'''simple docstring'''


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 						A   :





							def __init__(      self				:							Dict     ,		__a				:							Optional[int]     ,		__a				:							List[Any]=2     ,		__a				:							Any=True     ,		__a				:							str=False     ,		__a				:							Tuple=1_0     ,		__a				:							str=3     ,		__a				:							Optional[int]=3_2 * 8     ,		__a				:							List[str]=3_2 * 8     ,		__a				:							Union[str, Any]=4     ,		__a				:							Dict=6_4     ,		)							->	Optional[int]:
								__UpperCAmelCase							=			parent
								__UpperCAmelCase							=			batch_size
								__UpperCAmelCase							=			is_training
								__UpperCAmelCase							=			use_auxiliary_loss
								__UpperCAmelCase							=			num_queries
								__UpperCAmelCase							=			num_channels
								__UpperCAmelCase							=			min_size
								__UpperCAmelCase							=			max_size
								__UpperCAmelCase							=			num_labels
								__UpperCAmelCase							=			hidden_dim
								__UpperCAmelCase							=			hidden_dim





							def       snake_case__						(      self				:							Optional[Any]	)							->	List[Any]:
								__UpperCAmelCase							=			floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]	).to(
								    __a	)

								__UpperCAmelCase							=			torch.ones([self.batch_size, self.min_size, self.max_size]     ,		device=__a	)

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

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





							def       snake_case__						(      self				:							List[str]	)							->	Optional[Any]:
								__UpperCAmelCase							=			MaskaFormerConfig(
								    hidden_size=self.hidden_dim     ,		)
								__UpperCAmelCase							=			self.num_queries
								__UpperCAmelCase							=			self.num_labels

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

								__UpperCAmelCase							=			6_4
								__UpperCAmelCase							=			1_2_8
								__UpperCAmelCase							=			self.hidden_dim
								__UpperCAmelCase							=			self.hidden_dim
								__UpperCAmelCase							=			self.hidden_dim
								return config





							def       snake_case__						(      self				:							List[Any]	)							->	List[str]:
								__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase							=			self.prepare_config_and_inputs()
								__UpperCAmelCase							=			{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
								return config, inputs_dict





							def       snake_case__						(      self				:							Tuple     ,		__a				:							Optional[int]     ,		__a				:							Dict	)							->	Union[str, Any]:
								__UpperCAmelCase							=			output.encoder_hidden_states
								__UpperCAmelCase							=			output.pixel_decoder_hidden_states
								__UpperCAmelCase							=			output.transformer_decoder_hidden_states

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





							def       snake_case__						(      self				:							Tuple     ,		__a				:							List[Any]     ,		__a				:							Optional[Any]     ,		__a				:							Dict     ,		__a				:							Dict=False	)							->	str:
								with torch.no_grad():
									__UpperCAmelCase							=			MaskaFormerModel(config=__a	)
									model.to(__a	)
									model.eval()

									__UpperCAmelCase							=			model(pixel_values=__a     ,		pixel_mask=__a	)
									__UpperCAmelCase							=			model(__a     ,		output_hidden_states=__a	)

								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(__a     ,		__a	)






							def       snake_case__						(      self				:							Optional[int]     ,		__a				:							str     ,		__a				:							List[Any]     ,		__a				:							Dict     ,		__a				:							Any     ,		__a				:							Optional[int]	)							->	List[Any]:
								__UpperCAmelCase							=			MaskaFormerForUniversalSegmentation(config=__a	)
								model.to(__a	)
								model.eval()

								def comm_check_on_output(__a				:							List[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():
									__UpperCAmelCase							=			model(pixel_values=__a     ,		pixel_mask=__a	)
									__UpperCAmelCase							=			model(__a	)

									comm_check_on_output(__a	)

									__UpperCAmelCase							=			model(
									    pixel_values=__a     ,		pixel_mask=__a     ,		mask_labels=__a     ,		class_labels=__a	)

								comm_check_on_output(__a	)

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






@require_torch
class 						A   (   UpperCAmelCase       ,	UpperCAmelCase       ,	unittest.TestCase   ):
							a_        =      (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
							a_        =      {'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

							a_        =      False
							a_        =      False
							a_        =      False
							a_        =      False





							def       snake_case__						(      self				:							str	)							->	List[str]:
								__UpperCAmelCase							=			MaskaFormerModelTester(self	)
								__UpperCAmelCase							=			ConfigTester(self     ,		config_class=__a     ,		has_text_modality=__a	)





							def       snake_case__						(      self				:							Tuple	)							->	Optional[Any]:
								self.config_tester.run_common_tests()





							def       snake_case__						(      self				:							Optional[int]	)							->	Tuple:
								__UpperCAmelCase				,			__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.create_and_check_maskaformer_model(__a     ,		**__a     ,		output_hidden_states=__a	)





							def       snake_case__						(      self				:							Optional[Any]	)							->	Any:
								__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__a	)





