


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       A__     ( unittest.TestCase    ):





      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 ,							):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
       UpperCamelCase			:      str 			=     parent
       UpperCamelCase			:      Optional[Any] 			=     batch_size
       UpperCamelCase			:      Any 			=     num_channels
       UpperCamelCase			:      str 			=     min_resolution
       UpperCamelCase			:      Union[str, Any] 			=     max_resolution
       UpperCamelCase			:      str 			=     do_resize
       UpperCamelCase			:      List[str] 			=     size
       UpperCamelCase			:      List[Any] 			=     do_normalize
       UpperCamelCase			:      int 			=     image_mean
       UpperCamelCase			:      Optional[Any] 			=     image_std
       UpperCamelCase			:      str 			=     do_rescale
       UpperCamelCase			:      Dict 			=     rescale_factor
       UpperCamelCase			:      Union[str, Any] 			=     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 ,							A_ ,							A_=False     ):
       '''simple docstring'''




       if not batched:
        UpperCamelCase			:      Tuple 			=     image_inputs[0]
        if isinstance(A_ ,							Image.Image     ):
         UpperCamelCase			:      Union[str, Any] 			=     image.size
        else:
         UpperCamelCase			:      Optional[Any] 			=     image.shape[1], image.shape[2]
        if w < h:
         UpperCamelCase			:      Dict 			=     int(self.size["shortest_edge"] * h / w     )
         UpperCamelCase			:      List[str] 			=     self.size["shortest_edge"]
        elif w > h:
         UpperCamelCase			:      List[str] 			=     self.size["shortest_edge"]
         UpperCamelCase			:      Optional[Any] 			=     int(self.size["shortest_edge"] * w / h     )
        else:
         UpperCamelCase			:      Any 			=     self.size["shortest_edge"]
         UpperCamelCase			:      str 			=     self.size["shortest_edge"]

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

       return expected_height, expected_width



@require_torch
@require_vision
class       A__     ( __snake_case ,     unittest.TestCase    ):
      _UpperCAmelCase			:List[str]							=     DetaImageProcessor if is_vision_available() else None





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     DetaImageProcessingTester(self     )





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self.image_processor_tester.prepare_image_processor_dict()





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     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_ ,							"do_rescale"     )     )
       self.assertTrue(hasattr(A_ ,							"do_pad"     )     )
       self.assertTrue(hasattr(A_ ,							"size"     )     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     self.image_processing_class.from_dict(self.image_processor_dict     )
       self.assertEqual(image_processor.size ,							{"shortest_edge": 18, "longest_edge": 1333}     )
       self.assertEqual(image_processor.do_pad ,							A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       pass





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Any 			=     self.image_processing_class(**self.image_processor_dict     )
       # create random PIL images
       UpperCamelCase			:      List[str] 			=     prepare_image_inputs(self.image_processor_tester ,							equal_resolution=A_     )
       for image in image_inputs:
        self.assertIsInstance(A_ ,							Image.Image     )

       # Test not batched input
       UpperCamelCase			:      Union[str, Any] 			=     image_processing(image_inputs[0] ,							return_tensors="pt"     ).pixel_values

       UpperCamelCase			:      Union[str, Any] 			=     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
       UpperCamelCase			:      Union[str, Any] 			=     self.image_processor_tester.get_expected_values(A_ ,							batched=A_     )

       UpperCamelCase			:      Optional[int] 			=     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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     self.image_processing_class(**self.image_processor_dict     )
       # create random numpy tensors
       UpperCamelCase			:      int 			=     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
       UpperCamelCase			:      Union[str, Any] 			=     image_processing(image_inputs[0] ,							return_tensors="pt"     ).pixel_values

       UpperCamelCase			:      Any 			=     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
       UpperCamelCase			:      List[str] 			=     image_processing(A_ ,							return_tensors="pt"     ).pixel_values

       UpperCamelCase			:      Union[str, Any] 			=     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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     self.image_processing_class(**self.image_processor_dict     )
       # create random PyTorch tensors
       UpperCamelCase			:      List[str] 			=     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
       UpperCamelCase			:      List[str] 			=     image_processing(image_inputs[0] ,							return_tensors="pt"     ).pixel_values

       UpperCamelCase			:      List[str] 			=     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
       UpperCamelCase			:      int 			=     image_processing(A_ ,							return_tensors="pt"     ).pixel_values

       UpperCamelCase			:      str 			=     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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Union[str, Any] 			=     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
       with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" ,							"r"     ) as f:
        UpperCamelCase			:      Union[str, Any] 			=     json.loads(f.read()     )

       UpperCamelCase			:      Optional[int] 			=     {"image_id": 3_9769, "annotations": target}

       # encode them
       UpperCamelCase			:      Dict 			=     DetaImageProcessor()
       UpperCamelCase			:      List[str] 			=     image_processing(images=A_ ,							annotations=A_ ,							return_tensors="pt"     )

       # verify pixel values
       UpperCamelCase			:      Optional[Any] 			=     torch.Size([1, 3, 800, 1066]     )
       self.assertEqual(encoding["pixel_values"].shape ,							A_     )

       UpperCamelCase			:      List[str] 			=     torch.tensor([0.27_96, 0.31_38, 0.34_81]     )
       self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] ,							A_ ,							atol=1e-4     )     )

