


import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     MODEL_MAPPING,
     SegformerForImageClassification,
     SegformerForSemanticSegmentation,
     SegformerModel,
 )
 from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import SegformerImageProcessor


class snake_case__			(    __SCREAMING_SNAKE_CASE					):




       def     UpperCAmelCase__    ( self				:    List[Any]			):
              snake_case__ :       Any	   =							self.config_class(**self.inputs_dict			)
              self.parent.assertTrue(hasattr(__lowerCamelCase    ,				'hidden_sizes'			)			)
              self.parent.assertTrue(hasattr(__lowerCamelCase    ,				'num_attention_heads'			)			)
              self.parent.assertTrue(hasattr(__lowerCamelCase    ,				'num_encoder_blocks'			)			)


class snake_case__			:




       def __init__( self				:    str    ,				_lowerCamelCase				:    Union[str, Any]    ,				_lowerCamelCase				:    Optional[int]=1_3    ,				_lowerCamelCase				:    Optional[int]=6_4    ,				_lowerCamelCase				:    str=3    ,				_lowerCamelCase				:    Optional[int]=4    ,				_lowerCamelCase				:    List[str]=[2, 2, 2, 2]    ,				_lowerCamelCase				:    List[str]=[8, 4, 2, 1]    ,				_lowerCamelCase				:    List[str]=[1_6, 3_2, 6_4, 1_2_8]    ,				_lowerCamelCase				:    int=[1, 4, 8, 1_6]    ,				_lowerCamelCase				:    Dict=[1, 2, 4, 8]    ,				_lowerCamelCase				:    Union[str, Any]=True    ,				_lowerCamelCase				:    int=True    ,				_lowerCamelCase				:    Tuple="gelu"    ,				_lowerCamelCase				:    Any=0.1    ,				_lowerCamelCase				:    str=0.1    ,				_lowerCamelCase				:    int=0.02    ,				_lowerCamelCase				:    Any=3    ,				_lowerCamelCase				:    str=None    ,				):
              snake_case__ :       Any	   =							parent
              snake_case__ :       Dict	   =							batch_size
              snake_case__ :       Tuple	   =							image_size
              snake_case__ :       Dict	   =							num_channels
              snake_case__ :       Any	   =							num_encoder_blocks
              snake_case__ :       int	   =							sr_ratios
              snake_case__ :       Any	   =							depths
              snake_case__ :       str	   =							hidden_sizes
              snake_case__ :       Tuple	   =							downsampling_rates
              snake_case__ :       Union[str, Any]	   =							num_attention_heads
              snake_case__ :       Union[str, Any]	   =							is_training
              snake_case__ :       int	   =							use_labels
              snake_case__ :       Tuple	   =							hidden_act
              snake_case__ :       List[Any]	   =							hidden_dropout_prob
              snake_case__ :       Optional[int]	   =							attention_probs_dropout_prob
              snake_case__ :       str	   =							initializer_range
              snake_case__ :       List[Any]	   =							num_labels
              snake_case__ :       Any	   =							scope




       def     UpperCAmelCase__    ( self				:    List[Any]			):
              snake_case__ :       Optional[int]	   =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

              snake_case__ :       str	   =							None
              if self.use_labels:
                     snake_case__ :       List[str]	   =							ids_tensor([self.batch_size, self.image_size, self.image_size]    ,				self.num_labels			)

              snake_case__ :       str	   =							self.get_config()
              return config, pixel_values, labels




       def     UpperCAmelCase__    ( self				:    int			):
              return SegformerConfig(
                  image_size=self.image_size    ,				num_channels=self.num_channels    ,				num_encoder_blocks=self.num_encoder_blocks    ,				depths=self.depths    ,				hidden_sizes=self.hidden_sizes    ,				num_attention_heads=self.num_attention_heads    ,				hidden_act=self.hidden_act    ,				hidden_dropout_prob=self.hidden_dropout_prob    ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,				initializer_range=self.initializer_range    ,				)




       def     UpperCAmelCase__    ( self				:    Optional[Any]    ,				_lowerCamelCase				:    Tuple    ,				_lowerCamelCase				:    int    ,				_lowerCamelCase				:    List[str]			):
              snake_case__ :       Any	   =							SegformerModel(config=__lowerCamelCase			)
              model.to(__lowerCamelCase			)
              model.eval()
              snake_case__ :       str	   =							model(__lowerCamelCase			)
              snake_case__ :       Union[str, Any]	   =							self.image_size // (self.downsampling_rates[-1] * 2)
              self.parent.assertEqual(
                  result.last_hidden_state.shape    ,				(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)			)




