


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 YolosImageProcessor







class        snake_case_   (unittest.TestCase      ):
					def __init__(      self   :int			,__snake_case   :Optional[Any]			,__snake_case   :List[Any]=7			,__snake_case   :int=3			,__snake_case   :Union[str, Any]=30			,__snake_case   :Tuple=4_00			,__snake_case   :int=True			,__snake_case   :Union[str, Any]=None			,__snake_case   :Dict=True			,__snake_case   :Optional[int]=[0.5, 0.5, 0.5]			,__snake_case   :int=[0.5, 0.5, 0.5]			,__snake_case   :List[str]=True			,__snake_case   :Optional[int]=1 / 2_55			,__snake_case   :Dict=True			,)					->    Any:
												# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
												a__             =     size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
												a__             =     parent
												a__             =     batch_size
												a__             =     num_channels
												a__             =     min_resolution
												a__             =     max_resolution
												a__             =     do_resize
												a__             =     size
												a__             =     do_normalize
												a__             =     image_mean
												a__             =     image_std
												a__             =     do_rescale
												a__             =     rescale_factor
												a__             =     do_pad
					def  lowerCamelCase__(      self   :int      )					->    Tuple:
												return {
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "do_normalize": self.do_normalize,
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_rescale": self.do_rescale,
												    "rescale_factor": self.rescale_factor,
												    "do_pad": self.do_pad,
												}



					def  lowerCamelCase__(      self   :int			,__snake_case   :Tuple			,__snake_case   :str=False      )					->    str:
												if not batched:
																			a__             =     image_inputs[0]
																			if isinstance(__snake_case			,Image.Image      ):
																										a__      ,					a__             =     image.size
																			else:
																										a__      ,					a__             =     image.shape[1], image.shape[2]
																			if w < h:
																										a__             =     int(self.size['shortest_edge'] * h / w      )
																										a__             =     self.size['shortest_edge']
																			elif w > h:
																										a__             =     self.size['shortest_edge']
																										a__             =     int(self.size['shortest_edge'] * w / h      )
																			else:
																										a__             =     self.size['shortest_edge']
																										a__             =     self.size['shortest_edge']

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

												return expected_height, expected_width







@require_torch
@require_vision
class        snake_case_   (lowerCamelCase_	,						unittest.TestCase      ):
					UpperCAmelCase__					:    Tuple          =			YolosImageProcessor if is_vision_available() else None
					def  lowerCamelCase__(      self   :Dict      )					->    Any:
												a__             =     YolosImageProcessingTester(self      )
					@property
					def  lowerCamelCase__(      self   :int      )					->    Tuple:
												return self.image_processor_tester.prepare_image_processor_dict()
					def  lowerCamelCase__(      self   :int      )					->    Optional[Any]:
												a__             =     self.image_processing_class(**self.image_processor_dict      )
												self.assertTrue(hasattr(__snake_case			,'image_mean'      )      )
												self.assertTrue(hasattr(__snake_case			,'image_std'      )      )
												self.assertTrue(hasattr(__snake_case			,'do_normalize'      )      )
												self.assertTrue(hasattr(__snake_case			,'do_resize'      )      )
												self.assertTrue(hasattr(__snake_case			,'size'      )      )
					def  lowerCamelCase__(      self   :List[str]      )					->    str:
												a__             =     self.image_processing_class.from_dict(self.image_processor_dict      )
												self.assertEqual(image_processor.size			,{'shortest_edge': 18, 'longest_edge': 13_33}      )
												self.assertEqual(image_processor.do_pad			,__snake_case      )

												a__             =     self.image_processing_class.from_dict(
												    self.image_processor_dict			,size=42			,max_size=84			,pad_and_return_pixel_mask=__snake_case      )
												self.assertEqual(image_processor.size			,{'shortest_edge': 42, 'longest_edge': 84}      )
												self.assertEqual(image_processor.do_pad			,__snake_case      )
					def  lowerCamelCase__(      self   :str      )					->    List[str]:
												pass
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    Any:
												# Initialize image_processing
												a__             =     self.image_processing_class(**self.image_processor_dict      )
												# create random PIL images
												a__             =     prepare_image_inputs(self.image_processor_tester			,equal_resolution=__snake_case      )
												for image in image_inputs:
																			self.assertIsInstance(__snake_case			,Image.Image      )

