
"""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 DeformableDetrImageProcessor
class       lowerCAmelCase_		(				unittest.TestCase ):



							'''simple docstring'''





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




							def    A__						( self				)       ->  List[str]:
													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    A__						( self    ,				snake_case_    ,				snake_case_=False				)       ->  List[Any]:
													if not batched:
																			__lowerCAmelCase     =  image_inputs[0]
																			if isinstance(snake_case_    ,				Image.Image				):
																									__lowerCAmelCase   ,     __lowerCAmelCase     =  image.size
																			else:
																									__lowerCAmelCase   ,     __lowerCAmelCase     =  image.shape[1], image.shape[2]
																			if w < h:
																									__lowerCAmelCase     =  int(self.size["""shortest_edge"""] * h / w				)
																									__lowerCAmelCase     =  self.size["""shortest_edge"""]
																			elif w > h:
																									__lowerCAmelCase     =  self.size["""shortest_edge"""]
																									__lowerCAmelCase     =  int(self.size["""shortest_edge"""] * w / h				)
																			else:
																									__lowerCAmelCase     =  self.size["""shortest_edge"""]
																									__lowerCAmelCase     =  self.size["""shortest_edge"""]

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

													return expected_height, expected_width
@require_torch
@require_vision
class       lowerCAmelCase_		(				A__				,				unittest.TestCase ):



							'''simple docstring'''





							_snake_case						      =		DeformableDetrImageProcessor if is_vision_available() else None




							def    A__						( self				)       ->  Tuple:
													__lowerCAmelCase     =  DeformableDetrImageProcessingTester(self				)




							@property
							def    A__						( self				)       ->  Tuple:
													return self.image_processor_tester.prepare_image_processor_dict()




							def    A__						( self				)       ->  Tuple:
													__lowerCAmelCase     =  self.image_processing_class(**self.image_processor_dict				)
													self.assertTrue(hasattr(snake_case_    ,				"""image_mean"""				)				)
													self.assertTrue(hasattr(snake_case_    ,				"""image_std"""				)				)
													self.assertTrue(hasattr(snake_case_    ,				"""do_normalize"""				)				)
													self.assertTrue(hasattr(snake_case_    ,				"""do_resize"""				)				)
													self.assertTrue(hasattr(snake_case_    ,				"""do_rescale"""				)				)
													self.assertTrue(hasattr(snake_case_    ,				"""do_pad"""				)				)
													self.assertTrue(hasattr(snake_case_    ,				"""size"""				)				)




							def    A__						( self				)       ->  Optional[Any]:
													__lowerCAmelCase     =  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    ,				snake_case_				)

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




							def    A__						( self				)       ->  Dict:
													pass




							def    A__						( self				)       ->  Optional[Any]:
													# Initialize image_processing
													__lowerCAmelCase     =  self.image_processing_class(**self.image_processor_dict				)
													# create random PIL images
													__lowerCAmelCase     =  prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=snake_case_				)
													for image in image_inputs:
																			self.assertIsInstance(snake_case_    ,				Image.Image				)

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

													__lowerCAmelCase   ,     __lowerCAmelCase     =  self.image_processor_tester.get_expected_values(snake_case_				)

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

													# Test batched
													__lowerCAmelCase   ,     __lowerCAmelCase     =  self.image_processor_tester.get_expected_values(snake_case_    ,				batched=snake_case_				)

													__lowerCAmelCase     =  image_processing(snake_case_    ,				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				)       ->  Optional[Any]:
													# Initialize image_processing
													__lowerCAmelCase     =  self.image_processing_class(**self.image_processor_dict				)
													# create random numpy tensors
													__lowerCAmelCase     =  prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=snake_case_    ,				numpify=snake_case_				)
													for image in image_inputs:
																			self.assertIsInstance(snake_case_    ,				np.ndarray				)

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

													__lowerCAmelCase   ,     __lowerCAmelCase     =  self.image_processor_tester.get_expected_values(snake_case_				)

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

													# Test batched
													__lowerCAmelCase     =  image_processing(snake_case_    ,				return_tensors="""pt"""				).pixel_values