							@unittest.skip(reason='''Mask2Former does not use inputs_embeds'''	)
							def       snake_case__						(      self				:							List[Any]	)							->	Dict:
								pass





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





							@unittest.skip(reason='''Mask2Former is not a generative model'''	)
							def       snake_case__						(      self				:							Optional[int]	)							->	Tuple:
								pass





							@unittest.skip(reason='''Mask2Former does not use token embeddings'''	)
							def       snake_case__						(      self				:							List[str]	)							->	Any:
								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				:							List[str]	)							->	Tuple:
								pass





							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''	)
							def       snake_case__						(      self				:							Optional[int]	)							->	Any:
								pass





							def       snake_case__						(      self				:							Dict	)							->	int:
								__UpperCAmelCase				,			__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs_for_common()

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

									__UpperCAmelCase							=			['''pixel_values''']
									self.assertListEqual(arg_names[:1]     ,		__a	)





							@slow
							def       snake_case__						(      self				:							Tuple	)							->	Any:
								for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
									__UpperCAmelCase							=			MaskaFormerModel.from_pretrained(__a	)
									self.assertIsNotNone(__a	)





							def       snake_case__						(      self				:							Union[str, Any]	)							->	int:
								__UpperCAmelCase							=			(self.model_tester.min_size,) * 2
								__UpperCAmelCase							=			{
								    '''pixel_values''': torch.randn((2, 3, *size)     ,		device=__a	),
								    '''mask_labels''': torch.randn((2, 1_0, *size)     ,		device=__a	),
								    '''class_labels''': torch.zeros(2     ,		1_0     ,		device=__a	).long(),
								}
								__UpperCAmelCase							=			self.model_tester.get_config()

								__UpperCAmelCase							=			MaskaFormerForUniversalSegmentation(__a	).to(__a	)
								__UpperCAmelCase							=			model(**__a	)
								self.assertTrue(outputs.loss is not None	)





							def       snake_case__						(      self				:							Optional[int]	)							->	int:
								__UpperCAmelCase				,			__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.create_and_check_maskaformer_model(__a     ,		**__a     ,		output_hidden_states=__a	)





							def       snake_case__						(      self				:							Optional[int]	)							->	Tuple:
								__UpperCAmelCase				,			__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs_for_common()

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





							def       snake_case__						(      self				:							List[Any]	)							->	Union[str, Any]:
								if not self.model_tester.is_training:
									return

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

								__UpperCAmelCase							=			model_class(__a	)
								model.to(__a	)
								model.train()

								__UpperCAmelCase							=			model(__a     ,		mask_labels=__a     ,		class_labels=__a	).loss
								loss.backward()






							def       snake_case__						(      self				:							str	)							->	Dict:
								__UpperCAmelCase							=			self.all_model_classes[1]
								__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
								__UpperCAmelCase							=			True
								__UpperCAmelCase							=			True

								__UpperCAmelCase							=			model_class(__a	).to(__a	)
								model.train()

								__UpperCAmelCase							=			model(__a     ,		mask_labels=__a     ,		class_labels=__a	)

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

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

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

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

								outputs.loss.backward(retain_graph=__a	)

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

__lowerCAmelCase	:       Dict						       =	1e-4





def       lowerCAmelCase					(		):


	"""simple docstring"""
	__UpperCAmelCase							=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
	return image






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





							@cached_property
							def       snake_case__						(      self				:							str	)							->	Optional[int]:
								return "facebook/mask2former-swin-small-coco-instance"





							@cached_property
							def       snake_case__						(      self				:							Optional[int]	)							->	Optional[Any]:
								return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints	) if is_vision_available() else None





							def       snake_case__						(      self				:							Any	)							->	Optional[Any]:
								__UpperCAmelCase							=			MaskaFormerModel.from_pretrained(self.model_checkpoints	).to(__a	)
								__UpperCAmelCase							=			self.default_image_processor
								__UpperCAmelCase							=			prepare_img()
								__UpperCAmelCase							=			image_processor(__a     ,		return_tensors='''pt'''	).to(__a	)
								__UpperCAmelCase							=			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(__a     ,		(1, 3, 3_8_4, 3_8_4)	)

								with torch.no_grad():
									__UpperCAmelCase							=			model(**__a	)