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

												a__      ,					a__             =     self.image_processor_tester.get_expected_values(__snake_case      )

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

												# Test batched
												a__      ,					a__             =     self.image_processor_tester.get_expected_values(__snake_case			,batched=__snake_case      )

												a__             =     image_processing(__snake_case			,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  lowerCamelCase__(      self   :Optional[int]      )					->    Dict:
												# Initialize image_processing
												a__             =     self.image_processing_class(**self.image_processor_dict      )
												# create random numpy tensors
												a__             =     prepare_image_inputs(self.image_processor_tester			,equal_resolution=__snake_case			,numpify=__snake_case      )
												for image in image_inputs:
																			self.assertIsInstance(__snake_case			,np.ndarray      )

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

												a__      ,					a__             =     self.image_processor_tester.get_expected_values(__snake_case      )

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

												# Test batched
												a__             =     image_processing(__snake_case			,return_tensors='pt'      ).pixel_values

												a__      ,					a__             =     self.image_processor_tester.get_expected_values(__snake_case			,batched=__snake_case      )

												self.assertEqual(
												    encoded_images.shape			,(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )			,)
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    int:
												# Initialize image_processing
												a__             =     self.image_processing_class(**self.image_processor_dict      )
												# create random PyTorch tensors
												a__             =     prepare_image_inputs(self.image_processor_tester			,equal_resolution=__snake_case			,torchify=__snake_case      )
												for image in image_inputs:
																			self.assertIsInstance(__snake_case			,torch.Tensor      )

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

												a__      ,					a__             =     self.image_processor_tester.get_expected_values(__snake_case      )

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

												# Test batched
												a__             =     image_processing(__snake_case			,return_tensors='pt'      ).pixel_values

												a__      ,					a__             =     self.image_processor_tester.get_expected_values(__snake_case			,batched=__snake_case      )

												self.assertEqual(
												    encoded_images.shape			,(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )			,)
					def  lowerCamelCase__(      self   :List[Any]      )					->    Dict:
												# Initialize image_processings
												a__             =     self.image_processing_class(**self.image_processor_dict      )
												a__             =     self.image_processing_class(do_resize=__snake_case			,do_normalize=__snake_case			,do_rescale=__snake_case      )
												# create random PyTorch tensors
												a__             =     prepare_image_inputs(self.image_processor_tester			,equal_resolution=__snake_case			,torchify=__snake_case      )
												for image in image_inputs:
																			self.assertIsInstance(__snake_case			,torch.Tensor      )

												# Test whether the method "pad" and calling the image processor return the same tensors
												a__             =     image_processing_a.pad(__snake_case			,return_tensors='pt'      )
												a__             =     image_processing_a(__snake_case			,return_tensors='pt'      )

												self.assertTrue(
												    torch.allclose(encoded_images_with_method['pixel_values']			,encoded_images['pixel_values']			,atol=1E-4      )      )
					@slow
					def  lowerCamelCase__(      self   :List[Any]      )					->    Union[str, Any]:
												# prepare image and target
												a__             =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
												with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'			,'r'      ) as f:
																			a__             =     json.loads(f.read()      )

												a__             =     {'image_id': 3_97_69, 'annotations': target}

												# encode them
												a__             =     YolosImageProcessor.from_pretrained('hustvl/yolos-small'      )
												a__             =     image_processing(images=__snake_case			,annotations=__snake_case			,return_tensors='pt'      )

