


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 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     def __init__(					self:str     ,		_a:int     ,		_a:str=7     ,		_a:Optional[int]=3     ,		_a:Dict=30     ,		_a:str=4_00     ,		_a:Optional[Any]=True     ,		_a:Optional[Any]=None     ,		_a:List[str]=True     ,		_a:List[str]=[0.5, 0.5, 0.5]     ,		_a:List[str]=[0.5, 0.5, 0.5]     ,		_a:Tuple=True     ,		_a:Tuple=1 / 2_55     ,		_a:int=True     ,		):
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       snake_case__						= size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
       snake_case__						= parent
       snake_case__						= batch_size
       snake_case__						= num_channels
       snake_case__						= min_resolution
       snake_case__						= max_resolution
       snake_case__						= do_resize
       snake_case__						= size
       snake_case__						= do_normalize
       snake_case__						= image_mean
       snake_case__						= image_std
       snake_case__						= do_rescale
       snake_case__						= rescale_factor
       snake_case__						= do_pad

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

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

       return expected_height, expected_width




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





     '''simple docstring'''



     __lowercase				:							Optional[int]	   =       ConditionalDetrImageProcessor if is_vision_available() else None

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= ConditionalDetrImageProcessingTester(self   )

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       return self.image_processor_tester.prepare_image_processor_dict()

     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       snake_case__						= 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 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       snake_case__						= 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     ,		_a   )

       snake_case__						= 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 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       pass

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       # Initialize image_processing
       snake_case__						= self.image_processing_class(**self.image_processor_dict   )
       # create random PIL images
       snake_case__						= prepare_image_inputs(self.image_processor_tester     ,		equal_resolution=_a   )
       for image in image_inputs:
         self.assertIsInstance(_a     ,		Image.Image   )

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

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

       snake_case__						= 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 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       # Initialize image_processing
       snake_case__						= self.image_processing_class(**self.image_processor_dict   )
       # create random numpy tensors
       snake_case__						= 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
       snake_case__						= image_processing(image_inputs[0]     ,		return_tensors='''pt'''   ).pixel_values

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

       snake_case__				,				snake_case__						= 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 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       # Initialize image_processing
       snake_case__						= self.image_processing_class(**self.image_processor_dict   )
       # create random PyTorch tensors
       snake_case__						= 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
       snake_case__						= image_processing(image_inputs[0]     ,		return_tensors='''pt'''   ).pixel_values

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

       snake_case__				,				snake_case__						= 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 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       # prepare image and target
       snake_case__						= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
       with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''     ,		'''r'''   ) as f:
         snake_case__						= json.loads(f.read()   )

       snake_case__						= {'''image_id''': 3_97_69, '''annotations''': target}

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

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

       snake_case__						= 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
       snake_case__						= 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
       snake_case__						= torch.Size([6, 4]   )
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape     ,		_a   )
       snake_case__						= 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
       snake_case__						= torch.tensor([3_97_69]   )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']     ,		_a   )   )
       # verify is_crowd
       snake_case__						= torch.tensor([0, 0, 0, 0, 0, 0]   )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']     ,		_a   )   )
       # verify class_labels
       snake_case__						= torch.tensor([75, 75, 63, 65, 17, 17]   )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']     ,		_a   )   )
       # verify orig_size
       snake_case__						= torch.tensor([4_80, 6_40]   )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']     ,		_a   )   )
       # verify size
       snake_case__						= torch.tensor([8_00, 10_66]   )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']     ,		_a   )   )







     @slow
     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       # prepare image, target and masks_path
       snake_case__						= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
       with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''     ,		'''r'''   ) as f:
         snake_case__						= json.loads(f.read()   )

       snake_case__						= {'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}

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

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

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

       snake_case__						= 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
       snake_case__						= 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
       snake_case__						= torch.Size([6, 4]   )
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape     ,		_a   )
       snake_case__						= 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
       snake_case__						= torch.tensor([3_97_69]   )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']     ,		_a   )   )
       # verify is_crowd
       snake_case__						= torch.tensor([0, 0, 0, 0, 0, 0]   )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']     ,		_a   )   )
       # verify class_labels
       snake_case__						= torch.tensor([17, 17, 63, 75, 75, 93]   )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']     ,		_a   )   )
       # verify masks
       snake_case__						= 82_28_73
       self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()     ,		_a   )
       # verify orig_size
       snake_case__						= torch.tensor([4_80, 6_40]   )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']     ,		_a   )   )
       # verify size
       snake_case__						= torch.tensor([8_00, 10_66]   )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']     ,		_a   )   )





from typing import TYPE_CHECKING

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


lowerCamelCase__							:						Tuple							     =   {
    """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__							:						Tuple							     =   ["""RobertaTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__							:						Optional[int]							     =   [
       """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__							:						List[str]							     =   [
       """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__							:						str							     =   [
       """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__							:						List[Any]							     =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

