






"""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 ConditionalDetrImageProcessor






class      a						(					unittest.TestCase		):






    def __init__(       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							str       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]=7       ,						__SCREAMING_SNAKE_CASE     :							List[str]=3       ,						__SCREAMING_SNAKE_CASE     :							Dict=30       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=400       ,						__SCREAMING_SNAKE_CASE     :							List[str]=True       ,						__SCREAMING_SNAKE_CASE     :							Tuple=None       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=True       ,						__SCREAMING_SNAKE_CASE     :							List[str]=[0.5, 0.5, 0.5]       ,						__SCREAMING_SNAKE_CASE     :							List[Any]=[0.5, 0.5, 0.5]       ,						__SCREAMING_SNAKE_CASE     :							List[str]=True       ,						__SCREAMING_SNAKE_CASE     :							Any=1 / 255       ,						__SCREAMING_SNAKE_CASE     :							List[Any]=True       ,						)     ->		Dict:
      # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
      lowerCamelCase_         =						size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
      lowerCamelCase_         =						parent
      lowerCamelCase_         =						batch_size
      lowerCamelCase_         =						num_channels
      lowerCamelCase_         =						min_resolution
      lowerCamelCase_         =						max_resolution
      lowerCamelCase_         =						do_resize
      lowerCamelCase_         =						size
      lowerCamelCase_         =						do_normalize
      lowerCamelCase_         =						image_mean
      lowerCamelCase_         =						image_std
      lowerCamelCase_         =						do_rescale
      lowerCamelCase_         =						rescale_factor
      lowerCamelCase_         =						do_pad






    def    UpperCamelCase       (       self     :							List[Any]					)     ->		Tuple:
      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    UpperCamelCase       (       self     :							int       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]=False					)     ->		Optional[int]:
      if not batched:
        lowerCamelCase_         =						image_inputs[0]
        if isinstance(__SCREAMING_SNAKE_CASE       ,						Image.Image					):
          lowerCamelCase_     ,	lowerCamelCase_         =						image.size
        else:
          lowerCamelCase_     ,	lowerCamelCase_         =						image.shape[1], image.shape[2]
        if w < h:
          lowerCamelCase_         =						int(self.size['shortest_edge'] * h / w					)
          lowerCamelCase_         =						self.size['shortest_edge']
        elif w > h:
          lowerCamelCase_         =						self.size['shortest_edge']
          lowerCamelCase_         =						int(self.size['shortest_edge'] * w / h					)
        else:
          lowerCamelCase_         =						self.size['shortest_edge']
          lowerCamelCase_         =						self.size['shortest_edge']

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

      return expected_height, expected_width






@require_torch
@require_vision
class      a						(					__snake_case  ,      unittest.TestCase		):
    SCREAMING_SNAKE_CASE						:     Optional[Any]						= ConditionalDetrImageProcessor if is_vision_available() else None






    def    UpperCamelCase       (       self     :							List[str]					)     ->		int:
      lowerCamelCase_         =						ConditionalDetrImageProcessingTester(self					)






    @property
    def    UpperCamelCase       (       self     :							str					)     ->		Optional[int]:
      return self.image_processor_tester.prepare_image_processor_dict()






    def    UpperCamelCase       (       self     :							List[str]					)     ->		Union[str, Any]:
      lowerCamelCase_         =						self.image_processing_class(**self.image_processor_dict					)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,						'image_mean'					)					)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,						'image_std'					)					)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,						'do_normalize'					)					)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,						'do_resize'					)					)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,						'size'					)					)






    def    UpperCamelCase       (       self     :							List[Any]					)     ->		Tuple:
      lowerCamelCase_         =						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       ,						__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						self.image_processing_class.from_dict(
          self.image_processor_dict       ,						size=42       ,						max_size=84       ,						pad_and_return_pixel_mask=__SCREAMING_SNAKE_CASE					)
      self.assertEqual(image_processor.size       ,						{'shortest_edge': 42, 'longest_edge': 84}					)
      self.assertEqual(image_processor.do_pad       ,						__SCREAMING_SNAKE_CASE					)






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






    def    UpperCamelCase       (       self     :							List[Any]					)     ->		Tuple:
      # Initialize image_processing
      lowerCamelCase_         =						self.image_processing_class(**self.image_processor_dict					)
      # create random PIL images
      lowerCamelCase_         =						prepare_image_inputs(self.image_processor_tester       ,						equal_resolution=__SCREAMING_SNAKE_CASE					)
      for image in image_inputs:
        self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,						Image.Image					)

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

      lowerCamelCase_     ,	lowerCamelCase_         =						self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE					)

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

      # Test batched
      lowerCamelCase_     ,	lowerCamelCase_         =						self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE       ,						batched=__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						image_processing(__SCREAMING_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    UpperCamelCase       (       self     :							Any					)     ->		Optional[int]:
      # Initialize image_processing
      lowerCamelCase_         =						self.image_processing_class(**self.image_processor_dict					)
      # create random numpy tensors
      lowerCamelCase_         =						prepare_image_inputs(self.image_processor_tester       ,						equal_resolution=__SCREAMING_SNAKE_CASE       ,						numpify=__SCREAMING_SNAKE_CASE					)
      for image in image_inputs:
        self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,						np.ndarray					)