													__lowerCAmelCase   ,     __lowerCAmelCase     =  self.image_processor_tester.get_expected_values(snake_case_    ,				batched=snake_case_				)

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




							def    A__						( self				)       ->  Union[str, Any]:
													# Initialize image_processing
													__lowerCAmelCase     =  self.image_processing_class(**self.image_processor_dict				)
													# create random PyTorch tensors
													__lowerCAmelCase     =  prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=snake_case_    ,				torchify=snake_case_				)
													for image in image_inputs:
																			self.assertIsInstance(snake_case_    ,				torch.Tensor				)

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

													__lowerCAmelCase   ,     __lowerCAmelCase     =  self.image_processor_tester.get_expected_values(snake_case_				)

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

													# Test batched
													__lowerCAmelCase     =  image_processing(snake_case_    ,				return_tensors="""pt"""				).pixel_values

													__lowerCAmelCase   ,     __lowerCAmelCase     =  self.image_processor_tester.get_expected_values(snake_case_    ,				batched=snake_case_				)

													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				)       ->  Union[str, Any]:
													# prepare image and target
													__lowerCAmelCase     =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
													with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""    ,				"""r"""				) as f:
																			__lowerCAmelCase     =  json.loads(f.read()				)

													__lowerCAmelCase     =  {"""image_id""": 39_769, """annotations""": target}

													# encode them
													__lowerCAmelCase     =  DeformableDetrImageProcessor()
													__lowerCAmelCase     =  image_processing(images=snake_case_    ,				annotations=snake_case_    ,				return_tensors="""pt"""				)

													# verify pixel values
													__lowerCAmelCase     =  torch.Size([1, 3, 800, 1_066]				)
													self.assertEqual(encoding["""pixel_values"""].shape    ,				snake_case_				)

													__lowerCAmelCase     =  torch.tensor([0.2_796, 0.3_138, 0.3_481]				)
													self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    ,				snake_case_    ,				atol=1e-4				)				)

													# verify area
													__lowerCAmelCase     =  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"""]    ,				snake_case_				)				)
													# verify boxes
													__lowerCAmelCase     =  torch.Size([6, 4]				)
													self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    ,				snake_case_				)
													__lowerCAmelCase     =  torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]				)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    ,				snake_case_    ,				atol=1e-3				)				)
													# verify image_id
													__lowerCAmelCase     =  torch.tensor([39_769]				)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    ,				snake_case_				)				)
													# verify is_crowd
													__lowerCAmelCase     =  torch.tensor([0, 0, 0, 0, 0, 0]				)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    ,				snake_case_				)				)
													# verify class_labels
													__lowerCAmelCase     =  torch.tensor([75, 75, 63, 65, 17, 17]				)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    ,				snake_case_				)				)
													# verify orig_size
													__lowerCAmelCase     =  torch.tensor([480, 640]				)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    ,				snake_case_				)				)
													# verify size
													__lowerCAmelCase     =  torch.tensor([800, 1_066]				)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    ,				snake_case_				)				)




							@slow
							def    A__						( self				)       ->  int:
													# prepare image, target and masks_path
													__lowerCAmelCase     =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
													with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""    ,				"""r"""				) as f:
																			__lowerCAmelCase     =  json.loads(f.read()				)

													__lowerCAmelCase     =  {"""file_name""": """000000039769.png""", """image_id""": 39_769, """segments_info""": target}

													__lowerCAmelCase     =  pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""				)

													# encode them
													__lowerCAmelCase     =  DeformableDetrImageProcessor(format="""coco_panoptic"""				)
													__lowerCAmelCase     =  image_processing(images=snake_case_    ,				annotations=snake_case_    ,				masks_path=snake_case_    ,				return_tensors="""pt"""				)

													# verify pixel values
													__lowerCAmelCase     =  torch.Size([1, 3, 800, 1_066]				)
													self.assertEqual(encoding["""pixel_values"""].shape    ,				snake_case_				)