       def     UpperCAmelCase__    ( self				:    Any    ,				_lowerCamelCase				:    Dict    ,				_lowerCamelCase				:    Any    ,				_lowerCamelCase				:    List[str]			):
              snake_case__ :       Any	   =							self.num_labels
              snake_case__ :       Union[str, Any]	   =							SegformerForSemanticSegmentation(__lowerCamelCase			)
              model.to(__lowerCamelCase			)
              model.eval()
              snake_case__ :       Any	   =							model(__lowerCamelCase			)
              self.parent.assertEqual(
                  result.logits.shape    ,				(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)			)
              snake_case__ :       Optional[int]	   =							model(__lowerCamelCase    ,				labels=__lowerCamelCase			)
              self.parent.assertEqual(
                  result.logits.shape    ,				(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)			)
              self.parent.assertGreater(result.loss    ,				0.0			)




       def     UpperCAmelCase__    ( self				:    Optional[Any]    ,				_lowerCamelCase				:    Union[str, Any]    ,				_lowerCamelCase				:    str    ,				_lowerCamelCase				:    str			):
              snake_case__ :       Tuple	   =							1
              snake_case__ :       List[Any]	   =							SegformerForSemanticSegmentation(config=__lowerCamelCase			)
              model.to(__lowerCamelCase			)
              model.eval()
              snake_case__ :       Dict	   =							torch.randint(0    ,				1    ,				(self.batch_size, self.image_size, self.image_size)			).to(__lowerCamelCase			)
              snake_case__ :       int	   =							model(__lowerCamelCase    ,				labels=__lowerCamelCase			)
              self.parent.assertGreater(result.loss    ,				0.0			)




       def     UpperCAmelCase__    ( self				:    Union[str, Any]			):
              snake_case__ :       int	   =							self.prepare_config_and_inputs()
              snake_case__   ,				snake_case__   ,				snake_case__ :       List[str]	   =							config_and_inputs
              snake_case__ :       List[Any]	   =							{'pixel_values': pixel_values}
              return config, inputs_dict


@require_torch
class snake_case__			(    __SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE    ,   unittest.TestCase					):
       _lowerCAmelCase   =(
           (
               SegformerModel,
               SegformerForSemanticSegmentation,
               SegformerForImageClassification,
           )
           if is_torch_available()
           else ()
       )
       _lowerCAmelCase   =(
           {
               '''feature-extraction''': SegformerModel,
               '''image-classification''': SegformerForImageClassification,
               '''image-segmentation''': SegformerForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       _lowerCAmelCase   =True
       _lowerCAmelCase   =False
       _lowerCAmelCase   =False
       _lowerCAmelCase   =False




       def     UpperCAmelCase__    ( self				:    Optional[int]			):
              snake_case__ :       str	   =							SegformerModelTester(self			)
              snake_case__ :       Optional[Any]	   =							SegformerConfigTester(self    ,				config_class=__lowerCamelCase			)




       def     UpperCAmelCase__    ( self				:    List[str]			):
              self.config_tester.run_common_tests()




       def     UpperCAmelCase__    ( self				:    Optional[int]			):
              snake_case__ :       Optional[int]	   =							self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*__lowerCamelCase			)




       def     UpperCAmelCase__    ( self				:    Tuple			):
              snake_case__ :       List[Any]	   =							self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_binary_image_segmentation(*__lowerCamelCase			)




       def     UpperCAmelCase__    ( self				:    Optional[Any]			):
              snake_case__ :       Dict	   =							self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_image_segmentation(*__lowerCamelCase			)




       @unittest.skip('SegFormer does not use inputs_embeds'			)
       def     UpperCAmelCase__    ( self				:    Any			):
              pass




       @unittest.skip('SegFormer does not have get_input_embeddings method and get_output_embeddings methods'			)
       def     UpperCAmelCase__    ( self				:    List[str]			):
              pass




       def     UpperCAmelCase__    ( self				:    Optional[Any]			):
              snake_case__   ,				snake_case__ :       Optional[int]	   =							self.model_tester.prepare_config_and_inputs_for_common()