       # verify area
       UpperCamelCase			:      Optional[int] 			=     torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]     )
       self.assertTrue(torch.allclose(encoding["labels"][0]["area"] ,							A_     )     )
       # verify boxes
       UpperCamelCase			:      Optional[Any] 			=     torch.Size([6, 4]     )
       self.assertEqual(encoding["labels"][0]["boxes"].shape ,							A_     )
       UpperCamelCase			:      List[Any] 			=     torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]     )
       self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] ,							A_ ,							atol=1e-3     )     )
       # verify image_id
       UpperCamelCase			:      Any 			=     torch.tensor([3_9769]     )
       self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] ,							A_     )     )
       # verify is_crowd
       UpperCamelCase			:      List[str] 			=     torch.tensor([0, 0, 0, 0, 0, 0]     )
       self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] ,							A_     )     )
       # verify class_labels
       UpperCamelCase			:      Optional[int] 			=     torch.tensor([75, 75, 63, 65, 17, 17]     )
       self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] ,							A_     )     )
       # verify orig_size
       UpperCamelCase			:      Any 			=     torch.tensor([480, 640]     )
       self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] ,							A_     )     )
       # verify size
       UpperCamelCase			:      Dict 			=     torch.tensor([800, 1066]     )
       self.assertTrue(torch.allclose(encoding["labels"][0]["size"] ,							A_     )     )





      @slow
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
       with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" ,							"r"     ) as f:
        UpperCamelCase			:      Optional[int] 			=     json.loads(f.read()     )

       UpperCamelCase			:      int 			=     {"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

       UpperCamelCase			:      Tuple 			=     pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"     )

       # encode them
       UpperCamelCase			:      Dict 			=     DetaImageProcessor(format="coco_panoptic"     )
       UpperCamelCase			:      Dict 			=     image_processing(images=A_ ,							annotations=A_ ,							masks_path=A_ ,							return_tensors="pt"     )

       # verify pixel values
       UpperCamelCase			:      List[Any] 			=     torch.Size([1, 3, 800, 1066]     )
       self.assertEqual(encoding["pixel_values"].shape ,							A_     )

       UpperCamelCase			:      Optional[int] 			=     torch.tensor([0.27_96, 0.31_38, 0.34_81]     )
       self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] ,							A_ ,							atol=1e-4     )     )

       # verify area
       UpperCamelCase			:      List[str] 			=     torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]     )
       self.assertTrue(torch.allclose(encoding["labels"][0]["area"] ,							A_     )     )
       # verify boxes
       UpperCamelCase			:      int 			=     torch.Size([6, 4]     )
       self.assertEqual(encoding["labels"][0]["boxes"].shape ,							A_     )
       UpperCamelCase			:      Tuple 			=     torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]     )
       self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] ,							A_ ,							atol=1e-3     )     )
       # verify image_id
       UpperCamelCase			:      List[Any] 			=     torch.tensor([3_9769]     )
       self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] ,							A_     )     )
       # verify is_crowd
       UpperCamelCase			:      int 			=     torch.tensor([0, 0, 0, 0, 0, 0]     )
       self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] ,							A_     )     )
       # verify class_labels
       UpperCamelCase			:      str 			=     torch.tensor([17, 17, 63, 75, 75, 93]     )
       self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] ,							A_     )     )
       # verify masks
       UpperCamelCase			:      Optional[int] 			=     82_2873
       self.assertEqual(encoding["labels"][0]["masks"].sum().item() ,							A_     )
       # verify orig_size
       UpperCamelCase			:      Dict 			=     torch.tensor([480, 640]     )
       self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] ,							A_     )     )
       # verify size
       UpperCamelCase			:      str 			=     torch.tensor([800, 1066]     )
       self.assertTrue(torch.allclose(encoding["labels"][0]["size"] ,							A_     )     )







from typing import TYPE_CHECKING

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


__lowerCamelCase :  int										=  {
    """configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
    """tokenization_convbert""": ["""ConvBertTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase :  Dict										=  ["""ConvBertTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase :  int										=  [
        """CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ConvBertForMaskedLM""",
        """ConvBertForMultipleChoice""",
        """ConvBertForQuestionAnswering""",
        """ConvBertForSequenceClassification""",
        """ConvBertForTokenClassification""",
        """ConvBertLayer""",
        """ConvBertModel""",
        """ConvBertPreTrainedModel""",
        """load_tf_weights_in_convbert""",
    ]


try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase :  str										=  [
        """TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFConvBertForMaskedLM""",
        """TFConvBertForMultipleChoice""",
        """TFConvBertForQuestionAnswering""",
        """TFConvBertForSequenceClassification""",
        """TFConvBertForTokenClassification""",
        """TFConvBertLayer""",
        """TFConvBertModel""",
        """TFConvBertPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
    from .tokenization_convbert import ConvBertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_convbert_fast import ConvBertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_convbert import (
            CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            ConvBertForMaskedLM,
            ConvBertForMultipleChoice,
            ConvBertForQuestionAnswering,
            ConvBertForSequenceClassification,
            ConvBertForTokenClassification,
            ConvBertLayer,
            ConvBertModel,
            ConvBertPreTrainedModel,
            load_tf_weights_in_convbert,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_convbert import (
            TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFConvBertForMaskedLM,
            TFConvBertForMultipleChoice,
            TFConvBertForQuestionAnswering,
            TFConvBertForSequenceClassification,
            TFConvBertForTokenClassification,
            TFConvBertLayer,
            TFConvBertModel,
            TFConvBertPreTrainedModel,
        )


else:
    import sys

    __lowerCamelCase :  List[str]										=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
