

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 				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=[0.5, 0.5, 0.5]   ,     snake_case=[0.5, 0.5, 0.5]   ,     snake_case=True   ,     snake_case=1 / 255   ,     snake_case=True   ,     ):
								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_normalize
								snake_case_           =					image_mean
								snake_case_           =					image_std
								snake_case_           =					do_rescale
								snake_case_           =					rescale_factor
								snake_case_           =					do_pad




	def 					a							(      self ):
								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   ,     snake_case   ,     snake_case=False ):




								if not batched:
															snake_case_           =					image_inputs[0]
															if isinstance(A_   ,     Image.Image ):
																						snake_case_           =					image.size
															else:
																						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_           =					self.get_expected_values([image] )
																						expected_values.append((expected_height, expected_width) )
															snake_case_           =					max(A_   ,     key=lambda snake_case : item[0] )[0]
															snake_case_           =					max(A_   ,     key=lambda snake_case : item[1] )[1]

								return expected_height, expected_width







@require_torch
@require_vision
class 				lowercase       ( __snake_case			,						unittest.TestCase    ):
	__SCREAMING_SNAKE_CASE      :				List[str]     =    DetaImageProcessor if is_vision_available() else None




	def 					a							(      self ):
								snake_case_           =					DetaImageProcessingTester(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(A_   ,     'image_mean' ) )
								self.assertTrue(hasattr(A_   ,     'image_std' ) )
								self.assertTrue(hasattr(A_   ,     'do_normalize' ) )
								self.assertTrue(hasattr(A_   ,     'do_resize' ) )
								self.assertTrue(hasattr(A_   ,     'do_rescale' ) )
								self.assertTrue(hasattr(A_   ,     'do_pad' ) )
								self.assertTrue(hasattr(A_   ,     'size' ) )




	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   ,     A_ )




	def 					a							(      self ):
								pass




	def 					a							(      self ):
								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=A_ )
								for image in image_inputs:
															self.assertIsInstance(A_   ,     Image.Image )

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

								snake_case_           =					self.image_processor_tester.get_expected_values(A_ )

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

								# Test batched
								snake_case_           =					self.image_processor_tester.get_expected_values(A_   ,     batched=A_ )

								snake_case_           =					image_processing(A_   ,     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 ):
								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=A_   ,     numpify=A_ )
								for image in image_inputs:
															self.assertIsInstance(A_   ,     np.ndarray )

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

								snake_case_           =					self.image_processor_tester.get_expected_values(A_ )

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

								# Test batched
								snake_case_           =					image_processing(A_   ,     return_tensors='pt' ).pixel_values

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

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




	def 					a							(      self ):
								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=A_   ,     torchify=A_ )
								for image in image_inputs:
															self.assertIsInstance(A_   ,     torch.Tensor )

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

								snake_case_           =					self.image_processor_tester.get_expected_values(A_ )

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

								# Test batched
								snake_case_           =					image_processing(A_   ,     return_tensors='pt' ).pixel_values

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

								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 ):
								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_           =					DetaImageProcessor()
								snake_case_           =					image_processing(images=A_   ,     annotations=A_   ,     return_tensors='pt' )

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

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

								# verify area
								snake_case_           =					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']   ,     A_ ) )
								# verify boxes
								snake_case_           =					torch.Size([6, 4] )
								self.assertEqual(encoding['labels'][0]['boxes'].shape   ,     A_ )
								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]   ,     A_   ,     atol=1e-3 ) )
								# verify image_id
								snake_case_           =					torch.tensor([3_9769] )
								self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,     A_ ) )
								# verify is_crowd
								snake_case_           =					torch.tensor([0, 0, 0, 0, 0, 0] )
								self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,     A_ ) )
								# verify class_labels
								snake_case_           =					torch.tensor([75, 75, 63, 65, 17, 17] )
								self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,     A_ ) )
								# verify orig_size
								snake_case_           =					torch.tensor([480, 640] )
								self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,     A_ ) )
								# verify size
								snake_case_           =					torch.tensor([800, 1066] )
								self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,     A_ ) )




	@slow
	def 					a							(      self ):
								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_           =					DetaImageProcessor(format='coco_panoptic' )
								snake_case_           =					image_processing(images=A_   ,     annotations=A_   ,     masks_path=A_   ,     return_tensors='pt' )

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

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

								# verify area
								snake_case_           =					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']   ,     A_ ) )
								# verify boxes
								snake_case_           =					torch.Size([6, 4] )
								self.assertEqual(encoding['labels'][0]['boxes'].shape   ,     A_ )
								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]   ,     A_   ,     atol=1e-3 ) )
								# verify image_id
								snake_case_           =					torch.tensor([3_9769] )
								self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,     A_ ) )
								# verify is_crowd
								snake_case_           =					torch.tensor([0, 0, 0, 0, 0, 0] )
								self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,     A_ ) )
								# verify class_labels
								snake_case_           =					torch.tensor([17, 17, 63, 75, 75, 93] )
								self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,     A_ ) )
								# verify masks
								snake_case_           =					82_2873
								self.assertEqual(encoding['labels'][0]['masks'].sum().item()   ,     A_ )
								# verify orig_size
								snake_case_           =					torch.tensor([480, 640] )
								self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,     A_ ) )
								# verify size
								snake_case_           =					torch.tensor([800, 1066] )
								self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,     A_ ) )







import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin







class 				lowercase       ( lowercase_			,						unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				int     =    PhobertTokenizer
				__SCREAMING_SNAKE_CASE      :				int     =    False




				def 					a							(      self ):
											super().setUp()

											# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
											snake_case_           =					['T@@', 'i', 'I', 'R@@', 'r', 'e@@']
											snake_case_           =					dict(zip(snake_case   ,     range(len(snake_case ) ) ) )
											snake_case_           =					['#version: 0.2', 'l à</w>']
											snake_case_           =					{'unk_token': '<unk>'}

											snake_case_           =					os.path.join(self.tmpdirname   ,     VOCAB_FILES_NAMES['vocab_file'] )
											snake_case_           =					os.path.join(self.tmpdirname   ,     VOCAB_FILES_NAMES['merges_file'] )

											with open(self.vocab_file   ,     'w'   ,     encoding='utf-8' ) as fp:
																		for token in vocab_tokens:
																									fp.write(F'''{token} {vocab_tokens[token]}\n''' )
											with open(self.merges_file   ,     'w'   ,     encoding='utf-8' ) as fp:
																		fp.write('\n'.join(snake_case ) )




				def 					a							(      self   ,     **snake_case ):
											kwargs.update(self.special_tokens_map )
											return PhobertTokenizer.from_pretrained(self.tmpdirname   ,     **snake_case )




				def 					a							(      self   ,     snake_case ):
											snake_case_           =					'Tôi là VinAI Research'
											snake_case_           =					'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
											return input_text, output_text




				def 					a							(      self ):
											snake_case_           =					PhobertTokenizer(self.vocab_file   ,     self.merges_file   ,     **self.special_tokens_map )
											snake_case_           =					'Tôi là VinAI Research'
											snake_case_           =					'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
											snake_case_           =					tokenizer.tokenize(snake_case )
											print(snake_case )
											self.assertListEqual(snake_case   ,     snake_case )

											snake_case_           =					tokens + [tokenizer.unk_token]

											snake_case_           =					[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case )   ,     snake_case )




