



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      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					def __init__(     self					:       Union[str, Any]				,					_snake_case					:       List[str]				,					_snake_case					:       List[str]=7				,					_snake_case					:       Dict=3				,					_snake_case					:       List[str]=30				,					_snake_case					:       Dict=4_00				,					_snake_case					:       List[str]=True				,					_snake_case					:       int=None				,					_snake_case					:       Optional[Any]=True				,					_snake_case					:       Tuple=[0.5, 0.5, 0.5]				,					_snake_case					:       List[Any]=[0.5, 0.5, 0.5]				,					_snake_case					:       Any=True				,					_snake_case					:       Optional[int]=1 / 2_55				,					_snake_case					:       Any=True				,					):






								"""simple docstring"""


								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     _a							(     self					:       str					):






								"""simple docstring"""


								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     _a							(     self					:       List[str]				,					_snake_case					:       Tuple				,					_snake_case					:       str=False					):






								"""simple docstring"""


								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      __lowerCAmelCase     (		UpperCAmelCase_							,     unittest.TestCase      ):
					"""simple docstring"""






					A__				:		Dict			       =    YolosImageProcessor if is_vision_available() else None



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								A__       =    YolosImageProcessingTester(self					)



					@property
					def     _a							(     self					:       int					):






								"""simple docstring"""


								return self.image_processor_tester.prepare_image_processor_dict()



					def     _a							(     self					:       str					):






								"""simple docstring"""


								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     _a							(     self					:       str					):






								"""simple docstring"""


								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     _a							(     self					:       List[str]					):






								"""simple docstring"""


								pass



					def     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


								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     _a							(     self					:       Tuple					):






								"""simple docstring"""


								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     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								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     _a							(     self					:       Any					):






								"""simple docstring"""


								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     _a							(     self					:       List[str]					):






								"""simple docstring"""


								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([0.2796, 0.3138, 0.3481]					)
								self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]				,					_snake_case				,					atol=1E-4					)					)

								# verify area
								A__       =    torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]					)
								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([0.5503, 0.2765, 0.0604, 0.2215]					)
								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     _a							(     self					:       List[str]					):






								"""simple docstring"""


								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([0.2796, 0.3138, 0.3481]					)
								self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]				,					_snake_case				,					atol=1E-4					)					)

								# verify area
								A__       =    torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]					)
								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([0.2625, 0.5437, 0.4688, 0.8625]					)
								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					)					)









# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__       =      {'''configuration_mra''': ['''MRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MraConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       =      [
							    '''MRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''MraForMaskedLM''',
							    '''MraForMultipleChoice''',
							    '''MraForQuestionAnswering''',
							    '''MraForSequenceClassification''',
							    '''MraForTokenClassification''',
							    '''MraLayer''',
							    '''MraModel''',
							    '''MraPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mra import (
														    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MraForMaskedLM,
														    MraForMultipleChoice,
														    MraForQuestionAnswering,
														    MraForSequenceClassification,
														    MraForTokenClassification,
														    MraLayer,
														    MraModel,
														    MraPreTrainedModel,
														)
else:
							import sys

							SCREAMING_SNAKE_CASE__       =      _LazyModule(__name__, globals()['''__file__'''], _import_structure)




