



'''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 	lowerCAmelCase_:
		'''simple docstring'''




		def __init__(   self  ,__UpperCAmelCase  ,__UpperCAmelCase=2  ,__UpperCAmelCase=True  ,__UpperCAmelCase=False  ,__UpperCAmelCase=10  ,__UpperCAmelCase=3  ,__UpperCAmelCase=32 * 8  ,__UpperCAmelCase=32 * 8  ,__UpperCAmelCase=4  ,__UpperCAmelCase=64  ,)   ->		Optional[Any]:
						lowerCAmelCase__			:  str											= parent
						lowerCAmelCase__			:  List[Any]											= batch_size
						lowerCAmelCase__			:  Dict											= is_training
						lowerCAmelCase__			:  int											= use_auxiliary_loss
						lowerCAmelCase__			:  int											= num_queries
						lowerCAmelCase__			:  Tuple											= num_channels
						lowerCAmelCase__			:  List[str]											= min_size
						lowerCAmelCase__			:  List[Any]											= max_size
						lowerCAmelCase__			:  Optional[int]											= num_labels
						lowerCAmelCase__			:  Dict											= hidden_dim
						lowerCAmelCase__			:  Optional[Any]											= hidden_dim




		def 							UpperCAmelCase_     (   self				)   ->		Any:
						lowerCAmelCase__			:  Dict											= floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]				).to(
						    lowerCAmelCase__				)

						lowerCAmelCase__			:  int											= torch.ones([self.batch_size, self.min_size, self.max_size]  ,device=lowerCAmelCase__				)

						lowerCAmelCase__			:  Dict											= (
						    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]  ,device=lowerCAmelCase__				) > 0.5
						).float()
						lowerCAmelCase__			:  Tuple											= (torch.rand((self.batch_size, self.num_labels)  ,device=lowerCAmelCase__				) > 0.5).long()

						lowerCAmelCase__			:  Tuple											= self.get_config()
						return config, pixel_values, pixel_mask, mask_labels, class_labels




		def 							UpperCAmelCase_     (   self				)   ->		Tuple:
						lowerCAmelCase__			:  List[Any]											= MaskaFormerConfig(
						    hidden_size=self.hidden_dim  ,)
						lowerCAmelCase__			:  Dict											= self.num_queries
						lowerCAmelCase__			:  List[str]											= self.num_labels

						lowerCAmelCase__			:  Any											= [1, 1, 1, 1]
						lowerCAmelCase__			:  int											= self.num_channels

						lowerCAmelCase__			:  Optional[Any]											= 64
						lowerCAmelCase__			:  Tuple											= 128
						lowerCAmelCase__			:  Tuple											= self.hidden_dim
						lowerCAmelCase__			:  str											= self.hidden_dim
						lowerCAmelCase__			:  Dict											= self.hidden_dim
						return config




		def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
						lowerCAmelCase__			:  str											= self.prepare_config_and_inputs()
						lowerCAmelCase__			:  List[str]											= {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
						return config, inputs_dict




		def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		List[Any]:
						lowerCAmelCase__			:  List[Any]											= output.encoder_hidden_states
						lowerCAmelCase__			:  Dict											= output.pixel_decoder_hidden_states
						lowerCAmelCase__			:  Optional[int]											= output.transformer_decoder_hidden_states

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




		def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=False				)   ->		Optional[int]:
						with torch.no_grad():
										lowerCAmelCase__			:  Any											= MaskaFormerModel(config=lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()

										lowerCAmelCase__			:  Dict											= model(pixel_values=lowerCAmelCase__  ,pixel_mask=lowerCAmelCase__				)
										lowerCAmelCase__			:  Any											= model(lowerCAmelCase__  ,output_hidden_states=lowerCAmelCase__				)

