


'''simple docstring'''







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 DeformableDetrImageProcessor
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     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   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
            _lowerCAmelCase     :				Dict       =							parent
            _lowerCAmelCase     :				Dict       =							batch_size
            _lowerCAmelCase     :				Optional[Any]       =							num_channels
            _lowerCAmelCase     :				Dict       =							min_resolution
            _lowerCAmelCase     :				Optional[Any]       =							max_resolution
            _lowerCAmelCase     :				int       =							do_resize
            _lowerCAmelCase     :				Union[str, Any]       =							size
            _lowerCAmelCase     :				Optional[Any]       =							do_normalize
            _lowerCAmelCase     :				Optional[Any]       =							image_mean
            _lowerCAmelCase     :				List[Any]       =							image_std
            _lowerCAmelCase     :				Dict       =							do_rescale
            _lowerCAmelCase     :				Union[str, Any]       =							rescale_factor
            _lowerCAmelCase     :				Union[str, Any]       =							do_pad







     def a						(			self ):

            '''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   ,						snake_case__   ,						snake_case__=False ):

            '''simple docstring'''

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

            else:
                   _lowerCAmelCase     :				Union[str, Any]       =							[]
                   for image in image_inputs:
                          _lowerCAmelCase      ,  _lowerCAmelCase     :				Tuple       =							self.get_expected_values([image] )
                          expected_values.append((expected_height, expected_width) )
                   _lowerCAmelCase     :				Tuple       =							max(snake_case__   ,						key=lambda snake_case__ : item[0] )[0]
                   _lowerCAmelCase     :				List[str]       =							max(snake_case__   ,						key=lambda snake_case__ : item[1] )[1]

            return expected_height, expected_width
@require_torch
@require_vision
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       unittest.TestCase						):



     """simple docstring"""






     __magic_name__           =     DeformableDetrImageProcessor if is_vision_available() else None







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							DeformableDetrImageProcessingTester(self )







     @property
     def a						(			self ):

            '''simple docstring'''

            return self.image_processor_tester.prepare_image_processor_dict()







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							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__   ,						'do_rescale' ) )
            self.assertTrue(hasattr(snake_case__   ,						'do_pad' ) )
            self.assertTrue(hasattr(snake_case__   ,						'size' ) )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							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__ )

            _lowerCAmelCase     :				int       =							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 ):

            '''simple docstring'''

            pass







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							self.image_processing_class(**self.image_processor_dict )
            # create random PIL images
            _lowerCAmelCase     :				Tuple       =							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
            _lowerCAmelCase     :				Optional[int]       =							image_processing(image_inputs[0]   ,						return_tensors='pt' ).pixel_values

            _lowerCAmelCase      ,  _lowerCAmelCase     :				List[str]       =							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
            _lowerCAmelCase      ,  _lowerCAmelCase     :				Union[str, Any]       =							self.image_processor_tester.get_expected_values(snake_case__   ,						batched=snake_case__ )

            _lowerCAmelCase     :				Union[str, Any]       =							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 ):

            '''simple docstring'''

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

            _lowerCAmelCase      ,  _lowerCAmelCase     :				str       =							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
            _lowerCAmelCase     :				Any       =							image_processing(snake_case__   ,						return_tensors='pt' ).pixel_values

            _lowerCAmelCase      ,  _lowerCAmelCase     :				Optional[Any]       =							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 ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							self.image_processing_class(**self.image_processor_dict )
            # create random PyTorch tensors
            _lowerCAmelCase     :				List[Any]       =							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
            _lowerCAmelCase     :				int       =							image_processing(image_inputs[0]   ,						return_tensors='pt' ).pixel_values

            _lowerCAmelCase      ,  _lowerCAmelCase     :				str       =							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
            _lowerCAmelCase     :				int       =							image_processing(snake_case__   ,						return_tensors='pt' ).pixel_values

            _lowerCAmelCase      ,  _lowerCAmelCase     :				Union[str, Any]       =							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 ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
            with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'   ,						'r' ) as f:
                   _lowerCAmelCase     :				Optional[Any]       =							json.loads(f.read() )

            _lowerCAmelCase     :				List[str]       =							{'image_id': 3_9769, 'annotations': target}

