

"""simple docstring"""



from typing import Dict, Optional

import numpy as np

import datasets


SCREAMING_SNAKE_CASE_					:	Tuple							  =						'\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n'

SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						'\nArgs:\n    predictions (`List[ndarray]`):\n        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    references (`List[ndarray]`):\n        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    num_labels (`int`):\n        Number of classes (categories).\n    ignore_index (`int`):\n        Index that will be ignored during evaluation.\n    nan_to_num (`int`, *optional*):\n        If specified, NaN values will be replaced by the number defined by the user.\n    label_map (`dict`, *optional*):\n        If specified, dictionary mapping old label indices to new label indices.\n    reduce_labels (`bool`, *optional*, defaults to `False`):\n        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n    `Dict[str, float | ndarray]` comprising various elements:\n    - *mean_iou* (`float`):\n        Mean Intersection-over-Union (IoU averaged over all categories).\n    - *mean_accuracy* (`float`):\n        Mean accuracy (averaged over all categories).\n    - *overall_accuracy* (`float`):\n        Overall accuracy on all images.\n    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n        Per category accuracy.\n    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n        Per category IoU.\n\nExamples:\n\n    >>> import numpy as np\n\n    >>> mean_iou = datasets.load_metric("mean_iou")\n\n    >>> # suppose one has 3 different segmentation maps predicted\n    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n    >>> predicted = [predicted_1, predicted_2, predicted_3]\n    >>> ground_truth = [actual_1, actual_2, actual_3]\n\n    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), \'per_category_accuracy\': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}\n'

SCREAMING_SNAKE_CASE_					:	int							  =						'\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}'





def  _snake_case     (    UpperCAmelCase_  :					str					,   UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					Optional[Any]					,   UpperCAmelCase_  :					bool					,   UpperCAmelCase_  :					Optional[Dict[int, int]] = None					,   UpperCAmelCase_  :					bool = False					,   ):
     if label_map is not None:
          for old_id, new_id in label_map.items():
               A__										=  new_id

    # turn into Numpy arrays
     A__										=  np.array(UpperCAmelCase_			)
     A__										=  np.array(UpperCAmelCase_			)

     if reduce_labels:
          A__										=  255
          A__										=  label - 1
          A__										=  255

     A__										=  label != ignore_index
     A__										=  np.not_equal(UpperCAmelCase_					,   UpperCAmelCase_			)
     A__										=  pred_label[mask]
     A__										=  np.array(UpperCAmelCase_			)[mask]

     A__										=  pred_label[pred_label == label]

     A__										=  np.histogram(UpperCAmelCase_					,   bins=UpperCAmelCase_					,   range=(0, num_labels - 1)			)[0]
     A__										=  np.histogram(UpperCAmelCase_					,   bins=UpperCAmelCase_					,   range=(0, num_labels - 1)			)[0]
     A__										=  np.histogram(UpperCAmelCase_					,   bins=UpperCAmelCase_					,   range=(0, num_labels - 1)			)[0]

     A__										=  area_pred_label + area_label - area_intersect

     return area_intersect, area_union, area_pred_label, area_label





def  _snake_case     (    UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					List[str]					,   UpperCAmelCase_  :					Any					,   UpperCAmelCase_  :					bool					,   UpperCAmelCase_  :					Optional[Dict[int, int]] = None					,   UpperCAmelCase_  :					bool = False					,   ):
     A__										=  np.zeros((num_labels,)					,   dtype=np.floataa			)
     A__										=  np.zeros((num_labels,)					,   dtype=np.floataa			)
     A__										=  np.zeros((num_labels,)					,   dtype=np.floataa			)
     A__										=  np.zeros((num_labels,)					,   dtype=np.floataa			)
     for result, gt_seg_map in zip(UpperCAmelCase_					,   UpperCAmelCase_			):
          A__			,				A__			,				A__			,				A__										=  intersect_and_union(
              UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_			)
          total_area_intersect += area_intersect
          total_area_union += area_union
          total_area_pred_label += area_pred_label
          total_area_label += area_label
     return total_area_intersect, total_area_union, total_area_pred_label, total_area_label





