



'''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		):



 '''simple docstring'''







 def __init__(self				,     A				,     A=7				,     A=3				,     A=30				,     A=400				,     A=True				,     A=None				,     A=True				,     A=[0.5, 0.5, 0.5]				,     A=[0.5, 0.5, 0.5]				,     A=True				,     A=1 / 255				,     A=True				,     )					->  int:


      """simple docstring"""



      _a											=						size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
      _a											=						parent
      _a											=						batch_size
      _a											=						num_channels
      _a											=						min_resolution
      _a											=						max_resolution
      _a											=						do_resize
      _a											=						size
      _a											=						do_normalize
      _a											=						image_mean
      _a											=						image_std
      _a											=						do_rescale
      _a											=						rescale_factor
      _a											=						do_pad



 def       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



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



 def       a__						(self				,     A				,     A=False )					->  str:


      """simple docstring"""



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

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

      return expected_height, expected_width






@require_torch
@require_vision
class __A    (						A     ,     unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase					: str    = ConditionalDetrImageProcessor if is_vision_available() else None



 def       a__						(self )					->  Tuple:


      """simple docstring"""



      _a											=						ConditionalDetrImageProcessingTester(self )



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


      """simple docstring"""



      return self.image_processor_tester.prepare_image_processor_dict()



 def       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



      _a											=						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				,     '''size''' ) )



 def       a__						(self )					->  List[Any]:


      """simple docstring"""



      _a											=						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 )

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



 def       a__						(self )					->  Tuple:


      """simple docstring"""



      pass



 def       a__						(self )					->  List[str]:


      """simple docstring"""



      _a											=						self.image_processing_class(**self.image_processor_dict )
      # create random PIL images
      _a											=						prepare_image_inputs(self.image_processor_tester				,     equal_resolution=A )
      for image in image_inputs:
           self.assertIsInstance(A				,     Image.Image )

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

      _a						,					_a											=						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
      _a						,					_a											=						self.image_processor_tester.get_expected_values(A				,     batched=A )

      _a											=						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       a__						(self )					->  List[Any]:


      """simple docstring"""



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

      _a						,					_a											=						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
      _a											=						image_processing(A				,     return_tensors='''pt''' ).pixel_values

      _a						,					_a											=						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       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



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

      _a						,					_a											=						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
      _a											=						image_processing(A				,     return_tensors='''pt''' ).pixel_values

      _a						,					_a											=						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       a__						(self )					->  Any:


      """simple docstring"""



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

      _a											=						{'''image_id''': 39_769, '''annotations''': target}

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

      # verify pixel values
      _a											=						torch.Size([1, 3, 800, 1_066] )
      self.assertEqual(encoding['''pixel_values'''].shape				,     A )

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

      # verify area
      _a											=						torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']				,     A ) )
      # verify boxes
      _a											=						torch.Size([6, 4] )
      self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape				,     A )
      _a											=						torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]				,     A				,     atol=1E-3 ) )
      # verify image_id
      _a											=						torch.tensor([39_769] )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']				,     A ) )
      # verify is_crowd
      _a											=						torch.tensor([0, 0, 0, 0, 0, 0] )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']				,     A ) )
      # verify class_labels
      _a											=						torch.tensor([75, 75, 63, 65, 17, 17] )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']				,     A ) )
      # verify orig_size
      _a											=						torch.tensor([480, 640] )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']				,     A ) )
      # verify size
      _a											=						torch.tensor([800, 1_066] )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']				,     A ) )



 @slow
 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



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

      _a											=						{'''file_name''': '''000000039769.png''', '''image_id''': 39_769, '''segments_info''': target}

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

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

      # verify pixel values
      _a											=						torch.Size([1, 3, 800, 1_066] )
      self.assertEqual(encoding['''pixel_values'''].shape				,     A )

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

      # verify area
      _a											=						torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']				,     A ) )
      # verify boxes
      _a											=						torch.Size([6, 4] )
      self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape				,     A )
      _a											=						torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]				,     A				,     atol=1E-3 ) )
      # verify image_id
      _a											=						torch.tensor([39_769] )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']				,     A ) )
      # verify is_crowd
      _a											=						torch.tensor([0, 0, 0, 0, 0, 0] )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']				,     A ) )
      # verify class_labels
      _a											=						torch.tensor([17, 17, 63, 75, 75, 93] )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']				,     A ) )
      # verify masks
      _a											=						822_873
      self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()				,     A )
      # verify orig_size
      _a											=						torch.tensor([480, 640] )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']				,     A ) )
      # verify size
      _a											=						torch.tensor([800, 1_066] )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']				,     A ) )








'''simple docstring'''





from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
  import tensorflow as tf

  from ..tf_utils import stable_softmax


if is_torch_available():
  import torch


lowercase_	 =						logging.get_logger(__name__)






@add_end_docstrings(
    A     ,     R'\n        top_k (`int`, defaults to 5):\n            The number of predictions to return.\n        targets (`str` or `List[str]`, *optional*):\n            When passed, the model will limit the scores to the passed targets instead of looking up in the whole\n            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting\n            token will be used (with a warning, and that might be slower).\n\n    '     ,     )
class __A    (						A		):



 '''simple docstring'''







 def       a__						(self				,     A )					->  np.ndarray:


      """simple docstring"""



      if self.framework == "tf":
           _a											=						tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()
      elif self.framework == "pt":
           _a											=						torch.nonzero(input_ids == self.tokenizer.mask_token_id				,     as_tuple=A )
      else:
           raise ValueError('''Unsupported framework''' )
      return masked_index



 def       a__						(self				,     A )					->  np.ndarray:


      """simple docstring"""



