



"""simple docstring"""




import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, 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 MaskFormerForInstanceSegmentation, MaskFormerModel

 if is_vision_available():
  from transformers import MaskFormerImageProcessor

if is_vision_available():
 from PIL import Image






class       UpperCAmelCase_    :



      def __init__(	self	:      Any	,      A	:      Union[str, Any]	,      A	:      Union[str, Any]=2	,      A	:      int=True	,      A	:      str=False	,      A	:      Any=1_0	,      A	:      Optional[int]=3	,      A	:      Any=3_2 * 4	,      A	:      str=3_2 * 6	,      A	:      Dict=4	,      A	:      Any=3_2	,      ):
           _UpperCAmelCase     :      Any														=       parent
           _UpperCAmelCase     :      int														=       batch_size
           _UpperCAmelCase     :      Optional[int]														=       is_training
           _UpperCAmelCase     :      Optional[int]														=       use_auxiliary_loss
           _UpperCAmelCase     :      Tuple														=       num_queries
           _UpperCAmelCase     :      List[str]														=       num_channels
           _UpperCAmelCase     :      Union[str, Any]														=       min_size
           _UpperCAmelCase     :      List[Any]														=       max_size
           _UpperCAmelCase     :      Tuple														=       num_labels
           _UpperCAmelCase     :      Union[str, Any]														=       mask_feature_size



      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      List[Any]														=       floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]		).to(
               A		)

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

           _UpperCAmelCase     :      List[str]														=       (
               torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]	,      device=A		) > 0.5
           ).float()
           _UpperCAmelCase     :      Tuple														=       (torch.rand((self.batch_size, self.num_labels)	,      device=A		) > 0.5).long()

           _UpperCAmelCase     :      int														=       self.get_config()
           return config, pixel_values, pixel_mask, mask_labels, class_labels



      def 		snake_case_   (	self	:      Union[str, Any]		):
           return MaskFormerConfig.from_backbone_and_decoder_configs(
               backbone_config=SwinConfig(
                   depths=[1, 1, 1, 1]	,      )	,      decoder_config=DetrConfig(
                   decoder_ffn_dim=1_2_8	,      num_queries=self.num_queries	,      decoder_attention_heads=2	,      d_model=self.mask_feature_size	,      )	,      mask_feature_size=self.mask_feature_size	,      fpn_feature_size=self.mask_feature_size	,      num_channels=self.num_channels	,      num_labels=self.num_labels	,      )



      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase     :      Optional[Any]														=       self.prepare_config_and_inputs()
           _UpperCAmelCase     :      Optional[Any]														=       {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
           return config, inputs_dict



      def 		snake_case_   (	self	:      List[str]	,      A	:      List[Any]	,      A	:      List[Any]		):
           _UpperCAmelCase     :      str														=       output.encoder_hidden_states
           _UpperCAmelCase     :      Tuple														=       output.pixel_decoder_hidden_states
           _UpperCAmelCase     :      Optional[int]														=       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_config.decoder_layers		)



      def 		snake_case_   (	self	:      Tuple	,      A	:      Optional[Any]	,      A	:      Tuple	,      A	:      str	,      A	:      Tuple=False		):
           with torch.no_grad():
                _UpperCAmelCase     :      List[Any]														=       MaskFormerModel(config=A		)
                model.to(A		)
                model.eval()

                _UpperCAmelCase     :      Tuple														=       model(pixel_values=A	,      pixel_mask=A		)
                _UpperCAmelCase     :      int														=       model(A	,      output_hidden_states=A		)
           # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
           # encoder and pixel decoder
           self.parent.assertEqual(
               output.transformer_decoder_last_hidden_state.shape	,      (self.batch_size, self.num_queries, self.mask_feature_size)	,      )
           # 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	:      str	,      A	:      str	,      A	:      Union[str, Any]	,      A	:      Optional[int]	,      A	:      int	,      A	:      Optional[Any]		):
           _UpperCAmelCase     :      int														=       MaskFormerForInstanceSegmentation(config=A		)
           model.to(A		)
           model.eval()

           def comm_check_on_output(A	:      List[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():
                _UpperCAmelCase     :      List[str]														=       model(pixel_values=A	,      pixel_mask=A		)
                _UpperCAmelCase     :      int														=       model(A		)

                comm_check_on_output(A		)

                _UpperCAmelCase     :      List[str]														=       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       UpperCAmelCase_    (							_UpperCamelCase			,     _UpperCamelCase			,     unittest.TestCase		):
      __SCREAMING_SNAKE_CASE		:  Tuple						=					(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					(
          {'feature-extraction': MaskFormerModel, 'image-segmentation': MaskFormerForInstanceSegmentation}
          if is_torch_available()
          else {}
      )

      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					False
      __SCREAMING_SNAKE_CASE		:  Any						=					False
      __SCREAMING_SNAKE_CASE		:  List[str]						=					False
      __SCREAMING_SNAKE_CASE		:  List[Any]						=					False



