

'''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 							lowercase_						(		unittest.TestCase			):

  '''simple docstring'''





  def __init__(  self ,			a_ ,			a_=7 ,			a_=3 ,			a_=3_0 ,			a_=4_0_0 ,			a_=True ,			a_=None ,			a_=True ,			a_=[0.5, 0.5, 0.5] ,			a_=[0.5, 0.5, 0.5] ,			a_=True ,			a_=1 / 2_5_5 ,			a_=True ,			)				->					Optional[Any]:


    """simple docstring"""
    # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
    UpperCAmelCase    				=						size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
    UpperCAmelCase    				=						parent
    UpperCAmelCase    				=						batch_size
    UpperCAmelCase    				=						num_channels
    UpperCAmelCase    				=						min_resolution
    UpperCAmelCase    				=						max_resolution
    UpperCAmelCase    				=						do_resize
    UpperCAmelCase    				=						size
    UpperCAmelCase    				=						do_normalize
    UpperCAmelCase    				=						image_mean
    UpperCAmelCase    				=						image_std
    UpperCAmelCase    				=						do_rescale
    UpperCAmelCase    				=						rescale_factor
    UpperCAmelCase    				=						do_pad
  def     snake_case_      (  self  )				->					str:


    """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     snake_case_      (  self ,			a_ ,			a_=False  )				->					List[Any]:


    """simple docstring"""
    if not batched:
      UpperCAmelCase    				=						image_inputs[0]
      if isinstance(a_ ,			Image.Image  ):
        UpperCAmelCase				,   UpperCAmelCase    				=						image.size
      else:
        UpperCAmelCase				,   UpperCAmelCase    				=						image.shape[1], image.shape[2]
      if w < h:
        UpperCAmelCase    				=						int(self.size['shortest_edge'] * h / w  )
        UpperCAmelCase    				=						self.size['shortest_edge']
      elif w > h:
        UpperCAmelCase    				=						self.size['shortest_edge']
        UpperCAmelCase    				=						int(self.size['shortest_edge'] * w / h  )
      else:
        UpperCAmelCase    				=						self.size['shortest_edge']
        UpperCAmelCase    				=						self.size['shortest_edge']

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

    return expected_height, expected_width




@require_torch
@require_vision
class 							lowercase_						(		a			,							unittest.TestCase			):

  '''simple docstring'''





  __lowerCAmelCase       :			Tuple     	= DetaImageProcessor if is_vision_available() else None
  def     snake_case_      (  self  )				->					Any:


    """simple docstring"""
    UpperCAmelCase    				=						DetaImageProcessingTester(self  )
  @property
  def     snake_case_      (  self  )				->					List[Any]:


    """simple docstring"""
    return self.image_processor_tester.prepare_image_processor_dict()
  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						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     snake_case_      (  self  )				->					str:


    """simple docstring"""
    UpperCAmelCase    				=						self.image_processing_class.from_dict(self.image_processor_dict  )
    self.assertEqual(image_processor.size ,			{'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}  )
    self.assertEqual(image_processor.do_pad ,			a_  )
  def     snake_case_      (  self  )				->					str:


    """simple docstring"""
    pass
  def     snake_case_      (  self  )				->					List[str]:


    """simple docstring"""
    # Initialize image_processing
    UpperCAmelCase    				=						self.image_processing_class(**self.image_processor_dict  )
    # create random PIL images
    UpperCAmelCase    				=						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    				=						image_processing(image_inputs[0] ,			return_tensors='pt'  ).pixel_values

    UpperCAmelCase				,   UpperCAmelCase    				=						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				,   UpperCAmelCase    				=						self.image_processor_tester.get_expected_values(a_ ,			batched=a_  )

    UpperCAmelCase    				=						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  )				->					Any:


    """simple docstring"""
    # Initialize image_processing
    UpperCAmelCase    				=						self.image_processing_class(**self.image_processor_dict  )
    # create random numpy tensors
    UpperCAmelCase    				=						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    				=						image_processing(image_inputs[0] ,			return_tensors='pt'  ).pixel_values

    UpperCAmelCase				,   UpperCAmelCase    				=						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    				=						image_processing(a_ ,			return_tensors='pt'  ).pixel_values

    UpperCAmelCase				,   UpperCAmelCase    				=						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  )				->					Optional[Any]:


    """simple docstring"""
    # Initialize image_processing
    UpperCAmelCase    				=						self.image_processing_class(**self.image_processor_dict  )
    # create random PyTorch tensors
    UpperCAmelCase    				=						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    				=						image_processing(image_inputs[0] ,			return_tensors='pt'  ).pixel_values

    UpperCAmelCase				,   UpperCAmelCase    				=						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    				=						image_processing(a_ ,			return_tensors='pt'  ).pixel_values

