




import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor







class 						lowercase							(   unittest.TestCase		):

     def __init__(  self		,     _a		,     _a=7		,     _a=3		,     _a=18		,     _a=30		,     _a=400		,     _a=True		,     _a=None		,     _a=True		,     _a=False		,     _a=True		,     _a=True		,     _a=[0.5, 0.5, 0.5]		,     _a=[0.5, 0.5, 0.5]		,     )						->							Dict:
           _A							:     str			=						parent
           _A							:     int			=						batch_size
           _A							:     Optional[int]			=						num_channels
           _A							:     List[Any]			=						image_size
           _A							:     int			=						min_resolution
           _A							:     Optional[int]			=						max_resolution
           _A							:     Any			=						do_resize
           _A							:     List[str]			=						size if size is not None else {"""height""": 18, """width""": 20}
           _A							:     Optional[int]			=						do_thumbnail
           _A							:     str			=						do_align_axis
           _A							:     List[Any]			=						do_pad
           _A							:     Optional[Any]			=						do_normalize
           _A							:     Tuple			=						image_mean
           _A							:     List[str]			=						image_std



     def 					a__	(  self				)						->							Optional[int]:
           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_thumbnail": self.do_thumbnail,
               "do_align_long_axis": self.do_align_axis,
               "do_pad": self.do_pad,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
           }







@require_torch
@require_vision
class 						lowercase							(   UpperCamelCase__,unittest.TestCase		):
     _a							 =						DonutImageProcessor if is_vision_available() else None

     def 					a__	(  self				)						->							Optional[int]:
           _A							:     List[str]			=						DonutImageProcessingTester(self				)

     @property
     def 					a__	(  self				)						->							List[Any]:
           return self.image_processor_tester.prepare_image_processor_dict()

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     Union[str, Any]			=						self.image_processing_class(**self.image_processor_dict				)
           self.assertTrue(hasattr(_a		,     """do_resize"""				)				)
           self.assertTrue(hasattr(_a		,     """size"""				)				)
           self.assertTrue(hasattr(_a		,     """do_thumbnail"""				)				)
           self.assertTrue(hasattr(_a		,     """do_align_long_axis"""				)				)
           self.assertTrue(hasattr(_a		,     """do_pad"""				)				)
           self.assertTrue(hasattr(_a		,     """do_normalize"""				)				)
           self.assertTrue(hasattr(_a		,     """image_mean"""				)				)
           self.assertTrue(hasattr(_a		,     """image_std"""				)				)

     def 					a__	(  self				)						->							List[Any]:
           _A							:     Union[str, Any]			=						self.image_processing_class.from_dict(self.image_processor_dict				)
           self.assertEqual(image_processor.size		,     {"""height""": 18, """width""": 20}				)

           _A							:     Optional[int]			=						self.image_processing_class.from_dict(self.image_processor_dict		,     size=42				)
           self.assertEqual(image_processor.size		,     {"""height""": 42, """width""": 42}				)

           # Previous config had dimensions in (width, height) order
           _A							:     List[str]			=						self.image_processing_class.from_dict(self.image_processor_dict		,     size=(42, 84)				)
           self.assertEqual(image_processor.size		,     {"""height""": 84, """width""": 42}				)

     def 					a__	(  self				)						->							Union[str, Any]:
           pass

     @is_flaky()
     def 					a__	(  self				)						->							Optional[int]:
           # Initialize image_processing
           _A							:     List[Any]			=						self.image_processing_class(**self.image_processor_dict				)
           # create random PIL images
           _A							:     Dict			=						prepare_image_inputs(self.image_processor_tester		,     equal_resolution=_a				)
           for image in image_inputs:
                 self.assertIsInstance(_a		,     Image.Image				)

           # Test not batched input
           _A							:     Union[str, Any]			=						image_processing(image_inputs[0]		,     return_tensors="""pt"""				).pixel_values
           self.assertEqual(
               encoded_images.shape		,     (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,     )

           # Test batched
           _A							:     Any			=						image_processing(_a		,     return_tensors="""pt"""				).pixel_values
           self.assertEqual(
               encoded_images.shape		,     (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,     )

     @is_flaky()
     def 					a__	(  self				)						->							Dict:
           # Initialize image_processing
           _A							:     str			=						self.image_processing_class(**self.image_processor_dict				)
           # create random numpy tensors
           _A							:     Tuple			=						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
           _A							:     int			=						image_processing(image_inputs[0]		,     return_tensors="""pt"""				).pixel_values
           self.assertEqual(
               encoded_images.shape		,     (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,     )

           # Test batched
           _A							:     List[str]			=						image_processing(_a		,     return_tensors="""pt"""				).pixel_values
           self.assertEqual(
               encoded_images.shape		,     (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,     )



     @is_flaky()
     def 					a__	(  self				)						->							Optional[int]:
           # Initialize image_processing
           _A							:     str			=						self.image_processing_class(**self.image_processor_dict				)
           # create random PyTorch tensors
           _A							:     Optional[int]			=						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
           _A							:     Any			=						image_processing(image_inputs[0]		,     return_tensors="""pt"""				).pixel_values
           self.assertEqual(
               encoded_images.shape		,     (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,     )

           # Test batched
           _A							:     str			=						image_processing(_a		,     return_tensors="""pt"""				).pixel_values
           self.assertEqual(
               encoded_images.shape		,     (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,     )





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()





