






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 ConditionalDetrImageProcessor







class    A_	(unittest.TestCase							):
							"""simple docstring"""




							def __init__(		self			:List[Any]       ,							lowerCAmelCase__			:Tuple       ,							lowerCAmelCase__			:List[Any]=7       ,							lowerCAmelCase__			:Dict=3       ,							lowerCAmelCase__			:Union[str, Any]=30       ,							lowerCAmelCase__			:Optional[Any]=400       ,							lowerCAmelCase__			:Any=True       ,							lowerCAmelCase__			:List[Any]=None       ,							lowerCAmelCase__			:Tuple=True       ,							lowerCAmelCase__			:str=[0.5, 0.5, 0.5]       ,							lowerCAmelCase__			:List[Any]=[0.5, 0.5, 0.5]       ,							lowerCAmelCase__			:Any=True       ,							lowerCAmelCase__			:Union[str, Any]=1 / 255       ,							lowerCAmelCase__			:List[str]=True       ,							)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
										snake_case_				:					str   =       parent
										snake_case_				:					int   =       batch_size
										snake_case_				:					Optional[int]   =       num_channels
										snake_case_				:					Tuple   =       min_resolution
										snake_case_				:					Dict   =       max_resolution
										snake_case_				:					Union[str, Any]   =       do_resize
										snake_case_				:					Dict   =       size
										snake_case_				:					Any   =       do_normalize
										snake_case_				:					str   =       image_mean
										snake_case_				:					Dict   =       image_std
										snake_case_				:					Optional[Any]   =       do_rescale
										snake_case_				:					Union[str, Any]   =       rescale_factor
										snake_case_				:					List[Any]   =       do_pad

							def 							_A	(		self			:Union[str, Any]				)				->  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			:Tuple       ,							lowerCAmelCase__			:List[str]       ,							lowerCAmelCase__			:Optional[int]=False				)				->  Tuple:


										'''simple docstring'''




										if not batched:
													snake_case_				:					Optional[int]   =       image_inputs[0]
													if isinstance(lowerCAmelCase__       ,							Image.Image				):
																snake_case_				:					Union[str, Any]   =       image.size
													else:
																snake_case_				:					Optional[Any]   =       image.shape[1], image.shape[2]
													if w < h:
																snake_case_				:					Union[str, Any]   =       int(self.size["shortest_edge"] * h / w				)
																snake_case_				:					Optional[int]   =       self.size["shortest_edge"]
													elif w > h:
																snake_case_				:					Tuple   =       self.size["shortest_edge"]
																snake_case_				:					Dict   =       int(self.size["shortest_edge"] * w / h				)
													else:
																snake_case_				:					List[str]   =       self.size["shortest_edge"]
																snake_case_				:					List[str]   =       self.size["shortest_edge"]

										else:
													snake_case_				:					Dict   =       []
													for image in image_inputs:
																snake_case_				:					List[str]   =       self.get_expected_values([image]				)
																expected_values.append((expected_height, expected_width)				)
													snake_case_				:					Optional[Any]   =       max(lowerCAmelCase__       ,							key=lambda lowerCAmelCase__				: item[0]				)[0]
													snake_case_				:					Optional[Any]   =       max(lowerCAmelCase__       ,							key=lambda lowerCAmelCase__				: item[1]				)[1]

										return expected_height, expected_width







@require_torch
@require_vision
class    A_	(_UpperCamelCase						,					unittest.TestCase							):
							"""simple docstring"""




							a__   		=	ConditionalDetrImageProcessor if is_vision_available() else None

							def 							_A	(		self			:str				)				->  Any:


										'''simple docstring'''




										snake_case_				:					str   =       ConditionalDetrImageProcessingTester(self				)

							@property
							def 							_A	(		self			:Tuple				)				->  Tuple:


										'''simple docstring'''




										return self.image_processor_tester.prepare_image_processor_dict()

							def 							_A	(		self			:List[Any]				)				->  Any:


										'''simple docstring'''




										snake_case_				:					List[Any]   =       self.image_processing_class(**self.image_processor_dict				)
										self.assertTrue(hasattr(lowerCAmelCase__       ,							"image_mean"				)				)
										self.assertTrue(hasattr(lowerCAmelCase__       ,							"image_std"				)				)
										self.assertTrue(hasattr(lowerCAmelCase__       ,							"do_normalize"				)				)
										self.assertTrue(hasattr(lowerCAmelCase__       ,							"do_resize"				)				)
										self.assertTrue(hasattr(lowerCAmelCase__       ,							"size"				)				)

							def 							_A	(		self			:Union[str, Any]				)				->  Optional[int]:


										'''simple docstring'''




										snake_case_				:					List[Any]   =       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       ,							lowerCAmelCase__				)

										snake_case_				:					Tuple   =       self.image_processing_class.from_dict(
										    self.image_processor_dict       ,							size=42       ,							max_size=84       ,							pad_and_return_pixel_mask=lowerCAmelCase__				)
										self.assertEqual(image_processor.size       ,							{"shortest_edge": 42, "longest_edge": 84}				)
										self.assertEqual(image_processor.do_pad       ,							lowerCAmelCase__				)

							def 							_A	(		self			:Optional[int]				)				->  Optional[int]:


										'''simple docstring'''




										pass

							def 							_A	(		self			:Optional[Any]				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					List[str]   =       self.image_processing_class(**self.image_processor_dict				)
										# create random PIL images
										snake_case_				:					Optional[Any]   =       prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=lowerCAmelCase__				)
										for image in image_inputs:
													self.assertIsInstance(lowerCAmelCase__       ,							Image.Image				)

										# Test not batched input
										snake_case_				:					int   =       image_processing(image_inputs[0]       ,							return_tensors="pt"				).pixel_values

										snake_case_				:					int   =       self.image_processor_tester.get_expected_values(lowerCAmelCase__				)

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

										# Test batched
										snake_case_				:					str   =       self.image_processor_tester.get_expected_values(lowerCAmelCase__       ,							batched=lowerCAmelCase__				)

										snake_case_				:					Dict   =       image_processing(lowerCAmelCase__       ,							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			:Any				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					List[str]   =       self.image_processing_class(**self.image_processor_dict				)
										# create random numpy tensors
										snake_case_				:					int   =       prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=lowerCAmelCase__       ,							numpify=lowerCAmelCase__				)
										for image in image_inputs:
													self.assertIsInstance(lowerCAmelCase__       ,							np.ndarray				)

										# Test not batched input
										snake_case_				:					Tuple   =       image_processing(image_inputs[0]       ,							return_tensors="pt"				).pixel_values

										snake_case_				:					List[str]   =       self.image_processor_tester.get_expected_values(lowerCAmelCase__				)

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

										# Test batched
										snake_case_				:					List[Any]   =       image_processing(lowerCAmelCase__       ,							return_tensors="pt"				).pixel_values

										snake_case_				:					Optional[int]   =       self.image_processor_tester.get_expected_values(lowerCAmelCase__       ,							batched=lowerCAmelCase__				)

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

							def 							_A	(		self			:List[str]				)				->  Optional[int]:


										'''simple docstring'''




										snake_case_				:					Dict   =       self.image_processing_class(**self.image_processor_dict				)
										# create random PyTorch tensors
										snake_case_				:					Union[str, Any]   =       prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=lowerCAmelCase__       ,							torchify=lowerCAmelCase__				)
										for image in image_inputs:
													self.assertIsInstance(lowerCAmelCase__       ,							torch.Tensor				)

										# Test not batched input
										snake_case_				:					Union[str, Any]   =       image_processing(image_inputs[0]       ,							return_tensors="pt"				).pixel_values

										snake_case_				:					Any   =       self.image_processor_tester.get_expected_values(lowerCAmelCase__				)

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

										# Test batched
										snake_case_				:					Optional[Any]   =       image_processing(lowerCAmelCase__       ,							return_tensors="pt"				).pixel_values

										snake_case_				:					Optional[int]   =       self.image_processor_tester.get_expected_values(lowerCAmelCase__       ,							batched=lowerCAmelCase__				)

										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			:int				)				->  int:


										'''simple docstring'''




										snake_case_				:					int   =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
										with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"       ,							"r"				) as f:
													snake_case_				:					str   =       json.loads(f.read()				)

										snake_case_				:					Dict   =       {"image_id": 39_769, "annotations": target}

										# encode them
										snake_case_				:					Union[str, Any]   =       ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50"				)
										snake_case_				:					List[str]   =       image_processing(images=lowerCAmelCase__       ,							annotations=lowerCAmelCase__       ,							return_tensors="pt"				)

										# verify pixel values
										snake_case_				:					Union[str, Any]   =       torch.Size([1, 3, 800, 1_066]				)
										self.assertEqual(encoding["pixel_values"].shape       ,							lowerCAmelCase__				)

										snake_case_				:					int   =       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]       ,							lowerCAmelCase__       ,							atol=1E-4				)				)

										# verify area
										snake_case_				:					Any   =       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"]       ,							lowerCAmelCase__				)				)
										# verify boxes
										snake_case_				:					Union[str, Any]   =       torch.Size([6, 4]				)
										self.assertEqual(encoding["labels"][0]["boxes"].shape       ,							lowerCAmelCase__				)
										snake_case_				:					List[Any]   =       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]       ,							lowerCAmelCase__       ,							atol=1E-3				)				)
										# verify image_id
										snake_case_				:					str   =       torch.tensor([39_769]				)
										self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,							lowerCAmelCase__				)				)
										# verify is_crowd
										snake_case_				:					Optional[Any]   =       torch.tensor([0, 0, 0, 0, 0, 0]				)
										self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,							lowerCAmelCase__				)				)
										# verify class_labels
										snake_case_				:					str   =       torch.tensor([75, 75, 63, 65, 17, 17]				)
										self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,							lowerCAmelCase__				)				)
										# verify orig_size
										snake_case_				:					str   =       torch.tensor([480, 640]				)
										self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,							lowerCAmelCase__				)				)
										# verify size
										snake_case_				:					Optional[Any]   =       torch.tensor([800, 1_066]				)
										self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,							lowerCAmelCase__				)				)






							@slow
							def 							_A	(		self			:Dict				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Any   =       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_				:					str   =       json.loads(f.read()				)

										snake_case_				:					Tuple   =       {"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

										snake_case_				:					str   =       pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"				)

										# encode them
										snake_case_				:					Union[str, Any]   =       ConditionalDetrImageProcessor(format="coco_panoptic"				)
										snake_case_				:					Dict   =       image_processing(images=lowerCAmelCase__       ,							annotations=lowerCAmelCase__       ,							masks_path=lowerCAmelCase__       ,							return_tensors="pt"				)

										# verify pixel values
										snake_case_				:					Optional[int]   =       torch.Size([1, 3, 800, 1_066]				)
										self.assertEqual(encoding["pixel_values"].shape       ,							lowerCAmelCase__				)

										snake_case_				:					Union[str, Any]   =       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]       ,							lowerCAmelCase__       ,							atol=1E-4				)				)

										# verify area
										snake_case_				:					Tuple   =       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"]       ,							lowerCAmelCase__				)				)
										# verify boxes
										snake_case_				:					Optional[Any]   =       torch.Size([6, 4]				)
										self.assertEqual(encoding["labels"][0]["boxes"].shape       ,							lowerCAmelCase__				)
										snake_case_				:					List[str]   =       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]       ,							lowerCAmelCase__       ,							atol=1E-3				)				)
										# verify image_id
										snake_case_				:					int   =       torch.tensor([39_769]				)
										self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,							lowerCAmelCase__				)				)
										# verify is_crowd
										snake_case_				:					int   =       torch.tensor([0, 0, 0, 0, 0, 0]				)
										self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,							lowerCAmelCase__				)				)
										# verify class_labels
										snake_case_				:					Union[str, Any]   =       torch.tensor([17, 17, 63, 75, 75, 93]				)
										self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,							lowerCAmelCase__				)				)
										# verify masks
										snake_case_				:					Union[str, Any]   =       822_873
										self.assertEqual(encoding["labels"][0]["masks"].sum().item()       ,							lowerCAmelCase__				)
										# verify orig_size
										snake_case_				:					int   =       torch.tensor([480, 640]				)
										self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,							lowerCAmelCase__				)				)
										# verify size
										snake_case_				:					str   =       torch.tensor([800, 1_066]				)
										self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,							lowerCAmelCase__				)				)












