




'''simple docstring'''

import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

							if is_vision_available():
														from transformers import MaskFormerImageProcessor

if is_vision_available():
							from PIL import Image






class 						a				:


				def __init__(		self    :					str       ,				lowercase_    :					Any       ,				lowercase_    :					Dict=2       ,				lowercase_    :					Optional[Any]=True       ,				lowercase_    :					Optional[int]=False       ,				lowercase_    :					List[Any]=10       ,				lowercase_    :					Dict=3       ,				lowercase_    :					str=32 * 4       ,				lowercase_    :					Dict=32 * 6       ,				lowercase_    :					Union[str, Any]=4       ,				lowercase_    :					str=32       ,				):
									snake_case_									=				parent
									snake_case_									=				batch_size
									snake_case_									=				is_training
									snake_case_									=				use_auxiliary_loss
									snake_case_									=				num_queries
									snake_case_									=				num_channels
									snake_case_									=				min_size
									snake_case_									=				max_size
									snake_case_									=				num_labels
									snake_case_									=				mask_feature_size


				def   A_ (		self    :					Optional[Any]					):
									snake_case_									=				floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]					).to(
									    lowercase_					)

									snake_case_									=				torch.ones([self.batch_size, self.min_size, self.max_size]       ,				device=lowercase_					)

									snake_case_									=				(
									    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]       ,				device=lowercase_					) > 0.5
									).float()
									snake_case_									=				(torch.rand((self.batch_size, self.num_labels)       ,				device=lowercase_					) > 0.5).long()

									snake_case_									=				self.get_config()
									return config, pixel_values, pixel_mask, mask_labels, class_labels


				def   A_ (		self    :					Any					):
									return MaskFormerConfig.from_backbone_and_decoder_configs(
									    backbone_config=SwinConfig(
									        depths=[1, 1, 1, 1]       ,				)       ,				decoder_config=DetrConfig(
									        decoder_ffn_dim=128       ,				num_queries=self.num_queries       ,				decoder_attention_heads=2       ,				d_model=self.mask_feature_size       ,				)       ,				mask_feature_size=self.mask_feature_size       ,				fpn_feature_size=self.mask_feature_size       ,				num_channels=self.num_channels       ,				num_labels=self.num_labels       ,				)


				def   A_ (		self    :					List[Any]					):
									snake_case_			,snake_case_			,snake_case_			,snake_case_			,snake_case_									=				self.prepare_config_and_inputs()
									snake_case_									=				{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
									return config, inputs_dict


				def   A_ (		self    :					Any       ,				lowercase_    :					int       ,				lowercase_    :					Tuple					):
									snake_case_									=				output.encoder_hidden_states
									snake_case_									=				output.pixel_decoder_hidden_states
									snake_case_									=				output.transformer_decoder_hidden_states

									self.parent.assertTrue(len(lowercase_					)       ,				len(config.backbone_config.depths					)					)
									self.parent.assertTrue(len(lowercase_					)       ,				len(config.backbone_config.depths					)					)
									self.parent.assertTrue(len(lowercase_					)       ,				config.decoder_config.decoder_layers					)


				def   A_ (		self    :					Any       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					Tuple       ,				lowercase_    :					Any       ,				lowercase_    :					List[str]=False					):
									with torch.no_grad():
														snake_case_									=				MaskFormerModel(config=lowercase_					)
														model.to(lowercase_					)
														model.eval()

														snake_case_									=				model(pixel_values=lowercase_       ,				pixel_mask=lowercase_					)
														snake_case_									=				model(lowercase_       ,				output_hidden_states=lowercase_					)
									# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
									# encoder and pixel decoder
									self.parent.assertEqual(
									    output.transformer_decoder_last_hidden_state.shape       ,				(self.batch_size, self.num_queries, self.mask_feature_size)       ,				)
									# let's ensure the other two hidden state exists
									self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None					)
									self.parent.assertTrue(output.encoder_last_hidden_state is not None					)

									if output_hidden_states:
														self.check_output_hidden_state(lowercase_       ,				lowercase_					)


				def   A_ (		self    :					Optional[int]       ,				lowercase_    :					Optional[Any]       ,				lowercase_    :					Optional[Any]       ,				lowercase_    :					str       ,				lowercase_    :					Tuple       ,				lowercase_    :					Union[str, Any]					):
									snake_case_									=				MaskFormerForInstanceSegmentation(config=lowercase_					)
									model.to(lowercase_					)
									model.eval()

