


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 DetrImageProcessor

class       __magic_name__	(     unittest.TestCase):


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





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     Optional[int]			)  ->      Optional[Any]:





      '''simple docstring'''






      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_rescale": self.do_rescale,
          "rescale_factor": self.rescale_factor,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_pad": self.do_pad,
      }


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     List[Any]							,	lowerCamelCase__							:     Any=False			)  ->      Dict:





      '''simple docstring'''






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

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

      return expected_height, expected_width



@require_torch
@require_vision
class       __magic_name__	(     __lowerCAmelCase					,				unittest.TestCase):
     A:		Tuple			=      DetrImageProcessor if is_vision_available() else None


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





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   DetrImageProcessingTester(self			)


     @property
     def      UpperCAmelCase__	(      self							:     Optional[int]			)  ->      Optional[Any]:





      '''simple docstring'''






      return self.image_processor_tester.prepare_image_processor_dict()


     def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      Optional[int]:





      '''simple docstring'''






      UpperCamelCase__					:    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_rescale'''			)			)
      self.assertTrue(hasattr(lowerCamelCase__							,	'''rescale_factor'''			)			)
      self.assertTrue(hasattr(lowerCamelCase__							,	'''do_resize'''			)			)
      self.assertTrue(hasattr(lowerCamelCase__							,	'''size'''			)			)
      self.assertTrue(hasattr(lowerCamelCase__							,	'''do_pad'''			)			)


     def      UpperCAmelCase__	(      self							:     Optional[int]			)  ->      Any:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   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							,	lowerCamelCase__			)

      UpperCamelCase__					:    Dict          =   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      UpperCAmelCase__	(      self							:     Dict			)  ->      str:





      '''simple docstring'''






      pass


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





      '''simple docstring'''






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

      UpperCamelCase__       ,				UpperCamelCase__					:    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
      UpperCamelCase__       ,				UpperCamelCase__					:    str          =   self.image_processor_tester.get_expected_values(lowerCamelCase__							,	batched=lowerCamelCase__			)

      UpperCamelCase__					:    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      UpperCAmelCase__	(      self							:     List[Any]			)  ->      Optional[Any]:





      '''simple docstring'''






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

      UpperCamelCase__       ,				UpperCamelCase__					:    List[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
      UpperCamelCase__					:    Optional[Any]          =   image_processing(lowerCamelCase__							,	return_tensors='''pt'''			).pixel_values

      UpperCamelCase__       ,				UpperCamelCase__					:    Dict          =   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      UpperCAmelCase__	(      self							:     int			)  ->      int:





      '''simple docstring'''






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

      UpperCamelCase__       ,				UpperCamelCase__					:    Optional[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
      UpperCamelCase__					:    str          =   image_processing(lowerCamelCase__							,	return_tensors='''pt'''			).pixel_values

      UpperCamelCase__       ,				UpperCamelCase__					:    List[str]          =   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      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      Optional[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
      with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''							,	'''r'''			) as f:
       UpperCamelCase__					:    Any          =   json.loads(f.read()			)

      UpperCamelCase__					:    Union[str, Any]          =   {'''image_id''': 39769, '''annotations''': target}

      # encode them
      UpperCamelCase__					:    Optional[Any]          =   DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''			)
      UpperCamelCase__					:    int          =   image_processing(images=lowerCamelCase__							,	annotations=lowerCamelCase__							,	return_tensors='''pt'''			)

      # verify pixel values
      UpperCamelCase__					:    List[str]          =   torch.Size([1, 3, 800, 1066]			)
      self.assertEqual(encoding['''pixel_values'''].shape							,	lowerCamelCase__			)

      UpperCamelCase__					:    int          =   torch.tensor([0.2796, 0.3138, 0.3481]			)
      self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]							,	lowerCamelCase__							,	atol=1E-4			)			)

      # verify area
      UpperCamelCase__					:    Dict          =   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''']							,	lowerCamelCase__			)			)
      # verify boxes
      UpperCamelCase__					:    Optional[Any]          =   torch.Size([6, 4]			)
      self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape							,	lowerCamelCase__			)
      UpperCamelCase__					:    List[Any]          =   torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]			)
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]							,	lowerCamelCase__							,	atol=1E-3			)			)
      # verify image_id
      UpperCamelCase__					:    Union[str, Any]          =   torch.tensor([39769]			)
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']							,	lowerCamelCase__			)			)
      # verify is_crowd
      UpperCamelCase__					:    str          =   torch.tensor([0, 0, 0, 0, 0, 0]			)
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']							,	lowerCamelCase__			)			)
      # verify class_labels
      UpperCamelCase__					:    str          =   torch.tensor([75, 75, 63, 65, 17, 17]			)
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']							,	lowerCamelCase__			)			)
      # verify orig_size
      UpperCamelCase__					:    List[str]          =   torch.tensor([480, 640]			)
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']							,	lowerCamelCase__			)			)
      # verify size
      UpperCamelCase__					:    List[Any]          =   torch.tensor([800, 1066]			)
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							,	lowerCamelCase__			)			)


     @slow
     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
      with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''							,	'''r'''			) as f:
       UpperCamelCase__					:    Tuple          =   json.loads(f.read()			)

      UpperCamelCase__					:    Optional[Any]          =   {'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

      UpperCamelCase__					:    Tuple          =   pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''			)

