





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     :
			'''simple docstring'''




			def __init__(     self  :       Any				,      __lowerCAmelCase  :       List[str]				,      __lowerCAmelCase  :       Tuple=2				,      __lowerCAmelCase  :       Union[str, Any]=True				,      __lowerCAmelCase  :       Optional[Any]=False				,      __lowerCAmelCase  :       Tuple=10				,      __lowerCAmelCase  :       List[Any]=3				,      __lowerCAmelCase  :       Tuple=32 * 8				,      __lowerCAmelCase  :       int=32 * 8				,      __lowerCAmelCase  :       List[Any]=4				,      __lowerCAmelCase  :       Tuple=64				,      )      ->      Optional[int]:







					"""simple docstring"""






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






			def   a_			(     self  :       List[Any]  )      ->      Optional[Any]:







					"""simple docstring"""






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

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

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

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






			def   a_			(     self  :       Union[str, Any]  )      ->      Optional[Any]:







					"""simple docstring"""






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

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

					A__     =  64
					A__     =  1_28
					A__     =  self.hidden_dim
					A__     =  self.hidden_dim
					A__     =  self.hidden_dim
					return config






			def   a_			(     self  :       int  )      ->      str:







					"""simple docstring"""






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






			def   a_			(     self  :       Optional[int]				,      __lowerCAmelCase  :       List[str]				,      __lowerCAmelCase  :       Any  )      ->      List[Any]:







					"""simple docstring"""






					A__     =  output.encoder_hidden_states
					A__     =  output.pixel_decoder_hidden_states
					A__     =  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   a_			(     self  :       Any				,      __lowerCAmelCase  :       Union[str, Any]				,      __lowerCAmelCase  :       List[str]				,      __lowerCAmelCase  :       Optional[Any]				,      __lowerCAmelCase  :       Optional[Any]=False  )      ->      List[Any]:







					"""simple docstring"""






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

							A__     =  model(pixel_values=__lowerCAmelCase				,      pixel_mask=__lowerCAmelCase  )
							A__     =  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   a_			(     self  :       Any				,      __lowerCAmelCase  :       Any				,      __lowerCAmelCase  :       Union[str, Any]				,      __lowerCAmelCase  :       List[Any]				,      __lowerCAmelCase  :       List[str]				,      __lowerCAmelCase  :       Tuple  )      ->      Tuple:







					"""simple docstring"""






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

					def comm_check_on_output(__lowerCAmelCase  :       Optional[Any]  ):
							# 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():
							A__     =  model(pixel_values=__lowerCAmelCase				,      pixel_mask=__lowerCAmelCase  )
							A__     =  model(__lowerCAmelCase  )

							comm_check_on_output(__lowerCAmelCase  )

							A__     =  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     A     (SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE  ,	unittest.TestCase       ):
			'''simple docstring'''




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

			__lowerCamelCase      :			Any							     =       False
			__lowerCamelCase      :			Optional[Any]							     =       False
			__lowerCamelCase      :			Dict							     =       False
			__lowerCamelCase      :			Any							     =       False






			def   a_			(     self  :       str  )      ->      Optional[int]:







					"""simple docstring"""






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






			def   a_			(     self  :       Any  )      ->      Optional[Any]:







					"""simple docstring"""






					self.config_tester.run_common_tests()






			def   a_			(     self  :       Union[str, Any]  )      ->      List[Any]:







					"""simple docstring"""






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






			def   a_			(     self  :       Any  )      ->      int:







					"""simple docstring"""






					A__     =  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   a_			(     self  :       Optional[Any]  )      ->      Optional[int]:







					"""simple docstring"""






					pass






			@unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method"""  )
			def   a_			(     self  :       int  )      ->      Dict:







					"""simple docstring"""






					pass






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







					"""simple docstring"""






					pass






			@unittest.skip(reason="""Mask2Former does not use token embeddings"""  )
			def   a_			(     self  :       Optional[int]  )      ->      Optional[Any]:







					"""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   a_			(     self  :       Union[str, Any]  )      ->      Any:







					"""simple docstring"""






					pass






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







					"""simple docstring"""






					pass






			def   a_			(     self  :       int  )      ->      List[str]:







					"""simple docstring"""






					A__ ,     A__     =  self.model_tester.prepare_config_and_inputs_for_common()

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

							A__     =  ["""pixel_values"""]
							self.assertListEqual(arg_names[:1]				,      __lowerCAmelCase  )






			@slow
			def   a_			(     self  :       str  )      ->      List[Any]:







					"""simple docstring"""






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






			def   a_			(     self  :       int  )      ->      Optional[int]:







					"""simple docstring"""






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

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






			def   a_			(     self  :       List[str]  )      ->      str:







					"""simple docstring"""






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






			def   a_			(     self  :       List[Any]  )      ->      Any:







					"""simple docstring"""






					A__ ,     A__     =  self.model_tester.prepare_config_and_inputs_for_common()

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






			def   a_			(     self  :       Any  )      ->      Tuple:







					"""simple docstring"""






					if not self.model_tester.is_training:
							return

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

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

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







			def   a_			(     self  :       Optional[int]  )      ->      Tuple:







					"""simple docstring"""






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

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

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

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

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

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

					A__     =  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  )

A       :   Dict					= 1e-4







def   __lowerCamelCase   (				)						->							Tuple:






		"""simple docstring"""

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






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




			@cached_property
			def   a_			(     self  :       List[str]  )      ->      Optional[Any]:







					"""simple docstring"""






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






			@cached_property
			def   a_			(     self  :       Tuple  )      ->      int:







					"""simple docstring"""






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






			def   a_			(     self  :       Optional[int]  )      ->      List[Any]:







					"""simple docstring"""






					A__     =  MaskaFormerModel.from_pretrained(self.model_checkpoints  ).to(__lowerCAmelCase  )
					A__     =  self.default_image_processor
					A__     =  prepare_img()
					A__     =  image_processor(__lowerCAmelCase				,      return_tensors="""pt"""  ).to(__lowerCAmelCase  )
					A__     =  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, 3_84, 3_84)  )

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

					A__     =  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  )  )

					A__     =  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  )  )

					A__     =  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   a_			(     self  :       str  )      ->      Any:







					"""simple docstring"""






					A__     =  MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints  ).to(__lowerCAmelCase  ).eval()
					A__     =  self.default_image_processor
					A__     =  prepare_img()
					A__     =  image_processor(__lowerCAmelCase				,      return_tensors="""pt"""  ).to(__lowerCAmelCase  )
					A__     =  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, 3_84, 3_84)  )

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







					"""simple docstring"""






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

					A__     =  image_processor(
					    [np.zeros((3, 8_00, 13_33)  ), np.zeros((3, 8_00, 13_33)  )]				,      segmentation_maps=[np.zeros((3_84, 3_84)  ).astype(np.floataa  ), np.zeros((3_84, 3_84)  ).astype(np.floataa  )]				,      return_tensors="""pt"""				,      )

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

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

					self.assertTrue(outputs.loss is not None  )










import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
					A       :   List[Any]					= argparse.ArgumentParser()

					parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

					parser.add_argument(
					    '''--txt2img_unclip''',
					    default='''kakaobrain/karlo-v1-alpha''',
					    type=str,
					    required=False,
					    help='''The pretrained txt2img unclip.''',
					)

					A       :   List[Any]					= parser.parse_args()

					A       :   int					= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

					A       :   Tuple					= CLIPImageProcessor()
					A       :   int					= CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

					A       :   Tuple					= UnCLIPImageVariationPipeline(
					    decoder=txtaimg.decoder,
					    text_encoder=txtaimg.text_encoder,
					    tokenizer=txtaimg.tokenizer,
					    text_proj=txtaimg.text_proj,
					    feature_extractor=feature_extractor,
					    image_encoder=image_encoder,
					    super_res_first=txtaimg.super_res_first,
					    super_res_last=txtaimg.super_res_last,
					    decoder_scheduler=txtaimg.decoder_scheduler,
					    super_res_scheduler=txtaimg.super_res_scheduler,
					)

					imgaimg.save_pretrained(args.dump_path)



