


'''simple docstring'''




import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetrImageProcessor

class 		UpperCAmelCase_      (  unittest.TestCase		):





     def __init__( self       :      List[str]    ,      UpperCAmelCase__       :      Tuple    ,      UpperCAmelCase__       :      Optional[int]=7    ,      UpperCAmelCase__       :      Union[str, Any]=3    ,      UpperCAmelCase__       :      Any=3_0    ,      UpperCAmelCase__       :      str=4_0_0    ,      UpperCAmelCase__       :      Union[str, Any]=True    ,      UpperCAmelCase__       :      Optional[int]=None    ,      UpperCAmelCase__       :      Tuple=True    ,      UpperCAmelCase__       :      Optional[int]=1 / 2_5_5    ,      UpperCAmelCase__       :      Union[str, Any]=True    ,      UpperCAmelCase__       :      List[Any]=[0.5, 0.5, 0.5]    ,      UpperCAmelCase__       :      Any=[0.5, 0.5, 0.5]    ,      UpperCAmelCase__       :      Optional[int]=True    ,      )				->  Any:
        # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
        lowerCAmelCase     =			size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
        lowerCAmelCase     =			parent
        lowerCAmelCase     =			batch_size
        lowerCAmelCase     =			num_channels
        lowerCAmelCase     =			min_resolution
        lowerCAmelCase     =			max_resolution
        lowerCAmelCase     =			do_resize
        lowerCAmelCase     =			size
        lowerCAmelCase     =			do_rescale
        lowerCAmelCase     =			rescale_factor
        lowerCAmelCase     =			do_normalize
        lowerCAmelCase     =			image_mean
        lowerCAmelCase     =			image_std
        lowerCAmelCase     =			do_pad





     def 							__UpperCAmelCase      ( self       :      int    )				->  Dict:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_pad": self.do_pad,
        }





     def 							__UpperCAmelCase      ( self       :      List[Any]    ,      UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      Dict=False    )				->  str:
        if not batched:
           lowerCAmelCase     =			image_inputs[0]
           if isinstance(UpperCAmelCase__    ,      Image.Image    ):
              lowerCAmelCase     ,  lowerCAmelCase     =			image.size
           else:
              lowerCAmelCase     ,  lowerCAmelCase     =			image.shape[1], image.shape[2]
           if w < h:
              lowerCAmelCase     =			int(self.size['shortest_edge'] * h / w    )
              lowerCAmelCase     =			self.size['shortest_edge']
           elif w > h:
              lowerCAmelCase     =			self.size['shortest_edge']
              lowerCAmelCase     =			int(self.size['shortest_edge'] * w / h    )
           else:
              lowerCAmelCase     =			self.size['shortest_edge']
              lowerCAmelCase     =			self.size['shortest_edge']

        else:
           lowerCAmelCase     =			[]
           for image in image_inputs:
              lowerCAmelCase     ,  lowerCAmelCase     =			self.get_expected_values([image]    )
              expected_values.append((expected_height, expected_width)    )
           lowerCAmelCase     =			max(UpperCAmelCase__    ,      key=lambda UpperCAmelCase__    : item[0]    )[0]
           lowerCAmelCase     =			max(UpperCAmelCase__    ,      key=lambda UpperCAmelCase__    : item[1]    )[1]

        return expected_height, expected_width




@require_torch
@require_vision
class 		UpperCAmelCase_      (  __lowercase       ,	unittest.TestCase		):
     lowerCamelCase				:	List[str]		=     DetrImageProcessor if is_vision_available() else None





     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  List[Any]:
        lowerCAmelCase     =			DetrImageProcessingTester(self    )





     @property
     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  int:
        return self.image_processor_tester.prepare_image_processor_dict()





     def 							__UpperCAmelCase      ( self       :      str    )				->  str:
        lowerCAmelCase     =			self.image_processing_class(**self.image_processor_dict    )
        self.assertTrue(hasattr(UpperCAmelCase__    ,      'image_mean'    )    )
        self.assertTrue(hasattr(UpperCAmelCase__    ,      'image_std'    )    )
        self.assertTrue(hasattr(UpperCAmelCase__    ,      'do_normalize'    )    )
        self.assertTrue(hasattr(UpperCAmelCase__    ,      'do_rescale'    )    )
        self.assertTrue(hasattr(UpperCAmelCase__    ,      'rescale_factor'    )    )
        self.assertTrue(hasattr(UpperCAmelCase__    ,      'do_resize'    )    )
        self.assertTrue(hasattr(UpperCAmelCase__    ,      'size'    )    )
        self.assertTrue(hasattr(UpperCAmelCase__    ,      'do_pad'    )    )