      # encode them
      UpperCamelCase__					:    Optional[Any]          =   DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''			)
      UpperCamelCase__					:    Dict          =   image_processing(images=lowerCamelCase__							,	annotations=lowerCamelCase__							,	masks_path=lowerCamelCase__							,	return_tensors='''pt'''			)

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

      UpperCamelCase__					:    int          =   torch.tensor([0.2796, 0.3138, 0.3481]			)
      self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]							,	lowerCamelCase__							,	atol=1E-4			)			)

      # verify area
      UpperCamelCase__					:    Optional[Any]          =   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''']							,	lowerCamelCase__			)			)
      # verify boxes
      UpperCamelCase__					:    List[Any]          =   torch.Size([6, 4]			)
      self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape							,	lowerCamelCase__			)
      UpperCamelCase__					:    Union[str, Any]          =   torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]			)
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]							,	lowerCamelCase__							,	atol=1E-3			)			)
      # verify image_id
      UpperCamelCase__					:    Union[str, Any]          =   torch.tensor([39769]			)
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']							,	lowerCamelCase__			)			)
      # verify is_crowd
      UpperCamelCase__					:    Optional[Any]          =   torch.tensor([0, 0, 0, 0, 0, 0]			)
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']							,	lowerCamelCase__			)			)
      # verify class_labels
      UpperCamelCase__					:    List[str]          =   torch.tensor([17, 17, 63, 75, 75, 93]			)
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']							,	lowerCamelCase__			)			)
      # verify masks
      UpperCamelCase__					:    Optional[int]          =   822873
      self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()							,	lowerCamelCase__			)
      # verify orig_size
      UpperCamelCase__					:    int          =   torch.tensor([480, 640]			)
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']							,	lowerCamelCase__			)			)
      # verify size
      UpperCamelCase__					:    List[Any]          =   torch.tensor([800, 1066]			)
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							,	lowerCamelCase__			)			)






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_camembert import CamembertTokenizer
else:
       __UpperCamelCase       :       Tuple						       =				None


__UpperCamelCase       :       Optional[Any]						       =				logging.get_logger(__name__)

__UpperCamelCase       :       Any						       =				{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__UpperCamelCase       :       Tuple						       =				{
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
    },
}

__UpperCamelCase       :       Optional[Any]						       =				{
    "camembert-base": 512,
}

__UpperCamelCase       :       Optional[Any]						       =				"▁"

class       __magic_name__	(     __lowerCAmelCase):
     A:		int			=      VOCAB_FILES_NAMES
     A:		Union[str, Any]			=      PRETRAINED_VOCAB_FILES_MAP
     A:		Optional[int]			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A:		List[str]			=      ["input_ids", "attention_mask"]
     A:		Dict			=      CamembertTokenizer


     def __init__(      self							:     int							,	lowerCamelCase__							:     Any=None							,	lowerCamelCase__							:     Any=None							,	lowerCamelCase__							:     Dict="<s>"							,	lowerCamelCase__							:     List[str]="</s>"							,	lowerCamelCase__							:     int="</s>"							,	lowerCamelCase__							:     List[str]="<s>"							,	lowerCamelCase__							:     Optional[int]="<unk>"							,	lowerCamelCase__							:     Union[str, Any]="<pad>"							,	lowerCamelCase__							:     List[Any]="<mask>"							,	lowerCamelCase__							:     List[Any]=["<s>NOTUSED", "</s>NOTUSED"]							,	**lowerCamelCase__							:     Optional[int]							,	)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   AddedToken(lowerCamelCase__							,	lstrip=lowerCamelCase__							,	rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__							,	lowerCamelCase__			) else mask_token

      super().__init__(
          lowerCamelCase__							,	tokenizer_file=lowerCamelCase__							,	bos_token=lowerCamelCase__							,	eos_token=lowerCamelCase__							,	sep_token=lowerCamelCase__							,	cls_token=lowerCamelCase__							,	unk_token=lowerCamelCase__							,	pad_token=lowerCamelCase__							,	mask_token=lowerCamelCase__							,	additional_special_tokens=lowerCamelCase__							,	**lowerCamelCase__							,	)

      UpperCamelCase__					:    Tuple          =   vocab_file
      UpperCamelCase__					:    Optional[Any]          =   False if not self.vocab_file else True


     def      UpperCAmelCase__	(      self							:     Any							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






      if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      UpperCamelCase__					:    Any          =   [self.cls_token_id]
      UpperCamelCase__					:    Dict          =   [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[Any]          =   [self.sep_token_id]
      UpperCamelCase__					:    Tuple          =   [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 + sep + token_ids_a + sep			) * [0]


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     Optional[str] = None			)  ->      Tuple[str]:





      '''simple docstring'''






      if not self.can_save_slow_tokenizer:
       raise ValueError(
           '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
           '''tokenizer.'''			)

      if not os.path.isdir(lowerCamelCase__			):
       logger.error(F"Vocabulary path ({save_directory}) should be a directory"			)
       return
      UpperCamelCase__					:    List[str]          =   os.path.join(
          lowerCamelCase__							,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

      if os.path.abspath(self.vocab_file			) != os.path.abspath(lowerCamelCase__			):
       copyfile(self.vocab_file							,	lowerCamelCase__			)

      return (out_vocab_file,)


