import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import GLPNImageProcessor




class 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






      def __init__(					self     :				int    ,  _UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				Optional[int]=7    ,  _UpperCamelCase     :				Optional[Any]=3    ,  _UpperCamelCase     :				Any=18    ,  _UpperCamelCase     :				int=30    ,  _UpperCamelCase     :				Optional[int]=400    ,  _UpperCamelCase     :				List[Any]=True    ,  _UpperCamelCase     :				int=32    ,  _UpperCamelCase     :				Optional[int]=True    ,  )				->str:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						parent
          _lowerCamelCase     :		List[str] 						=						batch_size
          _lowerCamelCase     :		int 						=						num_channels
          _lowerCamelCase     :		List[str] 						=						image_size
          _lowerCamelCase     :		Any 						=						min_resolution
          _lowerCamelCase     :		Optional[Any] 						=						max_resolution
          _lowerCamelCase     :		Optional[int] 						=						do_resize
          _lowerCamelCase     :		str 						=						size_divisor
          _lowerCamelCase     :		Dict 						=						do_rescale




      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->Dict:


          """simple docstring"""


          return {
              "do_resize": self.do_resize,
              "size_divisor": self.size_divisor,
              "do_rescale": self.do_rescale,
          }





@require_torch
@require_vision
class 	__snake_case	( snake_case__ ,  unittest.TestCase		):





      '''simple docstring'''






      _snake_case    		=       GLPNImageProcessor if is_vision_available() else None



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->int:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						GLPNImageProcessingTester(self)



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Optional[int]:


          """simple docstring"""


          return self.image_processor_tester.prepare_image_processor_dict()



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(_A    ,  """do_resize"""))
          self.assertTrue(hasattr(_A    ,  """size_divisor"""))
          self.assertTrue(hasattr(_A    ,  """resample"""))
          self.assertTrue(hasattr(_A    ,  """do_rescale"""))



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


          """simple docstring"""


          pass



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->Dict:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          _lowerCamelCase     :		Any 						=						prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=_A)
          for image in image_inputs:
              self.assertIsInstance(_A    ,  Image.Image)

          # Test not batched input (GLPNImageProcessor doesn't support batching)
          _lowerCamelCase     :		Tuple 						=						image_processing(image_inputs[0]    ,  return_tensors="""pt""").pixel_values
          self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0)
          self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						self.image_processing_class(**self.image_processor_dict)
          # create random numpy tensors
          _lowerCamelCase     :		Any 						=						prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=_A    ,  numpify=_A)
          for image in image_inputs:
              self.assertIsInstance(_A    ,  np.ndarray)

          # Test not batched input (GLPNImageProcessor doesn't support batching)
          _lowerCamelCase     :		List[str] 						=						image_processing(image_inputs[0]    ,  return_tensors="""pt""").pixel_values
          self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0)
          self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0)




      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->int:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						self.image_processing_class(**self.image_processor_dict)
          # create random PyTorch tensors
          _lowerCamelCase     :		List[Any] 						=						prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=_A    ,  torchify=_A)
          for image in image_inputs:
              self.assertIsInstance(_A    ,  torch.Tensor)

          # Test not batched input (GLPNImageProcessor doesn't support batching)
          _lowerCamelCase     :		int 						=						image_processing(image_inputs[0]    ,  return_tensors="""pt""").pixel_values
          self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0)
          self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0)





from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				float)				->float:


          """simple docstring"""


          return 0.0







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		int 						=						min([-20, np.min(fft_results[1 : samplerate // 2 - 1]	)]	)
    _lowerCamelCase     :		Tuple 						=						max([20, np.max(fft_results[1 : samplerate // 2 - 1]	)]	)
    return lowest, highest







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						512
    _lowerCamelCase     :		Tuple 						=						[1] + [0] * (size - 1)
    _lowerCamelCase     :		Optional[Any] 						=						[filter_type.process(__A	) for item in inputs]

    _lowerCamelCase     :		Optional[Any] 						=						[0] * (samplerate - size)  # zero-padding
    outputs += filler
    _lowerCamelCase     :		Tuple 						=						np.abs(np.fft.fft(__A	)	)
    _lowerCamelCase     :		List[Any] 						=						20 * np.logaa(__A	)

    # 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     :		Any 						=						get_bounds(__A   , __A	)
    plt.ylim(max([-80, bounds[0]]	)   , min([80, bounds[1]]	)	)
    plt.ylabel("""Gain (dB)"""	)

    plt.plot(__A	)
    plt.show()







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						512
    _lowerCamelCase     :		Union[str, Any] 						=						[1] + [0] * (size - 1)
    _lowerCamelCase     :		int 						=						[filter_type.process(__A	) for item in inputs]

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

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