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

      lowerCamelCase_     ,	lowerCamelCase_         =						self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE					)

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

      # Test batched
      lowerCamelCase_         =						image_processing(__SCREAMING_SNAKE_CASE       ,						return_tensors='pt'					).pixel_values

      lowerCamelCase_     ,	lowerCamelCase_         =						self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE       ,						batched=__SCREAMING_SNAKE_CASE					)

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






    def    UpperCamelCase       (       self     :							List[str]					)     ->		Union[str, Any]:
      # Initialize image_processing
      lowerCamelCase_         =						self.image_processing_class(**self.image_processor_dict					)
      # create random PyTorch tensors
      lowerCamelCase_         =						prepare_image_inputs(self.image_processor_tester       ,						equal_resolution=__SCREAMING_SNAKE_CASE       ,						torchify=__SCREAMING_SNAKE_CASE					)
      for image in image_inputs:
        self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,						torch.Tensor					)

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

      lowerCamelCase_     ,	lowerCamelCase_         =						self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE					)

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

      # Test batched
      lowerCamelCase_         =						image_processing(__SCREAMING_SNAKE_CASE       ,						return_tensors='pt'					).pixel_values

      lowerCamelCase_     ,	lowerCamelCase_         =						self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE       ,						batched=__SCREAMING_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    UpperCamelCase       (       self     :							Any					)     ->		Dict:
      # prepare image and target
      lowerCamelCase_         =						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
      with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'       ,						'r'					) as f:
        lowerCamelCase_         =						json.loads(f.read()					)

      lowerCamelCase_         =						{'image_id': 39769, 'annotations': target}

      # encode them
      lowerCamelCase_         =						ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'					)
      lowerCamelCase_         =						image_processing(images=__SCREAMING_SNAKE_CASE       ,						annotations=__SCREAMING_SNAKE_CASE       ,						return_tensors='pt'					)

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

      lowerCamelCase_         =						torch.tensor([0.2_796, 0.3_138, 0.3_481]					)
      self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,						__SCREAMING_SNAKE_CASE       ,						atol=1e-4					)					)

      # verify area
      lowerCamelCase_         =						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']       ,						__SCREAMING_SNAKE_CASE					)					)
      # verify boxes
      lowerCamelCase_         =						torch.Size([6, 4]					)
      self.assertEqual(encoding['labels'][0]['boxes'].shape       ,						__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]					)
      self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,						__SCREAMING_SNAKE_CASE       ,						atol=1e-3					)					)
      # verify image_id
      lowerCamelCase_         =						torch.tensor([39769]					)
      self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,						__SCREAMING_SNAKE_CASE					)					)
      # verify is_crowd
      lowerCamelCase_         =						torch.tensor([0, 0, 0, 0, 0, 0]					)
      self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,						__SCREAMING_SNAKE_CASE					)					)
      # verify class_labels
      lowerCamelCase_         =						torch.tensor([75, 75, 63, 65, 17, 17]					)
      self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,						__SCREAMING_SNAKE_CASE					)					)
      # verify orig_size
      lowerCamelCase_         =						torch.tensor([480, 640]					)
      self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,						__SCREAMING_SNAKE_CASE					)					)
      # verify size
      lowerCamelCase_         =						torch.tensor([800, 1066]					)
      self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,						__SCREAMING_SNAKE_CASE					)					)






    @slow
    def    UpperCamelCase       (       self     :							List[Any]					)     ->		List[Any]:
      # prepare image, target and masks_path
      lowerCamelCase_         =						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
      with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'       ,						'r'					) as f:
        lowerCamelCase_         =						json.loads(f.read()					)

      lowerCamelCase_         =						{'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

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

      # encode them
      lowerCamelCase_         =						ConditionalDetrImageProcessor(format='coco_panoptic'					)
      lowerCamelCase_         =						image_processing(images=__SCREAMING_SNAKE_CASE       ,						annotations=__SCREAMING_SNAKE_CASE       ,						masks_path=__SCREAMING_SNAKE_CASE       ,						return_tensors='pt'					)

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

      lowerCamelCase_         =						torch.tensor([0.2_796, 0.3_138, 0.3_481]					)
      self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,						__SCREAMING_SNAKE_CASE       ,						atol=1e-4					)					)

