






'''simple docstring'''


import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
   import torch


class 						lowercase__	(  __a   ):






   '''simple docstring'''







   _snake_case		   =							'''char'''
   _snake_case		   =							'''bpe'''
   _snake_case		   =							'''wp'''




snake_case_ :  List[str]     =					(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)


class 						lowercase__	(  __a   ):






   '''simple docstring'''







   _snake_case		   =							['''image_processor''', '''char_tokenizer''']
   _snake_case		   =							'''ViTImageProcessor'''
   _snake_case		   =							'''MgpstrTokenizer'''







   def __init__(			self ,   lowerCamelCase__=None ,   lowerCamelCase__=None ,   **lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  None
      if "feature_extractor" in kwargs:
         warnings.warn(
             '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
             ''' instead.''' ,   a_ ,   )
         UpperCamelCase       =  kwargs.pop('''feature_extractor'''   )

      UpperCamelCase       =  image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
         raise ValueError('''You need to specify an `image_processor`.'''   )
      if tokenizer is None:
         raise ValueError('''You need to specify a `tokenizer`.'''   )

      UpperCamelCase       =  tokenizer
      UpperCamelCase       =  AutoTokenizer.from_pretrained('''gpt2'''   )
      UpperCamelCase       =  AutoTokenizer.from_pretrained('''bert-base-uncased'''   )

      super().__init__(a_ ,   a_   )







   def __call__(			self ,   lowerCamelCase__=None ,   lowerCamelCase__=None ,   lowerCamelCase__=None ,   **lowerCamelCase__   ):

      '''simple docstring'''







      if images is None and text is None:
         raise ValueError('''You need to specify either an `images` or `text` input to process.'''   )

      if images is not None:
         UpperCamelCase       =  self.image_processor(a_ ,   return_tensors=a_ ,   **a_   )
      if text is not None:
         UpperCamelCase       =  self.char_tokenizer(a_ ,   return_tensors=a_ ,   **a_   )

      if text is None:
         return inputs
      elif images is None:
         return encodings
      else:
         UpperCamelCase       =  encodings["""input_ids"""]
         return inputs







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  sequences
      UpperCamelCase       =  char_preds.size(0   )

      UpperCamelCase       =  self._decode_helper(a_ ,   '''char'''   )
      UpperCamelCase       =  self._decode_helper(a_ ,   '''bpe'''   )
      UpperCamelCase       =  self._decode_helper(a_ ,   '''wp'''   )

      UpperCamelCase       =  []
      UpperCamelCase       =  []
      for i in range(a_   ):
         UpperCamelCase       =  [char_scores[i], bpe_scores[i], wp_scores[i]]
         UpperCamelCase       =  [char_strs[i], bpe_strs[i], wp_strs[i]]
         UpperCamelCase       =  scores.index(max(a_   )   )
         final_strs.append(strs[max_score_index]   )
         final_scores.append(scores[max_score_index]   )

      UpperCamelCase       =  {}
      UpperCamelCase       =  final_strs
      UpperCamelCase       =  final_scores
      UpperCamelCase       =  char_strs
      UpperCamelCase       =  bpe_strs
      UpperCamelCase       =  wp_strs
      return out







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      if format == DecodeType.CHARACTER:
         UpperCamelCase       =  self.char_decode
         UpperCamelCase       =  1
         UpperCamelCase       =  """[s]"""
      elif format == DecodeType.BPE:
         UpperCamelCase       =  self.bpe_decode
         UpperCamelCase       =  2
         UpperCamelCase       =  """#"""
      elif format == DecodeType.WORDPIECE:
         UpperCamelCase       =  self.wp_decode
         UpperCamelCase       =  1_0_2
         UpperCamelCase       =  """[SEP]"""
      else:
         raise ValueError(f'Format {format} is not supported.'   )