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      Optional[int]														=       MaskFormerModelTester(self		)
           _UpperCAmelCase     :      Optional[Any]														=       ConfigTester(self	,      config_class=A	,      has_text_modality=A		)



      def 		snake_case_   (	self	:      Dict		):
           self.config_tester.run_common_tests()



      def 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      Union[str, Any]														=       self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.create_and_check_maskformer_model(A	,      **A	,      output_hidden_states=A		)



      def 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      Optional[int]														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*A		)



      @unittest.skip(reason="MaskFormer does not use inputs_embeds"		)
      def 		snake_case_   (	self	:      Optional[Any]		):
           pass



      @unittest.skip(reason="MaskFormer does not have a get_input_embeddings method"		)
      def 		snake_case_   (	self	:      int		):
           pass



      @unittest.skip(reason="MaskFormer is not a generative model"		)
      def 		snake_case_   (	self	:      Optional[int]		):
           pass



      @unittest.skip(reason="MaskFormer does not use token embeddings"		)
      def 		snake_case_   (	self	:      Optional[int]		):
           pass



      @require_torch_multi_gpu
      @unittest.skip(
          reason="MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`"		)
      def 		snake_case_   (	self	:      Any		):
           pass



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



      def 		snake_case_   (	self	:      int		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      Optional[Any]														=       self.model_tester.prepare_config_and_inputs_for_common()

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

                _UpperCAmelCase     :      Dict														=       ["pixel_values"]
                self.assertListEqual(arg_names[:1]	,      A		)



      @slow
      def 		snake_case_   (	self	:      int		):
           for model_name in ["facebook/maskformer-swin-small-coco"]:
                _UpperCAmelCase     :      Optional[Any]														=       MaskFormerModel.from_pretrained(A		)
                self.assertIsNotNone(A		)



      def 		snake_case_   (	self	:      Tuple		):
           _UpperCAmelCase     :      Union[str, Any]														=       (self.model_tester.min_size,) * 2
           _UpperCAmelCase     :      List[str]														=       {
               "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     :      Tuple														=       MaskFormerForInstanceSegmentation(MaskFormerConfig()		).to(A		)
           _UpperCAmelCase     :      List[str]														=       model(**A		)
           self.assertTrue(outputs.loss is not None		)



      def 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      str														=       self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.create_and_check_maskformer_model(A	,      **A	,      output_hidden_states=A		)



      def 		snake_case_   (	self	:      Optional[int]		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      int														=       self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                _UpperCAmelCase     :      Tuple														=       model_class(A		).to(A		)
                _UpperCAmelCase     :      Any														=       model(**A	,      output_attentions=A		)
                self.assertTrue(outputs.attentions is not None		)



      def 		snake_case_   (	self	:      Tuple		):
           if not self.model_tester.is_training:
                return
           # only MaskFormerForInstanceSegmentation has the loss
           _UpperCAmelCase     :      List[str]														=       self.all_model_classes[1]
           _UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase     :      Any														=       self.model_tester.prepare_config_and_inputs()

           _UpperCAmelCase     :      Dict														=       model_class(A		)
           model.to(A		)
           model.train()

           _UpperCAmelCase     :      Tuple														=       model(A	,      mask_labels=A	,      class_labels=A		).loss
           loss.backward()



      def 		snake_case_   (	self	:      Tuple		):
           # only MaskFormerForInstanceSegmentation has the loss
           _UpperCAmelCase     :      Optional[Any]														=       self.all_model_classes[1]
           _UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase     :      Union[str, Any]														=       self.model_tester.prepare_config_and_inputs()
           _UpperCAmelCase     :      Tuple														=       True
           _UpperCAmelCase     :      Optional[Any]														=       True

           _UpperCAmelCase     :      Union[str, Any]														=       model_class(A		)
           model.to(A		)
           model.train()

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

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

           _UpperCAmelCase     :      str														=       outputs.pixel_decoder_hidden_states[0]
           pixel_decoder_hidden_states.retain_grad()
           # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
           _UpperCAmelCase     :      Optional[int]														=       outputs.transformer_decoder_hidden_states[0]
           transformer_decoder_hidden_states.retain_grad()

           _UpperCAmelCase     :      Dict														=       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							:   int				=				1E-4

def 		__snake_case		( )    ->  Union[str, Any]:





     '''simple docstring'''

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






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



      @cached_property
      def 		snake_case_   (	self	:      Dict		):
           return (
               MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco"		)
               if is_vision_available()
               else None
           )



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      Any														=       MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco"		).to(A		)
           _UpperCAmelCase     :      List[Any]														=       self.default_image_processor
           _UpperCAmelCase     :      Optional[Any]														=       prepare_img()
           _UpperCAmelCase     :      str														=       image_processor(A	,      return_tensors="pt"		).to(A		)
           _UpperCAmelCase     :      int														=       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, 8_0_0, 1_0_8_8)		)

           with torch.no_grad():
                _UpperCAmelCase     :      Any														=       model(**A		)

