



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 					__lowerCAmelCase :




				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						,				):



									"""simple docstring"""





									_lowerCAmelCase						=    parent
									_lowerCAmelCase						=    batch_size
									_lowerCAmelCase						=    is_training
									_lowerCAmelCase						=    use_auxiliary_loss
									_lowerCAmelCase						=    num_queries
									_lowerCAmelCase						=    num_channels
									_lowerCAmelCase						=    min_size
									_lowerCAmelCase						=    max_size
									_lowerCAmelCase						=    num_labels
									_lowerCAmelCase						=    hidden_dim
									_lowerCAmelCase						=    hidden_dim




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]		).to(
									    _snake_case		)

									_lowerCAmelCase						=    torch.ones([self.batch_size, self.min_size, self.max_size]						,				device=_snake_case		)

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

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




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    MaskaFormerConfig(
									    hidden_size=self.hidden_dim						,				)
									_lowerCAmelCase						=    self.num_queries
									_lowerCAmelCase						=    self.num_labels

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

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




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase	,	_lowerCAmelCase	,	_lowerCAmelCase	,	_lowerCAmelCase	,	_lowerCAmelCase						=    self.prepare_config_and_inputs()
									_lowerCAmelCase						=    {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
									return config, inputs_dict




				def 							snake_case   (							self						,				_snake_case						,				_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    output.encoder_hidden_states
									_lowerCAmelCase						=    output.pixel_decoder_hidden_states
									_lowerCAmelCase						=    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 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case=False		):



									"""simple docstring"""





									with torch.no_grad():
														_lowerCAmelCase						=    MaskaFormerModel(config=_snake_case		)
														model.to(_snake_case		)
														model.eval()

														_lowerCAmelCase						=    model(pixel_values=_snake_case						,				pixel_mask=_snake_case		)
														_lowerCAmelCase						=    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 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    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():
														_lowerCAmelCase						=    model(pixel_values=_snake_case						,				pixel_mask=_snake_case		)
														_lowerCAmelCase						=    model(_snake_case		)

														comm_check_on_output(_snake_case		)

														_lowerCAmelCase						=    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 					__lowerCAmelCase (     lowerCamelCase__       ,						lowerCamelCase__       ,						unittest.TestCase       ):
				__lowerCamelCase =				(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
				__lowerCamelCase =				{'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

				__lowerCamelCase =				False
				__lowerCamelCase =				False
				__lowerCamelCase =				False
				__lowerCamelCase =				False




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    MaskaFormerModelTester(self		)
									_lowerCAmelCase						=    ConfigTester(self						,				config_class=_snake_case						,				has_text_modality=_snake_case		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									self.config_tester.run_common_tests()




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase	,	_lowerCAmelCase						=    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 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    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 							snake_case   (							self		):



									"""simple docstring"""





									pass




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



									"""simple docstring"""





									pass




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



									"""simple docstring"""





									pass




				@unittest.skip(reason="""Mask2Former does not use token embeddings"""		)
				def 							snake_case   (							self		):



									"""simple docstring"""





									pass




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



									"""simple docstring"""





									pass




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



									"""simple docstring"""





									pass




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase	,	_lowerCAmelCase						=    self.model_tester.prepare_config_and_inputs_for_common()

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

														_lowerCAmelCase						=    ["""pixel_values"""]
														self.assertListEqual(arg_names[:1]						,				_snake_case		)




				@slow
				def 							snake_case   (							self		):



									"""simple docstring"""





									for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
														_lowerCAmelCase						=    MaskaFormerModel.from_pretrained(_snake_case		)
														self.assertIsNotNone(_snake_case		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    (self.model_tester.min_size,) * 2
									_lowerCAmelCase						=    {
									    """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(),
									}
									_lowerCAmelCase						=    self.model_tester.get_config()

									_lowerCAmelCase						=    MaskaFormerForUniversalSegmentation(_snake_case		).to(_snake_case		)
									_lowerCAmelCase						=    model(**_snake_case		)
									self.assertTrue(outputs.loss is not None		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase	,	_lowerCAmelCase						=    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 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase	,	_lowerCAmelCase						=    self.model_tester.prepare_config_and_inputs_for_common()

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




				def 							snake_case   (							self		):



