




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 YolosImageProcessor



class       a					(       unittest.TestCase      ):

     '''simple docstring'''





     def __init__(     self       :      Dict ,   __snake_case       :      List[Any] ,   __snake_case       :      List[str]=7 ,   __snake_case       :      Union[str, Any]=3 ,   __snake_case       :      Any=30 ,   __snake_case       :      List[str]=4_00 ,   __snake_case       :      Union[str, Any]=True ,   __snake_case       :      Union[str, Any]=None ,   __snake_case       :      Union[str, Any]=True ,   __snake_case       :      Dict=[0.5, 0.5, 0.5] ,   __snake_case       :      Union[str, Any]=[0.5, 0.5, 0.5] ,   __snake_case       :      Any=True ,   __snake_case       :      Any=1 / 2_55 ,   __snake_case       :      Optional[int]=True ,   ):
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         UpperCAmelCase_										=					size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
         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   lowerCamelCase_       (     self       :      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   lowerCamelCase_       (     self       :      Any ,   __snake_case       :      Tuple ,   __snake_case       :      Optional[int]=False    ):
         if not batched:
             UpperCAmelCase_										=					image_inputs[0]
             if isinstance(__snake_case ,   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(__snake_case ,   key=lambda __snake_case    : item[0]    )[0]
             UpperCAmelCase_										=					max(__snake_case ,   key=lambda __snake_case    : item[1]    )[1]

         return expected_height, expected_width



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

     '''simple docstring'''





     lowerCAmelCase    :	List[str]       		=  YolosImageProcessor if is_vision_available() else None
     def   lowerCamelCase_       (     self       :      Tuple    ):
         UpperCAmelCase_										=					YolosImageProcessingTester(self    )
     @property
     def   lowerCamelCase_       (     self       :      Tuple    ):
         return self.image_processor_tester.prepare_image_processor_dict()
     def   lowerCamelCase_       (     self       :      Dict    ):
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         self.assertTrue(hasattr(__snake_case ,   '''image_mean'''    )    )
         self.assertTrue(hasattr(__snake_case ,   '''image_std'''    )    )
         self.assertTrue(hasattr(__snake_case ,   '''do_normalize'''    )    )
         self.assertTrue(hasattr(__snake_case ,   '''do_resize'''    )    )
         self.assertTrue(hasattr(__snake_case ,   '''size'''    )    )
     def   lowerCamelCase_       (     self       :      List[str]    ):
         UpperCAmelCase_										=					self.image_processing_class.from_dict(self.image_processor_dict    )
         self.assertEqual(image_processor.size ,   {'''shortest_edge''': 18, '''longest_edge''': 13_33}    )
         self.assertEqual(image_processor.do_pad ,   __snake_case    )

         UpperCAmelCase_										=					self.image_processing_class.from_dict(
             self.image_processor_dict ,   size=42 ,   max_size=84 ,   pad_and_return_pixel_mask=__snake_case    )
         self.assertEqual(image_processor.size ,   {'''shortest_edge''': 42, '''longest_edge''': 84}    )
         self.assertEqual(image_processor.do_pad ,   __snake_case    )
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         pass
     def   lowerCamelCase_       (     self       :      Optional[Any]    ):
         # Initialize image_processing
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         # create random PIL images
         UpperCAmelCase_										=					prepare_image_inputs(self.image_processor_tester ,   equal_resolution=__snake_case    )
         for image in image_inputs:
             self.assertIsInstance(__snake_case ,   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(__snake_case    )

         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(__snake_case ,   batched=__snake_case    )

         UpperCAmelCase_										=					image_processing(__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   lowerCamelCase_       (     self       :      Tuple    ):
         # Initialize image_processing
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         # create random numpy tensors
         UpperCAmelCase_										=					prepare_image_inputs(self.image_processor_tester ,   equal_resolution=__snake_case ,   numpify=__snake_case    )
         for image in image_inputs:
             self.assertIsInstance(__snake_case ,   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(__snake_case    )