      UpperCamelCase       =  [], []
      UpperCamelCase       =  pred_logits.size(0   )
      UpperCamelCase       =  pred_logits.size(1   )
      UpperCamelCase       =  pred_logits.topk(1 ,   dim=-1 ,   largest=a_ ,   sorted=a_   )
      UpperCamelCase       =  preds_index.view(-1 ,   a_   )[:, 1:]
      UpperCamelCase       =  decoder(a_   )
      UpperCamelCase       =  torch.nn.functional.softmax(a_ ,   dim=2   ).max(dim=2   )
      UpperCamelCase       =  preds_max_prob[:, 1:]

      for index in range(a_   ):
         UpperCamelCase       =  preds_str[index].find(a_   )
         UpperCamelCase       =  preds_str[index][:pred_eos]
         UpperCamelCase       =  preds_index[index].cpu().tolist()
         UpperCamelCase       =  pred_index.index(a_   ) if eos_token in pred_index else -1
         UpperCamelCase       =  preds_max_prob[index][: pred_eos_index + 1]
         UpperCamelCase       =  pred_max_prob.cumprod(dim=0   )[-1] if pred_max_prob.nelement() != 0 else 0.0
         dec_strs.append(a_   )
         conf_scores.append(a_   )

      return dec_strs, conf_scores







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  [seq.replace(''' ''' ,   ''''''   ) for seq in self.char_tokenizer.batch_decode(a_   )]
      return decode_strs







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return self.bpe_tokenizer.batch_decode(a_   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  [seq.replace(''' ''' ,   ''''''   ) for seq in self.wp_tokenizer.batch_decode(a_   )]
      return decode_strs







'''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 						lowercase__	(  unittest.TestCase   ):






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__=7 ,   lowerCamelCase__=3 ,   lowerCamelCase__=3_0 ,   lowerCamelCase__=4_0_0 ,   lowerCamelCase__=True ,   lowerCamelCase__=None ,   lowerCamelCase__=True ,   lowerCamelCase__=[0.5, 0.5, 0.5] ,   lowerCamelCase__=[0.5, 0.5, 0.5] ,   lowerCamelCase__=True ,   lowerCamelCase__=1 / 2_5_5 ,   lowerCamelCase__=True ,   ):

      '''simple docstring'''







      UpperCamelCase       =  size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
      UpperCamelCase       =  parent
      UpperCamelCase       =  batch_size
      UpperCamelCase       =  num_channels
      UpperCamelCase       =  min_resolution
      UpperCamelCase       =  max_resolution
      UpperCamelCase       =  do_resize
      UpperCamelCase       =  size
      UpperCamelCase       =  do_normalize
      UpperCamelCase       =  image_mean
      UpperCamelCase       =  image_std
      UpperCamelCase       =  do_rescale
      UpperCamelCase       =  rescale_factor
      UpperCamelCase       =  do_pad







   def       UpperCAmelCase				(			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       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__=False   ):

      '''simple docstring'''







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

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

      return expected_height, expected_width




@require_torch
@require_vision
class 						lowercase__	(  snake_case_,					unittest.TestCase   ):






   '''simple docstring'''







   _snake_case		   =							DeformableDetrImageProcessor if is_vision_available() else None







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  DeformableDetrImageProcessingTester(self   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return self.image_processor_tester.prepare_image_processor_dict()







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  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_resize'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''do_rescale'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''do_pad'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''size'''   )   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.image_processing_class.from_dict(self.image_processor_dict   )
      self.assertEqual(image_processor.size ,   {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}   )
      self.assertEqual(image_processor.do_pad ,   lowerCamelCase__   )

      UpperCamelCase       =  self.image_processing_class.from_dict(
          self.image_processor_dict ,   size=4_2 ,   max_size=8_4 ,   pad_and_return_pixel_mask=lowerCamelCase__   )
      self.assertEqual(image_processor.size ,   {'''shortest_edge''': 4_2, '''longest_edge''': 8_4}   )
      self.assertEqual(image_processor.do_pad ,   lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      pass







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict   )
      # create random PIL images
      UpperCamelCase       =  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       =  image_processing(image_inputs[0] ,   return_tensors='''pt'''   ).pixel_values

      UpperCamelCase     , UpperCamelCase       =  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       =  self.image_processor_tester.get_expected_values(lowerCamelCase__ ,   batched=lowerCamelCase__   )

