



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 DetaImageProcessor






class 		lowerCAmelCase_ (			unittest.TestCase ):







		"""simple docstring"""
		def __init__(		self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE=7     ,    _SCREAMING_SNAKE_CASE=3     ,    _SCREAMING_SNAKE_CASE=30     ,    _SCREAMING_SNAKE_CASE=400     ,    _SCREAMING_SNAKE_CASE=True     ,    _SCREAMING_SNAKE_CASE=None     ,    _SCREAMING_SNAKE_CASE=True     ,    _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]     ,    _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]     ,    _SCREAMING_SNAKE_CASE=True     ,    _SCREAMING_SNAKE_CASE=1 / 255     ,    _SCREAMING_SNAKE_CASE=True     ,    )     ->  List[str]:
									__UpperCamelCase          =  size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
									__UpperCamelCase          =  parent
									__UpperCamelCase          =  batch_size
									__UpperCamelCase          =  num_channels
									__UpperCamelCase          =  min_resolution
									__UpperCamelCase          =  max_resolution
									__UpperCamelCase          =  do_resize
									__UpperCamelCase          =  size
									__UpperCamelCase          =  do_normalize
									__UpperCamelCase          =  image_mean
									__UpperCamelCase          =  image_std
									__UpperCamelCase          =  do_rescale
									__UpperCamelCase          =  rescale_factor
									__UpperCamelCase          =  do_pad
		def 						__lowercase(		self			)     ->  int:
									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 						__lowercase(		self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE=False			)     ->  Any:


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

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

									return expected_height, expected_width






@require_torch
@require_vision
class 		lowerCAmelCase_ (			__snake_case ,       unittest.TestCase ):







		"""simple docstring"""
		UpperCAmelCase__													=       DetaImageProcessor if is_vision_available() else None
		def 						__lowercase(		self			)     ->  Union[str, Any]:
									__UpperCamelCase          =  DetaImageProcessingTester(self			)
		@property
		def 						__lowercase(		self			)     ->  Optional[int]:
									return self.image_processor_tester.prepare_image_processor_dict()
		def 						__lowercase(		self			)     ->  int:
									__UpperCamelCase          =  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_resize'			)			)
									self.assertTrue(hasattr(__UpperCamelCase     ,    'do_rescale'			)			)
									self.assertTrue(hasattr(__UpperCamelCase     ,    'do_pad'			)			)
									self.assertTrue(hasattr(__UpperCamelCase     ,    'size'			)			)
		def 						__lowercase(		self			)     ->  Optional[int]:
									__UpperCamelCase          =  self.image_processing_class.from_dict(self.image_processor_dict			)
									self.assertEqual(image_processor.size     ,    {'shortest_edge': 18, 'longest_edge': 1_333}			)
									self.assertEqual(image_processor.do_pad     ,    __UpperCamelCase			)
		def 						__lowercase(		self			)     ->  Any:
									pass
		def 						__lowercase(		self			)     ->  str:
									__UpperCamelCase          =  self.image_processing_class(**self.image_processor_dict			)
									# create random PIL images
									__UpperCamelCase          =  prepare_image_inputs(self.image_processor_tester     ,    equal_resolution=__UpperCamelCase			)
									for image in image_inputs:
																self.assertIsInstance(__UpperCamelCase     ,    Image.Image			)

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

									__UpperCamelCase     ,				__UpperCamelCase          =  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
									__UpperCamelCase     ,				__UpperCamelCase          =  self.image_processor_tester.get_expected_values(__UpperCamelCase     ,    batched=__UpperCamelCase			)

									__UpperCamelCase          =  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 						__lowercase(		self			)     ->  List[Any]:
									__UpperCamelCase          =  self.image_processing_class(**self.image_processor_dict			)
									# create random numpy tensors
									__UpperCamelCase          =  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
									__UpperCamelCase          =  image_processing(image_inputs[0]     ,    return_tensors='pt'			).pixel_values

									__UpperCamelCase     ,				__UpperCamelCase          =  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
									__UpperCamelCase          =  image_processing(__UpperCamelCase     ,    return_tensors='pt'			).pixel_values

									__UpperCamelCase     ,				__UpperCamelCase          =  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 						__lowercase(		self			)     ->  Dict:
									__UpperCamelCase          =  self.image_processing_class(**self.image_processor_dict			)
									# create random PyTorch tensors
									__UpperCamelCase          =  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
									__UpperCamelCase          =  image_processing(image_inputs[0]     ,    return_tensors='pt'			).pixel_values

									__UpperCamelCase     ,				__UpperCamelCase          =  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
									__UpperCamelCase          =  image_processing(__UpperCamelCase     ,    return_tensors='pt'			).pixel_values