									def comm_check_on_output(lowercase_    :					Union[str, Any]					):
														# let's still check that all the required stuff is there
														self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None					)
														self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None					)
														self.parent.assertTrue(result.encoder_last_hidden_state is not None					)
														# okay, now we need to check the logits shape
														# due to the encoder compression, masks have a //4 spatial size
														self.parent.assertEqual(
														    result.masks_queries_logits.shape       ,				(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)       ,				)
														# + 1 for null class
														self.parent.assertEqual(
														    result.class_queries_logits.shape       ,				(self.batch_size, self.num_queries, self.num_labels + 1)					)

									with torch.no_grad():
														snake_case_									=				model(pixel_values=lowercase_       ,				pixel_mask=lowercase_					)
														snake_case_									=				model(lowercase_					)

														comm_check_on_output(lowercase_					)

														snake_case_									=				model(
														    pixel_values=lowercase_       ,				pixel_mask=lowercase_       ,				mask_labels=lowercase_       ,				class_labels=lowercase_					)

									comm_check_on_output(lowercase_					)

									self.parent.assertTrue(result.loss is not None					)
									self.parent.assertEqual(result.loss.shape       ,				torch.Size([1]					)					)







@require_torch
class 						a				(   _lowerCamelCase   ,     _lowerCamelCase   ,     unittest.TestCase     ):
				snake_case_      =		(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
				snake_case_      =		(
				    {"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
				    if is_torch_available()
				    else {}
				)

				snake_case_      =		False
				snake_case_      =		False
				snake_case_      =		False
				snake_case_      =		False


				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				MaskFormerModelTester(self					)
									snake_case_									=				ConfigTester(self       ,				config_class=lowercase_       ,				has_text_modality=lowercase_					)


				def   A_ (		self    :					Any					):
									self.config_tester.run_common_tests()


				def   A_ (		self    :					Dict					):
									snake_case_			,snake_case_									=				self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskformer_model(lowercase_       ,				**lowercase_       ,				output_hidden_states=lowercase_					)


				def   A_ (		self    :					Tuple					):
									snake_case_									=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*lowercase_					)


				@unittest.skip(reason='''MaskFormer does not use inputs_embeds'''					)
				def   A_ (		self    :					List[Any]					):
									pass


				@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method'''					)
				def   A_ (		self    :					str					):
									pass


				@unittest.skip(reason='''MaskFormer is not a generative model'''					)
				def   A_ (		self    :					Tuple					):
									pass


				@unittest.skip(reason='''MaskFormer does not use token embeddings'''					)
				def   A_ (		self    :					Optional[Any]					):
									pass


				@require_torch_multi_gpu
				@unittest.skip(
				    reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''					)
				def   A_ (		self    :					List[str]					):
									pass


				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''					)
				def   A_ (		self    :					Union[str, Any]					):
									pass


				def   A_ (		self    :					str					):
									snake_case_			,snake_case_									=				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														snake_case_									=				model_class(lowercase_					)
														snake_case_									=				inspect.signature(model.forward					)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														snake_case_									=				[*signature.parameters.keys()]

														snake_case_									=				['''pixel_values''']
														self.assertListEqual(arg_names[:1]       ,				lowercase_					)


				@slow
				def   A_ (		self    :					List[str]					):
									for model_name in ["facebook/maskformer-swin-small-coco"]:
														snake_case_									=				MaskFormerModel.from_pretrained(lowercase_					)
														self.assertIsNotNone(lowercase_					)


				def   A_ (		self    :					Optional[int]					):
									snake_case_									=				(self.model_tester.min_size,) * 2
									snake_case_									=				{
									    '''pixel_values''': torch.randn((2, 3, *size)       ,				device=lowercase_					),
									    '''mask_labels''': torch.randn((2, 10, *size)       ,				device=lowercase_					),
									    '''class_labels''': torch.zeros(2       ,				10       ,				device=lowercase_					).long(),
									}

									snake_case_									=				MaskFormerForInstanceSegmentation(MaskFormerConfig()					).to(lowercase_					)
									snake_case_									=				model(**lowercase_					)
									self.assertTrue(outputs.loss is not None					)


				def   A_ (		self    :					List[str]					):
									snake_case_			,snake_case_									=				self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskformer_model(lowercase_       ,				**lowercase_       ,				output_hidden_states=lowercase_					)


				def   A_ (		self    :					str					):
									snake_case_			,snake_case_									=				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														snake_case_									=				model_class(lowercase_					).to(lowercase_					)
														snake_case_									=				model(**lowercase_       ,				output_attentions=lowercase_					)
														self.assertTrue(outputs.attentions is not None					)