      _a											=						self.get_masked_index(A )
      _a											=						np.prod(masked_index.shape )
      if numel < 1:
           raise PipelineException(
               '''fill-mask'''				,     self.model.base_model_prefix				,     f'''No mask_token ({self.tokenizer.mask_token}) found on the input'''				,     )



 def       a__						(self				,     A )					->  Optional[int]:


      """simple docstring"""



      if isinstance(A				,     A ):
           for model_input in model_inputs:
                self._ensure_exactly_one_mask_token(model_input['''input_ids'''][0] )
      else:
           for input_ids in model_inputs["input_ids"]:
                self._ensure_exactly_one_mask_token(A )



 def       a__						(self				,     A				,     A=None				,     **A )					->  Dict[str, GenericTensor]:


      """simple docstring"""



      if return_tensors is None:
           _a											=						self.framework
      _a											=						self.tokenizer(A				,     return_tensors=A )
      self.ensure_exactly_one_mask_token(A )
      return model_inputs



 def       a__						(self				,     A )					->  Optional[Any]:


      """simple docstring"""



      _a											=						self.model(**A )
      _a											=						model_inputs['''input_ids''']
      return model_outputs



 def       a__						(self				,     A				,     A=5				,     A=None )					->  Tuple:


      """simple docstring"""



      if target_ids is not None and target_ids.shape[0] < top_k:
           _a											=						target_ids.shape[0]
      _a											=						model_outputs['''input_ids'''][0]
      _a											=						model_outputs['''logits''']

      if self.framework == "tf":
           _a											=						tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()[:, 0]

           _a											=						outputs.numpy()

           _a											=						outputs[0, masked_index, :]
           _a											=						stable_softmax(A				,     axis=-1 )
           if target_ids is not None:
                _a											=						tf.gather_nd(tf.squeeze(A				,     0 )				,     target_ids.reshape(-1				,     1 ) )
                _a											=						tf.expand_dims(A				,     0 )

           _a											=						tf.math.top_k(A				,     k=A )
           _a						,					_a											=						topk.values.numpy(), topk.indices.numpy()
      else:
           _a											=						torch.nonzero(input_ids == self.tokenizer.mask_token_id				,     as_tuple=A ).squeeze(-1 )
           # Fill mask pipeline supports only one ${mask_token} per sample

           _a											=						outputs[0, masked_index, :]
           _a											=						logits.softmax(dim=-1 )
           if target_ids is not None:
                _a											=						probs[..., target_ids]

           _a						,					_a											=						probs.topk(A )

      _a											=						[]
      _a											=						values.shape[0] == 1
      for i, (_values, _predictions) in enumerate(zip(values.tolist()				,     predictions.tolist() ) ):
           _a											=						[]
           for v, p in zip(_values				,     _predictions ):
                # Copy is important since we're going to modify this array in place
                _a											=						input_ids.numpy().copy()
                if target_ids is not None:
                     _a											=						target_ids[p].tolist()

                _a											=						p
                # Filter padding out:
                _a											=						tokens[np.where(tokens != self.tokenizer.pad_token_id )]
                # Originally we skip special tokens to give readable output.
                # For multi masks though, the other [MASK] would be removed otherwise
                # making the output look odd, so we add them back
                _a											=						self.tokenizer.decode(A				,     skip_special_tokens=A )
                _a											=						{'''score''': v, '''token''': p, '''token_str''': self.tokenizer.decode([p] ), '''sequence''': sequence}
                row.append(A )
           result.append(A )
      if single_mask:
           return result[0]
      return result



 def       a__						(self				,     A				,     A=None )					->  Optional[Any]:


      """simple docstring"""



      if isinstance(A				,     A ):
           _a											=						[targets]
      try:
           _a											=						self.tokenizer.get_vocab()
      except Exception:
           _a											=						{}
      _a											=						[]
      for target in targets:
           _a											=						vocab.get(A				,     A )
           if id_ is None:
                _a											=						self.tokenizer(
                    A				,     add_special_tokens=A				,     return_attention_mask=A				,     return_token_type_ids=A				,     max_length=1				,     truncation=A				,     )['''input_ids''']
                if len(A ) == 0:
                     logger.warning(
                         f'''The specified target token `{target}` does not exist in the model vocabulary. '''
                         '''We cannot replace it with anything meaningful, ignoring it''' )
                     continue
                _a											=						input_ids[0]
                # XXX: If users encounter this pass
                # it becomes pretty slow, so let's make sure
                # The warning enables them to fix the input to
                # get faster performance.
                logger.warning(
                    f'''The specified target token `{target}` does not exist in the model vocabulary. '''
                    f'''Replacing with `{self.tokenizer.convert_ids_to_tokens(id_ )}`.''' )
           target_ids.append(id_ )
      _a											=						list(set(A ) )
      if len(A ) == 0:
           raise ValueError('''At least one target must be provided when passed.''' )
      _a											=						np.array(A )
      return target_ids



 def       a__						(self				,     A=None				,     A=None )					->  List[str]:


      """simple docstring"""



      _a											=						{}

      if targets is not None:
           _a											=						self.get_target_ids(A				,     A )
           _a											=						target_ids

      if top_k is not None:
           _a											=						top_k

      if self.tokenizer.mask_token_id is None:
           raise PipelineException(
               '''fill-mask'''				,     self.model.base_model_prefix				,     '''The tokenizer does not define a `mask_token`.''' )
      return {}, {}, postprocess_params



 def __call__(self				,     A				,     *A				,     **A )					->  str:


      """simple docstring"""



      _a											=						super().__call__(A				,     **A )
      if isinstance(A				,     A ) and len(A ) == 1:
           return outputs[0]
      return outputs