      # verify area
      lowerCamelCase_         =						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']       ,						__SCREAMING_SNAKE_CASE					)					)
      # verify boxes
      lowerCamelCase_         =						torch.Size([6, 4]					)
      self.assertEqual(encoding['labels'][0]['boxes'].shape       ,						__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]					)
      self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,						__SCREAMING_SNAKE_CASE       ,						atol=1e-3					)					)
      # verify image_id
      lowerCamelCase_         =						torch.tensor([39769]					)
      self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,						__SCREAMING_SNAKE_CASE					)					)
      # verify is_crowd
      lowerCamelCase_         =						torch.tensor([0, 0, 0, 0, 0, 0]					)
      self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,						__SCREAMING_SNAKE_CASE					)					)
      # verify class_labels
      lowerCamelCase_         =						torch.tensor([17, 17, 63, 75, 75, 93]					)
      self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,						__SCREAMING_SNAKE_CASE					)					)
      # verify masks
      lowerCamelCase_         =						822873
      self.assertEqual(encoding['labels'][0]['masks'].sum().item()       ,						__SCREAMING_SNAKE_CASE					)
      # verify orig_size
      lowerCamelCase_         =						torch.tensor([480, 640]					)
      self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,						__SCREAMING_SNAKE_CASE					)					)
      # verify size
      lowerCamelCase_         =						torch.tensor([800, 1066]					)
      self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,						__SCREAMING_SNAKE_CASE					)					)












"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


_SCREAMING_SNAKE_CASE					:				Union[str, Any]					    =						{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_SCREAMING_SNAKE_CASE					:				Union[str, Any]					    =						{
    '''vocab_file''': {
        '''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''unc-nlp/lxmert-base-uncased''': (
            '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

_SCREAMING_SNAKE_CASE					:				Any					    =						{
    '''unc-nlp/lxmert-base-uncased''': 512,
}

_SCREAMING_SNAKE_CASE					:				int					    =						{
    '''unc-nlp/lxmert-base-uncased''': {'''do_lower_case''': True},
}






class      a						(					__snake_case		):
    SCREAMING_SNAKE_CASE						:     int						= VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE						:     Optional[Any]						= PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE						:     Any						= PRETRAINED_INIT_CONFIGURATION
    SCREAMING_SNAKE_CASE						:     Any						= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE						:     Optional[Any]						= LxmertTokenizer






    def __init__(       self     :							str       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=None       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=None       ,						__SCREAMING_SNAKE_CASE     :							str=True       ,						__SCREAMING_SNAKE_CASE     :							List[Any]="[UNK]"       ,						__SCREAMING_SNAKE_CASE     :							int="[SEP]"       ,						__SCREAMING_SNAKE_CASE     :							int="[PAD]"       ,						__SCREAMING_SNAKE_CASE     :							List[Any]="[CLS]"       ,						__SCREAMING_SNAKE_CASE     :							Any="[MASK]"       ,						__SCREAMING_SNAKE_CASE     :							List[str]=True       ,						__SCREAMING_SNAKE_CASE     :							List[str]=None       ,						**__SCREAMING_SNAKE_CASE     :							Any       ,						)     ->		Any:
      super().__init__(
          __SCREAMING_SNAKE_CASE       ,						tokenizer_file=__SCREAMING_SNAKE_CASE       ,						do_lower_case=__SCREAMING_SNAKE_CASE       ,						unk_token=__SCREAMING_SNAKE_CASE       ,						sep_token=__SCREAMING_SNAKE_CASE       ,						pad_token=__SCREAMING_SNAKE_CASE       ,						cls_token=__SCREAMING_SNAKE_CASE       ,						mask_token=__SCREAMING_SNAKE_CASE       ,						tokenize_chinese_chars=__SCREAMING_SNAKE_CASE       ,						strip_accents=__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE       ,						)

      lowerCamelCase_         =						json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
      if (
          normalizer_state.get('lowercase'       ,						__SCREAMING_SNAKE_CASE					) != do_lower_case
          or normalizer_state.get('strip_accents'       ,						__SCREAMING_SNAKE_CASE					) != strip_accents
          or normalizer_state.get('handle_chinese_chars'       ,						__SCREAMING_SNAKE_CASE					) != tokenize_chinese_chars
      ):
        lowerCamelCase_         =						getattr(__SCREAMING_SNAKE_CASE       ,						normalizer_state.pop('type'					)					)
        lowerCamelCase_         =						do_lower_case
        lowerCamelCase_         =						strip_accents
        lowerCamelCase_         =						tokenize_chinese_chars
        lowerCamelCase_         =						normalizer_class(**__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						do_lower_case






    def    UpperCamelCase       (       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							Tuple=None					)     ->		Dict:
      lowerCamelCase_         =						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

      if token_ids_a:
        output += token_ids_a + [self.sep_token_id]

      return output






    def    UpperCamelCase       (       self     :							str       ,						__SCREAMING_SNAKE_CASE     :							List[int]       ,						__SCREAMING_SNAKE_CASE     :							Optional[List[int]] = None					)     ->		List[int]:
      lowerCamelCase_         =						[self.sep_token_id]
      lowerCamelCase_         =						[self.cls_token_id]
      if token_ids_a is None:
        return len(cls + token_ids_a + sep					) * [0]
      return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]






    def    UpperCamelCase       (       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							str       ,						__SCREAMING_SNAKE_CASE     :							Optional[str] = None					)     ->		Tuple[str]:
      lowerCamelCase_         =						self._tokenizer.model.save(__SCREAMING_SNAKE_CASE       ,						name=__SCREAMING_SNAKE_CASE					)
      return tuple(__SCREAMING_SNAKE_CASE					)




