"""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 DetaImageProcessor



class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def __init__(		self							:     Union[str, Any]	,      A_							:     List[Any]	,      A_							:     Any=7	,      A_							:     Optional[int]=3	,      A_							:     List[Any]=30	,      A_							:     Tuple=400	,      A_							:     str=True	,      A_							:     Any=None	,      A_							:     List[Any]=True	,      A_							:     int=[0.5, 0.5, 0.5]	,      A_							:     str=[0.5, 0.5, 0.5]	,      A_							:     Union[str, Any]=True	,      A_							:     Optional[int]=1 / 255	,      A_							:     List[Any]=True	,      )    ->       Dict:
         # 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''': 1_333}
         __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  lowercase					(		self							:     Tuple    )    ->       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							:     List[Any]	,      A_							:     str	,      A_							:     Union[str, Any]=False    )    ->       List[str]:
         if not batched:
              __snake_case				=							image_inputs[0]
              if isinstance(A_	,      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(A_	,      key=lambda A_    : item[0]    )[0]
              __snake_case				=							max(A_	,      key=lambda A_    : item[1]    )[1]

         return expected_height, expected_width






@require_torch
@require_vision
class      _A	(						_UpperCAmelCase							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					Dict							=					DetaImageProcessor if is_vision_available() else None
    def  lowercase					(		self							:     Any    )    ->       int:
         __snake_case				=							DetaImageProcessingTester(self    )
    @property
    def  lowercase					(		self							:     Dict    )    ->       Dict:
         return self.image_processor_tester.prepare_image_processor_dict()
    def  lowercase					(		self							:     Union[str, Any]    )    ->       str:
         __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  lowercase					(		self							:     Optional[Any]    )    ->       List[str]:
         __snake_case				=							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	,      A_    )
    def  lowercase					(		self							:     List[Any]    )    ->       List[Any]:
         pass
    def  lowercase					(		self							:     List[Any]    )    ->       List[Any]:
         # 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=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		,	__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		,	__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  lowercase					(		self							:     List[Any]    )    ->       Any:
         # 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=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		,	__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		,	__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  lowercase					(		self							:     List[str]    )    ->       Any:
         # 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=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		,	__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		,	__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  lowercase					(		self							:     List[Any]    )    ->       int:
         # 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''': 39_769, '''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, 1_066]    )
         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([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''']	,      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([39_769]    )
         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, 1_066]    )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']	,      A_    )    )







    @slow
    def  lowercase					(		self							:     Optional[Any]    )    ->       str:
         # 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''': 39_769, '''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, 1_066]    )
         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_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''']	,      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([39_769]    )
         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				=							822_873
         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, 1_066]    )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']	,      A_    )    )
"""simple docstring"""



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor



@require_vision
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def  lowercase					(		self							:     Optional[Any]    )    ->       Optional[Any]:
         __snake_case				=							tempfile.mkdtemp()

         __snake_case				=							[
             '''[UNK]''',
             '''[CLS]''',
             '''[SEP]''',
             '''[PAD]''',
             '''[MASK]''',
             '''的''',
             '''价''',
             '''格''',
             '''是''',
             '''15''',
             '''便''',
             '''alex''',
             '''##andra''',
             '''，''',
             '''。''',
             '''-''',
             '''t''',
             '''shirt''',
         ]
         __snake_case				=							os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES['''vocab_file''']    )
         with open(self.vocab_file	,      '''w'''	,      encoding='''utf-8'''    ) as vocab_writer:
              vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]    )    )

         __snake_case				=							{
             '''do_resize''': True,
             '''size''': {'''height''': 224, '''width''': 224},
             '''do_center_crop''': True,
             '''crop_size''': {'''height''': 18, '''width''': 18},
             '''do_normalize''': True,
             '''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
             '''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
             '''do_convert_rgb''': True,
         }
         __snake_case				=							os.path.join(self.tmpdirname	,      A_    )
         with open(self.image_processor_file	,      '''w'''	,      encoding='''utf-8'''    ) as fp:
              json.dump(A_	,      A_    )
    def  lowercase					(		self							:     str	,      **A_							:     str    )    ->       Dict:
         return BertTokenizer.from_pretrained(self.tmpdirname	,      **A_    )
    def  lowercase					(		self							:     Optional[int]	,      **A_							:     Optional[int]    )    ->       Tuple:
         return BertTokenizerFast.from_pretrained(self.tmpdirname	,      **A_    )
    def  lowercase					(		self							:     Tuple	,      **A_							:     Any    )    ->       Any:
         return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname	,      **A_    )
    def  lowercase					(		self							:     Optional[Any]    )    ->       int:
         shutil.rmtree(self.tmpdirname    )
    def  lowercase					(		self							:     Dict    )    ->       Dict:
         __snake_case				=							[np.random.randint(255	,      size=(3, 30, 400)	,      dtype=np.uinta    )]