     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  List[str]:
        lowerCAmelCase     =			self.image_processing_class.from_dict(self.image_processor_dict    )
        self.assertEqual(image_processor.size    ,      {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}    )
        self.assertEqual(image_processor.do_pad    ,      UpperCAmelCase__    )

        lowerCAmelCase     =			self.image_processing_class.from_dict(
            self.image_processor_dict    ,      size=4_2    ,      max_size=8_4    ,      pad_and_return_pixel_mask=UpperCAmelCase__    )
        self.assertEqual(image_processor.size    ,      {'shortest_edge': 4_2, 'longest_edge': 8_4}    )
        self.assertEqual(image_processor.do_pad    ,      UpperCAmelCase__    )





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





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  str:
        # Initialize image_processing
        lowerCAmelCase     =			self.image_processing_class(**self.image_processor_dict    )
        # create random PIL images
        lowerCAmelCase     =			prepare_image_inputs(self.image_processor_tester    ,      equal_resolution=UpperCAmelCase__    )
        for image in image_inputs:
           self.assertIsInstance(UpperCAmelCase__    ,      Image.Image    )

        # Test not batched input
        lowerCAmelCase     =			image_processing(image_inputs[0]    ,      return_tensors='pt'    ).pixel_values

        lowerCAmelCase     ,  lowerCAmelCase     =			self.image_processor_tester.get_expected_values(UpperCAmelCase__    )

        self.assertEqual(
            encoded_images.shape    ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,      )

        # Test batched
        lowerCAmelCase     ,  lowerCAmelCase     =			self.image_processor_tester.get_expected_values(UpperCAmelCase__    ,      batched=UpperCAmelCase__    )

        lowerCAmelCase     =			image_processing(UpperCAmelCase__    ,      return_tensors='pt'    ).pixel_values
        self.assertEqual(
            encoded_images.shape    ,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )    ,      )





     def 							__UpperCAmelCase      ( self       :      int    )				->  Tuple:
        # Initialize image_processing
        lowerCAmelCase     =			self.image_processing_class(**self.image_processor_dict    )
        # create random numpy tensors
        lowerCAmelCase     =			prepare_image_inputs(self.image_processor_tester    ,      equal_resolution=UpperCAmelCase__    ,      numpify=UpperCAmelCase__    )
        for image in image_inputs:
           self.assertIsInstance(UpperCAmelCase__    ,      np.ndarray    )

        # Test not batched input
        lowerCAmelCase     =			image_processing(image_inputs[0]    ,      return_tensors='pt'    ).pixel_values

        lowerCAmelCase     ,  lowerCAmelCase     =			self.image_processor_tester.get_expected_values(UpperCAmelCase__    )

        self.assertEqual(
            encoded_images.shape    ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,      )

        # Test batched
        lowerCAmelCase     =			image_processing(UpperCAmelCase__    ,      return_tensors='pt'    ).pixel_values

        lowerCAmelCase     ,  lowerCAmelCase     =			self.image_processor_tester.get_expected_values(UpperCAmelCase__    ,      batched=UpperCAmelCase__    )

        self.assertEqual(
            encoded_images.shape    ,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )    ,      )





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Any:
        # Initialize image_processing
        lowerCAmelCase     =			self.image_processing_class(**self.image_processor_dict    )
        # create random PyTorch tensors
        lowerCAmelCase     =			prepare_image_inputs(self.image_processor_tester    ,      equal_resolution=UpperCAmelCase__    ,      torchify=UpperCAmelCase__    )
        for image in image_inputs:
           self.assertIsInstance(UpperCAmelCase__    ,      torch.Tensor    )

        # Test not batched input
        lowerCAmelCase     =			image_processing(image_inputs[0]    ,      return_tensors='pt'    ).pixel_values

        lowerCAmelCase     ,  lowerCAmelCase     =			self.image_processor_tester.get_expected_values(UpperCAmelCase__    )

        self.assertEqual(
            encoded_images.shape    ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,      )

        # Test batched
        lowerCAmelCase     =			image_processing(UpperCAmelCase__    ,      return_tensors='pt'    ).pixel_values

        lowerCAmelCase     ,  lowerCAmelCase     =			self.image_processor_tester.get_expected_values(UpperCAmelCase__    ,      batched=UpperCAmelCase__    )

        self.assertEqual(
            encoded_images.shape    ,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )    ,      )