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

                     snake_case__ :       List[Any]	   =							['pixel_values']
                     self.assertListEqual(arg_names[:1]    ,				__lowerCamelCase			)




       def     UpperCAmelCase__    ( self				:    Optional[Any]			):
              snake_case__   ,				snake_case__ :       List[str]	   =							self.model_tester.prepare_config_and_inputs_for_common()
              snake_case__ :       str	   =							True

              for model_class in self.all_model_classes:
                     snake_case__ :       Any	   =							True
                     snake_case__ :       Union[str, Any]	   =							False
                     snake_case__ :       Dict	   =							True
                     snake_case__ :       Optional[int]	   =							model_class(__lowerCamelCase			)
                     model.to(__lowerCamelCase			)
                     model.eval()
                     with torch.no_grad():
                            snake_case__ :       Any	   =							model(**self._prepare_for_class(__lowerCamelCase    ,				__lowerCamelCase			)			)
                     snake_case__ :       str	   =							outputs.attentions

                     snake_case__ :       List[Any]	   =							sum(self.model_tester.depths			)
                     self.assertEqual(len(__lowerCamelCase			)    ,				__lowerCamelCase			)

                     # check that output_attentions also work using config
                     del inputs_dict["output_attentions"]
                     snake_case__ :       str	   =							True
                     snake_case__ :       str	   =							model_class(__lowerCamelCase			)
                     model.to(__lowerCamelCase			)
                     model.eval()
                     with torch.no_grad():
                            snake_case__ :       Tuple	   =							model(**self._prepare_for_class(__lowerCamelCase    ,				__lowerCamelCase			)			)
                     snake_case__ :       List[str]	   =							outputs.attentions

                     self.assertEqual(len(__lowerCamelCase			)    ,				__lowerCamelCase			)

                     # verify the first attentions (first block, first layer)
                     snake_case__ :       Tuple	   =							(self.model_tester.image_size // 4) ** 2
                     snake_case__ :       int	   =							(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
                     self.assertListEqual(
                         list(attentions[0].shape[-3:]			)    ,				[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]    ,				)

                     # verify the last attentions (last block, last layer)
                     snake_case__ :       Tuple	   =							(self.model_tester.image_size // 3_2) ** 2
                     snake_case__ :       Dict	   =							(self.model_tester.image_size // (3_2 * self.model_tester.sr_ratios[-1])) ** 2
                     self.assertListEqual(
                         list(attentions[-1].shape[-3:]			)    ,				[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]    ,				)
                     snake_case__ :       List[str]	   =							len(__lowerCamelCase			)

                     # Check attention is always last and order is fine
                     snake_case__ :       Any	   =							True
                     snake_case__ :       List[Any]	   =							True
                     snake_case__ :       List[str]	   =							model_class(__lowerCamelCase			)
                     model.to(__lowerCamelCase			)
                     model.eval()
                     with torch.no_grad():
                            snake_case__ :       Optional[int]	   =							model(**self._prepare_for_class(__lowerCamelCase    ,				__lowerCamelCase			)			)

                     self.assertEqual(out_len + 1    ,				len(__lowerCamelCase			)			)

                     snake_case__ :       Dict	   =							outputs.attentions

                     self.assertEqual(len(__lowerCamelCase			)    ,				__lowerCamelCase			)
                     # verify the first attentions (first block, first layer)
                     snake_case__ :       Dict	   =							(self.model_tester.image_size // 4) ** 2
                     snake_case__ :       List[Any]	   =							(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
                     self.assertListEqual(
                         list(self_attentions[0].shape[-3:]			)    ,				[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]    ,				)




       def     UpperCAmelCase__    ( self				:    Optional[Any]			):
              def check_hidden_states_output(_lowerCamelCase				:    Optional[Any]    ,				_lowerCamelCase				:    List[Any]    ,				_lowerCamelCase				:    List[str]			):
                     snake_case__ :       Tuple	   =							model_class(__lowerCamelCase			)
                     model.to(__lowerCamelCase			)
                     model.eval()

                     with torch.no_grad():
                            snake_case__ :       Union[str, Any]	   =							model(**self._prepare_for_class(__lowerCamelCase    ,				__lowerCamelCase			)			)

                     snake_case__ :       Any	   =							outputs.hidden_states

                     snake_case__ :       Any	   =							self.model_tester.num_encoder_blocks
                     self.assertEqual(len(__lowerCamelCase			)    ,				__lowerCamelCase			)

