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 DetrImageProcessor

class 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''




    def __init__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__=7       ,						lowerCamelCase__=3       ,						lowerCamelCase__=30       ,						lowerCamelCase__=400       ,						lowerCamelCase__=True       ,						lowerCamelCase__=None       ,						lowerCamelCase__=True       ,						lowerCamelCase__=1 / 255       ,						lowerCamelCase__=True       ,						lowerCamelCase__=[0.5, 0.5, 0.5]       ,						lowerCamelCase__=[0.5, 0.5, 0.5]       ,						lowerCamelCase__=True       ,						):
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       lowerCAmelCase_:							Any       =	size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
       lowerCAmelCase_:							List[str]       =	parent
       lowerCAmelCase_:							Any       =	batch_size
       lowerCAmelCase_:							Dict       =	num_channels
       lowerCAmelCase_:							Any       =	min_resolution
       lowerCAmelCase_:							str       =	max_resolution
       lowerCAmelCase_:							Dict       =	do_resize
       lowerCAmelCase_:							Optional[int]       =	size
       lowerCAmelCase_:							Dict       =	do_rescale
       lowerCAmelCase_:							Optional[Any]       =	rescale_factor
       lowerCAmelCase_:							List[str]       =	do_normalize
       lowerCAmelCase_:							Union[str, Any]       =	image_mean
       lowerCAmelCase_:							Optional[Any]       =	image_std
       lowerCAmelCase_:							Optional[int]       =	do_pad




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




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

       else:
          lowerCAmelCase_:							List[str]       =	[]
          for image in image_inputs:
             lowerCAmelCase_  ,	lowerCAmelCase_:							Tuple       =	self.get_expected_values([image] )
             expected_values.append((expected_height, expected_width) )
          lowerCAmelCase_:							Dict       =	max(lowerCamelCase__       ,						key=lambda lowerCamelCase__ : item[0] )[0]
          lowerCAmelCase_:							List[Any]       =	max(lowerCamelCase__       ,						key=lambda lowerCamelCase__ : item[1] )[1]

       return expected_height, expected_width





@require_torch
@require_vision
class 					_lowercase    (     UpperCAmelCase__ ,  unittest.TestCase			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    DetrImageProcessor if is_vision_available() else None




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	DetrImageProcessingTester(self )




    @property
    def      _a    (    self ):
       return self.image_processor_tester.prepare_image_processor_dict()




    def      _a    (    self ):
       lowerCAmelCase_:							str       =	self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(lowerCamelCase__       ,						"image_mean" ) )
       self.assertTrue(hasattr(lowerCamelCase__       ,						"image_std" ) )
       self.assertTrue(hasattr(lowerCamelCase__       ,						"do_normalize" ) )
       self.assertTrue(hasattr(lowerCamelCase__       ,						"do_rescale" ) )
       self.assertTrue(hasattr(lowerCamelCase__       ,						"rescale_factor" ) )
       self.assertTrue(hasattr(lowerCamelCase__       ,						"do_resize" ) )
       self.assertTrue(hasattr(lowerCamelCase__       ,						"size" ) )
       self.assertTrue(hasattr(lowerCamelCase__       ,						"do_pad" ) )




    def      _a    (    self ):
       lowerCAmelCase_:							Dict       =	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       ,						lowerCamelCase__ )

       lowerCAmelCase_:							str       =	self.image_processing_class.from_dict(
           self.image_processor_dict       ,						size=42       ,						max_size=84       ,						pad_and_return_pixel_mask=lowerCamelCase__ )
       self.assertEqual(image_processor.size       ,						{"shortest_edge": 42, "longest_edge": 84} )
       self.assertEqual(image_processor.do_pad       ,						lowerCamelCase__ )




    def      _a    (    self ):
       pass




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

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

       lowerCAmelCase_  ,	lowerCAmelCase_:							Union[str, Any]       =	self.image_processor_tester.get_expected_values(lowerCamelCase__ )