												# verify pixel values
												a__             =     torch.Size([1, 3, 8_00, 10_66]      )
												self.assertEqual(encoding['pixel_values'].shape			,__snake_case      )

												a__             =     torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
												self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]			,__snake_case			,atol=1E-4      )      )

												# verify area
												a__             =     torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['area']			,__snake_case      )      )
												# verify boxes
												a__             =     torch.Size([6, 4]      )
												self.assertEqual(encoding['labels'][0]['boxes'].shape			,__snake_case      )
												a__             =     torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]			,__snake_case			,atol=1E-3      )      )
												# verify image_id
												a__             =     torch.tensor([3_97_69]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,__snake_case      )      )
												# verify is_crowd
												a__             =     torch.tensor([0, 0, 0, 0, 0, 0]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,__snake_case      )      )
												# verify class_labels
												a__             =     torch.tensor([75, 75, 63, 65, 17, 17]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,__snake_case      )      )
												# verify orig_size
												a__             =     torch.tensor([4_80, 6_40]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,__snake_case      )      )
												# verify size
												a__             =     torch.tensor([8_00, 10_66]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,__snake_case      )      )



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

												a__             =     {'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

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

												# encode them
												a__             =     YolosImageProcessor(format='coco_panoptic'      )
												a__             =     image_processing(images=__snake_case			,annotations=__snake_case			,masks_path=__snake_case			,return_tensors='pt'      )

												# verify pixel values
												a__             =     torch.Size([1, 3, 8_00, 10_66]      )
												self.assertEqual(encoding['pixel_values'].shape			,__snake_case      )

												a__             =     torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
												self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]			,__snake_case			,atol=1E-4      )      )

												# verify area
												a__             =     torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['area']			,__snake_case      )      )
												# verify boxes
												a__             =     torch.Size([6, 4]      )
												self.assertEqual(encoding['labels'][0]['boxes'].shape			,__snake_case      )
												a__             =     torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]			,__snake_case			,atol=1E-3      )      )
												# verify image_id
												a__             =     torch.tensor([3_97_69]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,__snake_case      )      )
												# verify is_crowd
												a__             =     torch.tensor([0, 0, 0, 0, 0, 0]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,__snake_case      )      )
												# verify class_labels
												a__             =     torch.tensor([17, 17, 63, 75, 75, 93]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,__snake_case      )      )
												# verify masks
												a__             =     82_28_73
												self.assertEqual(encoding['labels'][0]['masks'].sum().item()			,__snake_case      )
												# verify orig_size
												a__             =     torch.tensor([4_80, 6_40]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,__snake_case      )      )
												# verify size
												a__             =     torch.tensor([8_00, 10_66]      )
												self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,__snake_case      )      )








import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate







@dataclass(frozen=lowerCamelCase_      )
class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    str          =			field(default='''audio-classification'''	,						metadata={'''include_in_asdict_even_if_is_default''': True}      )
					UpperCAmelCase__					:    ClassVar[Features]          =			Features({'''audio''': Audio()}      )
					UpperCAmelCase__					:    ClassVar[Features]          =			Features({'''labels''': ClassLabel}      )
					UpperCAmelCase__					:    str          =			"audio"
					UpperCAmelCase__					:    str          =			"labels"
					def  lowerCamelCase__(      self   :Optional[int]			,__snake_case   :int      )					->    str:
												if self.label_column not in features:
																			raise ValueError(F'Column {self.label_column} is not present in features.'      )
												if not isinstance(features[self.label_column]			,__snake_case      ):
																			raise ValueError(F'Column {self.label_column} is not a ClassLabel.'      )
												a__             =     copy.deepcopy(self      )
												a__             =     self.label_schema.copy()
												a__             =     features[self.label_column]
												a__             =     label_schema
												return task_template



					@property
					def  lowerCamelCase__(      self   :Dict      )					->    Dict[str, str]:
												return {
												    self.audio_column: "audio",
												    self.label_column: "labels",
												}




