

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 			lowercase    (	unittest.TestCase      ):




				def __init__( self				,       snake_case				,       snake_case=7				,       snake_case=3				,       snake_case=30				,       snake_case=400				,       snake_case=True				,       snake_case=None				,       snake_case=True				,       snake_case=1 / 255				,       snake_case=True				,       snake_case=[0.5, 0.5, 0.5]				,       snake_case=[0.5, 0.5, 0.5]				,       snake_case=True				,       ):
											# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
											snake_case_          =   size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
											snake_case_          =   parent
											snake_case_          =   batch_size
											snake_case_          =   num_channels
											snake_case_          =   min_resolution
											snake_case_          =   max_resolution
											snake_case_          =   do_resize
											snake_case_          =   size
											snake_case_          =   do_rescale
											snake_case_          =   rescale_factor
											snake_case_          =   do_normalize
											snake_case_          =   image_mean
											snake_case_          =   image_std
											snake_case_          =   do_pad




				def    a							( self	):
											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    a							( self				,       snake_case				,       snake_case=False	):
											if not batched:
																		snake_case_          =   image_inputs[0]
																		if isinstance(snake_case				,       Image.Image	):
																									snake_case_    ,   snake_case_          =   image.size
																		else:
																									snake_case_    ,   snake_case_          =   image.shape[1], image.shape[2]
																		if w < h:
																									snake_case_          =   int(self.size['shortest_edge'] * h / w	)
																									snake_case_          =   self.size['shortest_edge']
																		elif w > h:
																									snake_case_          =   self.size['shortest_edge']
																									snake_case_          =   int(self.size['shortest_edge'] * w / h	)
																		else:
																									snake_case_          =   self.size['shortest_edge']
																									snake_case_          =   self.size['shortest_edge']

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

											return expected_height, expected_width







@require_torch
@require_vision
class 			lowercase    (	lowercase_     ,  unittest.TestCase      ):
				__SCREAMING_SNAKE_CASE :			Any	      =      DetrImageProcessor if is_vision_available() else None




				def    a							( self	):
											snake_case_          =   DetrImageProcessingTester(self	)




				@property
				def    a							( self	):
											return self.image_processor_tester.prepare_image_processor_dict()




				def    a							( self	):
											snake_case_          =   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_rescale'	)	)
											self.assertTrue(hasattr(snake_case				,       'rescale_factor'	)	)
											self.assertTrue(hasattr(snake_case				,       'do_resize'	)	)
											self.assertTrue(hasattr(snake_case				,       'size'	)	)
											self.assertTrue(hasattr(snake_case				,       'do_pad'	)	)




				def    a							( self	):
											snake_case_          =   self.image_processing_class.from_dict(self.image_processor_dict	)
											self.assertEqual(image_processor.size				,       {'shortest_edge': 18, 'longest_edge': 1333}	)
											self.assertEqual(image_processor.do_pad				,       snake_case	)

											snake_case_          =   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	):
											pass




				def    a							( self	):
											# Initialize image_processing
											snake_case_          =   self.image_processing_class(**self.image_processor_dict	)
											# create random PIL images
											snake_case_          =   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
											snake_case_          =   image_processing(image_inputs[0]				,       return_tensors='pt'	).pixel_values

											snake_case_    ,   snake_case_          =   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
											snake_case_    ,   snake_case_          =   self.image_processor_tester.get_expected_values(snake_case				,       batched=snake_case	)

											snake_case_          =   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	):
											# Initialize image_processing
											snake_case_          =   self.image_processing_class(**self.image_processor_dict	)
											# create random numpy tensors
											snake_case_          =   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
											snake_case_          =   image_processing(image_inputs[0]				,       return_tensors='pt'	).pixel_values

											snake_case_    ,   snake_case_          =   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
											snake_case_          =   image_processing(snake_case				,       return_tensors='pt'	).pixel_values

											snake_case_    ,   snake_case_          =   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	):
											# Initialize image_processing
											snake_case_          =   self.image_processing_class(**self.image_processor_dict	)
											# create random PyTorch tensors
											snake_case_          =   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
											snake_case_          =   image_processing(image_inputs[0]				,       return_tensors='pt'	).pixel_values

											snake_case_    ,   snake_case_          =   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
											snake_case_          =   image_processing(snake_case				,       return_tensors='pt'	).pixel_values

											snake_case_    ,   snake_case_          =   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,
											    )				,       )




