



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   UpperCAmelCase_    (       unittest.TestCase):







							def __init__(   self,			__a,			__a=7,			__a=3,			__a=30,			__a=400,			__a=True,			__a=None,			__a=True,			__a=1 / 255,			__a=True,			__a=[0.5, 0.5, 0.5],			__a=[0.5, 0.5, 0.5],			__a=True,			):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
														_lowerCAmelCase     :							Dict    =    parent
														_lowerCAmelCase     :							Optional[int]    =    batch_size
														_lowerCAmelCase     :							Tuple    =    num_channels
														_lowerCAmelCase     :							Any    =    min_resolution
														_lowerCAmelCase     :							Tuple    =    max_resolution
														_lowerCAmelCase     :							Optional[Any]    =    do_resize
														_lowerCAmelCase     :							Any    =    size
														_lowerCAmelCase     :							Union[str, Any]    =    do_rescale
														_lowerCAmelCase     :							List[Any]    =    rescale_factor
														_lowerCAmelCase     :							Tuple    =    do_normalize
														_lowerCAmelCase     :							Union[str, Any]    =    image_mean
														_lowerCAmelCase     :							Tuple    =    image_std
														_lowerCAmelCase     :							Tuple    =    do_pad







							def    snake_case__      (   self):

														'''simple docstring'''

														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    snake_case__      (   self,			__a,			__a=False):

														'''simple docstring'''

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

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

														return expected_height, expected_width



@require_torch
@require_vision
class   UpperCAmelCase_    (       a   ,					unittest.TestCase):
							lowerCamelCase__   			=		DetrImageProcessor if is_vision_available() else None







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    DetrImageProcessingTester(self)







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return self.image_processor_tester.prepare_image_processor_dict()







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    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_rescale"))
														self.assertTrue(hasattr(__a,			"rescale_factor"))
														self.assertTrue(hasattr(__a,			"do_resize"))
														self.assertTrue(hasattr(__a,			"size"))
														self.assertTrue(hasattr(__a,			"do_pad"))







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    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)

														_lowerCAmelCase     :							int    =    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    snake_case__      (   self):

														'''simple docstring'''

														pass







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    self.image_processing_class(**self.image_processor_dict)
														# create random PIL images
														_lowerCAmelCase     :							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
														_lowerCAmelCase     :							Tuple    =    image_processing(image_inputs[0],			return_tensors="pt").pixel_values

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Tuple    =    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
														_lowerCAmelCase   ,    _lowerCAmelCase     :							Dict    =    self.image_processor_tester.get_expected_values(__a,			batched=__a)

														_lowerCAmelCase     :							Optional[Any]    =    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    snake_case__      (   self):

														'''simple docstring'''

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

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[int]    =    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
														_lowerCAmelCase     :							List[Any]    =    image_processing(__a,			return_tensors="pt").pixel_values

														_lowerCAmelCase   ,    _lowerCAmelCase     :							int    =    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    snake_case__      (   self):

														'''simple docstring'''

														_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=__a,			torchify=__a)
														for image in image_inputs:
																					self.assertIsInstance(__a,			torch.Tensor)

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

														_lowerCAmelCase   ,    _lowerCAmelCase     :							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
														_lowerCAmelCase     :							Any    =    image_processing(__a,			return_tensors="pt").pixel_values

														_lowerCAmelCase   ,    _lowerCAmelCase     :							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,
														    ),			)







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

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

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

														# encode them
														_lowerCAmelCase     :							str    =    DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
														_lowerCAmelCase     :							str    =    image_processing(images=__a,			annotations=__a,			return_tensors="pt")

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

														_lowerCAmelCase     :							Union[str, Any]    =    torch.tensor([0.2_796, 0.3_138, 0.3_481])
														self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3],			__a,			atol=1E-4))

														# verify area
														_lowerCAmelCase     :							int    =    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"],			__a))
														# verify boxes
														_lowerCAmelCase     :							Union[str, Any]    =    torch.Size([6, 4])
														self.assertEqual(encoding["labels"][0]["boxes"].shape,			__a)
														_lowerCAmelCase     :							int    =    torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215])
														self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0],			__a,			atol=1E-3))
														# verify image_id
														_lowerCAmelCase     :							List[str]    =    torch.tensor([3_9769])
														self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"],			__a))
														# verify is_crowd
														_lowerCAmelCase     :							int    =    torch.tensor([0, 0, 0, 0, 0, 0])
														self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"],			__a))
														# verify class_labels
														_lowerCAmelCase     :							List[str]    =    torch.tensor([75, 75, 63, 65, 17, 17])
														self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"],			__a))
														# verify orig_size
														_lowerCAmelCase     :							Optional[int]    =    torch.tensor([480, 640])
														self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"],			__a))
														# verify size
														_lowerCAmelCase     :							Any    =    torch.tensor([800, 1066])
														self.assertTrue(torch.allclose(encoding["labels"][0]["size"],			__a))







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							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:
																					_lowerCAmelCase     :							Union[str, Any]    =    json.loads(f.read())

														_lowerCAmelCase     :							Tuple    =    {"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

														_lowerCAmelCase     :							Optional[Any]    =    pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")

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

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

														_lowerCAmelCase     :							str    =    torch.tensor([0.2_796, 0.3_138, 0.3_481])
														self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3],			__a,			atol=1E-4))

														# verify area
														_lowerCAmelCase     :							List[Any]    =    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"],			__a))
														# verify boxes
														_lowerCAmelCase     :							Union[str, Any]    =    torch.Size([6, 4])
														self.assertEqual(encoding["labels"][0]["boxes"].shape,			__a)
														_lowerCAmelCase     :							List[str]    =    torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625])
														self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0],			__a,			atol=1E-3))
														# verify image_id
														_lowerCAmelCase     :							List[str]    =    torch.tensor([3_9769])
														self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"],			__a))
														# verify is_crowd
														_lowerCAmelCase     :							List[Any]    =    torch.tensor([0, 0, 0, 0, 0, 0])
														self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"],			__a))
														# verify class_labels
														_lowerCAmelCase     :							int    =    torch.tensor([17, 17, 63, 75, 75, 93])
														self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"],			__a))
														# verify masks
														_lowerCAmelCase     :							Dict    =    82_2873
														self.assertEqual(encoding["labels"][0]["masks"].sum().item(),			__a)
														# verify orig_size
														_lowerCAmelCase     :							Tuple    =    torch.tensor([480, 640])
														self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"],			__a))
														# verify size
														_lowerCAmelCase     :							Tuple    =    torch.tensor([800, 1066])
														self.assertTrue(torch.allclose(encoding["labels"][0]["size"],			__a))




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__   	=							{
    '''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TimesformerModel''',
      '''TimesformerForVideoClassification''',
      '''TimesformerPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_timesformer import (
        TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimesformerForVideoClassification,
        TimesformerModel,
        TimesformerPreTrainedModel,
    )

else:
  import sys

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

