






'''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 DetaImageProcessor






class        __SCREAMING_SNAKE_CASE (     unittest.TestCase			):




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




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

       else:
             SCREAMING_SNAKE_CASE__       :			Tuple													=[]
             for image in image_inputs:
                   SCREAMING_SNAKE_CASE__       :			Tuple													=self.get_expected_values([image] )
                   expected_values.append((expected_height, expected_width) )
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=max(__lowercase   , key=lambda __lowercase : item[0] )[0]
             SCREAMING_SNAKE_CASE__       :			Dict													=max(__lowercase   , key=lambda __lowercase : item[1] )[1]

       return expected_height, expected_width






@require_torch
@require_vision
class        __SCREAMING_SNAKE_CASE (     lowerCamelCase		,			unittest.TestCase			):
 snake_case_												=							DetaImageProcessor if is_vision_available() else None




 def        __magic_name__		( self		:							Union[str, Any] )		->  Any:
       SCREAMING_SNAKE_CASE__       :			Any													=DetaImageProcessingTester(self )




 @property
 def        __magic_name__		( self		:							Optional[int] )		->  str:
       return self.image_processor_tester.prepare_image_processor_dict()




 def        __magic_name__		( self		:							Any )		->  Optional[int]:
       SCREAMING_SNAKE_CASE__       :			str													=self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(__lowercase   , '''image_mean''' ) )
       self.assertTrue(hasattr(__lowercase   , '''image_std''' ) )
       self.assertTrue(hasattr(__lowercase   , '''do_normalize''' ) )
       self.assertTrue(hasattr(__lowercase   , '''do_resize''' ) )
       self.assertTrue(hasattr(__lowercase   , '''do_rescale''' ) )
       self.assertTrue(hasattr(__lowercase   , '''do_pad''' ) )
       self.assertTrue(hasattr(__lowercase   , '''size''' ) )




 def        __magic_name__		( self		:							List[Any] )		->  str:
       SCREAMING_SNAKE_CASE__       :			str													=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   , __lowercase )




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




 def        __magic_name__		( self		:							int )		->  Tuple:
       # Initialize image_processing
       SCREAMING_SNAKE_CASE__       :			List[Any]													=self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       SCREAMING_SNAKE_CASE__       :			List[str]													=prepare_image_inputs(self.image_processor_tester   , equal_resolution=__lowercase )
       for image in image_inputs:
             self.assertIsInstance(__lowercase   , Image.Image )

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

       SCREAMING_SNAKE_CASE__       :			Tuple													=self.image_processor_tester.get_expected_values(__lowercase )

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

       # Test batched
       SCREAMING_SNAKE_CASE__       :			List[Any]													=self.image_processor_tester.get_expected_values(__lowercase   , batched=__lowercase )

       SCREAMING_SNAKE_CASE__       :			Optional[int]													=image_processing(__lowercase   , 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        __magic_name__		( self		:							List[Any] )		->  Optional[int]:
       # Initialize image_processing
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=prepare_image_inputs(self.image_processor_tester   , equal_resolution=__lowercase   , numpify=__lowercase )
       for image in image_inputs:
             self.assertIsInstance(__lowercase   , np.ndarray )

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

       SCREAMING_SNAKE_CASE__       :			List[str]													=self.image_processor_tester.get_expected_values(__lowercase )

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

       # Test batched
       SCREAMING_SNAKE_CASE__       :			Dict													=image_processing(__lowercase   , return_tensors='''pt''' ).pixel_values

       SCREAMING_SNAKE_CASE__       :			Optional[int]													=self.image_processor_tester.get_expected_values(__lowercase   , batched=__lowercase )

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




 def        __magic_name__		( self		:							Optional[Any] )		->  Any:
       # Initialize image_processing
       SCREAMING_SNAKE_CASE__       :			List[Any]													=self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=prepare_image_inputs(self.image_processor_tester   , equal_resolution=__lowercase   , torchify=__lowercase )
       for image in image_inputs:
             self.assertIsInstance(__lowercase   , torch.Tensor )

       # Test not batched input
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=image_processing(image_inputs[0]   , return_tensors='''pt''' ).pixel_values

       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=self.image_processor_tester.get_expected_values(__lowercase )

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

       # Test batched
       SCREAMING_SNAKE_CASE__       :			List[Any]													=image_processing(__lowercase   , return_tensors='''pt''' ).pixel_values

       SCREAMING_SNAKE_CASE__       :			Optional[int]													=self.image_processor_tester.get_expected_values(__lowercase   , batched=__lowercase )