				@slow
				def    a							( self	):
											# prepare image and target
											snake_case_          =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
											with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'				,       'r'	) as f:
																		snake_case_          =   json.loads(f.read()	)

											snake_case_          =   {'image_id': 3_9769, 'annotations': target}

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

											# verify pixel values
											snake_case_          =   torch.Size([1, 3, 800, 1066]	)
											self.assertEqual(encoding['pixel_values'].shape				,       snake_case	)

											snake_case_          =   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
											snake_case_          =   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
											snake_case_          =   torch.Size([6, 4]	)
											self.assertEqual(encoding['labels'][0]['boxes'].shape				,       snake_case	)
											snake_case_          =   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
											snake_case_          =   torch.tensor([3_9769]	)
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']				,       snake_case	)	)
											# verify is_crowd
											snake_case_          =   torch.tensor([0, 0, 0, 0, 0, 0]	)
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']				,       snake_case	)	)
											# verify class_labels
											snake_case_          =   torch.tensor([75, 75, 63, 65, 17, 17]	)
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']				,       snake_case	)	)
											# verify orig_size
											snake_case_          =   torch.tensor([480, 640]	)
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']				,       snake_case	)	)
											# verify size
											snake_case_          =   torch.tensor([800, 1066]	)
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']				,       snake_case	)	)




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

											snake_case_          =   {'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

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

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

											# verify pixel values
											snake_case_          =   torch.Size([1, 3, 800, 1066]	)
											self.assertEqual(encoding['pixel_values'].shape				,       snake_case	)

											snake_case_          =   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
											snake_case_          =   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
											snake_case_          =   torch.Size([6, 4]	)
											self.assertEqual(encoding['labels'][0]['boxes'].shape				,       snake_case	)
											snake_case_          =   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
											snake_case_          =   torch.tensor([3_9769]	)
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']				,       snake_case	)	)
											# verify is_crowd
											snake_case_          =   torch.tensor([0, 0, 0, 0, 0, 0]	)
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']				,       snake_case	)	)
											# verify class_labels
											snake_case_          =   torch.tensor([17, 17, 63, 75, 75, 93]	)
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']				,       snake_case	)	)
											# verify masks
											snake_case_          =   82_2873
											self.assertEqual(encoding['labels'][0]['masks'].sum().item()				,       snake_case	)
											# verify orig_size
											snake_case_          =   torch.tensor([480, 640]	)
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']				,       snake_case	)	)
											# verify size
											snake_case_          =   torch.tensor([800, 1066]	)
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']				,       snake_case	)	)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase		:       Tuple						    =	{
    """configuration_xlm_roberta_xl""": [
        """XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaXLConfig""",
        """XLMRobertaXLOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase		:       int						    =	[
					    """XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """XLMRobertaXLForCausalLM""",
					    """XLMRobertaXLForMaskedLM""",
					    """XLMRobertaXLForMultipleChoice""",
					    """XLMRobertaXLForQuestionAnswering""",
					    """XLMRobertaXLForSequenceClassification""",
					    """XLMRobertaXLForTokenClassification""",
					    """XLMRobertaXLModel""",
					    """XLMRobertaXLPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_xlm_roberta_xl import (
					    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    XLMRobertaXLConfig,
					    XLMRobertaXLOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm_roberta_xl import (
										    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMRobertaXLForCausalLM,
										    XLMRobertaXLForMaskedLM,
										    XLMRobertaXLForMultipleChoice,
										    XLMRobertaXLForQuestionAnswering,
										    XLMRobertaXLForSequenceClassification,
										    XLMRobertaXLForTokenClassification,
										    XLMRobertaXLModel,
										    XLMRobertaXLPreTrainedModel,
										)

else:
					import sys

					_UpperCAmelCase		:       Optional[Any]						    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)