           _UpperCAmelCase     :      Any														=       torch.tensor(
               [[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]]		).to(A		)
           self.assertTrue(
               torch.allclose(
                   outputs.encoder_last_hidden_state[0, 0, :3, :3]	,      A	,      atol=A		)		)

           _UpperCAmelCase     :      Any														=       torch.tensor(
               [[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]]		).to(A		)
           self.assertTrue(
               torch.allclose(
                   outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]	,      A	,      atol=A		)		)

           _UpperCAmelCase     :      Union[str, Any]														=       torch.tensor(
               [[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]]		).to(A		)
           self.assertTrue(
               torch.allclose(
                   outputs.transformer_decoder_last_hidden_state[0, :3, :3]	,      A	,      atol=A		)		)



      def 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      Any														=       (
               MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco"		)
               .to(A		)
               .eval()
           )
           _UpperCAmelCase     :      Union[str, Any]														=       self.default_image_processor
           _UpperCAmelCase     :      str														=       prepare_img()
           _UpperCAmelCase     :      Optional[Any]														=       image_processor(A	,      return_tensors="pt"		).to(A		)
           _UpperCAmelCase     :      Optional[int]														=       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, 8_0_0, 1_0_8_8)		)

           with torch.no_grad():
                _UpperCAmelCase     :      str														=       model(**A		)
           # masks_queries_logits
           _UpperCAmelCase     :      str														=       outputs.masks_queries_logits
           self.assertEqual(
               masks_queries_logits.shape	,      (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)	,      )
           _UpperCAmelCase     :      List[Any]														=       [
               [-1.3_737_124, -1.7_724_937, -1.9_364_233],
               [-1.5_977_281, -1.9_867_939, -2.1_523_695],
               [-1.5_795_398, -1.9_269_832, -2.093_942],
           ]
           _UpperCAmelCase     :      Dict														=       torch.tensor(A		).to(A		)
           self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]	,      A	,      atol=A		)		)
           # class_queries_logits
           _UpperCAmelCase     :      Optional[Any]														=       outputs.class_queries_logits
           self.assertEqual(
               class_queries_logits.shape	,      (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)		)
           _UpperCAmelCase     :      List[str]														=       torch.tensor(
               [
                   [1.6512e00, -5.2572e00, -3.3519e00],
                   [3.6169e-02, -5.9025e00, -2.9313e00],
                   [1.0766e-04, -7.7630e00, -5.1263e00],
               ]		).to(A		)
           self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]	,      A	,      atol=A		)		)



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      List[Any]														=       (
               MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff"		)
               .to(A		)
               .eval()
           )
           _UpperCAmelCase     :      Optional[Any]														=       self.default_image_processor
           _UpperCAmelCase     :      List[str]														=       prepare_img()
           _UpperCAmelCase     :      str														=       image_processor(A	,      return_tensors="pt"		).to(A		)
           _UpperCAmelCase     :      int														=       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, 8_0_0, 1_0_8_8)		)

           with torch.no_grad():
                _UpperCAmelCase     :      Dict														=       model(**A		)
           # masks_queries_logits
           _UpperCAmelCase     :      int														=       outputs.masks_queries_logits
           self.assertEqual(
               masks_queries_logits.shape	,      (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)	,      )
           _UpperCAmelCase     :      List[Any]														=       [[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
           _UpperCAmelCase     :      int														=       torch.tensor(A		).to(A		)
           self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]	,      A	,      atol=A		)		)
           # class_queries_logits
           _UpperCAmelCase     :      Dict														=       outputs.class_queries_logits
           self.assertEqual(
               class_queries_logits.shape	,      (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)		)
           _UpperCAmelCase     :      Union[str, Any]														=       torch.tensor(
               [[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]]		).to(A		)
           self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]	,      A	,      atol=A		)		)



      def 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase     :      List[Any]														=       (
               MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco"		)
               .to(A		)
               .eval()
           )
           _UpperCAmelCase     :      Tuple														=       self.default_image_processor

           _UpperCAmelCase     :      Any														=       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     :      List[Any]														=       inputs["pixel_values"].to(A		)
           _UpperCAmelCase     :      int														=       [el.to(A		) for el in inputs["mask_labels"]]
           _UpperCAmelCase     :      List[Any]														=       [el.to(A		) for el in inputs["class_labels"]]

           with torch.no_grad():
                _UpperCAmelCase     :      Optional[Any]														=       model(**A		)

           self.assertTrue(outputs.loss is not None		)





"""simple docstring"""




def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  bool:





     '''simple docstring'''

     _UpperCAmelCase     :      str														=       0
     for ch in input_str:
          _UpperCAmelCase     :      Optional[int]														=       ord(SCREAMING_SNAKE_CASE__							)
          _UpperCAmelCase     :      int														=       pow(2		,    SCREAMING_SNAKE_CASE__							)

          # If we already turned on bit for current character's unicode
          if bitmap >> ch_unicode & 1 == 1:
               return False
          bitmap |= ch_bit_index_on
     return True


if __name__ == "__main__":
 import doctest

 doctest.testmod()