						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(lowerCAmelCase__  ,lowerCAmelCase__				)






		def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		Tuple:
						lowerCAmelCase__			:  int											= MaskaFormerForUniversalSegmentation(config=lowerCAmelCase__				)
						model.to(lowerCAmelCase__				)
						model.eval()

						def comm_check_on_output(__UpperCAmelCase				):
										# 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__			:  Optional[Any]											= model(pixel_values=lowerCAmelCase__  ,pixel_mask=lowerCAmelCase__				)
										lowerCAmelCase__			:  List[str]											= model(lowerCAmelCase__				)

										comm_check_on_output(lowerCAmelCase__				)

										lowerCAmelCase__			:  Union[str, Any]											= model(
										    pixel_values=lowerCAmelCase__  ,pixel_mask=lowerCAmelCase__  ,mask_labels=lowerCAmelCase__  ,class_labels=lowerCAmelCase__				)

						comm_check_on_output(lowerCAmelCase__				)

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







@require_torch
class 	lowerCAmelCase_(    a__						,			a__						,			unittest.TestCase					):
		'''simple docstring'''




		__lowercase							:				int           =				(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
		__lowercase							:				int           =				{"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}

		__lowercase							:				List[str]           =				False
		__lowercase							:				str           =				False
		__lowercase							:				Tuple           =				False
		__lowercase							:				List[str]           =				False




		def 							UpperCAmelCase_     (   self				)   ->		Tuple:
						lowerCAmelCase__			:  Union[str, Any]											= MaskaFormerModelTester(self				)
						lowerCAmelCase__			:  Dict											= ConfigTester(self  ,config_class=lowerCAmelCase__  ,has_text_modality=lowerCAmelCase__				)




		def 							UpperCAmelCase_     (   self				)   ->		List[str]:
						self.config_tester.run_common_tests()




		def 							UpperCAmelCase_     (   self				)   ->		Dict:
						lowerCAmelCase__			:  List[Any]											= self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase__  ,**lowerCAmelCase__  ,output_hidden_states=lowerCAmelCase__				)




		def 							UpperCAmelCase_     (   self				)   ->		int:
						lowerCAmelCase__			:  str											= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCAmelCase__				)




		@unittest.skip(reason="""Mask2Former does not use inputs_embeds"""				)
		def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
						pass




		@unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method"""				)
		def 							UpperCAmelCase_     (   self				)   ->		List[str]:
						pass




		@unittest.skip(reason="""Mask2Former is not a generative model"""				)
		def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
						pass




		@unittest.skip(reason="""Mask2Former does not use token embeddings"""				)
		def 							UpperCAmelCase_     (   self				)   ->		Tuple:
						pass




		@require_torch_multi_gpu
		@unittest.skip(
		    reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""				)
		def 							UpperCAmelCase_     (   self				)   ->		List[str]:
						pass




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




		def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
						lowerCAmelCase__			:  Union[str, Any]											= self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										lowerCAmelCase__			:  Any											= model_class(lowerCAmelCase__				)
										lowerCAmelCase__			:  Union[str, Any]											= inspect.signature(model.forward				)
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										lowerCAmelCase__			:  int											= [*signature.parameters.keys()]

										lowerCAmelCase__			:  List[str]											= ["pixel_values"]
										self.assertListEqual(arg_names[:1]  ,lowerCAmelCase__				)




		@slow
		def 							UpperCAmelCase_     (   self				)   ->		str:
						for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
										lowerCAmelCase__			:  Optional[Any]											= MaskaFormerModel.from_pretrained(lowerCAmelCase__				)
										self.assertIsNotNone(lowerCAmelCase__				)




		def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
						lowerCAmelCase__			:  Dict											= (self.model_tester.min_size,) * 2
						lowerCAmelCase__			:  List[str]											= {
						    "pixel_values": torch.randn((2, 3, *size)  ,device=lowerCAmelCase__				),
						    "mask_labels": torch.randn((2, 10, *size)  ,device=lowerCAmelCase__				),
						    "class_labels": torch.zeros(2  ,10  ,device=lowerCAmelCase__				).long(),
						}
						lowerCAmelCase__			:  Dict											= self.model_tester.get_config()