         __snake_case				=							[Image.fromarray(np.moveaxis(A_	,      0	,      -1    )    ) for x in image_inputs]

         return image_inputs
    def  lowercase					(		self							:     Tuple    )    ->       Any:
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_rust_tokenizer()
         __snake_case				=							self.get_image_processor()

         __snake_case				=							ChineseCLIPProcessor(tokenizer=A_	,      image_processor=A_    )
         processor_slow.save_pretrained(self.tmpdirname    )
         __snake_case				=							ChineseCLIPProcessor.from_pretrained(self.tmpdirname	,      use_fast=A_    )

         __snake_case				=							ChineseCLIPProcessor(tokenizer=A_	,      image_processor=A_    )
         processor_fast.save_pretrained(self.tmpdirname    )
         __snake_case				=							ChineseCLIPProcessor.from_pretrained(self.tmpdirname    )

         self.assertEqual(processor_slow.tokenizer.get_vocab()	,      tokenizer_slow.get_vocab()    )
         self.assertEqual(processor_fast.tokenizer.get_vocab()	,      tokenizer_fast.get_vocab()    )
         self.assertEqual(tokenizer_slow.get_vocab()	,      tokenizer_fast.get_vocab()    )
         self.assertIsInstance(processor_slow.tokenizer	,      A_    )
         self.assertIsInstance(processor_fast.tokenizer	,      A_    )

         self.assertEqual(processor_slow.image_processor.to_json_string()	,      image_processor.to_json_string()    )
         self.assertEqual(processor_fast.image_processor.to_json_string()	,      image_processor.to_json_string()    )
         self.assertIsInstance(processor_slow.image_processor	,      A_    )
         self.assertIsInstance(processor_fast.image_processor	,      A_    )
    def  lowercase					(		self							:     int    )    ->       Union[str, Any]:
         __snake_case				=							ChineseCLIPProcessor(tokenizer=self.get_tokenizer()	,      image_processor=self.get_image_processor()    )
         processor.save_pretrained(self.tmpdirname    )

         __snake_case				=							self.get_tokenizer(cls_token='''(CLS)'''	,      sep_token='''(SEP)'''    )
         __snake_case				=							self.get_image_processor(do_normalize=A_    )

         __snake_case				=							ChineseCLIPProcessor.from_pretrained(
             self.tmpdirname	,      cls_token='''(CLS)'''	,      sep_token='''(SEP)'''	,      do_normalize=A_    )

         self.assertEqual(processor.tokenizer.get_vocab()	,      tokenizer_add_kwargs.get_vocab()    )
         self.assertIsInstance(processor.tokenizer	,      A_    )

         self.assertEqual(processor.image_processor.to_json_string()	,      image_processor_add_kwargs.to_json_string()    )
         self.assertIsInstance(processor.image_processor	,      A_    )
    def  lowercase					(		self							:     Optional[int]    )    ->       int:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							ChineseCLIPProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							image_processor(A_	,      return_tensors='''np'''    )
         __snake_case				=							processor(images=A_	,      return_tensors='''np'''    )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()	,      input_processor[key].sum()	,      delta=1E-2    )
    def  lowercase					(		self							:     str    )    ->       str:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							ChineseCLIPProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							'''Alexandra，T-shirt的价格是15便士。'''

         __snake_case				=							processor(text=A_    )

         __snake_case				=							tokenizer(A_    )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]	,      encoded_processor[key]    )
    def  lowercase					(		self							:     Optional[int]    )    ->       Dict:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							ChineseCLIPProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							'''Alexandra，T-shirt的价格是15便士。'''
         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							processor(text=A_	,      images=A_    )

         self.assertListEqual(list(inputs.keys()    )	,      ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']    )

         # test if it raises when no input is passed
         with pytest.raises(A_    ):
              processor()
    def  lowercase					(		self							:     str    )    ->       int:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							ChineseCLIPProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         __snake_case				=							processor.batch_decode(A_    )
         __snake_case				=							tokenizer.batch_decode(A_    )

         self.assertListEqual(A_	,      A_    )







    def  lowercase					(		self							:     int    )    ->       Optional[Any]:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							ChineseCLIPProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							'''Alexandra，T-shirt的价格是15便士。'''
         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							processor(text=A_	,      images=A_    )

         self.assertListEqual(list(inputs.keys()    )	,      processor.model_input_names    )