"""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	, __lowerCAmelCase	, __lowerCAmelCase=2	, __lowerCAmelCase=True	, __lowerCAmelCase=False	, __lowerCAmelCase=10	, __lowerCAmelCase=3	, __lowerCAmelCase=32 * 8	, __lowerCAmelCase=32 * 8	, __lowerCAmelCase=4	, __lowerCAmelCase=64	, ):







            """simple docstring"""



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

      def        A__ (		self		):







            """simple docstring"""



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

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

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

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

      def        A__ (		self		):







            """simple docstring"""



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

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

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

      def        A__ (		self		):







            """simple docstring"""



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

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			output.encoder_hidden_states
            lowercase														=			output.pixel_decoder_hidden_states
            lowercase														=			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	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase=False		):







            """simple docstring"""



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

                  lowercase														=			model(pixel_values=__lowerCAmelCase	, pixel_mask=__lowerCAmelCase		)
                  lowercase														=			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	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



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

            def comm_check_on_output(__lowerCAmelCase		):
                  # 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():
                  lowercase														=			model(pixel_values=__lowerCAmelCase	, pixel_mask=__lowerCAmelCase		)
                  lowercase														=			model(__lowerCAmelCase		)

                  comm_check_on_output(__lowerCAmelCase		)

                  lowercase														=			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		( lowerCAmelCase						,					lowerCAmelCase						,					unittest.TestCase			):
      snake_case__    :     str													=			(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
      snake_case__    :     List[str]													=			{'feature-extraction': MaskaFormerModel} if is_torch_available() else {}

      snake_case__    :     Any													=			False
      snake_case__    :     List[str]													=			False
      snake_case__    :     Optional[Any]													=			False
      snake_case__    :     Union[str, Any]													=			False

      def        A__ (		self		):







            """simple docstring"""



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

      def        A__ (		self		):







            """simple docstring"""



            self.config_tester.run_common_tests()

      def        A__ (		self		):







            """simple docstring"""



            lowercase							,							lowercase														=			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		):







            """simple docstring"""



            lowercase														=			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		):







            """simple docstring"""



            pass

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







            """simple docstring"""



            pass

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







            """simple docstring"""



            pass

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







            """simple docstring"""



            pass

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







            """simple docstring"""



            pass

      def        A__ (		self		):







            """simple docstring"""



            lowercase							,							lowercase														=			self.model_tester.prepare_config_and_inputs_for_common()

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

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

      @slow
      def        A__ (		self		):







            """simple docstring"""



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

      def        A__ (		self		):







            """simple docstring"""



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

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

      def        A__ (		self		):







            """simple docstring"""



            lowercase							,							lowercase														=			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		):







            """simple docstring"""



            lowercase							,							lowercase														=			self.model_tester.prepare_config_and_inputs_for_common()

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

      def        A__ (		self		):







            """simple docstring"""



            if not self.model_tester.is_training:
                  return

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

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

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







      def        A__ (		self		):







            """simple docstring"""



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

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

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

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

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

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

            lowercase														=			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					: str        =1E-4


def     UpperCAmelCase__ (       )				->					Any:
      '''simple docstring'''


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






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

      @cached_property
      def        A__ (		self		):







            """simple docstring"""



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

      @cached_property
      def        A__ (		self		):







            """simple docstring"""



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

      def        A__ (		self		):







            """simple docstring"""



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

            lowercase														=			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		)		)

            lowercase														=			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		)		)

            lowercase														=			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		):







            """simple docstring"""



            lowercase														=			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints		).to(__lowerCAmelCase		).eval()
            lowercase														=			self.default_image_processor
            lowercase														=			prepare_img()
            lowercase														=			image_processor(__lowerCAmelCase	, return_tensors="""pt"""		).to(__lowerCAmelCase		)
            lowercase														=			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():
                  lowercase														=			model(**__lowerCAmelCase		)
            # masks_queries_logits
            lowercase														=			outputs.masks_queries_logits
            self.assertEqual(
                masks_queries_logits.shape	, (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)		)
            lowercase														=			[
                [-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],
            ]
            lowercase														=			torch.tensor(__lowerCAmelCase		).to(__lowerCAmelCase		)
            self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]	, __lowerCAmelCase	, atol=__lowerCAmelCase		)		)
            # class_queries_logits
            lowercase														=			outputs.class_queries_logits
            self.assertEqual(class_queries_logits.shape	, (1, model.config.num_queries, model.config.num_labels + 1)		)
            lowercase														=			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		):







            """simple docstring"""



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

            lowercase														=			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"""	, )

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

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

            self.assertTrue(outputs.loss is not None		)




"""simple docstring"""






def     UpperCAmelCase__ (       lowerCAmelCase__  :str      ,	lowerCAmelCase__  :int	)				->					list:
      '''simple docstring'''


      lowercase														=			word.split()

      def justify(lowerCAmelCase__  :list      ,	lowerCAmelCase__  :int      ,	lowerCAmelCase__  :int	) -> str:
            lowercase														=			max_width - width
            lowercase														=			len(lowerCAmelCase__	)
            if len(lowerCAmelCase__	) == 1:
                  # if there is only word in line
                  # just insert overall_spaces_count for the remainder of line
                  return line[0] + " " * overall_spaces_count
            else:
                  lowercase														=			words_count - 1
                  # num_spaces_between_words_list[i] : tells you to insert
                  # num_spaces_between_words_list[i] spaces
                  # after word on line[i]
                  lowercase														=			spaces_to_insert_between_words * [
                      overall_spaces_count // spaces_to_insert_between_words
                  ]
                  lowercase														=			(
                      overall_spaces_count % spaces_to_insert_between_words
                  )
                  # distribute spaces via round robin to the left words
                  for i in range(lowerCAmelCase__	):
                        num_spaces_between_words_list[i] += 1
                  lowercase														=			[]
                  for i in range(lowerCAmelCase__	):
                        # add the word
                        aligned_words_list.append(line[i]	)
                        # add the spaces to insert
                        aligned_words_list.append(num_spaces_between_words_list[i] * """ """	)
                  # just add the last word to the sentence
                  aligned_words_list.append(line[-1]	)
                  # join the aligned words list to form a justified line
                  return "".join(lowerCAmelCase__	)

      lowercase														=			[]
      lowercase														=			[]
      lowercase														=			0
      for word in words:
            if width + len(lowerCAmelCase__	) + len(lowerCAmelCase__	) <= max_width:
                  # keep adding words until we can fill out max_width
                  # width = sum of length of all words (without overall_spaces_count)
                  # len(word) = length of current word
                  # len(line) = number of overall_spaces_count to insert between words
                  line.append(lowerCAmelCase__	)
                  width += len(lowerCAmelCase__	)
            else:
                  # justify the line and add it to result
                  answer.append(justify(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	)	)
                  # reset new line and new width
                  lowercase							,							lowercase														=			[word], len(lowerCAmelCase__	)
      lowercase														=			max_width - width - len(lowerCAmelCase__	)
      answer.append(""" """.join(lowerCAmelCase__	) + (remaining_spaces + 1) * """ """	)
      return answer


if __name__ == "__main__":
       from doctest import testmod

       testmod()