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

       # Test batched
       lowerCAmelCase_  ,	lowerCAmelCase_:							Union[str, Any]       =	self.image_processor_tester.get_expected_values(lowerCamelCase__       ,						batched=lowerCamelCase__ )

       lowerCAmelCase_:							List[str]       =	image_processing(lowerCamelCase__       ,						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 ):
       # Initialize image_processing
       lowerCAmelCase_:							List[str]       =	self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       lowerCAmelCase_:							int       =	prepare_image_inputs(self.image_processor_tester       ,						equal_resolution=lowerCamelCase__       ,						numpify=lowerCamelCase__ )
       for image in image_inputs:
          self.assertIsInstance(lowerCamelCase__       ,						np.ndarray )

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

       lowerCAmelCase_  ,	lowerCAmelCase_:							Union[str, Any]       =	self.image_processor_tester.get_expected_values(lowerCamelCase__ )

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

       # Test batched
       lowerCAmelCase_:							Optional[Any]       =	image_processing(lowerCamelCase__       ,						return_tensors="pt" ).pixel_values

       lowerCAmelCase_  ,	lowerCAmelCase_:							str       =	self.image_processor_tester.get_expected_values(lowerCamelCase__       ,						batched=lowerCamelCase__ )

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




    def      _a    (    self ):
       # Initialize image_processing
       lowerCAmelCase_:							Optional[int]       =	self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       lowerCAmelCase_:							Any       =	prepare_image_inputs(self.image_processor_tester       ,						equal_resolution=lowerCamelCase__       ,						torchify=lowerCamelCase__ )
       for image in image_inputs:
          self.assertIsInstance(lowerCamelCase__       ,						torch.Tensor )

       # Test not batched input
       lowerCAmelCase_:							Dict       =	image_processing(image_inputs[0]       ,						return_tensors="pt" ).pixel_values

       lowerCAmelCase_  ,	lowerCAmelCase_:							str       =	self.image_processor_tester.get_expected_values(lowerCamelCase__ )

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

       # Test batched
       lowerCAmelCase_:							Union[str, Any]       =	image_processing(lowerCamelCase__       ,						return_tensors="pt" ).pixel_values

       lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[Any]       =	self.image_processor_tester.get_expected_values(lowerCamelCase__       ,						batched=lowerCamelCase__ )

       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 ):
       # prepare image and target
       lowerCAmelCase_:							int       =	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
       with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"       ,						"r" ) as f:
          lowerCAmelCase_:							Dict       =	json.loads(f.read() )

       lowerCAmelCase_:							int       =	{"image_id": 39_769, "annotations": target}

       # encode them
       lowerCAmelCase_:							Dict       =	DetrImageProcessor.from_pretrained("facebook/detr-resnet-50" )
       lowerCAmelCase_:							Tuple       =	image_processing(images=lowerCamelCase__       ,						annotations=lowerCamelCase__       ,						return_tensors="pt" )

       # verify pixel values
       lowerCAmelCase_:							Dict       =	torch.Size([1, 3, 800, 1_066] )
       self.assertEqual(encoding["pixel_values"].shape       ,						lowerCamelCase__ )

       lowerCAmelCase_:							Union[str, Any]       =	torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
       self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,						lowerCamelCase__       ,						atol=1E-4 ) )

       # verify area
       lowerCAmelCase_:							str       =	torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
       self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,						lowerCamelCase__ ) )
       # verify boxes
       lowerCAmelCase_:							Union[str, Any]       =	torch.Size([6, 4] )
       self.assertEqual(encoding["labels"][0]["boxes"].shape       ,						lowerCamelCase__ )
       lowerCAmelCase_:							List[Any]       =	torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
       self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,						lowerCamelCase__       ,						atol=1E-3 ) )
       # verify image_id
       lowerCAmelCase_:							Dict       =	torch.tensor([39_769] )
       self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,						lowerCamelCase__ ) )
       # verify is_crowd
       lowerCAmelCase_:							List[Any]       =	torch.tensor([0, 0, 0, 0, 0, 0] )
       self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,						lowerCamelCase__ ) )
       # verify class_labels
       lowerCAmelCase_:							str       =	torch.tensor([75, 75, 63, 65, 17, 17] )
       self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,						lowerCamelCase__ ) )
       # verify orig_size
       lowerCAmelCase_:							Dict       =	torch.tensor([480, 640] )
       self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,						lowerCamelCase__ ) )
       # verify size
       lowerCAmelCase_:							Union[str, Any]       =	torch.tensor([800, 1_066] )
       self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,						lowerCamelCase__ ) )