						lowerCAmelCase__			:  List[Any]											= MaskaFormerForUniversalSegmentation(lowerCAmelCase__				).to(lowerCAmelCase__				)
						lowerCAmelCase__			:  Union[str, Any]											= model(**lowerCAmelCase__				)
						self.assertTrue(outputs.loss is not None				)




		def 							UpperCAmelCase_     (   self				)   ->		Dict:
						lowerCAmelCase__			:  int											= self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase__  ,**lowerCAmelCase__  ,output_hidden_states=lowerCAmelCase__				)




		def 							UpperCAmelCase_     (   self				)   ->		List[str]:
						lowerCAmelCase__			:  Union[str, Any]											= self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										lowerCAmelCase__			:  Optional[int]											= model_class(lowerCAmelCase__				).to(lowerCAmelCase__				)
										lowerCAmelCase__			:  Tuple											= model(**lowerCAmelCase__  ,output_attentions=lowerCAmelCase__				)
										self.assertTrue(outputs.attentions is not None				)




		def 							UpperCAmelCase_     (   self				)   ->		Tuple:
						if not self.model_tester.is_training:
										return

						lowerCAmelCase__			:  List[Any]											= self.all_model_classes[1]
						lowerCAmelCase__			:  Tuple											= self.model_tester.prepare_config_and_inputs()

						lowerCAmelCase__			:  Tuple											= model_class(lowerCAmelCase__				)
						model.to(lowerCAmelCase__				)
						model.train()

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






		def 							UpperCAmelCase_     (   self				)   ->		int:
						lowerCAmelCase__			:  str											= self.all_model_classes[1]
						lowerCAmelCase__			:  List[Any]											= self.model_tester.prepare_config_and_inputs()
						lowerCAmelCase__			:  List[Any]											= True
						lowerCAmelCase__			:  Tuple											= True

						lowerCAmelCase__			:  str											= model_class(lowerCAmelCase__				).to(lowerCAmelCase__				)
						model.train()

						lowerCAmelCase__			:  Optional[Any]											= model(lowerCAmelCase__  ,mask_labels=lowerCAmelCase__  ,class_labels=lowerCAmelCase__				)

						lowerCAmelCase__			:  int											= outputs.encoder_hidden_states[0]
						encoder_hidden_states.retain_grad()

						lowerCAmelCase__			:  Union[str, Any]											= outputs.pixel_decoder_hidden_states[0]
						pixel_decoder_hidden_states.retain_grad()

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

						lowerCAmelCase__			:  Dict											= outputs.attentions[0]
						attentions.retain_grad()

						outputs.loss.backward(retain_graph=lowerCAmelCase__				)

						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										=     1e-4
def        _SCREAMING_SNAKE_CASE  (	):



				"""simple docstring"""





				lowerCAmelCase__			:  Optional[Any]											= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
				return image






@require_vision
@slow
class 	lowerCAmelCase_(    unittest.TestCase					):
		'''simple docstring'''




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




		@cached_property
		def 							UpperCAmelCase_     (   self				)   ->		Dict:
						return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints				) if is_vision_available() else None




		def 							UpperCAmelCase_     (   self				)   ->		Tuple:
						lowerCAmelCase__			:  int											= MaskaFormerModel.from_pretrained(self.model_checkpoints				).to(lowerCAmelCase__				)
						lowerCAmelCase__			:  Dict											= self.default_image_processor
						lowerCAmelCase__			:  List[Any]											= prepare_img()
						lowerCAmelCase__			:  List[str]											= image_processor(lowerCAmelCase__  ,return_tensors="""pt"""				).to(lowerCAmelCase__				)
						lowerCAmelCase__			:  Dict											= 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(lowerCAmelCase__  ,(1, 3, 384, 384)				)

						with torch.no_grad():
										lowerCAmelCase__			:  Tuple											= model(**lowerCAmelCase__				)

						lowerCAmelCase__			:  List[str]											= 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(lowerCAmelCase__				)
						self.assertTrue(
						    torch.allclose(
						        outputs.encoder_last_hidden_state[0, 0, :3, :3]  ,lowerCAmelCase__  ,atol=lowerCAmelCase__				)				)