													__lowerCAmelCase     =  torch.tensor([0.2_796, 0.3_138, 0.3_481]				)
													self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    ,				snake_case_    ,				atol=1e-4				)				)

													# verify area
													__lowerCAmelCase     =  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"""]    ,				snake_case_				)				)
													# verify boxes
													__lowerCAmelCase     =  torch.Size([6, 4]				)
													self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    ,				snake_case_				)
													__lowerCAmelCase     =  torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]				)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    ,				snake_case_    ,				atol=1e-3				)				)
													# verify image_id
													__lowerCAmelCase     =  torch.tensor([39_769]				)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    ,				snake_case_				)				)
													# verify is_crowd
													__lowerCAmelCase     =  torch.tensor([0, 0, 0, 0, 0, 0]				)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    ,				snake_case_				)				)
													# verify class_labels
													__lowerCAmelCase     =  torch.tensor([17, 17, 63, 75, 75, 93]				)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    ,				snake_case_				)				)
													# verify masks
													__lowerCAmelCase     =  822_873
													self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()    ,				snake_case_				)
													# verify orig_size
													__lowerCAmelCase     =  torch.tensor([480, 640]				)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    ,				snake_case_				)				)
													# verify size
													__lowerCAmelCase     =  torch.tensor([800, 1_066]				)
													self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    ,				snake_case_				)				)


"""simple docstring"""
from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					     =	{
    '''snap-research/efficientformer-l1-300''': (
        '''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'''
    ),
}
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		'''efficientformer'''




							def __init__( self    ,				snake_case_ = [3, 2, 6, 4]    ,				snake_case_ = [48, 96, 224, 448]    ,				snake_case_ = [True, True, True, True]    ,				snake_case_ = 448    ,				snake_case_ = 32    ,				snake_case_ = 4    ,				snake_case_ = 7    ,				snake_case_ = 5    ,				snake_case_ = 8    ,				snake_case_ = 4    ,				snake_case_ = 0.0    ,				snake_case_ = 16    ,				snake_case_ = 3    ,				snake_case_ = 3    ,				snake_case_ = 3    ,				snake_case_ = 2    ,				snake_case_ = 1    ,				snake_case_ = 0.0    ,				snake_case_ = 1    ,				snake_case_ = True    ,				snake_case_ = True    ,				snake_case_ = 1e-5    ,				snake_case_ = "gelu"    ,				snake_case_ = 0.02    ,				snake_case_ = 1e-1_2    ,				snake_case_ = 224    ,				snake_case_ = 1e-0_5    ,				**snake_case_    ,				)       ->  None:
													super().__init__(**snake_case_				)

													__lowerCAmelCase     =  hidden_act
													__lowerCAmelCase     =  hidden_dropout_prob
													__lowerCAmelCase     =  hidden_sizes
													__lowerCAmelCase     =  num_hidden_layers
													__lowerCAmelCase     =  num_attention_heads
													__lowerCAmelCase     =  initializer_range
													__lowerCAmelCase     =  layer_norm_eps
													__lowerCAmelCase     =  patch_size
													__lowerCAmelCase     =  num_channels
													__lowerCAmelCase     =  depths
													__lowerCAmelCase     =  mlp_expansion_ratio
													__lowerCAmelCase     =  downsamples
													__lowerCAmelCase     =  dim
													__lowerCAmelCase     =  key_dim
													__lowerCAmelCase     =  attention_ratio
													__lowerCAmelCase     =  resolution
													__lowerCAmelCase     =  pool_size
													__lowerCAmelCase     =  downsample_patch_size
													__lowerCAmelCase     =  downsample_stride
													__lowerCAmelCase     =  downsample_pad
													__lowerCAmelCase     =  drop_path_rate
													__lowerCAmelCase     =  num_metaad_blocks
													__lowerCAmelCase     =  distillation
													__lowerCAmelCase     =  use_layer_scale
													__lowerCAmelCase     =  layer_scale_init_value
													__lowerCAmelCase     =  image_size
													__lowerCAmelCase     =  batch_norm_eps
