

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		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      List[Any]=2       ,SCREAMING_SNAKE_CASE__		:      Tuple=True       ,SCREAMING_SNAKE_CASE__		:      str=False       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=1_0       ,SCREAMING_SNAKE_CASE__		:      List[Any]=3       ,SCREAMING_SNAKE_CASE__		:      Any=3_2 * 4       ,SCREAMING_SNAKE_CASE__		:      Tuple=3_2 * 6       ,SCREAMING_SNAKE_CASE__		:      int=4       ,SCREAMING_SNAKE_CASE__		:      List[Any]=3_2       ,):
								__lowerCamelCase    :    str 	=			parent
								__lowerCamelCase    :    str 	=			batch_size
								__lowerCamelCase    :    List[Any] 	=			is_training
								__lowerCamelCase    :    Optional[int] 	=			use_auxiliary_loss
								__lowerCamelCase    :    int 	=			num_queries
								__lowerCamelCase    :    List[str] 	=			num_channels
								__lowerCamelCase    :    Optional[int] 	=			min_size
								__lowerCamelCase    :    Optional[Any] 	=			max_size
								__lowerCamelCase    :    Dict 	=			num_labels
								__lowerCamelCase    :    Union[str, Any] 	=			mask_feature_size
							def    lowerCAmelCase					(		self		:      str):
								__lowerCamelCase    :    Optional[int] 	=			floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]).to(
								    SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    List[str] 	=			torch.ones([self.batch_size, self.min_size, self.max_size]       ,device=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    List[Any] 	=			(
								    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]       ,device=SCREAMING_SNAKE_CASE__) > 0.5
								).float()
								__lowerCamelCase    :    Optional[int] 	=			(torch.rand((self.batch_size, self.num_labels)       ,device=SCREAMING_SNAKE_CASE__) > 0.5).long()

								__lowerCamelCase    :    Any 	=			self.get_config()
								return config, pixel_values, pixel_mask, mask_labels, class_labels
							def    lowerCAmelCase					(		self		:      Any):
								return MaskFormerConfig.from_backbone_and_decoder_configs(
								    backbone_config=SwinConfig(
								        depths=[1, 1, 1, 1]       ,)       ,decoder_config=DetrConfig(
								        decoder_ffn_dim=1_2_8       ,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    lowerCAmelCase					(		self		:      Tuple):
								__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    List[str] 	=			self.prepare_config_and_inputs()
								__lowerCamelCase    :    Dict 	=			{'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
								return config, inputs_dict
							def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      List[Any]):
								__lowerCamelCase    :    List[Any] 	=			output.encoder_hidden_states
								__lowerCamelCase    :    Any 	=			output.pixel_decoder_hidden_states
								__lowerCamelCase    :    str 	=			output.transformer_decoder_hidden_states

								self.parent.assertTrue(len(SCREAMING_SNAKE_CASE__)       ,len(config.backbone_config.depths))
								self.parent.assertTrue(len(SCREAMING_SNAKE_CASE__)       ,len(config.backbone_config.depths))
								self.parent.assertTrue(len(SCREAMING_SNAKE_CASE__)       ,config.decoder_config.decoder_layers)
							def    lowerCAmelCase					(		self		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=False):
								with torch.no_grad():
									__lowerCamelCase    :    Tuple 	=			MaskFormerModel(config=SCREAMING_SNAKE_CASE__)
									model.to(SCREAMING_SNAKE_CASE__)
									model.eval()

									__lowerCamelCase    :    List[Any] 	=			model(pixel_values=SCREAMING_SNAKE_CASE__       ,pixel_mask=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Tuple 	=			model(SCREAMING_SNAKE_CASE__       ,output_hidden_states=SCREAMING_SNAKE_CASE__)
								# 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(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)


							def    lowerCAmelCase					(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      str):
								__lowerCamelCase    :    str 	=			MaskFormerForInstanceSegmentation(config=SCREAMING_SNAKE_CASE__)
								model.to(SCREAMING_SNAKE_CASE__)
								model.eval()

								def comm_check_on_output(SCREAMING_SNAKE_CASE__		:      str):
									# 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():
									__lowerCamelCase    :    int 	=			model(pixel_values=SCREAMING_SNAKE_CASE__       ,pixel_mask=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    List[Any] 	=			model(SCREAMING_SNAKE_CASE__)

									comm_check_on_output(SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    Optional[int] 	=			model(
									    pixel_values=SCREAMING_SNAKE_CASE__       ,pixel_mask=SCREAMING_SNAKE_CASE__       ,mask_labels=SCREAMING_SNAKE_CASE__       ,class_labels=SCREAMING_SNAKE_CASE__)

								comm_check_on_output(SCREAMING_SNAKE_CASE__)