									"""simple docstring"""





									if not self.model_tester.is_training:
														return

									_lowerCAmelCase						=    self.all_model_classes[1]
									_lowerCAmelCase	,	_lowerCAmelCase	,	_lowerCAmelCase	,	_lowerCAmelCase	,	_lowerCAmelCase						=    self.model_tester.prepare_config_and_inputs()

									_lowerCAmelCase						=    model_class(_snake_case		)
									model.to(_snake_case		)
									model.train()

									_lowerCAmelCase						=    model(_snake_case						,				mask_labels=_snake_case						,				class_labels=_snake_case		).loss
									loss.backward()




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.all_model_classes[1]
									_lowerCAmelCase	,	_lowerCAmelCase	,	_lowerCAmelCase	,	_lowerCAmelCase	,	_lowerCAmelCase						=    self.model_tester.prepare_config_and_inputs()
									_lowerCAmelCase						=    True
									_lowerCAmelCase						=    True

									_lowerCAmelCase						=    model_class(_snake_case		).to(_snake_case		)
									model.train()

									_lowerCAmelCase						=    model(_snake_case						,				mask_labels=_snake_case						,				class_labels=_snake_case		)

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

									_lowerCAmelCase						=    outputs.pixel_decoder_hidden_states[0]
									pixel_decoder_hidden_states.retain_grad()

									_lowerCAmelCase						=    outputs.transformer_decoder_hidden_states[0]
									transformer_decoder_hidden_states.retain_grad()

									_lowerCAmelCase						=    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		)

A__	      =       1e-4


def 		_UpperCAmelCase				(      ):





					"""simple docstring"""

					_lowerCAmelCase						=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
					return image





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




				@cached_property
				def 							snake_case   (							self		):



									"""simple docstring"""





									return "facebook/mask2former-swin-small-coco-instance"




				@cached_property
				def 							snake_case   (							self		):



									"""simple docstring"""





									return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints		) if is_vision_available() else None




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    MaskaFormerModel.from_pretrained(self.model_checkpoints		).to(_snake_case		)
									_lowerCAmelCase						=    self.default_image_processor
									_lowerCAmelCase						=    prepare_img()
									_lowerCAmelCase						=    image_processor(_snake_case						,				return_tensors="""pt"""		).to(_snake_case		)
									_lowerCAmelCase						=    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():
														_lowerCAmelCase						=    model(**_snake_case		)

									_lowerCAmelCase						=    torch.tensor(
									    [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]		).to(_snake_case		)
									self.assertTrue(
									    torch.allclose(
									        outputs.encoder_last_hidden_state[0, 0, :3, :3]						,				_snake_case						,				atol=_snake_case		)		)

									_lowerCAmelCase						=    torch.tensor(
									    [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]		).to(_snake_case		)
									self.assertTrue(
									    torch.allclose(
									        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]						,				_snake_case						,				atol=_snake_case		)		)

									_lowerCAmelCase						=    torch.tensor(
									    [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]		).to(_snake_case		)
									self.assertTrue(
									    torch.allclose(
									        outputs.transformer_decoder_last_hidden_state[0, :3, :3]						,				_snake_case						,				atol=_snake_case		)		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints		).to(_snake_case		).eval()
									_lowerCAmelCase						=    self.default_image_processor
									_lowerCAmelCase						=    prepare_img()
									_lowerCAmelCase						=    image_processor(_snake_case						,				return_tensors="""pt"""		).to(_snake_case		)
									_lowerCAmelCase						=    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():
														_lowerCAmelCase						=    model(**_snake_case		)
									# masks_queries_logits
									_lowerCAmelCase						=    outputs.masks_queries_logits
									self.assertEqual(
									    masks_queries_logits.shape						,				(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)		)
									_lowerCAmelCase						=    [
									    [-8.7839, -9.0056, -8.8121],
									    [-7.4104, -7.0313, -6.5401],
									    [-6.6105, -6.3427, -6.4675],
									]
									_lowerCAmelCase						=    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
									_lowerCAmelCase						=    outputs.class_queries_logits
									self.assertEqual(class_queries_logits.shape						,				(1, model.config.num_queries, model.config.num_labels + 1)		)
									_lowerCAmelCase						=    torch.tensor(
									    [
									        [1.8324, -8.0835, -4.1922],
									        [0.8450, -9.0050, -3.6053],
									        [0.3045, -7.7293, -3.0275],
									    ]		).to(_snake_case		)
									self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]						,				_snake_case						,				atol=_snake_case		)		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints		).to(_snake_case		).eval()
									_lowerCAmelCase						=    self.default_image_processor