            # encode them
            _lowerCAmelCase     :				Dict       =							DeformableDetrImageProcessor()
            _lowerCAmelCase     :				Dict       =							image_processing(images=snake_case__   ,						annotations=snake_case__   ,						return_tensors='pt' )

            # verify pixel values
            _lowerCAmelCase     :				Optional[Any]       =							torch.Size([1, 3, 800, 1066] )
            self.assertEqual(encoding['pixel_values'].shape   ,						snake_case__ )

            _lowerCAmelCase     :				List[str]       =							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
            _lowerCAmelCase     :				Optional[Any]       =							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
            _lowerCAmelCase     :				Dict       =							torch.Size([6, 4] )
            self.assertEqual(encoding['labels'][0]['boxes'].shape   ,						snake_case__ )
            _lowerCAmelCase     :				str       =							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
            _lowerCAmelCase     :				str       =							torch.tensor([3_9769] )
            self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,						snake_case__ ) )
            # verify is_crowd
            _lowerCAmelCase     :				List[Any]       =							torch.tensor([0, 0, 0, 0, 0, 0] )
            self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,						snake_case__ ) )
            # verify class_labels
            _lowerCAmelCase     :				Tuple       =							torch.tensor([75, 75, 63, 65, 17, 17] )
            self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,						snake_case__ ) )
            # verify orig_size
            _lowerCAmelCase     :				List[str]       =							torch.tensor([480, 640] )
            self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,						snake_case__ ) )
            # verify size
            _lowerCAmelCase     :				Dict       =							torch.tensor([800, 1066] )
            self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,						snake_case__ ) )







     @slow
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
            with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'   ,						'r' ) as f:
                   _lowerCAmelCase     :				List[str]       =							json.loads(f.read() )

            _lowerCAmelCase     :				List[Any]       =							{'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

            _lowerCAmelCase     :				Dict       =							pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )

            # encode them
            _lowerCAmelCase     :				Dict       =							DeformableDetrImageProcessor(format='coco_panoptic' )
            _lowerCAmelCase     :				Dict       =							image_processing(images=snake_case__   ,						annotations=snake_case__   ,						masks_path=snake_case__   ,						return_tensors='pt' )

            # verify pixel values
            _lowerCAmelCase     :				Tuple       =							torch.Size([1, 3, 800, 1066] )
            self.assertEqual(encoding['pixel_values'].shape   ,						snake_case__ )

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





'''simple docstring'''







import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       unittest.TestCase						):



     """simple docstring"""






     __magic_name__           =     RoFormerTokenizer
     __magic_name__           =     RoFormerTokenizerFast
     __magic_name__           =     True
     __magic_name__           =     True







     def a						(			self ):

            '''simple docstring'''

            super().setUp()







     def a						(			self   ,						**snake_case__ ):

            '''simple docstring'''

            return self.tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'   ,						**snake_case__ )







     def a						(			self   ,						**snake_case__ ):

            '''simple docstring'''

            return self.rust_tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'   ,						**snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							'永和服装饰品有限公司,今天天气非常好'
            _lowerCAmelCase     :				str       =							'永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'
            return input_text, output_text







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							self.get_tokenizer()
            _lowerCAmelCase      ,  _lowerCAmelCase     :				int       =							self.get_chinese_input_output_texts()
            _lowerCAmelCase     :				List[Any]       =							tokenizer.tokenize(snake_case__ )

            self.assertListEqual(snake_case__   ,						output_text.split() )

            _lowerCAmelCase     :				Optional[Any]       =							tokens + [tokenizer.unk_token]
            _lowerCAmelCase     :				List[str]       =							[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ )   ,						snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							self.get_rust_tokenizer()
            _lowerCAmelCase      ,  _lowerCAmelCase     :				Any       =							self.get_chinese_input_output_texts()
            _lowerCAmelCase     :				Optional[int]       =							tokenizer.tokenize(snake_case__ )
            self.assertListEqual(snake_case__   ,						output_text.split() )
            _lowerCAmelCase     :				Tuple       =							tokens + [tokenizer.unk_token]
            _lowerCAmelCase     :				List[Any]       =							[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ )   ,						snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            pass







     def a						(			self ):

            '''simple docstring'''

            pass







     def a						(			self ):

            '''simple docstring'''

            pass