'''simple docstring'''




import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class    A_	(a_						,					unittest.TestCase							):
							"""simple docstring"""




							a__   		=	MgpstrTokenizer
							a__   		=	False
							a__   		=	{}
							a__   		=	False

							def 							_A	(		self			:List[str]				)				->  List[str]:


										'''simple docstring'''




										super().setUp()

										# fmt: off
										snake_case_				:					Dict   =       ["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
										# fmt: on
										snake_case_				:					List[str]   =       dict(zip(lowerCAmelCase__       ,							range(len(lowerCAmelCase__				)				)				)				)

										snake_case_				:					Tuple   =       os.path.join(self.tmpdirname       ,							VOCAB_FILES_NAMES["vocab_file"]				)
										with open(self.vocab_file       ,							"w"       ,							encoding="utf-8"				) as fp:
													fp.write(json.dumps(lowerCAmelCase__				) + "\n"				)

							def 							_A	(		self			:Optional[Any]       ,							**lowerCAmelCase__			:Optional[Any]				)				->  Dict:


										'''simple docstring'''




										return MgpstrTokenizer.from_pretrained(self.tmpdirname       ,							**lowerCAmelCase__				)

							def 							_A	(		self			:Dict       ,							lowerCAmelCase__			:Any				)				->  str:


										'''simple docstring'''




										snake_case_				:					Dict   =       "tester"
										snake_case_				:					Tuple   =       "tester"
										return input_text, output_text

							@unittest.skip("MGP-STR always lower cases letters."				)
							def 							_A	(		self			:Dict				)				->  str:


										'''simple docstring'''




										pass

							def 							_A	(		self			:Tuple				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					List[str]   =       self.get_tokenizers(do_lower_case=lowerCAmelCase__				)
										for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''				):
																snake_case_				:					Tuple   =       "[SPECIAL_TOKEN]"

																tokenizer.add_special_tokens({"cls_token": special_token}				)
																snake_case_				:					str   =       tokenizer.encode([special_token]       ,							add_special_tokens=lowerCAmelCase__				)
																self.assertEqual(len(lowerCAmelCase__				)       ,							1				)

																snake_case_				:					Tuple   =       tokenizer.decode(lowerCAmelCase__       ,							skip_special_tokens=lowerCAmelCase__				)
																self.assertTrue(special_token not in decoded				)

							def 							_A	(		self			:int				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Dict   =       self.get_tokenizers()
										for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''				):
																snake_case_,	snake_case_				:					str   =       self.get_input_output_texts(lowerCAmelCase__				)

																snake_case_				:					Union[str, Any]   =       tokenizer.tokenize(lowerCAmelCase__				)
																snake_case_				:					List[Any]   =       tokenizer.convert_tokens_to_ids(lowerCAmelCase__				)
																snake_case_				:					Dict   =       tokenizer.encode(lowerCAmelCase__       ,							add_special_tokens=lowerCAmelCase__				)
																self.assertListEqual(lowerCAmelCase__       ,							lowerCAmelCase__				)

																snake_case_				:					List[str]   =       tokenizer.convert_ids_to_tokens(lowerCAmelCase__				)
																self.assertNotEqual(len(lowerCAmelCase__				)       ,							0				)
																snake_case_				:					List[str]   =       tokenizer.decode(lowerCAmelCase__				)
																self.assertIsInstance(lowerCAmelCase__       ,							lowerCAmelCase__				)

																self.assertEqual(text_a.replace(" "       ,							""				)       ,							lowerCAmelCase__				)

							@unittest.skip("MGP-STR tokenizer only handles one sequence."				)
							def 							_A	(		self			:Union[str, Any]				)				->  Any:


										'''simple docstring'''




										pass






							@unittest.skip("inputs cannot be pretokenized in MgpstrTokenizer"				)
							def 							_A	(		self			:int				)				->  Dict:


										'''simple docstring'''




										pass