      UpperCamelCase       =  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   ):

      '''simple docstring'''







      UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict   )
      # create random numpy tensors
      UpperCamelCase       =  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       =  image_processing(image_inputs[0] ,   return_tensors='''pt'''   ).pixel_values

      UpperCamelCase     , UpperCamelCase       =  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       =  image_processing(lowerCamelCase__ ,   return_tensors='''pt'''   ).pixel_values

      UpperCamelCase     , UpperCamelCase       =  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   ):

      '''simple docstring'''







      UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict   )
      # create random PyTorch tensors
      UpperCamelCase       =  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       =  image_processing(image_inputs[0] ,   return_tensors='''pt'''   ).pixel_values

      UpperCamelCase     , UpperCamelCase       =  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       =  image_processing(lowerCamelCase__ ,   return_tensors='''pt'''   ).pixel_values

      UpperCamelCase     , UpperCamelCase       =  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   ):

      '''simple docstring'''







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

      UpperCamelCase       =  {'''image_id''': 3_9_7_6_9, '''annotations''': target}

      # encode them
      UpperCamelCase       =  DeformableDetrImageProcessor()
      UpperCamelCase       =  image_processing(images=lowerCamelCase__ ,   annotations=lowerCamelCase__ ,   return_tensors='''pt'''   )

      # verify pixel values
      UpperCamelCase       =  torch.Size([1, 3, 8_0_0, 1_0_6_6]   )
      self.assertEqual(encoding['''pixel_values'''].shape ,   lowerCamelCase__   )

      UpperCamelCase       =  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       =  torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] ,   lowerCamelCase__   )   )
      # verify boxes
      UpperCamelCase       =  torch.Size([6, 4]   )
      self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,   lowerCamelCase__   )
      UpperCamelCase       =  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       =  torch.tensor([3_9_7_6_9]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,   lowerCamelCase__   )   )
      # verify is_crowd
      UpperCamelCase       =  torch.tensor([0, 0, 0, 0, 0, 0]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,   lowerCamelCase__   )   )
      # verify class_labels
      UpperCamelCase       =  torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,   lowerCamelCase__   )   )
      # verify orig_size
      UpperCamelCase       =  torch.tensor([4_8_0, 6_4_0]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,   lowerCamelCase__   )   )
      # verify size
      UpperCamelCase       =  torch.tensor([8_0_0, 1_0_6_6]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,   lowerCamelCase__   )   )







   @slow
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







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

      UpperCamelCase       =  {'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

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

      # encode them
      UpperCamelCase       =  DeformableDetrImageProcessor(format='''coco_panoptic'''   )
      UpperCamelCase       =  image_processing(images=lowerCamelCase__ ,   annotations=lowerCamelCase__ ,   masks_path=lowerCamelCase__ ,   return_tensors='''pt'''   )

      # verify pixel values
      UpperCamelCase       =  torch.Size([1, 3, 8_0_0, 1_0_6_6]   )
      self.assertEqual(encoding['''pixel_values'''].shape ,   lowerCamelCase__   )

      UpperCamelCase       =  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       =  torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] ,   lowerCamelCase__   )   )
      # verify boxes
      UpperCamelCase       =  torch.Size([6, 4]   )
      self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,   lowerCamelCase__   )
      UpperCamelCase       =  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       =  torch.tensor([3_9_7_6_9]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,   lowerCamelCase__   )   )
      # verify is_crowd
      UpperCamelCase       =  torch.tensor([0, 0, 0, 0, 0, 0]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,   lowerCamelCase__   )   )
      # verify class_labels
      UpperCamelCase       =  torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,   lowerCamelCase__   )   )
      # verify masks
      UpperCamelCase       =  8_2_2_8_7_3
      self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() ,   lowerCamelCase__   )
      # verify orig_size
      UpperCamelCase       =  torch.tensor([4_8_0, 6_4_0]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,   lowerCamelCase__   )   )
      # verify size
      UpperCamelCase       =  torch.tensor([8_0_0, 1_0_6_6]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,   lowerCamelCase__   )   )