								__UpperCAmelCase							=			torch.tensor(
								    [[-0.2_7_9_0, -1.0_7_1_7, -1.1_6_6_8], [-0.5_1_2_8, -0.3_1_2_8, -0.4_9_8_7], [-0.5_8_3_2, 0.1_9_7_1, -0.0_1_9_7]]	).to(__a	)
								self.assertTrue(
								    torch.allclose(
								        outputs.encoder_last_hidden_state[0, 0, :3, :3]     ,		__a     ,		atol=__a	)	)

								__UpperCAmelCase							=			torch.tensor(
								    [[0.8_9_7_3, 1.1_8_4_7, 1.1_7_7_6], [1.1_9_3_4, 1.5_0_4_0, 1.5_1_2_8], [1.1_1_5_3, 1.4_4_8_6, 1.4_9_5_1]]	).to(__a	)
								self.assertTrue(
								    torch.allclose(
								        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]     ,		__a     ,		atol=__a	)	)

								__UpperCAmelCase							=			torch.tensor(
								    [[2.1_1_5_2, 1.7_0_0_0, -0.8_6_0_3], [1.5_8_0_8, 1.8_0_0_4, -0.9_3_5_3], [1.6_0_4_3, 1.7_4_9_5, -0.5_9_9_9]]	).to(__a	)
								self.assertTrue(
								    torch.allclose(
								        outputs.transformer_decoder_last_hidden_state[0, :3, :3]     ,		__a     ,		atol=__a	)	)





							def       snake_case__						(      self				:							List[str]	)							->	Any:
								__UpperCAmelCase							=			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints	).to(__a	).eval()
								__UpperCAmelCase							=			self.default_image_processor
								__UpperCAmelCase							=			prepare_img()
								__UpperCAmelCase							=			image_processor(__a     ,		return_tensors='''pt'''	).to(__a	)
								__UpperCAmelCase							=			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(__a     ,		(1, 3, 3_8_4, 3_8_4)	)

								with torch.no_grad():
									__UpperCAmelCase							=			model(**__a	)
								# masks_queries_logits
								__UpperCAmelCase							=			outputs.masks_queries_logits
								self.assertEqual(
								    masks_queries_logits.shape     ,		(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)	)
								__UpperCAmelCase							=			[
								    [-8.7_8_3_9, -9.0_0_5_6, -8.8_1_2_1],
								    [-7.4_1_0_4, -7.0_3_1_3, -6.5_4_0_1],
								    [-6.6_1_0_5, -6.3_4_2_7, -6.4_6_7_5],
								]
								__UpperCAmelCase							=			torch.tensor(__a	).to(__a	)
								self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]     ,		__a     ,		atol=__a	)	)
								# class_queries_logits
								__UpperCAmelCase							=			outputs.class_queries_logits
								self.assertEqual(class_queries_logits.shape     ,		(1, model.config.num_queries, model.config.num_labels + 1)	)
								__UpperCAmelCase							=			torch.tensor(
								    [
								        [1.8_3_2_4, -8.0_8_3_5, -4.1_9_2_2],
								        [0.8_4_5_0, -9.0_0_5_0, -3.6_0_5_3],
								        [0.3_0_4_5, -7.7_2_9_3, -3.0_2_7_5],
								    ]	).to(__a	)
								self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]     ,		__a     ,		atol=__a	)	)






							def       snake_case__						(      self				:							Optional[int]	)							->	str:
								__UpperCAmelCase							=			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints	).to(__a	).eval()
								__UpperCAmelCase							=			self.default_image_processor

								__UpperCAmelCase							=			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'''     ,		)

								__UpperCAmelCase							=			inputs['''pixel_values'''].to(__a	)
								__UpperCAmelCase							=			[el.to(__a	) for el in inputs['''mask_labels''']]
								__UpperCAmelCase							=			[el.to(__a	) for el in inputs['''class_labels''']]

								with torch.no_grad():
									__UpperCAmelCase							=			model(**__a	)

								self.assertTrue(outputs.loss is not None	)

'''simple docstring'''


from pathlib import Path

import fire





def       lowerCAmelCase					(		UpperCamelCase__				:   str    ,					UpperCamelCase__				:   str    ,					UpperCamelCase__				:   int       ):


	"""simple docstring"""
	__UpperCAmelCase							=			Path(UpperCamelCase__       )
	__UpperCAmelCase							=			Path(UpperCamelCase__       )
	dest_dir.mkdir(exist_ok=UpperCamelCase__       )
	for path in src_dir.iterdir():
		__UpperCAmelCase							=			[x.rstrip() for x in list(path.open().readlines()       )][:n]
		__UpperCAmelCase							=			dest_dir.joinpath(path.name       )
		print(UpperCamelCase__       )
		dest_path.open('''w'''       ).write('''\n'''.join(UpperCamelCase__       )       )


if __name__ == "__main__":
					fire.Fire(minify)