def  _snake_case     (    UpperCAmelCase_  :					Optional[int]					,   UpperCAmelCase_  :					Any					,   UpperCAmelCase_  :					Tuple					,   UpperCAmelCase_  :					bool					,   UpperCAmelCase_  :					Optional[int] = None					,   UpperCAmelCase_  :					Optional[Dict[int, int]] = None					,   UpperCAmelCase_  :					bool = False					,   ):
     A__			,				A__			,				A__			,				A__										=  total_intersect_and_union(
         UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_			)

     # compute metrics
     A__										=  {}

     A__										=  total_area_intersect.sum() / total_area_label.sum()
     A__										=  total_area_intersect / total_area_union
     A__										=  total_area_intersect / total_area_label

     A__										=  np.nanmean(UpperCAmelCase_			)
     A__										=  np.nanmean(UpperCAmelCase_			)
     A__										=  all_acc
     A__										=  iou
     A__										=  acc

     if nan_to_num is not None:
          A__										=  {metric: np.nan_to_num(UpperCAmelCase_					,   nan=UpperCAmelCase_			) for metric, metric_value in metrics.items()}

     return metrics



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,	_KWARGS_DESCRIPTION				)
class 					a						(       datasets.Metric				):

    def 				UpperCamelCase	(     self: str       ):






         """simple docstring"""


         return datasets.MetricInfo(
             description=_DESCRIPTION    ,						citation=_CITATION    ,						inputs_description=_KWARGS_DESCRIPTION    ,						features=datasets.Features(
                 # 1st Seq - height dim, 2nd - width dim
                 {
                     """predictions""": datasets.Sequence(datasets.Sequence(datasets.Value("""uint16"""       )       )       ),
                     """references""": datasets.Sequence(datasets.Sequence(datasets.Value("""uint16"""       )       )       ),
                 }       )    ,						reference_urls=[
                 """https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py"""
             ]    ,						)





    def 				UpperCamelCase	(     self: str    ,						UpperCamelCase: Union[str, Any]    ,						UpperCamelCase: Optional[Any]    ,						UpperCamelCase: int    ,						UpperCamelCase: bool    ,						UpperCamelCase: Optional[int] = None    ,						UpperCamelCase: Optional[Dict[int, int]] = None    ,						UpperCamelCase: bool = False    ,						):






         """simple docstring"""


         A__										=  mean_iou(
             results=UpperCamelCase    ,						gt_seg_maps=UpperCamelCase    ,						num_labels=UpperCamelCase    ,						ignore_index=UpperCamelCase    ,						nan_to_num=UpperCamelCase    ,						label_map=UpperCamelCase    ,						reduce_labels=UpperCamelCase    ,						)
         return iou_result









"""simple docstring"""



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 					a						(       _lowerCamelCase				):



    """simple docstring"""



    def 				UpperCamelCase	(     self: Dict       ):






         """simple docstring"""


         A__										=  self.config_class(**self.inputs_dict       )
         self.parent.assertTrue(hasattr(UpperCamelCase    ,						"""hidden_sizes"""       )       )
         self.parent.assertTrue(hasattr(UpperCamelCase    ,						"""num_attention_heads"""       )       )
         self.parent.assertTrue(hasattr(UpperCamelCase    ,						"""num_encoder_blocks"""       )       )



class 					a						:



    """simple docstring"""



    def __init__(     self: str    ,						UpperCamelCase: Dict    ,						UpperCamelCase: int=13    ,						UpperCamelCase: Optional[int]=64    ,						UpperCamelCase: List[Any]=3    ,						UpperCamelCase: List[Any]=4    ,						UpperCamelCase: Optional[Any]=[2, 2, 2, 2]    ,						UpperCamelCase: Any=[8, 4, 2, 1]    ,						UpperCamelCase: Optional[int]=[16, 32, 64, 1_28]    ,						UpperCamelCase: str=[1, 4, 8, 16]    ,						UpperCamelCase: Dict=[1, 2, 4, 8]    ,						UpperCamelCase: Optional[Any]=True    ,						UpperCamelCase: Union[str, Any]=True    ,						UpperCamelCase: List[str]="gelu"    ,						UpperCamelCase: Tuple=0.1    ,						UpperCamelCase: Optional[int]=0.1    ,						UpperCamelCase: Tuple=0.02    ,						UpperCamelCase: int=3    ,						UpperCamelCase: str=None    ,						):






         """simple docstring"""


         A__										=  parent
         A__										=  batch_size
         A__										=  image_size
         A__										=  num_channels
         A__										=  num_encoder_blocks
         A__										=  sr_ratios
         A__										=  depths
         A__										=  hidden_sizes
         A__										=  downsampling_rates
         A__										=  num_attention_heads
         A__										=  is_training
         A__										=  use_labels
         A__										=  hidden_act
         A__										=  hidden_dropout_prob
         A__										=  attention_probs_dropout_prob
         A__										=  initializer_range
         A__										=  num_labels
         A__										=  scope

    def 				UpperCamelCase	(     self: Tuple       ):






         """simple docstring"""


         A__										=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

         A__										=  self.get_config()
         return config, pixel_values, labels

    def 				UpperCamelCase	(     self: Optional[int]       ):






         """simple docstring"""


         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: Tuple    ,						UpperCamelCase: str    ,						UpperCamelCase: Optional[Any]    ,						UpperCamelCase: int       ):






         """simple docstring"""


         A__										=  SegformerModel(config=UpperCamelCase       )
         model.to(UpperCamelCase       )
         model.eval()
         A__										=  model(UpperCamelCase       )
         A__										=  A__										=  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: List[str]    ,						UpperCamelCase: Tuple    ,						UpperCamelCase: str    ,						UpperCamelCase: List[str]       ):






         """simple docstring"""


         A__										=  self.num_labels
         A__										=  SegformerForSemanticSegmentation(UpperCamelCase       )
         model.to(UpperCamelCase       )
         model.eval()
         A__										=  model(UpperCamelCase       )
         self.parent.assertEqual(
             result.logits.shape    ,						(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)       )
         A__										=  model(UpperCamelCase    ,						labels=UpperCamelCase       )
         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]    ,						UpperCamelCase: List[str]    ,						UpperCamelCase: str    ,						UpperCamelCase: Tuple       ):






         """simple docstring"""


         A__										=  1
         A__										=  SegformerForSemanticSegmentation(config=UpperCamelCase       )
         model.to(UpperCamelCase       )
         model.eval()
         A__										=  torch.randint(0    ,						1    ,						(self.batch_size, self.image_size, self.image_size)       ).to(UpperCamelCase       )
         A__										=  model(UpperCamelCase    ,						labels=UpperCamelCase       )
         self.parent.assertGreater(result.loss    ,						0.0       )





    def 				UpperCamelCase	(     self: Tuple       ):






         """simple docstring"""


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



@require_torch
class 					a						(       _lowerCamelCase,	_lowerCamelCase,	unittest.TestCase				):



    """simple docstring"""



    UpperCAmelCase						 =					(
        (
            SegformerModel,
            SegformerForSemanticSegmentation,
            SegformerForImageClassification,
        )
        if is_torch_available()
        else ()
    )
    UpperCAmelCase						 =					(
        {
            "feature-extraction": SegformerModel,
            "image-classification": SegformerForImageClassification,
            "image-segmentation": SegformerForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    UpperCAmelCase						 =					True
    UpperCAmelCase						 =					False
    UpperCAmelCase						 =					False
    UpperCAmelCase						 =					False