									__UpperCamelCase     ,				__UpperCamelCase          =  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 						__lowercase(		self			)     ->  Tuple:
									__UpperCamelCase          =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
									with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'     ,    'r'			) as f:
																__UpperCamelCase          =  json.loads(f.read()			)

									__UpperCamelCase          =  {'image_id': 39_769, 'annotations': target}

									# encode them
									__UpperCamelCase          =  DetaImageProcessor()
									__UpperCamelCase          =  image_processing(images=__UpperCamelCase     ,    annotations=__UpperCamelCase     ,    return_tensors='pt'			)

									# verify pixel values
									__UpperCamelCase          =  torch.Size([1, 3, 800, 1_066]			)
									self.assertEqual(encoding['pixel_values'].shape     ,    __UpperCamelCase			)

									__UpperCamelCase          =  torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]			)
									self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]     ,    __UpperCamelCase     ,    atol=1e-4			)			)

									# verify area
									__UpperCamelCase          =  torch.tensor([5_8_8_7.9_6_0_0, 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
									__UpperCamelCase          =  torch.Size([6, 4]			)
									self.assertEqual(encoding['labels'][0]['boxes'].shape     ,    __UpperCamelCase			)
									__UpperCamelCase          =  torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]			)
									self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]     ,    __UpperCamelCase     ,    atol=1e-3			)			)
									# verify image_id
									__UpperCamelCase          =  torch.tensor([39_769]			)
									self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     ,    __UpperCamelCase			)			)
									# verify is_crowd
									__UpperCamelCase          =  torch.tensor([0, 0, 0, 0, 0, 0]			)
									self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     ,    __UpperCamelCase			)			)
									# verify class_labels
									__UpperCamelCase          =  torch.tensor([75, 75, 63, 65, 17, 17]			)
									self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     ,    __UpperCamelCase			)			)
									# verify orig_size
									__UpperCamelCase          =  torch.tensor([480, 640]			)
									self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     ,    __UpperCamelCase			)			)
									# verify size
									__UpperCamelCase          =  torch.tensor([800, 1_066]			)
									self.assertTrue(torch.allclose(encoding['labels'][0]['size']     ,    __UpperCamelCase			)			)



		@slow
		def 						__lowercase(		self			)     ->  List[Any]:
									__UpperCamelCase          =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
									with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'     ,    'r'			) as f:
																__UpperCamelCase          =  json.loads(f.read()			)

									__UpperCamelCase          =  {'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

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

									# encode them
									__UpperCamelCase          =  DetaImageProcessor(format='coco_panoptic'			)
									__UpperCamelCase          =  image_processing(images=__UpperCamelCase     ,    annotations=__UpperCamelCase     ,    masks_path=__UpperCamelCase     ,    return_tensors='pt'			)

									# verify pixel values
									__UpperCamelCase          =  torch.Size([1, 3, 800, 1_066]			)
									self.assertEqual(encoding['pixel_values'].shape     ,    __UpperCamelCase			)

									__UpperCamelCase          =  torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]			)
									self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]     ,    __UpperCamelCase     ,    atol=1e-4			)			)

									# verify area
									__UpperCamelCase          =  torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]			)
									self.assertTrue(torch.allclose(encoding['labels'][0]['area']     ,    __UpperCamelCase			)			)
									# verify boxes
									__UpperCamelCase          =  torch.Size([6, 4]			)
									self.assertEqual(encoding['labels'][0]['boxes'].shape     ,    __UpperCamelCase			)
									__UpperCamelCase          =  torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]			)
									self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]     ,    __UpperCamelCase     ,    atol=1e-3			)			)
									# verify image_id
									__UpperCamelCase          =  torch.tensor([39_769]			)
									self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     ,    __UpperCamelCase			)			)
									# verify is_crowd
									__UpperCamelCase          =  torch.tensor([0, 0, 0, 0, 0, 0]			)
									self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     ,    __UpperCamelCase			)			)
									# verify class_labels
									__UpperCamelCase          =  torch.tensor([17, 17, 63, 75, 75, 93]			)
									self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     ,    __UpperCamelCase			)			)
									# verify masks
									__UpperCamelCase          =  822_873
									self.assertEqual(encoding['labels'][0]['masks'].sum().item()     ,    __UpperCamelCase			)
									# verify orig_size
									__UpperCamelCase          =  torch.tensor([480, 640]			)
									self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     ,    __UpperCamelCase			)			)
									# verify size
									__UpperCamelCase          =  torch.tensor([800, 1_066]			)
									self.assertTrue(torch.allclose(encoding['labels'][0]['size']     ,    __UpperCamelCase			)			)






from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig


A									=  logging.get_logger(__name__)

A									=  'T5Config'


class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			__A    	=	"""mt5"""
			__A    	=	MTaConfig







class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			__A    	=	"""mt5"""
			__A    	=	MTaConfig







class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			__A    	=	"""mt5"""
			__A    	=	MTaConfig