                     # verify the first hidden states (first block)
                     self.assertListEqual(
                         list(hidden_states[0].shape[-3:]			)    ,				[
                             self.model_tester.hidden_sizes[0],
                             self.model_tester.image_size // 4,
                             self.model_tester.image_size // 4,
                         ]    ,				)

              snake_case__   ,				snake_case__ :       Tuple	   =							self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     snake_case__ :       List[str]	   =							True
                     check_hidden_states_output(__lowerCamelCase    ,				__lowerCamelCase    ,				__lowerCamelCase			)

                     # check that output_hidden_states also work using config
                     del inputs_dict["output_hidden_states"]
                     snake_case__ :       Dict	   =							True

                     check_hidden_states_output(__lowerCamelCase    ,				__lowerCamelCase    ,				__lowerCamelCase			)




       def     UpperCAmelCase__    ( self				:    Dict			):
              if not self.model_tester.is_training:
                     return

              snake_case__   ,				snake_case__ :       Optional[int]	   =							self.model_tester.prepare_config_and_inputs_for_common()
              snake_case__ :       Optional[Any]	   =							True

              for model_class in self.all_model_classes:
                     if model_class in get_values(__lowerCamelCase			):
                            continue

                     snake_case__ :       Any	   =							model_class(__lowerCamelCase			)
                     model.to(__lowerCamelCase			)
                     model.train()
                     snake_case__ :       List[Any]	   =							self._prepare_for_class(__lowerCamelCase    ,				__lowerCamelCase    ,				return_labels=__lowerCamelCase			)
                     snake_case__ :       Tuple	   =							model(**__lowerCamelCase			).loss
                     loss.backward()




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




       @slow
       def     UpperCAmelCase__    ( self				:    Optional[Any]			):
              for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     snake_case__ :       List[str]	   =							SegformerModel.from_pretrained(__lowerCamelCase			)
                     self.assertIsNotNone(__lowerCamelCase			)





def 	lowercase__(		):
       snake_case__ :       Dict	   =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
       return image


@require_torch
class snake_case__			(    unittest.TestCase					):




       @slow
       def     UpperCAmelCase__    ( self				:    int			):
              snake_case__ :       Any	   =							SegformerImageProcessor(
                  image_scale=(5_1_2, 5_1_2)    ,				keep_ratio=__lowerCamelCase    ,				align=__lowerCamelCase    ,				do_random_crop=__lowerCamelCase			)
              snake_case__ :       List[Any]	   =							SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'			).to(
                  __lowerCamelCase			)

              snake_case__ :       Optional[int]	   =							prepare_img()
              snake_case__ :       List[Any]	   =							image_processor(images=__lowerCamelCase    ,				return_tensors='pt'			)
              snake_case__ :       List[str]	   =							encoded_inputs.pixel_values.to(__lowerCamelCase			)

              with torch.no_grad():
                     snake_case__ :       Union[str, Any]	   =							model(__lowerCamelCase			)

              snake_case__ :       List[str]	   =							torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8)			)
              self.assertEqual(outputs.logits.shape    ,				__lowerCamelCase			)

              snake_case__ :       List[str]	   =							torch.tensor(
                  [
                      [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
                      [[-1_2.1_3_9_1, -1_3.3_1_2_2, -1_3.9_5_5_4], [-1_2.8_7_3_2, -1_3.9_3_5_2, -1_4.3_5_6_3], [-1_2.9_4_3_8, -1_3.8_2_2_6, -1_4.2_5_1_3]],
                      [[-1_2.5_1_3_4, -1_3.4_6_8_6, -1_4.4_9_1_5], [-1_2.8_6_6_9, -1_4.4_3_4_3, -1_4.7_7_5_8], [-1_3.2_5_2_3, -1_4.5_8_1_9, -1_5.0_6_9_4]],
                  ]			).to(__lowerCamelCase			)
              self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]    ,				__lowerCamelCase    ,				atol=1E-4			)			)