				def   A_ (		self    :					Dict					):
									if not self.model_tester.is_training:
														return
									# only MaskFormerForInstanceSegmentation has the loss
									snake_case_									=				self.all_model_classes[1]
									snake_case_			,snake_case_			,snake_case_			,snake_case_			,snake_case_									=				self.model_tester.prepare_config_and_inputs()

									snake_case_									=				model_class(lowercase_					)
									model.to(lowercase_					)
									model.train()

									snake_case_									=				model(lowercase_       ,				mask_labels=lowercase_       ,				class_labels=lowercase_					).loss
									loss.backward()


				def   A_ (		self    :					List[str]					):
									# only MaskFormerForInstanceSegmentation has the loss
									snake_case_									=				self.all_model_classes[1]
									snake_case_			,snake_case_			,snake_case_			,snake_case_			,snake_case_									=				self.model_tester.prepare_config_and_inputs()
									snake_case_									=				True
									snake_case_									=				True

									snake_case_									=				model_class(lowercase_					)
									model.to(lowercase_					)
									model.train()

									snake_case_									=				model(lowercase_       ,				mask_labels=lowercase_       ,				class_labels=lowercase_					)

									snake_case_									=				outputs.encoder_hidden_states[0]
									encoder_hidden_states.retain_grad()

									snake_case_									=				outputs.pixel_decoder_hidden_states[0]
									pixel_decoder_hidden_states.retain_grad()
									# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
									snake_case_									=				outputs.transformer_decoder_hidden_states[0]
									transformer_decoder_hidden_states.retain_grad()

									snake_case_									=				outputs.attentions[0]
									attentions.retain_grad()

									outputs.loss.backward(retain_graph=lowercase_					)

									self.assertIsNotNone(encoder_hidden_states.grad					)
									self.assertIsNotNone(pixel_decoder_hidden_states.grad					)
									self.assertIsNotNone(transformer_decoder_hidden_states.grad					)
									self.assertIsNotNone(attentions.grad					)







a  :				List[Any]									=  1E-4


def 		__magic_name__    (	)		->					Optional[int]:

					'''simple docstring'''

					snake_case_									=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
					return image






@require_vision
@slow
class 						a				(   unittest.TestCase     ):


				@cached_property
				def   A_ (		self    :					Tuple					):
									return (
									    MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco'''					)
									    if is_vision_available()
									    else None
									)


				def   A_ (		self    :					Optional[int]					):
									snake_case_									=				MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco'''					).to(lowercase_					)
									snake_case_									=				self.default_image_processor
									snake_case_									=				prepare_img()
									snake_case_									=				image_processor(lowercase_       ,				return_tensors='''pt'''					).to(lowercase_					)
									snake_case_									=				inputs['''pixel_values'''].shape
									# check size is divisible by 32
									self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0					)
									# check size
									self.assertEqual(lowercase_       ,				(1, 3, 800, 1088)					)

									with torch.no_grad():
														snake_case_									=				model(**lowercase_					)

									snake_case_									=				torch.tensor(
									    [[-0.0482, 0.9228, 0.4951], [-0.2547, 0.8017, 0.8527], [-0.0069, 0.3385, -0.0089]]					).to(lowercase_					)
									self.assertTrue(
									    torch.allclose(
									        outputs.encoder_last_hidden_state[0, 0, :3, :3]       ,				lowercase_       ,				atol=lowercase_					)					)

									snake_case_									=				torch.tensor(
									    [[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]]					).to(lowercase_					)
									self.assertTrue(
									    torch.allclose(
									        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]       ,				lowercase_       ,				atol=lowercase_					)					)

									snake_case_									=				torch.tensor(
									    [[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]]					).to(lowercase_					)
									self.assertTrue(
									    torch.allclose(
									        outputs.transformer_decoder_last_hidden_state[0, :3, :3]       ,				lowercase_       ,				atol=lowercase_					)					)


				def   A_ (		self    :					List[str]					):
									snake_case_									=				(
									    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''					)
									    .to(lowercase_					)
									    .eval()
									)
									snake_case_									=				self.default_image_processor
									snake_case_									=				prepare_img()
									snake_case_									=				image_processor(lowercase_       ,				return_tensors='''pt'''					).to(lowercase_					)
									snake_case_									=				inputs['''pixel_values'''].shape
									# check size is divisible by 32
									self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0					)
									# check size
									self.assertEqual(lowercase_       ,				(1, 3, 800, 1088)					)