    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         A__										=  SegformerModelTester(self       )
         A__										=  SegformerConfigTester(self    ,						config_class=UpperCamelCase       )

    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         self.config_tester.run_common_tests()

    def 				UpperCamelCase	(     self: Dict       ):






         """simple docstring"""


         A__										=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*UpperCamelCase       )

    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


         A__										=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_binary_image_segmentation(*UpperCamelCase       )

    def 				UpperCamelCase	(     self: Dict       ):






         """simple docstring"""


         A__										=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_segmentation(*UpperCamelCase       )

    @unittest.skip("""SegFormer does not use inputs_embeds"""       )
    def 				UpperCamelCase	(     self: List[str]       ):






         """simple docstring"""


         pass

    @unittest.skip("""SegFormer does not have get_input_embeddings method and get_output_embeddings methods"""       )
    def 				UpperCamelCase	(     self: str       ):






         """simple docstring"""


         pass

    def 				UpperCamelCase	(     self: Optional[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(UpperCamelCase       )
              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]    ,						UpperCamelCase       )

    def 				UpperCamelCase	(     self: List[Any]       ):






         """simple docstring"""


         A__			,				A__										=  self.model_tester.prepare_config_and_inputs_for_common()
         A__										=  True

         for model_class in self.all_model_classes:
              A__										=  True
              A__										=  False
              A__										=  True
              A__										=  model_class(UpperCamelCase       )
              model.to(UpperCamelCase       )
              model.eval()
              with torch.no_grad():
                   A__										=  model(**self._prepare_for_class(UpperCamelCase    ,						UpperCamelCase       )       )
              A__										=  outputs.attentions

              A__										=  sum(self.model_tester.depths       )
              self.assertEqual(len(UpperCamelCase       )    ,						UpperCamelCase       )

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

              self.assertEqual(len(UpperCamelCase       )    ,						UpperCamelCase       )

              # verify the first attentions (first block, first layer)
              A__										=  (self.model_tester.image_size // 4) ** 2
              A__										=  (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)
              A__										=  (self.model_tester.image_size // 32) ** 2
              A__										=  (self.model_tester.image_size // (32 * 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]    ,						)
              A__										=  len(UpperCamelCase       )

              # Check attention is always last and order is fine
              A__										=  True
              A__										=  True
              A__										=  model_class(UpperCamelCase       )
              model.to(UpperCamelCase       )
              model.eval()
              with torch.no_grad():
                   A__										=  model(**self._prepare_for_class(UpperCamelCase    ,						UpperCamelCase       )       )

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

              A__										=  outputs.attentions

              self.assertEqual(len(UpperCamelCase       )    ,						UpperCamelCase       )
              # verify the first attentions (first block, first layer)
              A__										=  (self.model_tester.image_size // 4) ** 2
              A__										=  (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: Union[str, Any]       ):






         """simple docstring"""


         def check_hidden_states_output(UpperCamelCase: Dict    ,						UpperCamelCase: Tuple    ,						UpperCamelCase: Dict       ):
              A__										=  model_class(UpperCamelCase       )
              model.to(UpperCamelCase       )
              model.eval()

              with torch.no_grad():
                   A__										=  model(**self._prepare_for_class(UpperCamelCase    ,						UpperCamelCase       )       )

              A__										=  outputs.hidden_states

              A__										=  self.model_tester.num_encoder_blocks
              self.assertEqual(len(UpperCamelCase       )    ,						UpperCamelCase       )

              # 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,
                  ]    ,						)

         A__			,				A__										=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              A__										=  True
              check_hidden_states_output(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase       )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              A__										=  True

              check_hidden_states_output(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase       )

    def 				UpperCamelCase	(     self: Union[str, Any]       ):






         """simple docstring"""


         if not self.model_tester.is_training:
              return

         A__			,				A__										=  self.model_tester.prepare_config_and_inputs_for_common()
         A__										=  True