    UpperCAmelCase				,   UpperCAmelCase    				=						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  )				->					str:


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

    UpperCAmelCase    				=						{'image_id': 3_9_7_6_9, 'annotations': target}

    # encode them
    UpperCAmelCase    				=						DetaImageProcessor()
    UpperCAmelCase    				=						image_processing(images=a_ ,			annotations=a_ ,			return_tensors='pt'  )

    # verify pixel values
    UpperCAmelCase    				=						torch.Size([1, 3, 8_0_0, 1_0_6_6]  )
    self.assertEqual(encoding['pixel_values'].shape ,			a_  )

    UpperCAmelCase    				=						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
    UpperCAmelCase    				=						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    				=						torch.Size([6, 4]  )
    self.assertEqual(encoding['labels'][0]['boxes'].shape ,			a_  )
    UpperCAmelCase    				=						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
    UpperCAmelCase    				=						torch.tensor([3_9_7_6_9]  )
    self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,			a_  )  )
    # verify is_crowd
    UpperCAmelCase    				=						torch.tensor([0, 0, 0, 0, 0, 0]  )
    self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,			a_  )  )
    # verify class_labels
    UpperCAmelCase    				=						torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]  )
    self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,			a_  )  )
    # verify orig_size
    UpperCAmelCase    				=						torch.tensor([4_8_0, 6_4_0]  )
    self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,			a_  )  )
    # verify size
    UpperCAmelCase    				=						torch.tensor([8_0_0, 1_0_6_6]  )
    self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,			a_  )  )





  @slow
  def     snake_case_      (  self  )				->					List[Any]:


    """simple docstring"""
    # prepare image, target and masks_path
    UpperCAmelCase    				=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
    with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt' ,			'r'  ) as f:
      UpperCAmelCase    				=						json.loads(f.read()  )

    UpperCAmelCase    				=						{'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

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

    # encode them
    UpperCAmelCase    				=						DetaImageProcessor(format='coco_panoptic'  )
    UpperCAmelCase    				=						image_processing(images=a_ ,			annotations=a_ ,			masks_path=a_ ,			return_tensors='pt'  )

    # verify pixel values
    UpperCAmelCase    				=						torch.Size([1, 3, 8_0_0, 1_0_6_6]  )
    self.assertEqual(encoding['pixel_values'].shape ,			a_  )

    UpperCAmelCase    				=						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
    UpperCAmelCase    				=						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    				=						torch.Size([6, 4]  )
    self.assertEqual(encoding['labels'][0]['boxes'].shape ,			a_  )
    UpperCAmelCase    				=						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
    UpperCAmelCase    				=						torch.tensor([3_9_7_6_9]  )
    self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,			a_  )  )
    # verify is_crowd
    UpperCAmelCase    				=						torch.tensor([0, 0, 0, 0, 0, 0]  )
    self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,			a_  )  )
    # verify class_labels
    UpperCAmelCase    				=						torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]  )
    self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,			a_  )  )
    # verify masks
    UpperCAmelCase    				=						8_2_2_8_7_3
    self.assertEqual(encoding['labels'][0]['masks'].sum().item() ,			a_  )
    # verify orig_size
    UpperCAmelCase    				=						torch.tensor([4_8_0, 6_4_0]  )
    self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,			a_  )  )
    # verify size
    UpperCAmelCase    				=						torch.tensor([8_0_0, 1_0_6_6]  )
    self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,			a_  )  )





'''simple docstring'''




from typing import TYPE_CHECKING

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


_a					:			Any							=    {
    'configuration_groupvit': [
        'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'GroupViTConfig',
        'GroupViTOnnxConfig',
        'GroupViTTextConfig',
        'GroupViTVisionConfig',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			Union[str, Any]							=    [
      'GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'GroupViTModel',
      'GroupViTPreTrainedModel',
      'GroupViTTextModel',
      'GroupViTVisionModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			Tuple							=    [
      'TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFGroupViTModel',
      'TFGroupViTPreTrainedModel',
      'TFGroupViTTextModel',
      'TFGroupViTVisionModel',
  ]

if TYPE_CHECKING:
  from .configuration_groupvit import (
      GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      GroupViTConfig,
      GroupViTOnnxConfig,
      GroupViTTextConfig,
      GroupViTVisionConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_groupvit import (
        GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        GroupViTModel,
        GroupViTPreTrainedModel,
        GroupViTTextModel,
        GroupViTVisionModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_groupvit import (
        TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFGroupViTModel,
        TFGroupViTPreTrainedModel,
        TFGroupViTTextModel,
        TFGroupViTVisionModel,
    )

else:
  import sys

  _a					:			Union[str, Any]							=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


