
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  :  List[str]      ,				snake_case__  :  List[str]      ,				snake_case__  :  Dict=2      ,				snake_case__  :  str=True      ,				snake_case__  :  Optional[int]=False      ,				snake_case__  :  Optional[int]=10      ,				snake_case__  :  Optional[int]=3      ,				snake_case__  :  Optional[int]=32 * 8      ,				snake_case__  :  Optional[Any]=32 * 8      ,				snake_case__  :  str=4      ,				snake_case__  :  Dict=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       UpperCAmelCase__					(						self  :  int ):
								"""simple docstring"""







								__lowerCAmelCase         =							floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
								    lowercase__ )

								__lowerCAmelCase         =							torch.ones([self.batch_size, self.min_size, self.max_size]      ,				device=lowercase__ )

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

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







							def       UpperCAmelCase__					(						self  :  str ):
								"""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       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  List[Any]      ,				snake_case__  :  Tuple ):
								"""simple docstring"""







								__lowerCAmelCase         =							output.encoder_hidden_states
								__lowerCAmelCase         =							output.pixel_decoder_hidden_states
								__lowerCAmelCase         =							output.transformer_decoder_hidden_states

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







							def       UpperCAmelCase__					(						self  :  Optional[Any]      ,				snake_case__  :  Any      ,				snake_case__  :  Union[str, Any]      ,				snake_case__  :  Dict      ,				snake_case__  :  Dict=False ):
								"""simple docstring"""







								with torch.no_grad():
									__lowerCAmelCase         =							MaskaFormerModel(config=lowercase__ )
									model.to(lowercase__ )
									model.eval()

									__lowerCAmelCase         =							model(pixel_values=lowercase__      ,				pixel_mask=lowercase__ )
									__lowerCAmelCase         =							model(lowercase__      ,				output_hidden_states=lowercase__ )

								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(lowercase__      ,				lowercase__ )







							def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  str      ,				snake_case__  :  Any      ,				snake_case__  :  Optional[Any]      ,				snake_case__  :  List[str]      ,				snake_case__  :  str ):
								"""simple docstring"""







								__lowerCAmelCase         =							MaskaFormerForUniversalSegmentation(config=lowercase__ )
								model.to(lowercase__ )
								model.eval()

								def comm_check_on_output(snake_case__  :  Tuple ):
									# 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=lowercase__      ,				pixel_mask=lowercase__ )
									__lowerCAmelCase         =							model(lowercase__ )

									comm_check_on_output(lowercase__ )

									__lowerCAmelCase         =							model(
									    pixel_values=lowercase__      ,				pixel_mask=lowercase__      ,				mask_labels=lowercase__      ,				class_labels=lowercase__ )

								comm_check_on_output(lowercase__ )

								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					):
							lowercase_		:						Any   							=  (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
							lowercase_		:						List[str]   							=  {'feature-extraction': MaskaFormerModel} if is_torch_available() else {}

							lowercase_		:						Tuple   							=  False
							lowercase_		:						Dict   							=  False
							lowercase_		:						Any   							=  False
							lowercase_		:						List[str]   							=  False







							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								__lowerCAmelCase         =							MaskaFormerModelTester(self )
								__lowerCAmelCase         =							ConfigTester(self      ,				config_class=lowercase__      ,				has_text_modality=lowercase__ )







							def       UpperCAmelCase__					(						self  :  int ):
								"""simple docstring"""







								self.config_tester.run_common_tests()







							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.create_and_check_maskaformer_model(lowercase__      ,				**lowercase__      ,				output_hidden_states=lowercase__ )







							def       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowercase__ )







							@unittest.skip(reason="Mask2Former does not use inputs_embeds" )
							def       UpperCAmelCase__					(						self  :  Tuple ):
								"""simple docstring"""







								pass







							@unittest.skip(reason="Mask2Former does not have a get_input_embeddings method" )
							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







								pass







							@unittest.skip(reason="Mask2Former is not a generative model" )
							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								pass







							@unittest.skip(reason="Mask2Former does not use token embeddings" )
							def       UpperCAmelCase__					(						self  :  Tuple ):
								"""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       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								pass







							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								pass







							def       UpperCAmelCase__					(						self  :  Tuple ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									__lowerCAmelCase         =							model_class(lowercase__ )
									__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]      ,				lowercase__ )







							@slow
							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







								__lowerCAmelCase         =							(self.model_tester.min_size,) * 2
								__lowerCAmelCase         =							{
								    "pixel_values": torch.randn((2, 3, *size)      ,				device=lowercase__ ),
								    "mask_labels": torch.randn((2, 10, *size)      ,				device=lowercase__ ),
								    "class_labels": torch.zeros(2      ,				10      ,				device=lowercase__ ).long(),
								}
								__lowerCAmelCase         =							self.model_tester.get_config()