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






@require_torch
class 	A_		(	SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							unittest.TestCase    ):
							_UpperCAmelCase   :  str										=				(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
							_UpperCAmelCase   :  Dict										=				(
							    {'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
							    if is_torch_available()
							    else {}
							)

							_UpperCAmelCase   :  Tuple										=				False
							_UpperCAmelCase   :  Optional[Any]										=				False
							_UpperCAmelCase   :  Tuple										=				False
							_UpperCAmelCase   :  Tuple										=				False
							def    lowerCAmelCase					(		self		:      str):
								__lowerCamelCase    :    int 	=			MaskFormerModelTester(self)
								__lowerCamelCase    :    Tuple 	=			ConfigTester(self       ,config_class=SCREAMING_SNAKE_CASE__       ,has_text_modality=SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Optional[int]):
								self.config_tester.run_common_tests()
							def    lowerCAmelCase					(		self		:      int):
								__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.create_and_check_maskformer_model(SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__       ,output_hidden_states=SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      List[Any]):
								__lowerCamelCase    :    List[str] 	=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*SCREAMING_SNAKE_CASE__)
							@unittest.skip(reason='MaskFormer does not use inputs_embeds')
							def    lowerCAmelCase					(		self		:      int):
								pass
							@unittest.skip(reason='MaskFormer does not have a get_input_embeddings method')
							def    lowerCAmelCase					(		self		:      Tuple):
								pass
							@unittest.skip(reason='MaskFormer is not a generative model')
							def    lowerCAmelCase					(		self		:      Any):
								pass
							@unittest.skip(reason='MaskFormer does not use token embeddings')
							def    lowerCAmelCase					(		self		:      int):
								pass
							@require_torch_multi_gpu
							@unittest.skip(
							    reason='MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`')
							def    lowerCAmelCase					(		self		:      str):
								pass
							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
							def    lowerCAmelCase					(		self		:      int):
								pass
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase	,			__lowerCamelCase    :    int 	=			self.model_tester.prepare_config_and_inputs_for_common()

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

									__lowerCamelCase    :    Optional[int] 	=			['pixel_values']
									self.assertListEqual(arg_names[:1]       ,SCREAMING_SNAKE_CASE__)
							@slow
							def    lowerCAmelCase					(		self		:      List[Any]):
								for model_name in ["facebook/maskformer-swin-small-coco"]:
									__lowerCamelCase    :    str 	=			MaskFormerModel.from_pretrained(SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      int):
								__lowerCamelCase    :    Any 	=			(self.model_tester.min_size,) * 2
								__lowerCamelCase    :    Any 	=			{
								    'pixel_values': torch.randn((2, 3, *size)       ,device=SCREAMING_SNAKE_CASE__),
								    'mask_labels': torch.randn((2, 1_0, *size)       ,device=SCREAMING_SNAKE_CASE__),
								    'class_labels': torch.zeros(2       ,1_0       ,device=SCREAMING_SNAKE_CASE__).long(),
								}

								__lowerCamelCase    :    List[str] 	=			MaskFormerForInstanceSegmentation(MaskFormerConfig()).to(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[int] 	=			model(**SCREAMING_SNAKE_CASE__)
								self.assertTrue(outputs.loss is not None)
							def    lowerCAmelCase					(		self		:      List[str]):
								__lowerCamelCase	,			__lowerCamelCase    :    Optional[int] 	=			self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.create_and_check_maskformer_model(SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__       ,output_hidden_states=SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase	,			__lowerCamelCase    :    Dict 	=			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									__lowerCamelCase    :    Optional[int] 	=			model_class(SCREAMING_SNAKE_CASE__).to(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Any 	=			model(**SCREAMING_SNAKE_CASE__       ,output_attentions=SCREAMING_SNAKE_CASE__)
									self.assertTrue(outputs.attentions is not None)
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								if not self.model_tester.is_training:
									return
								# only MaskFormerForInstanceSegmentation has the loss
								__lowerCamelCase    :    Optional[Any] 	=			self.all_model_classes[1]
								__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Any 	=			self.model_tester.prepare_config_and_inputs()

								__lowerCamelCase    :    Dict 	=			model_class(SCREAMING_SNAKE_CASE__)
								model.to(SCREAMING_SNAKE_CASE__)
								model.train()

								__lowerCamelCase    :    Any 	=			model(SCREAMING_SNAKE_CASE__       ,mask_labels=SCREAMING_SNAKE_CASE__       ,class_labels=SCREAMING_SNAKE_CASE__).loss
								loss.backward()


							def    lowerCAmelCase					(		self		:      Optional[int]):
								# only MaskFormerForInstanceSegmentation has the loss
								__lowerCamelCase    :    Union[str, Any] 	=			self.all_model_classes[1]
								__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    int 	=			self.model_tester.prepare_config_and_inputs()
								__lowerCamelCase    :    Optional[Any] 	=			True
								__lowerCamelCase    :    Any 	=			True

