

import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, 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 MaskaFormerForUniversalSegmentation, MaskaFormerModel

					if is_vision_available():
										from transformers import MaskaFormerImageProcessor

if is_vision_available():
					from PIL import Image







class  lowercase				:




				def __init__(					self      ,   snake_case      ,   snake_case=2      ,   snake_case=True      ,   snake_case=False      ,   snake_case=10      ,   snake_case=3      ,   snake_case=32 * 8      ,   snake_case=32 * 8      ,   snake_case=4      ,   snake_case=64      ,   ):
											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_							=    hidden_dim
											snake_case_							=    hidden_dim




				def     a							(					self       ):
											snake_case_							=    floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]       ).to(
											    snake_case       )

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

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

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




				def     a							(					self       ):
											snake_case_							=    MaskaFormerConfig(
											    hidden_size=self.hidden_dim      ,   )
											snake_case_							=    self.num_queries
											snake_case_							=    self.num_labels

											snake_case_							=    [1, 1, 1, 1]
											snake_case_							=    self.num_channels

											snake_case_							=    64
											snake_case_							=    128
											snake_case_							=    self.hidden_dim
											snake_case_							=    self.hidden_dim
											snake_case_							=    self.hidden_dim
											return config




				def     a							(					self       ):
											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      ,   snake_case      ,   snake_case       ):
											snake_case_							=    output.encoder_hidden_states
											snake_case_							=    output.pixel_decoder_hidden_states
											snake_case_							=    output.transformer_decoder_hidden_states

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




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case=False       ):
											with torch.no_grad():
																		snake_case_							=    MaskaFormerModel(config=snake_case       )
																		model.to(snake_case       )
																		model.eval()

																		snake_case_							=    model(pixel_values=snake_case      ,   pixel_mask=snake_case       )
																		snake_case_							=    model(snake_case      ,   output_hidden_states=snake_case       )

											self.parent.assertEqual(
											    output.transformer_decoder_last_hidden_state.shape      ,   (self.batch_size, self.num_queries, self.hidden_dim)      ,   )
											# 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(snake_case      ,   snake_case       )




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    MaskaFormerForUniversalSegmentation(config=snake_case       )
											model.to(snake_case       )
											model.eval()

											def comm_check_on_output(snake_case       ):
																		# 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=snake_case      ,   pixel_mask=snake_case       )
																		snake_case_							=    model(snake_case       )

																		comm_check_on_output(snake_case       )

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

											comm_check_on_output(snake_case       )

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







@require_torch
class  lowercase				(       lowercase_	,  lowercase_	,  unittest.TestCase			):
				__SCREAMING_SNAKE_CASE					: str       			=			(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
				__SCREAMING_SNAKE_CASE					: Union[str, Any]       			=			{'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

				__SCREAMING_SNAKE_CASE					: List[str]       			=			False
				__SCREAMING_SNAKE_CASE					: Union[str, Any]       			=			False
				__SCREAMING_SNAKE_CASE					: Union[str, Any]       			=			False
				__SCREAMING_SNAKE_CASE					: Optional[Any]       			=			False




				def     a							(					self       ):
											snake_case_							=    MaskaFormerModelTester(self       )
											snake_case_							=    ConfigTester(self      ,   config_class=snake_case      ,   has_text_modality=snake_case       )




				def     a							(					self       ):
											self.config_tester.run_common_tests()




				def     a							(					self       ):
											snake_case_				,    snake_case_							=    self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.create_and_check_maskaformer_model(snake_case      ,   **snake_case      ,   output_hidden_states=snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*snake_case       )




				@unittest.skip(reason='Mask2Former does not use inputs_embeds'       )
				def     a							(					self       ):
											pass




				@unittest.skip(reason='Mask2Former does not have a get_input_embeddings method'       )
				def     a							(					self       ):
											pass




				@unittest.skip(reason='Mask2Former is not a generative model'       )
				def     a							(					self       ):
											pass




				@unittest.skip(reason='Mask2Former does not use token embeddings'       )
				def     a							(					self       ):
											pass




				@require_torch_multi_gpu
				@unittest.skip(
				    reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'       )
				def     a							(					self       ):
											pass




				@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'       )
				def     a							(					self       ):
											pass




				def     a							(					self       ):
											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(snake_case       )
																		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]      ,   snake_case       )




				@slow
				def     a							(					self       ):
											for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
																		snake_case_							=    MaskaFormerModel.from_pretrained(snake_case       )
																		self.assertIsNotNone(snake_case       )




				def     a							(					self       ):
											snake_case_							=    (self.model_tester.min_size,) * 2
											snake_case_							=    {
											    'pixel_values': torch.randn((2, 3, *size)      ,   device=snake_case       ),
											    'mask_labels': torch.randn((2, 10, *size)      ,   device=snake_case       ),
											    'class_labels': torch.zeros(2      ,   10      ,   device=snake_case       ).long(),
											}
											snake_case_							=    self.model_tester.get_config()

											snake_case_							=    MaskaFormerForUniversalSegmentation(snake_case       ).to(snake_case       )
											snake_case_							=    model(**snake_case       )
											self.assertTrue(outputs.loss is not None       )




				def     a							(					self       ):
											snake_case_				,    snake_case_							=    self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.create_and_check_maskaformer_model(snake_case      ,   **snake_case      ,   output_hidden_states=snake_case       )




				def     a							(					self       ):
											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(snake_case       ).to(snake_case       )
																		snake_case_							=    model(**snake_case      ,   output_attentions=snake_case       )
																		self.assertTrue(outputs.attentions is not None       )




				def     a							(					self       ):
											if not self.model_tester.is_training:
																		return