								__lowerCAmelCase         =							MaskaFormerForUniversalSegmentation(lowercase__ ).to(lowercase__ )
								__lowerCAmelCase         =							model(**lowercase__ )
								self.assertTrue(outputs.loss is not None )







							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.create_and_check_maskaformer_model(lowercase__      ,				**lowercase__      ,				output_hidden_states=lowercase__ )







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.model_tester.prepare_config_and_inputs_for_common()

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







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								if not self.model_tester.is_training:
									return

								__lowerCAmelCase         =							self.all_model_classes[1]
								__lowerCAmelCase         =							self.model_tester.prepare_config_and_inputs()

								__lowerCAmelCase         =							model_class(lowercase__ )
								model.to(lowercase__ )
								model.train()

								__lowerCAmelCase         =							model(lowercase__      ,				mask_labels=lowercase__      ,				class_labels=lowercase__ ).loss
								loss.backward()







							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







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

								__lowerCAmelCase         =							model_class(lowercase__ ).to(lowercase__ )
								model.train()

								__lowerCAmelCase         =							model(lowercase__      ,				mask_labels=lowercase__      ,				class_labels=lowercase__ )

								__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=lowercase__ )

								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_   	=	1E-4






def 		_UpperCAmelCase   (	):






	"""simple docstring"""


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







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







							@cached_property
							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







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







							@cached_property
							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  int ):
								"""simple docstring"""







								__lowerCAmelCase         =							MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(lowercase__ )
								__lowerCAmelCase         =							self.default_image_processor
								__lowerCAmelCase         =							prepare_img()
								__lowerCAmelCase         =							image_processor(lowercase__      ,				return_tensors="pt" ).to(lowercase__ )
								__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(lowercase__      ,				(1, 3, 384, 384) )

								with torch.no_grad():
									__lowerCAmelCase         =							model(**lowercase__ )

								__lowerCAmelCase         =							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(lowercase__ )
								self.assertTrue(
								    torch.allclose(
								        outputs.encoder_last_hidden_state[0, 0, :3, :3]      ,				lowercase__      ,				atol=lowercase__ ) )

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

								__lowerCAmelCase         =							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(lowercase__ )
								self.assertTrue(
								    torch.allclose(
								        outputs.transformer_decoder_last_hidden_state[0, :3, :3]      ,				lowercase__      ,				atol=lowercase__ ) )







							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(lowercase__ ).eval()
								__lowerCAmelCase         =							self.default_image_processor
								__lowerCAmelCase         =							prepare_img()
								__lowerCAmelCase         =							image_processor(lowercase__      ,				return_tensors="pt" ).to(lowercase__ )
								__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(lowercase__      ,				(1, 3, 384, 384) )

								with torch.no_grad():
									__lowerCAmelCase         =							model(**lowercase__ )
								# 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         =							[
								    [-8.7_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         =							torch.tensor(lowercase__ ).to(lowercase__ )
								self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]      ,				lowercase__      ,				atol=lowercase__ ) )
								# 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(
								    [
								        [1.8_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(lowercase__ )
								self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]      ,				lowercase__      ,				atol=lowercase__ ) )







							def       UpperCAmelCase__					(						self  :  Tuple ):
								"""simple docstring"""







								__lowerCAmelCase         =							MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(lowercase__ ).eval()
								__lowerCAmelCase         =							self.default_image_processor

								__lowerCAmelCase         =							image_processor(
								    [np.zeros((3, 800, 1_333) ), np.zeros((3, 800, 1_333) )]      ,				segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )]      ,				return_tensors="pt"      ,				)

								__lowerCAmelCase         =							inputs["pixel_values"].to(lowercase__ )
								__lowerCAmelCase         =							[el.to(lowercase__ ) for el in inputs["mask_labels"]]
								__lowerCAmelCase         =							[el.to(lowercase__ ) for el in inputs["class_labels"]]

								with torch.no_grad():
									__lowerCAmelCase         =							model(**lowercase__ )

								self.assertTrue(outputs.loss is not None )


import fire

from utils import calculate_rouge, save_json






def 		_UpperCAmelCase   (	UpperCamelCase:     Any			,							UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     List[Any]=None			,							**UpperCamelCase:     Optional[int]  ):






	"""simple docstring"""


	__lowerCAmelCase         =							[x.strip() for x in open(UpperCamelCase  ).readlines()]
	__lowerCAmelCase         =							[x.strip() for x in open(UpperCamelCase  ).readlines()][: len(UpperCamelCase  )]
	__lowerCAmelCase         =							calculate_rouge(UpperCamelCase			,							UpperCamelCase			,							**UpperCamelCase  )
	if save_path is not None:
		save_json(UpperCamelCase			,							UpperCamelCase			,							indent=UpperCamelCase  )
	return metrics  # these print nicely


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