								__lowerCamelCase    :    int 	=			model_class(SCREAMING_SNAKE_CASE__)
								model.to(SCREAMING_SNAKE_CASE__)
								model.train()

								__lowerCamelCase    :    Tuple 	=			model(SCREAMING_SNAKE_CASE__       ,mask_labels=SCREAMING_SNAKE_CASE__       ,class_labels=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Dict 	=			outputs.encoder_hidden_states[0]
								encoder_hidden_states.retain_grad()

								__lowerCamelCase    :    int 	=			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
								__lowerCamelCase    :    int 	=			outputs.transformer_decoder_hidden_states[0]
								transformer_decoder_hidden_states.retain_grad()

								__lowerCamelCase    :    Union[str, Any] 	=			outputs.attentions[0]
								attentions.retain_grad()

								outputs.loss.backward(retain_graph=SCREAMING_SNAKE_CASE__)

								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		=1E-4






def        SCREAMING_SNAKE_CASE__	(  )  ->					Tuple:
	__lowerCamelCase    :    Any 	=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
	return image






@require_vision
@slow
class 	A_		(	unittest.TestCase    ):
							@cached_property
							def    lowerCAmelCase					(		self		:      Dict):
								return (
								    MaskFormerImageProcessor.from_pretrained('facebook/maskformer-swin-small-coco')
								    if is_vision_available()
								    else None
								)
							def    lowerCAmelCase					(		self		:      int):
								__lowerCamelCase    :    Optional[Any] 	=			MaskFormerModel.from_pretrained('facebook/maskformer-swin-small-coco').to(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Dict 	=			self.default_image_processor
								__lowerCamelCase    :    Optional[Any] 	=			prepare_img()
								__lowerCamelCase    :    int 	=			image_processor(SCREAMING_SNAKE_CASE__       ,return_tensors='pt').to(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    List[Any] 	=			inputs['pixel_values'].shape
								# check size is divisible by 32
								self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0)
								# check size
								self.assertEqual(SCREAMING_SNAKE_CASE__       ,(1, 3, 8_0_0, 1_0_8_8))

								with torch.no_grad():
									__lowerCamelCase    :    List[str] 	=			model(**SCREAMING_SNAKE_CASE__)

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

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

								__lowerCamelCase    :    str 	=			torch.tensor(
								    [[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]]).to(SCREAMING_SNAKE_CASE__)
								self.assertTrue(
								    torch.allclose(
								        outputs.transformer_decoder_last_hidden_state[0, :3, :3]       ,SCREAMING_SNAKE_CASE__       ,atol=SCREAMING_SNAKE_CASE__))
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								__lowerCamelCase    :    str 	=			(
								    MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco')
								    .to(SCREAMING_SNAKE_CASE__)
								    .eval()
								)
								__lowerCamelCase    :    Optional[int] 	=			self.default_image_processor
								__lowerCamelCase    :    Optional[Any] 	=			prepare_img()
								__lowerCamelCase    :    int 	=			image_processor(SCREAMING_SNAKE_CASE__       ,return_tensors='pt').to(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Tuple 	=			inputs['pixel_values'].shape
								# check size is divisible by 32
								self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0)
								# check size
								self.assertEqual(SCREAMING_SNAKE_CASE__       ,(1, 3, 8_0_0, 1_0_8_8))

								with torch.no_grad():
									__lowerCamelCase    :    Union[str, Any] 	=			model(**SCREAMING_SNAKE_CASE__)
								# masks_queries_logits
								__lowerCamelCase    :    Dict 	=			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)       ,)
								__lowerCamelCase    :    Union[str, Any] 	=			[
								    [-1.3737124, -1.7724937, -1.9364233],
								    [-1.5977281, -1.9867939, -2.1523695],
								    [-1.5795398, -1.9269832, -2.093942],
								]
								__lowerCamelCase    :    str 	=			torch.tensor(SCREAMING_SNAKE_CASE__).to(SCREAMING_SNAKE_CASE__)
								self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]       ,SCREAMING_SNAKE_CASE__       ,atol=SCREAMING_SNAKE_CASE__))
								# class_queries_logits
								__lowerCamelCase    :    Tuple 	=			outputs.class_queries_logits
								self.assertEqual(
								    class_queries_logits.shape       ,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1))
								__lowerCamelCase    :    str 	=			torch.tensor(
								    [
								        [1.65_12E00, -5.25_72E00, -3.35_19E00],
								        [3.61_69E-02, -5.90_25E00, -2.93_13E00],
								        [1.07_66E-04, -7.76_30E00, -5.12_63E00],
								    ]).to(SCREAMING_SNAKE_CASE__)
								self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,SCREAMING_SNAKE_CASE__       ,atol=SCREAMING_SNAKE_CASE__))
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								__lowerCamelCase    :    str 	=			(
								    MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-resnet101-coco-stuff')
								    .to(SCREAMING_SNAKE_CASE__)
								    .eval()
								)
								__lowerCamelCase    :    Union[str, Any] 	=			self.default_image_processor
								__lowerCamelCase    :    Any 	=			prepare_img()
								__lowerCamelCase    :    List[Any] 	=			image_processor(SCREAMING_SNAKE_CASE__       ,return_tensors='pt').to(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    str 	=			inputs['pixel_values'].shape
								# check size is divisible by 32
								self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0)
								# check size
								self.assertEqual(SCREAMING_SNAKE_CASE__       ,(1, 3, 8_0_0, 1_0_8_8))