     @slow
     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  Any:
        # prepare image and target
        lowerCAmelCase     =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
        with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'    ,      'r'    ) as f:
           lowerCAmelCase     =			json.loads(f.read()    )

        lowerCAmelCase     =			{'image_id': 3_9_7_6_9, 'annotations': target}

        # encode them
        lowerCAmelCase     =			DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'    )
        lowerCAmelCase     =			image_processing(images=UpperCAmelCase__    ,      annotations=UpperCAmelCase__    ,      return_tensors='pt'    )

        # verify pixel values
        lowerCAmelCase     =			torch.Size([1, 3, 8_0_0, 1_0_6_6]    )
        self.assertEqual(encoding['pixel_values'].shape    ,      UpperCAmelCase__    )

        lowerCAmelCase     =			torch.tensor([0.2_796, 0.3_138, 0.3_481]    )
        self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,      UpperCAmelCase__    ,      atol=1E-4    )    )

        # verify area
        lowerCAmelCase     =			torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]    )
        self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,      UpperCAmelCase__    )    )
        # verify boxes
        lowerCAmelCase     =			torch.Size([6, 4]    )
        self.assertEqual(encoding['labels'][0]['boxes'].shape    ,      UpperCAmelCase__    )
        lowerCAmelCase     =			torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]    )
        self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,      UpperCAmelCase__    ,      atol=1E-3    )    )
        # verify image_id
        lowerCAmelCase     =			torch.tensor([3_9_7_6_9]    )
        self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,      UpperCAmelCase__    )    )
        # verify is_crowd
        lowerCAmelCase     =			torch.tensor([0, 0, 0, 0, 0, 0]    )
        self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,      UpperCAmelCase__    )    )
        # verify class_labels
        lowerCAmelCase     =			torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]    )
        self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,      UpperCAmelCase__    )    )
        # verify orig_size
        lowerCAmelCase     =			torch.tensor([4_8_0, 6_4_0]    )
        self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,      UpperCAmelCase__    )    )
        # verify size
        lowerCAmelCase     =			torch.tensor([8_0_0, 1_0_6_6]    )
        self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,      UpperCAmelCase__    )    )





     @slow
     def 							__UpperCAmelCase      ( self       :      str    )				->  str:
        # prepare image, target and masks_path
        lowerCAmelCase     =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
        with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'    ,      'r'    ) as f:
           lowerCAmelCase     =			json.loads(f.read()    )

        lowerCAmelCase     =			{'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

        lowerCAmelCase     =			pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'    )

        # encode them
        lowerCAmelCase     =			DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'    )
        lowerCAmelCase     =			image_processing(images=UpperCAmelCase__    ,      annotations=UpperCAmelCase__    ,      masks_path=UpperCAmelCase__    ,      return_tensors='pt'    )

        # verify pixel values
        lowerCAmelCase     =			torch.Size([1, 3, 8_0_0, 1_0_6_6]    )
        self.assertEqual(encoding['pixel_values'].shape    ,      UpperCAmelCase__    )

        lowerCAmelCase     =			torch.tensor([0.2_796, 0.3_138, 0.3_481]    )
        self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,      UpperCAmelCase__    ,      atol=1E-4    )    )

        # verify area
        lowerCAmelCase     =			torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]    )
        self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,      UpperCAmelCase__    )    )
        # verify boxes
        lowerCAmelCase     =			torch.Size([6, 4]    )
        self.assertEqual(encoding['labels'][0]['boxes'].shape    ,      UpperCAmelCase__    )
        lowerCAmelCase     =			torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]    )
        self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,      UpperCAmelCase__    ,      atol=1E-3    )    )
        # verify image_id
        lowerCAmelCase     =			torch.tensor([3_9_7_6_9]    )
        self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,      UpperCAmelCase__    )    )
        # verify is_crowd
        lowerCAmelCase     =			torch.tensor([0, 0, 0, 0, 0, 0]    )
        self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,      UpperCAmelCase__    )    )
        # verify class_labels
        lowerCAmelCase     =			torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]    )
        self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,      UpperCAmelCase__    )    )
        # verify masks
        lowerCAmelCase     =			8_2_2_8_7_3
        self.assertEqual(encoding['labels'][0]['masks'].sum().item()    ,      UpperCAmelCase__    )
        # verify orig_size
        lowerCAmelCase     =			torch.tensor([4_8_0, 6_4_0]    )
        self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,      UpperCAmelCase__    )    )
        # verify size
        lowerCAmelCase     =			torch.tensor([8_0_0, 1_0_6_6]    )
        self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,      UpperCAmelCase__    )    )









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()