											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(snake_case       )
											model.to(snake_case       )
											model.train()

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




				def     a							(					self       ):
											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(snake_case       ).to(snake_case       )
											model.train()

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

											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()

											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=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       )



_UpperCAmelCase		:   int										=       1e-4





def 					__lowerCamelCase	( ):







							'''simple docstring'''






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







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




				@cached_property
				def     a							(					self       ):
											return "facebook/mask2former-swin-small-coco-instance"




				@cached_property
				def     a							(					self       ):
											return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints       ) if is_vision_available() else None




				def     a							(					self       ):
											snake_case_							=    MaskaFormerModel.from_pretrained(self.model_checkpoints       ).to(snake_case       )
											snake_case_							=    self.default_image_processor
											snake_case_							=    prepare_img()
											snake_case_							=    image_processor(snake_case      ,   return_tensors='pt'       ).to(snake_case       )
											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(snake_case      ,   (1, 3, 384, 384)       )

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

											snake_case_							=    torch.tensor(
											    [[-0.27_90, -1.07_17, -1.16_68], [-0.51_28, -0.31_28, -0.49_87], [-0.58_32, 0.19_71, -0.01_97]]       ).to(snake_case       )
											self.assertTrue(
											    torch.allclose(
											        outputs.encoder_last_hidden_state[0, 0, :3, :3]      ,   snake_case      ,   atol=snake_case       )       )

											snake_case_							=    torch.tensor(
											    [[0.89_73, 1.18_47, 1.17_76], [1.19_34, 1.50_40, 1.51_28], [1.11_53, 1.44_86, 1.49_51]]       ).to(snake_case       )
											self.assertTrue(
											    torch.allclose(
											        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]      ,   snake_case      ,   atol=snake_case       )       )

											snake_case_							=    torch.tensor(
											    [[2.11_52, 1.70_00, -0.86_03], [1.58_08, 1.80_04, -0.93_53], [1.60_43, 1.74_95, -0.59_99]]       ).to(snake_case       )
											self.assertTrue(
											    torch.allclose(
											        outputs.transformer_decoder_last_hidden_state[0, :3, :3]      ,   snake_case      ,   atol=snake_case       )       )




				def     a							(					self       ):
											snake_case_							=    MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(snake_case       ).eval()
											snake_case_							=    self.default_image_processor
											snake_case_							=    prepare_img()
											snake_case_							=    image_processor(snake_case      ,   return_tensors='pt'       ).to(snake_case       )
											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(snake_case      ,   (1, 3, 384, 384)       )

											with torch.no_grad():
																		snake_case_							=    model(**snake_case       )
											# masks_queries_logits
											snake_case_							=    outputs.masks_queries_logits
											self.assertEqual(
											    masks_queries_logits.shape      ,   (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)       )
											snake_case_							=    [
											    [-8.78_39, -9.00_56, -8.81_21],
											    [-7.41_04, -7.03_13, -6.54_01],
											    [-6.61_05, -6.34_27, -6.46_75],
											]
											snake_case_							=    torch.tensor(snake_case       ).to(snake_case       )
											self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]      ,   snake_case      ,   atol=snake_case       )       )
											# class_queries_logits
											snake_case_							=    outputs.class_queries_logits
											self.assertEqual(class_queries_logits.shape      ,   (1, model.config.num_queries, model.config.num_labels + 1)       )
											snake_case_							=    torch.tensor(
											    [
											        [1.83_24, -8.08_35, -4.19_22],
											        [0.84_50, -9.00_50, -3.60_53],
											        [0.30_45, -7.72_93, -3.02_75],
											    ]       ).to(snake_case       )
											self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]      ,   snake_case      ,   atol=snake_case       )       )




				def     a							(					self       ):
											snake_case_							=    MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(snake_case       ).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(snake_case       )
											snake_case_							=    [el.to(snake_case       ) for el in inputs['mask_labels']]
											snake_case_							=    [el.to(snake_case       ) for el in inputs['class_labels']]

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

											self.assertTrue(outputs.loss is not None       )







from abc import ABC, abstractmethod
from argparse import ArgumentParser







class  lowercase				(       lowercase_			):




				@staticmethod
				@abstractmethod
				def     a							(					snake_case       ):
											raise NotImplementedError()




				@abstractmethod
				def     a							(					self       ):
											raise NotImplementedError()




