': 256}
           A__							=   get_size_dict(UpperCAmelCase__ ,			default_to_square=UpperCAmelCase__)
           A__							=   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
           A__							=   get_size_dict(UpperCAmelCase__ ,			param_name='''crop_size''')
           A__							=   do_resize
           A__							=   size
           A__							=   resample
           A__							=   do_center_crop
           A__							=   crop_size
           A__							=   do_rescale
           A__							=   rescale_factor
           A__							=   do_normalize
           A__							=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           A__							=   image_std if image_std is not None else IMAGENET_STANDARD_STD


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any] ,			UpperCAmelCase__				:     np.ndarray ,			UpperCAmelCase__				:     Dict[str, int] ,			UpperCAmelCase__				:     PILImageResampling = PILImageResampling.BICUBIC ,			UpperCAmelCase__				:     Optional[Union[str, ChannelDimension]] = None ,			**UpperCAmelCase__				:     int ,			)						->np.ndarray:





           '''simple docstring'''

           A__							=   get_size_dict(UpperCAmelCase__ ,			default_to_square=UpperCAmelCase__)
           if "shortest_edge" not in size:
                  raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""")
           A__							=   get_resize_output_image_size(UpperCAmelCase__ ,			size=size['''shortest_edge'''] ,			default_to_square=UpperCAmelCase__)
           return resize(UpperCAmelCase__ ,			size=UpperCAmelCase__ ,			resample=UpperCAmelCase__ ,			data_format=UpperCAmelCase__ ,			**UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any] ,			UpperCAmelCase__				:     np.ndarray ,			UpperCAmelCase__				:     Dict[str, int] ,			UpperCAmelCase__				:     Optional[Union[str, ChannelDimension]] = None ,			**UpperCAmelCase__				:     Optional[int] ,			)						->np.ndarray:





           '''simple docstring'''

           A__							=   get_size_dict(UpperCAmelCase__)
           if "height" not in size or "width" not in size:
                  raise ValueError(f"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}""")
           return center_crop(UpperCAmelCase__ ,			size=(size['''height'''], size['''width''']) ,			data_format=UpperCAmelCase__ ,			**UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple ,			UpperCAmelCase__				:     np.ndarray ,			UpperCAmelCase__				:     float ,			UpperCAmelCase__				:     Optional[Union[str, ChannelDimension]] = None ,			**UpperCAmelCase__				:     Optional[int])						->np.ndarray:





           '''simple docstring'''

           return rescale(UpperCAmelCase__ ,			scale=UpperCAmelCase__ ,			data_format=UpperCAmelCase__ ,			**UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     np.ndarray ,			UpperCAmelCase__				:     Union[float, List[float]] ,			UpperCAmelCase__				:     Union[float, List[float]] ,			UpperCAmelCase__				:     Optional[Union[str, ChannelDimension]] = None ,			**UpperCAmelCase__				:     Optional[Any] ,			)						->np.ndarray:





           '''simple docstring'''

           return normalize(UpperCAmelCase__ ,			mean=UpperCAmelCase__ ,			std=UpperCAmelCase__ ,			data_format=UpperCAmelCase__ ,			**UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     ImageInput ,			UpperCAmelCase__				:     Optional[bool] = None ,			UpperCAmelCase__				:     Dict[str, int] = None ,			UpperCAmelCase__				:     PILImageResampling = None ,			UpperCAmelCase__				:     bool = None ,			UpperCAmelCase__				:     Dict[str, int] = None ,			UpperCAmelCase__				:     Optional[bool] = None ,			UpperCAmelCase__				:     Optional[float] = None ,			UpperCAmelCase__				:     Optional[bool] = None ,			UpperCAmelCase__				:     Optional[Union[float, List[float]]] = None ,			UpperCAmelCase__				:     Optional[Union[float, List[float]]] = None ,			UpperCAmelCase__				:     Optional[Union[str, TensorType]] = None ,			UpperCAmelCase__				:     Union[str, ChannelDimension] = ChannelDimension.FIRST ,			**UpperCAmelCase__				:     Dict ,			)						->Any:





           '''simple docstring'''

           A__							=   do_resize if do_resize is not None else self.do_resize
           A__							=   size if size is not None else self.size
           A__							=   get_size_dict(UpperCAmelCase__ ,			default_to_square=UpperCAmelCase__)
           A__							=   resample if resample is not None else self.resample
           A__							=   do_center_crop if do_center_crop is not None else self.do_center_crop
           A__							=   crop_size if crop_size is not None else self.crop_size
           A__							=   get_size_dict(UpperCAmelCase__ ,			param_name='''crop_size''')
           A__							=   do_rescale if do_rescale is not None else self.do_rescale
           A__							=   rescale_factor if rescale_factor is not None else self.rescale_factor
           A__							=   do_normalize if do_normalize is not None else self.do_normalize
           A__							=   image_mean if image_mean is not None else self.image_mean
           A__							=   image_std if image_std is not None else self.image_std

           A__							=   make_list_of_images(UpperCAmelCase__)

           if not valid_images(UpperCAmelCase__):
                  raise ValueError(
                      '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                      '''torch.Tensor, tf.Tensor or jax.ndarray.''')

           if do_resize and size is None:
                  raise ValueError('''Size must be specified if do_resize is True.''')

           if do_center_crop and crop_size is None:
                  raise ValueError('''Crop size must be specified if do_center_crop is True.''')

           if do_rescale and rescale_factor is None:
                  raise ValueError('''Rescale factor must be specified if do_rescale is True.''')

           if do_normalize and (image_mean is None or image_std is None):
                  raise ValueError('''Image mean and std must be specified if do_normalize is True.''')

           # All transformations expect numpy arrays.
           A__							=   [to_numpy_array(UpperCAmelCase__) for image in images]

           if do_resize:
                  A__							=   [self.resize(image=UpperCAmelCase__ ,			size=UpperCAmelCase__ ,			resample=UpperCAmelCase__) for image in images]

           if do_center_crop:
                  A__							=   [self.center_crop(image=UpperCAmelCase__ ,			size=UpperCAmelCase__) for image in images]

           if do_rescale:
                  A__							=   [self.rescale(image=UpperCAmelCase__ ,			scale=UpperCAmelCase__) for image in images]

           if do_normalize:
                  A__							=   [self.normalize(image=UpperCAmelCase__ ,			mean=UpperCAmelCase__ ,			std=UpperCAmelCase__) for image in images]

           A__							=   [to_channel_dimension_format(UpperCAmelCase__ ,			UpperCAmelCase__) for image in images]

           A__							=   {'''pixel_values''': images}
           return BatchFeature(data=UpperCAmelCase__ ,			tensor_type=UpperCAmelCase__)





    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any] ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     List[Tuple] = None)						->Dict:





           '''simple docstring'''

           A__							=   outputs.logits

           # Resize logits and compute semantic segmentation maps
           if target_sizes is not None:
                  if len(UpperCAmelCase__) != len(UpperCAmelCase__):
                         raise ValueError(
                             '''Make sure that you pass in as many target sizes as the batch dimension of the logits''')

                  if is_torch_tensor(UpperCAmelCase__):
                         A__							=   target_sizes.numpy()

                  A__							=   []

                  for idx in range(len(UpperCAmelCase__)):
                         A__							=   torch.nn.functional.interpolate(
                             logits[idx].unsqueeze(dim=0) ,			size=target_sizes[idx] ,			mode='''bilinear''' ,			align_corners=UpperCAmelCase__)
                         A__							=   resized_logits[0].argmax(dim=0)
                         semantic_segmentation.append(UpperCAmelCase__)
           else:
                  A__							=   logits.argmax(dim=1)
                  A__							=   [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

           return semantic_segmentation








from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			UpperCAmelCase__				:     float)						->float:





           '''simple docstring'''

           return 0.0







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	tuple[int | float, int | float]:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	None:



       """simple docstring"""

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

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

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

       plt.plot(lowercase_						)
       plt.show()







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	None:



       """simple docstring"""

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

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

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