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

              A__										=  model_class(UpperCamelCase       )
              model.to(UpperCamelCase       )
              model.train()
              A__										=  self._prepare_for_class(UpperCamelCase    ,						UpperCamelCase    ,						return_labels=UpperCamelCase       )
              A__										=  model(**UpperCamelCase       ).loss
              loss.backward()

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






         """simple docstring"""


         pass





    @slow
    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              A__										=  SegformerModel.from_pretrained(UpperCamelCase       )
              self.assertIsNotNone(UpperCamelCase       )





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



@require_torch
class 					a						(       unittest.TestCase				):



    """simple docstring"""



    @slow
    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         A__										=  SegformerImageProcessor(
             image_scale=(5_12, 5_12)    ,						keep_ratio=UpperCamelCase    ,						align=UpperCamelCase    ,						do_random_crop=UpperCamelCase       )
         A__										=  SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512"""       ).to(
             UpperCamelCase       )

         A__										=  prepare_img()
         A__										=  image_processor(images=UpperCamelCase    ,						return_tensors="""pt"""       )
         A__										=  encoded_inputs.pixel_values.to(UpperCamelCase       )

         with torch.no_grad():
              A__										=  model(UpperCamelCase       )

         A__										=  torch.Size((1, model.config.num_labels, 1_28, 1_28)       )
         self.assertEqual(outputs.logits.shape    ,						UpperCamelCase       )

         A__										=  torch.tensor(
             [
                 [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
                 [[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
                 [[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
             ]       ).to(UpperCamelCase       )
         self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]    ,						UpperCamelCase    ,						atol=1e-4       )       )

    @slow
    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         A__										=  SegformerImageProcessor(
             image_scale=(5_12, 5_12)    ,						keep_ratio=UpperCamelCase    ,						align=UpperCamelCase    ,						do_random_crop=UpperCamelCase       )
         A__										=  SegformerForSemanticSegmentation.from_pretrained(
             """nvidia/segformer-b1-finetuned-cityscapes-1024-1024"""       ).to(UpperCamelCase       )

         A__										=  prepare_img()
         A__										=  image_processor(images=UpperCamelCase    ,						return_tensors="""pt"""       )
         A__										=  encoded_inputs.pixel_values.to(UpperCamelCase       )

         with torch.no_grad():
              A__										=  model(UpperCamelCase       )

         A__										=  torch.Size((1, model.config.num_labels, 1_28, 1_28)       )
         self.assertEqual(outputs.logits.shape    ,						UpperCamelCase       )

         A__										=  torch.tensor(
             [
                 [[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
                 [[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
                 [[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
             ]       ).to(UpperCamelCase       )
         self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]    ,						UpperCamelCase    ,						atol=1e-1       )       )





    @slow
    def 				UpperCamelCase	(     self: List[Any]       ):






         """simple docstring"""


         A__										=  SegformerImageProcessor(
             image_scale=(5_12, 5_12)    ,						keep_ratio=UpperCamelCase    ,						align=UpperCamelCase    ,						do_random_crop=UpperCamelCase       )
         A__										=  SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512"""       ).to(
             UpperCamelCase       )

         A__										=  prepare_img()
         A__										=  image_processor(images=UpperCamelCase    ,						return_tensors="""pt"""       )
         A__										=  encoded_inputs.pixel_values.to(UpperCamelCase       )

         with torch.no_grad():
              A__										=  model(UpperCamelCase       )

         A__										=  outputs.logits.detach().cpu()

         A__										=  image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase    ,						target_sizes=[(5_00, 3_00)]       )
         A__										=  torch.Size((5_00, 3_00)       )
         self.assertEqual(segmentation[0].shape    ,						UpperCamelCase       )

         A__										=  image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase       )
         A__										=  torch.Size((1_28, 1_28)       )
         self.assertEqual(segmentation[0].shape    ,						UpperCamelCase       )