    @slow
    def      _a    (    self ):
       # prepare image, target and masks_path
       lowerCAmelCase_:							List[Any]       =	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
       with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"       ,						"r" ) as f:
          lowerCAmelCase_:							List[str]       =	json.loads(f.read() )

       lowerCAmelCase_:							Union[str, Any]       =	{"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

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

       # encode them
       lowerCAmelCase_:							List[Any]       =	DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic" )
       lowerCAmelCase_:							List[str]       =	image_processing(images=lowerCamelCase__       ,						annotations=lowerCamelCase__       ,						masks_path=lowerCamelCase__       ,						return_tensors="pt" )

       # verify pixel values
       lowerCAmelCase_:							Dict       =	torch.Size([1, 3, 800, 1_066] )
       self.assertEqual(encoding["pixel_values"].shape       ,						lowerCamelCase__ )

       lowerCAmelCase_:							Any       =	torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
       self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,						lowerCamelCase__       ,						atol=1E-4 ) )

       # verify area
       lowerCAmelCase_:							Any       =	torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
       self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,						lowerCamelCase__ ) )
       # verify boxes
       lowerCAmelCase_:							List[Any]       =	torch.Size([6, 4] )
       self.assertEqual(encoding["labels"][0]["boxes"].shape       ,						lowerCamelCase__ )
       lowerCAmelCase_:							List[str]       =	torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
       self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,						lowerCamelCase__       ,						atol=1E-3 ) )
       # verify image_id
       lowerCAmelCase_:							int       =	torch.tensor([39_769] )
       self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,						lowerCamelCase__ ) )
       # verify is_crowd
       lowerCAmelCase_:							Dict       =	torch.tensor([0, 0, 0, 0, 0, 0] )
       self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,						lowerCamelCase__ ) )
       # verify class_labels
       lowerCAmelCase_:							str       =	torch.tensor([17, 17, 63, 75, 75, 93] )
       self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,						lowerCamelCase__ ) )
       # verify masks
       lowerCAmelCase_:							Union[str, Any]       =	822_873
       self.assertEqual(encoding["labels"][0]["masks"].sum().item()       ,						lowerCamelCase__ )
       # verify orig_size
       lowerCAmelCase_:							Dict       =	torch.tensor([480, 640] )
       self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,						lowerCamelCase__ ) )
       # verify size
       lowerCAmelCase_:							List[Any]       =	torch.tensor([800, 1_066] )
       self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,						lowerCamelCase__ ) )
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a  :      int            =    {
    """configuration_bridgetower""": [
        """BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BridgeTowerConfig""",
        """BridgeTowerTextConfig""",
        """BridgeTowerVisionConfig""",
    ],
    """processing_bridgetower""": ["""BridgeTowerProcessor"""],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      Any            =    ["""BridgeTowerImageProcessor"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      str            =    [
     """BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """BridgeTowerForContrastiveLearning""",
     """BridgeTowerForImageAndTextRetrieval""",
     """BridgeTowerForMaskedLM""",
     """BridgeTowerModel""",
     """BridgeTowerPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_bridgetower import (
     BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BridgeTowerConfig,
     BridgeTowerTextConfig,
     BridgeTowerVisionConfig,
 )
 from .processing_bridgetower import BridgeTowerProcessor

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_bridgetower import BridgeTowerImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bridgetower import (
      BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
      BridgeTowerForContrastiveLearning,
      BridgeTowerForImageAndTextRetrieval,
      BridgeTowerForMaskedLM,
      BridgeTowerModel,
      BridgeTowerPreTrainedModel,
  )


else:
 import sys

 a  :      List[Any]            =    _LazyModule(__name__, globals()["""__file__"""], _import_structure)