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


							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    int 	=			(
								    MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco')
								    .to(SCREAMING_SNAKE_CASE__)
								    .eval()
								)
								__lowerCamelCase    :    List[str] 	=			self.default_image_processor

								__lowerCamelCase    :    Union[str, Any] 	=			image_processor(
								    [np.zeros((3, 8_0_0, 1_3_3_3)), np.zeros((3, 8_0_0, 1_3_3_3))]       ,segmentation_maps=[np.zeros((3_8_4, 3_8_4)).astype(np.floataa), np.zeros((3_8_4, 3_8_4)).astype(np.floataa)]       ,return_tensors='pt'       ,)

								__lowerCamelCase    :    Optional[int] 	=			inputs['pixel_values'].to(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[Any] 	=			[el.to(SCREAMING_SNAKE_CASE__) for el in inputs['mask_labels']]
								__lowerCamelCase    :    str 	=			[el.to(SCREAMING_SNAKE_CASE__) for el in inputs['class_labels']]

								with torch.no_grad():
									__lowerCamelCase    :    Any 	=			model(**SCREAMING_SNAKE_CASE__)

								self.assertTrue(outputs.loss is not None)







# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


a		=[
    """EAGER""",
    """AOT_EAGER""",
    """INDUCTOR""",
    """NVFUSER""",
    """AOT_NVFUSER""",
    """AOT_CUDAGRAPHS""",
    """OFI""",
    """FX2TRT""",
    """ONNXRT""",
    """IPEX""",
]






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=None     ,			lowerCamelCase__=None     ,			lowerCamelCase__=None      )  ->					Optional[int]:
	__lowerCamelCase    :    int 	=			True
	while ask_again:
		__lowerCamelCase    :    Dict 	=			input(lowerCamelCase__      )
		try:
			if default is not None and len(lowerCamelCase__      ) == 0:
				return default
			return convert_value(lowerCamelCase__      ) if convert_value is not None else result
		except Exception:
			if error_message is not None:
				print(lowerCamelCase__      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=[]     ,			lowerCamelCase__=None     ,			lowerCamelCase__=0      )  ->					str:
	__lowerCamelCase    :    Union[str, Any] 	=			BulletMenu(lowerCamelCase__     ,			lowerCamelCase__      )
	__lowerCamelCase    :    Tuple 	=			menu.run(default_choice=lowerCamelCase__      )
	return convert_value(lowerCamelCase__      ) if convert_value is not None else result






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Dict:
	__lowerCamelCase    :    List[str] 	=			int(lowerCamelCase__      )
	return ComputeEnvironment(['LOCAL_MACHINE', 'AMAZON_SAGEMAKER'][value]      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[int]:
	__lowerCamelCase    :    Union[str, Any] 	=			int(lowerCamelCase__      )
	return DistributedType(['NO', 'MULTI_CPU', 'MULTI_XPU', 'MULTI_GPU', 'MULTI_NPU', 'TPU'][value]      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Union[str, Any]:
	__lowerCamelCase    :    Optional[Any] 	=			int(lowerCamelCase__      )
	return DynamoBackend(DYNAMO_BACKENDS[value]      ).value






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					int:
	__lowerCamelCase    :    Union[str, Any] 	=			int(lowerCamelCase__      )
	return PrecisionType(['no', 'fp16', 'bf16', 'fp8'][value]      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					str:
	__lowerCamelCase    :    Optional[Any] 	=			int(lowerCamelCase__      )
	return SageMakerDistributedType(['NO', 'DATA_PARALLEL', 'MODEL_PARALLEL'][value]      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[Any]:
	return {"yes": True, "no": False}[value.lower()]






class 	A_		(	argparse.RawDescriptionHelpFormatter    ):
							def    lowerCAmelCase					(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      str):
								__lowerCamelCase    :    int 	=			super()._format_usage(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Union[str, Any] 	=			usage.replace('<command> [<args>] '       ,'')
								return usage