									with torch.no_grad():
														snake_case_									=				model(**lowercase_					)
									# masks_queries_logits
									snake_case_									=				outputs.masks_queries_logits
									self.assertEqual(
									    masks_queries_logits.shape       ,				(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)       ,				)
									snake_case_									=				[
									    [-1.373_7124, -1.772_4937, -1.936_4233],
									    [-1.597_7281, -1.986_7939, -2.152_3695],
									    [-1.579_5398, -1.926_9832, -2.09_3942],
									]
									snake_case_									=				torch.tensor(lowercase_					).to(lowercase_					)
									self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]       ,				lowercase_       ,				atol=lowercase_					)					)
									# class_queries_logits
									snake_case_									=				outputs.class_queries_logits
									self.assertEqual(
									    class_queries_logits.shape       ,				(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)					)
									snake_case_									=				torch.tensor(
									    [
									        [1.6_512e00, -5.2_572e00, -3.3_519e00],
									        [3.6_169e-02, -5.9_025e00, -2.9_313e00],
									        [1.0_766e-04, -7.7_630e00, -5.1_263e00],
									    ]					).to(lowercase_					)
									self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,				lowercase_       ,				atol=lowercase_					)					)


				def   A_ (		self    :					Any					):
									snake_case_									=				(
									    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff'''					)
									    .to(lowercase_					)
									    .eval()
									)
									snake_case_									=				self.default_image_processor
									snake_case_									=				prepare_img()
									snake_case_									=				image_processor(lowercase_       ,				return_tensors='''pt'''					).to(lowercase_					)
									snake_case_									=				inputs['''pixel_values'''].shape
									# check size is divisible by 32
									self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0					)
									# check size
									self.assertEqual(lowercase_       ,				(1, 3, 800, 1088)					)

									with torch.no_grad():
														snake_case_									=				model(**lowercase_					)
									# masks_queries_logits
									snake_case_									=				outputs.masks_queries_logits
									self.assertEqual(
									    masks_queries_logits.shape       ,				(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)       ,				)
									snake_case_									=				[[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
									snake_case_									=				torch.tensor(lowercase_					).to(lowercase_					)
									self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]       ,				lowercase_       ,				atol=lowercase_					)					)
									# class_queries_logits
									snake_case_									=				outputs.class_queries_logits
									self.assertEqual(
									    class_queries_logits.shape       ,				(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)					)
									snake_case_									=				torch.tensor(
									    [[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]]					).to(lowercase_					)
									self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,				lowercase_       ,				atol=lowercase_					)					)


				def   A_ (		self    :					Dict					):
									snake_case_									=				(
									    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''					)
									    .to(lowercase_					)
									    .eval()
									)
									snake_case_									=				self.default_image_processor

									snake_case_									=				image_processor(
									    [np.zeros((3, 800, 1333)					), np.zeros((3, 800, 1333)					)]       ,				segmentation_maps=[np.zeros((384, 384)					).astype(np.floataa					), np.zeros((384, 384)					).astype(np.floataa					)]       ,				return_tensors='''pt'''       ,				)

									snake_case_									=				inputs['''pixel_values'''].to(lowercase_					)
									snake_case_									=				[el.to(lowercase_					) for el in inputs['''mask_labels''']]
									snake_case_									=				[el.to(lowercase_					) for el in inputs['''class_labels''']]

									with torch.no_grad():
														snake_case_									=				model(**lowercase_					)

									self.assertTrue(outputs.loss is not None					)


"""simple docstring"""




from __future__ import annotations

_a      :		List[str]           =   10





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       list[int]	)       ->      list[int]:
  _lowerCAmelCase						:       Optional[int]  			=     1
  _lowerCAmelCase						:       Union[str, Any]  			=     max(_lowerCamelCase	)
  while placement <= max_digit:
    # declare and initialize empty buckets
    _lowerCAmelCase						:       list[list]  			=     [[] for _ in range(_lowerCamelCase	)]
    # split list_of_ints between the buckets
    for i in list_of_ints:
      _lowerCAmelCase						:       Tuple  			=     int((i / placement) % RADIX	)
      buckets[tmp].append(_lowerCamelCase	)
    # put each buckets' contents into list_of_ints
    _lowerCAmelCase						:       List[str]  			=     0
    for b in range(_lowerCamelCase	):
      for i in buckets[b]:
        _lowerCAmelCase						:       Any  			=     i
        a += 1
        # move to next
    placement *= RADIX
  return list_of_ints


if __name__ == "__main__":
     import doctest

     doctest.testmod()