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

         # Test batched
         UpperCAmelCase_										=					image_processing(__snake_case ,   return_tensors='''pt'''    ).pixel_values

         UpperCAmelCase_			,							UpperCAmelCase_										=					self.image_processor_tester.get_expected_values(__snake_case ,   batched=__snake_case    )

         self.assertEqual(
             encoded_images.shape ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             ) ,   )
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         # Initialize image_processing
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         # create random PyTorch tensors
         UpperCAmelCase_										=					prepare_image_inputs(self.image_processor_tester ,   equal_resolution=__snake_case ,   torchify=__snake_case    )
         for image in image_inputs:
             self.assertIsInstance(__snake_case ,   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(__snake_case    )

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

         # Test batched
         UpperCAmelCase_										=					image_processing(__snake_case ,   return_tensors='''pt'''    ).pixel_values

         UpperCAmelCase_			,							UpperCAmelCase_										=					self.image_processor_tester.get_expected_values(__snake_case ,   batched=__snake_case    )

         self.assertEqual(
             encoded_images.shape ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             ) ,   )
     def   lowerCamelCase_       (     self       :      Any    ):
         # Initialize image_processings
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         UpperCAmelCase_										=					self.image_processing_class(do_resize=__snake_case ,   do_normalize=__snake_case ,   do_rescale=__snake_case    )
         # create random PyTorch tensors
         UpperCAmelCase_										=					prepare_image_inputs(self.image_processor_tester ,   equal_resolution=__snake_case ,   torchify=__snake_case    )
         for image in image_inputs:
             self.assertIsInstance(__snake_case ,   torch.Tensor    )

         # Test whether the method "pad" and calling the image processor return the same tensors
         UpperCAmelCase_										=					image_processing_a.pad(__snake_case ,   return_tensors='''pt'''    )
         UpperCAmelCase_										=					image_processing_a(__snake_case ,   return_tensors='''pt'''    )

         self.assertTrue(
             torch.allclose(encoded_images_with_method['''pixel_values'''] ,   encoded_images['''pixel_values'''] ,   atol=1E-4    )    )
     @slow
     def   lowerCamelCase_       (     self       :      List[Any]    ):
         # prepare image and target
         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_97_69, '''annotations''': target}

         # encode them
         UpperCAmelCase_										=					YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''    )
         UpperCAmelCase_										=					image_processing(images=__snake_case ,   annotations=__snake_case ,   return_tensors='''pt'''    )

         # verify pixel values
         UpperCAmelCase_										=					torch.Size([1, 3, 8_00, 10_66]    )
         self.assertEqual(encoding['''pixel_values'''].shape ,   __snake_case    )

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

         # verify area
         UpperCAmelCase_										=					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'''] ,   __snake_case    )    )
         # verify boxes
         UpperCAmelCase_										=					torch.Size([6, 4]    )
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,   __snake_case    )
         UpperCAmelCase_										=					torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]    )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] ,   __snake_case ,   atol=1E-3    )    )
         # verify image_id
         UpperCAmelCase_										=					torch.tensor([3_97_69]    )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,   __snake_case    )    )
         # verify is_crowd
         UpperCAmelCase_										=					torch.tensor([0, 0, 0, 0, 0, 0]    )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,   __snake_case    )    )
         # verify class_labels
         UpperCAmelCase_										=					torch.tensor([75, 75, 63, 65, 17, 17]    )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,   __snake_case    )    )
         # verify orig_size
         UpperCAmelCase_										=					torch.tensor([4_80, 6_40]    )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,   __snake_case    )    )
         # verify size
         UpperCAmelCase_										=					torch.tensor([8_00, 10_66]    )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,   __snake_case    )    )




     @slow
     def   lowerCamelCase_       (     self       :      Any    ):
         # prepare image, target and masks_path
         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_97_69, '''segments_info''': target}