						lowerCAmelCase__			:  int											= 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(lowerCAmelCase__				)
						self.assertTrue(
						    torch.allclose(
						        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]  ,lowerCAmelCase__  ,atol=lowerCAmelCase__				)				)

						lowerCAmelCase__			:  List[Any]											= 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(lowerCAmelCase__				)
						self.assertTrue(
						    torch.allclose(
						        outputs.transformer_decoder_last_hidden_state[0, :3, :3]  ,lowerCAmelCase__  ,atol=lowerCAmelCase__				)				)




		def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
						lowerCAmelCase__			:  Any											= MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints				).to(lowerCAmelCase__				).eval()
						lowerCAmelCase__			:  Tuple											= self.default_image_processor
						lowerCAmelCase__			:  Optional[Any]											= prepare_img()
						lowerCAmelCase__			:  Optional[Any]											= image_processor(lowerCAmelCase__  ,return_tensors="""pt"""				).to(lowerCAmelCase__				)
						lowerCAmelCase__			:  Tuple											= 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(lowerCAmelCase__  ,(1, 3, 384, 384)				)

						with torch.no_grad():
										lowerCAmelCase__			:  Tuple											= model(**lowerCAmelCase__				)
						# masks_queries_logits
						lowerCAmelCase__			:  Tuple											= outputs.masks_queries_logits
						self.assertEqual(
						    masks_queries_logits.shape  ,(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)				)
						lowerCAmelCase__			:  str											= [
						    [-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],
						]
						lowerCAmelCase__			:  List[str]											= torch.tensor(lowerCAmelCase__				).to(lowerCAmelCase__				)
						self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]  ,lowerCAmelCase__  ,atol=lowerCAmelCase__				)				)
						# class_queries_logits
						lowerCAmelCase__			:  Optional[int]											= outputs.class_queries_logits
						self.assertEqual(class_queries_logits.shape  ,(1, model.config.num_queries, model.config.num_labels + 1)				)
						lowerCAmelCase__			:  Any											= 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(lowerCAmelCase__				)
						self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]  ,lowerCAmelCase__  ,atol=lowerCAmelCase__				)				)






		def 							UpperCAmelCase_     (   self				)   ->		str:
						lowerCAmelCase__			:  Optional[int]											= MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints				).to(lowerCAmelCase__				).eval()
						lowerCAmelCase__			:  Optional[Any]											= self.default_image_processor

						lowerCAmelCase__			:  Dict											= 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__			:  Union[str, Any]											= inputs["pixel_values"].to(lowerCAmelCase__				)
						lowerCAmelCase__			:  str											= [el.to(lowerCAmelCase__				) for el in inputs["mask_labels"]]
						lowerCAmelCase__			:  List[str]											= [el.to(lowerCAmelCase__				) for el in inputs["class_labels"]]

						with torch.no_grad():
										lowerCAmelCase__			:  Optional[int]											= model(**lowerCAmelCase__				)

						self.assertTrue(outputs.loss is not None				)







'''simple docstring'''





_lowerCAmelCase										=     '''Input must be a string of 8 numbers plus letter'''
_lowerCAmelCase										=     '''TRWAGMYFPDXBNJZSQVHLCKE'''
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    if not isinstance(UpperCamelCase							,   UpperCamelCase      ):
        lowerCAmelCase__			:  List[Any]											= f"""Expected string as input, found {type(UpperCamelCase      ).__name__}"""
        raise TypeError(UpperCamelCase      )

    lowerCAmelCase__			:  Union[str, Any]											= spanish_id.replace("""-"""							,   """"""      ).upper()
    if len(UpperCamelCase      ) != 9:
        raise ValueError(UpperCamelCase      )

    try:
        lowerCAmelCase__			:  Optional[int]											= int(spanish_id_clean[0:8]      )
        lowerCAmelCase__			:  int											= spanish_id_clean[8]
    except ValueError as ex:
        raise ValueError(UpperCamelCase      ) from ex

    if letter.isdigit():
        raise ValueError(UpperCamelCase      )

    return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
 import doctest

 doctest.testmod()