									_lowerCAmelCase						=    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"""						,				)

									_lowerCAmelCase						=    inputs["""pixel_values"""].to(_snake_case		)
									_lowerCAmelCase						=    [el.to(_snake_case		) for el in inputs["""mask_labels"""]]
									_lowerCAmelCase						=    [el.to(_snake_case		) for el in inputs["""class_labels"""]]

									with torch.no_grad():
														_lowerCAmelCase						=    model(**_snake_case		)

									self.assertTrue(outputs.loss is not None		)





from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


__snake_case								=  logging.get_logger(__name__)


__snake_case								=  TypeVar("""DatasetType""", Dataset, IterableDataset)






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_ = None	,	UpperCamelCase_ = None	,	UpperCamelCase_ = None	,	UpperCamelCase_ = None	,	UpperCamelCase_ = "first_exhausted"	,	)		->		DatasetType:
					'''simple docstring'''


					from .arrow_dataset import Dataset
					from .iterable_dataset import IterableDataset

					if not datasets:
										raise ValueError('Unable to interleave an empty list of datasets.'		)
					for i, dataset in enumerate(UpperCamelCase_		):
										if not isinstance(UpperCamelCase_	,	(Dataset, IterableDataset)		):
															if isinstance(UpperCamelCase_	,	(DatasetDict, IterableDatasetDict)		):
																				if not dataset:
																									raise ValueError(
																									    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
																									    'is an empty dataset dictionary.'		)
																				raise ValueError(
																				    F'Dataset at position {i} has at least one split: {list(UpperCamelCase_		)}\n'
																				    F'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(UpperCamelCase_		)		)}\']'		)
															raise ValueError(
															    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCamelCase_		).__name__}.'		)
										if i == 0:
															SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= (
															    (Dataset, IterableDataset) if isinstance(UpperCamelCase_	,	UpperCamelCase_		) else (IterableDataset, Dataset)
															)
										elif not isinstance(UpperCamelCase_	,	UpperCamelCase_		):
															raise ValueError(
															    F'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'		)
					if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
										raise ValueError(F'{stopping_strategy} is not supported. Please enter a valid stopping_strategy.'		)
					if dataset_type is Dataset:
										return _interleave_map_style_datasets(
										    UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	info=UpperCamelCase_	,	split=UpperCamelCase_	,	stopping_strategy=UpperCamelCase_		)
					else:
										return _interleave_iterable_datasets(
										    UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	info=UpperCamelCase_	,	split=UpperCamelCase_	,	stopping_strategy=UpperCamelCase_		)






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_ = None	,	UpperCamelCase_ = None	,	UpperCamelCase_ = 0	,	)		->		DatasetType:
					'''simple docstring'''


					if not dsets:
										raise ValueError('Unable to concatenate an empty list of datasets.'		)
					for i, dataset in enumerate(UpperCamelCase_		):
										if not isinstance(UpperCamelCase_	,	(Dataset, IterableDataset)		):
															if isinstance(UpperCamelCase_	,	(DatasetDict, IterableDatasetDict)		):
																				if not dataset:
																									raise ValueError(
																									    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
																									    'is an empty dataset dictionary.'		)
																				raise ValueError(
																				    F'Dataset at position {i} has at least one split: {list(UpperCamelCase_		)}\n'
																				    F'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(UpperCamelCase_		)		)}\']'		)
															raise ValueError(
															    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCamelCase_		).__name__}.'		)
										if i == 0:
															SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= (
															    (Dataset, IterableDataset) if isinstance(UpperCamelCase_	,	UpperCamelCase_		) else (IterableDataset, Dataset)
															)
										elif not isinstance(UpperCamelCase_	,	UpperCamelCase_		):
															raise ValueError(
															    F'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'		)
					if dataset_type is Dataset:
										return _concatenate_map_style_datasets(UpperCamelCase_	,	info=UpperCamelCase_	,	split=UpperCamelCase_	,	axis=UpperCamelCase_		)
					else:
										return _concatenate_iterable_datasets(UpperCamelCase_	,	info=UpperCamelCase_	,	split=UpperCamelCase_	,	axis=UpperCamelCase_		)