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




 @slow
 def        __magic_name__		( self		:							List[str] )		->  Optional[Any]:
       # prepare image and target
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
       with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''   , '''r''' ) as f:
             SCREAMING_SNAKE_CASE__       :			int													=json.loads(f.read() )

       SCREAMING_SNAKE_CASE__       :			Dict													={'''image_id''': 3_97_69, '''annotations''': target}

       # encode them
       SCREAMING_SNAKE_CASE__       :			str													=DetaImageProcessor()
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=image_processing(images=__lowercase   , annotations=__lowercase   , return_tensors='''pt''' )

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

       SCREAMING_SNAKE_CASE__       :			Any													=torch.tensor([0.2796, 0.3138, 0.3481] )
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]   , __lowercase   , atol=1e-4 ) )

       # verify area
       SCREAMING_SNAKE_CASE__       :			Dict													=torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']   , __lowercase ) )
       # verify boxes
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=torch.Size([6, 4] )
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   , __lowercase )
       SCREAMING_SNAKE_CASE__       :			Tuple													=torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   , __lowercase   , atol=1e-3 ) )
       # verify image_id
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=torch.tensor([3_97_69] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   , __lowercase ) )
       # verify is_crowd
       SCREAMING_SNAKE_CASE__       :			Tuple													=torch.tensor([0, 0, 0, 0, 0, 0] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   , __lowercase ) )
       # verify class_labels
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=torch.tensor([75, 75, 63, 65, 17, 17] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   , __lowercase ) )
       # verify orig_size
       SCREAMING_SNAKE_CASE__       :			int													=torch.tensor([4_80, 6_40] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   , __lowercase ) )
       # verify size
       SCREAMING_SNAKE_CASE__       :			List[Any]													=torch.tensor([8_00, 10_66] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   , __lowercase ) )




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

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

       SCREAMING_SNAKE_CASE__       :			str													=pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )

       # encode them
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=DetaImageProcessor(format='''coco_panoptic''' )
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=image_processing(images=__lowercase   , annotations=__lowercase   , masks_path=__lowercase   , return_tensors='''pt''' )

       # verify pixel values
       SCREAMING_SNAKE_CASE__       :			List[Any]													=torch.Size([1, 3, 8_00, 10_66] )
       self.assertEqual(encoding['''pixel_values'''].shape   , __lowercase )

       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=torch.tensor([0.2796, 0.3138, 0.3481] )
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]   , __lowercase   , atol=1e-4 ) )

       # verify area
       SCREAMING_SNAKE_CASE__       :			int													=torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']   , __lowercase ) )
       # verify boxes
       SCREAMING_SNAKE_CASE__       :			Dict													=torch.Size([6, 4] )
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   , __lowercase )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   , __lowercase   , atol=1e-3 ) )
       # verify image_id
       SCREAMING_SNAKE_CASE__       :			List[str]													=torch.tensor([3_97_69] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   , __lowercase ) )
       # verify is_crowd
       SCREAMING_SNAKE_CASE__       :			Tuple													=torch.tensor([0, 0, 0, 0, 0, 0] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   , __lowercase ) )
       # verify class_labels
       SCREAMING_SNAKE_CASE__       :			List[str]													=torch.tensor([17, 17, 63, 75, 75, 93] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   , __lowercase ) )
       # verify masks
       SCREAMING_SNAKE_CASE__       :			Tuple													=82_28_73
       self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()   , __lowercase )
       # verify orig_size
       SCREAMING_SNAKE_CASE__       :			Dict													=torch.tensor([4_80, 6_40] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   , __lowercase ) )
       # verify size
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=torch.tensor([8_00, 10_66] )
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   , __lowercase ) )







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_					=	{
    'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					=	[
      'MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'MegatronBertForCausalLM',
      'MegatronBertForMaskedLM',
      'MegatronBertForMultipleChoice',
      'MegatronBertForNextSentencePrediction',
      'MegatronBertForPreTraining',
      'MegatronBertForQuestionAnswering',
      'MegatronBertForSequenceClassification',
      'MegatronBertForTokenClassification',
      'MegatronBertModel',
      'MegatronBertPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_megatron_bert import (
        MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        MegatronBertForCausalLM,
        MegatronBertForMaskedLM,
        MegatronBertForMultipleChoice,
        MegatronBertForNextSentencePrediction,
        MegatronBertForPreTraining,
        MegatronBertForQuestionAnswering,
        MegatronBertForSequenceClassification,
        MegatronBertForTokenClassification,
        MegatronBertModel,
        MegatronBertPreTrainedModel,
    )

else:
  import sys

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