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

         # encode them
         UpperCAmelCase_										=					YolosImageProcessor(format='''coco_panoptic'''    )
         UpperCAmelCase_										=					image_processing(images=__snake_case ,   annotations=__snake_case ,   masks_path=__snake_case ,   return_tensors='''pt'''    )

         # verify pixel values
         UpperCAmelCase_										=					torch.Size([1, 3, 8_00, 10_66]    )
         self.assertEqual(encoding['''pixel_values'''].shape ,   __snake_case    )

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

         # verify area
         UpperCAmelCase_										=					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'''] ,   __snake_case    )    )
         # verify boxes
         UpperCAmelCase_										=					torch.Size([6, 4]    )
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,   __snake_case    )
         UpperCAmelCase_										=					torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]    )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] ,   __snake_case ,   atol=1E-3    )    )
         # verify image_id
         UpperCAmelCase_										=					torch.tensor([3_97_69]    )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,   __snake_case    )    )
         # verify is_crowd
         UpperCAmelCase_										=					torch.tensor([0, 0, 0, 0, 0, 0]    )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,   __snake_case    )    )
         # verify class_labels
         UpperCAmelCase_										=					torch.tensor([17, 17, 63, 75, 75, 93]    )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,   __snake_case    )    )
         # verify masks
         UpperCAmelCase_										=					82_28_73
         self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() ,   __snake_case    )
         # verify orig_size
         UpperCAmelCase_										=					torch.tensor([4_80, 6_40]    )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,   __snake_case    )    )
         # verify size
         UpperCAmelCase_										=					torch.tensor([8_00, 10_66]    )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,   __snake_case    )    )











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase      			=						{
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						['RobertaTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						[
     'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
     'RobertaForCausalLM',
     'RobertaForMaskedLM',
     'RobertaForMultipleChoice',
     'RobertaForQuestionAnswering',
     'RobertaForSequenceClassification',
     'RobertaForTokenClassification',
     'RobertaModel',
     'RobertaPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						[
     'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFRobertaForCausalLM',
     'TFRobertaForMaskedLM',
     'TFRobertaForMultipleChoice',
     'TFRobertaForQuestionAnswering',
     'TFRobertaForSequenceClassification',
     'TFRobertaForTokenClassification',
     'TFRobertaMainLayer',
     'TFRobertaModel',
     'TFRobertaPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						[
     'FlaxRobertaForCausalLM',
     'FlaxRobertaForMaskedLM',
     'FlaxRobertaForMultipleChoice',
     'FlaxRobertaForQuestionAnswering',
     'FlaxRobertaForSequenceClassification',
     'FlaxRobertaForTokenClassification',
     'FlaxRobertaModel',
     'FlaxRobertaPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
 from .tokenization_roberta import RobertaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_roberta_fast import RobertaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_roberta import (
      ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      RobertaForCausalLM,
      RobertaForMaskedLM,
      RobertaForMultipleChoice,
      RobertaForQuestionAnswering,
      RobertaForSequenceClassification,
      RobertaForTokenClassification,
      RobertaModel,
      RobertaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_roberta import (
      TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRobertaForCausalLM,
      TFRobertaForMaskedLM,
      TFRobertaForMultipleChoice,
      TFRobertaForQuestionAnswering,
      TFRobertaForSequenceClassification,
      TFRobertaForTokenClassification,
      TFRobertaMainLayer,
      TFRobertaModel,
      TFRobertaPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_roberta import (
      FlaxRobertaForCausalLM,
      FlaxRobertaForMaskedLM,
      FlaxRobertaForMultipleChoice,
      FlaxRobertaForQuestionAnswering,
      FlaxRobertaForSequenceClassification,
      FlaxRobertaForTokenClassification,
      FlaxRobertaModel,
      FlaxRobertaPreTrainedModel,
  )

else:
 import sys

 _lowerCamelCase      			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