       @slow
       def     UpperCAmelCase__    ( self				:    List[Any]			):
              snake_case__ :       List[Any]	   =							SegformerImageProcessor(
                  image_scale=(5_1_2, 5_1_2)    ,				keep_ratio=__lowerCamelCase    ,				align=__lowerCamelCase    ,				do_random_crop=__lowerCamelCase			)
              snake_case__ :       str	   =							SegformerForSemanticSegmentation.from_pretrained(
                  'nvidia/segformer-b1-finetuned-cityscapes-1024-1024'			).to(__lowerCamelCase			)

              snake_case__ :       str	   =							prepare_img()
              snake_case__ :       Union[str, Any]	   =							image_processor(images=__lowerCamelCase    ,				return_tensors='pt'			)
              snake_case__ :       List[str]	   =							encoded_inputs.pixel_values.to(__lowerCamelCase			)

              with torch.no_grad():
                     snake_case__ :       Optional[int]	   =							model(__lowerCamelCase			)

              snake_case__ :       Optional[Any]	   =							torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8)			)
              self.assertEqual(outputs.logits.shape    ,				__lowerCamelCase			)

              snake_case__ :       str	   =							torch.tensor(
                  [
                      [[-1_3.5_7_4_8, -1_3.9_1_1_1, -1_2.6_5_0_0], [-1_4.3_5_0_0, -1_5.3_6_8_3, -1_4.2_3_2_8], [-1_4.7_5_3_2, -1_6.0_4_2_4, -1_5.6_0_8_7]],
                      [[-1_7.1_6_5_1, -1_5.8_7_2_5, -1_2.9_6_5_3], [-1_7.2_5_8_0, -1_7.3_7_1_8, -1_4.8_2_2_3], [-1_6.6_0_5_8, -1_6.8_7_8_3, -1_6.7_4_5_2]],
                      [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
                  ]			).to(__lowerCamelCase			)
              self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]    ,				__lowerCamelCase    ,				atol=1E-1			)			)




       @slow
       def     UpperCAmelCase__    ( self				:    Union[str, Any]			):
              snake_case__ :       List[str]	   =							SegformerImageProcessor(
                  image_scale=(5_1_2, 5_1_2)    ,				keep_ratio=__lowerCamelCase    ,				align=__lowerCamelCase    ,				do_random_crop=__lowerCamelCase			)
              snake_case__ :       Optional[int]	   =							SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'			).to(
                  __lowerCamelCase			)

              snake_case__ :       str	   =							prepare_img()
              snake_case__ :       Optional[Any]	   =							image_processor(images=__lowerCamelCase    ,				return_tensors='pt'			)
              snake_case__ :       Dict	   =							encoded_inputs.pixel_values.to(__lowerCamelCase			)

              with torch.no_grad():
                     snake_case__ :       Optional[int]	   =							model(__lowerCamelCase			)

              snake_case__ :       Any	   =							outputs.logits.detach().cpu()

              snake_case__ :       Optional[int]	   =							image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase    ,				target_sizes=[(5_0_0, 3_0_0)]			)
              snake_case__ :       Any	   =							torch.Size((5_0_0, 3_0_0)			)
              self.assertEqual(segmentation[0].shape    ,				__lowerCamelCase			)

              snake_case__ :       Optional[int]	   =							image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase			)
              snake_case__ :       Tuple	   =							torch.Size((1_2_8, 1_2_8)			)
              self.assertEqual(segmentation[0].shape    ,				__lowerCamelCase			)










"""simple docstring"""






import os
from pathlib import Path




def 			snake_case		(		)						->				Tuple:
  from torch.utils.cpp_extension import load

  _snake_case   	=      Path(lowerCAmelCase_	).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
  _snake_case   	=      [
      root / filename
      for filename in [
          '''vision.cpp''',
          os.path.join('''cpu'''  ,						'''ms_deform_attn_cpu.cpp'''	),
          os.path.join('''cuda'''  ,						'''ms_deform_attn_cuda.cu'''	),
      ]
  ]

  load(
      '''MultiScaleDeformableAttention'''  ,						lowerCAmelCase_  ,						with_cuda=lowerCAmelCase_  ,						extra_include_paths=[str(lowerCAmelCase_	)]  ,						extra_cflags=['''-DWITH_CUDA=1''']  ,						extra_cuda_cflags=[
          '''-DCUDA_HAS_FP16=1''',
          '''-D__CUDA_NO_HALF_OPERATORS__''',
          '''-D__CUDA_NO_HALF_CONVERSIONS__''',
          '''-D__CUDA_NO_HALF2_OPERATORS__''',
      ]  ,						)

  import MultiScaleDeformableAttention as MSDA

  return MSDA





