
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        lowerCAmelCase__      (   __lowercase	):

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


        '''simple docstring'''

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







class        lowerCAmelCase__      :

      def __init__(			self       ,							a       ,							a=13       ,							a=64       ,							a=3       ,							a=4       ,							a=[2, 2, 2, 2]       ,							a=[8, 4, 2, 1]       ,							a=[16, 32, 64, 1_28]       ,							a=[1, 4, 8, 16]       ,							a=[1, 2, 4, 8]       ,							a=True       ,							a=True       ,							a="gelu"       ,							a=0.1       ,							a=0.1       ,							a=0.02       ,							a=3       ,							a=None       ,							)					->      Any:


        '''simple docstring'''

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

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


        '''simple docstring'''

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

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

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

      def  A_				(			self				)					->      Dict:


        '''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  A_				(			self       ,							a       ,							a       ,							a				)					->      Tuple:


        '''simple docstring'''

        _UpperCamelCase						    =	SegformerModel(config=a				)
        model.to(a				)
        model.eval()
        _UpperCamelCase						    =	model(a				)
        _UpperCamelCase						    =	_UpperCamelCase						    =	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  A_				(			self       ,							a       ,							a       ,							a				)					->      List[Any]:


        '''simple docstring'''

        _UpperCamelCase						    =	self.num_labels
        _UpperCamelCase						    =	SegformerForSemanticSegmentation(a				)
        model.to(a				)
        model.eval()
        _UpperCamelCase						    =	model(a				)
        self.parent.assertEqual(
            result.logits.shape       ,							(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)				)
        _UpperCamelCase						    =	model(a       ,							labels=a				)
        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  A_				(			self       ,							a       ,							a       ,							a				)					->      Optional[Any]:


        '''simple docstring'''

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







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


        '''simple docstring'''

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







@require_torch
class        lowerCAmelCase__      (   __lowercase		,	__lowercase		,	unittest.TestCase	):
      UpperCamelCase_							: List[str]	=		(
          (
              SegformerModel,
              SegformerForSemanticSegmentation,
              SegformerForImageClassification,
          )
          if is_torch_available()
          else ()
      )
      UpperCamelCase_							: Optional[int]	=		(
          {
              "feature-extraction": SegformerModel,
              "image-classification": SegformerForImageClassification,
              "image-segmentation": SegformerForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      UpperCamelCase_							: List[Any]	=		True
      UpperCamelCase_							: Optional[Any]	=		False
      UpperCamelCase_							: List[str]	=		False
      UpperCamelCase_							: Optional[int]	=		False

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


        '''simple docstring'''

        _UpperCamelCase						    =	SegformerModelTester(self				)
        _UpperCamelCase						    =	SegformerConfigTester(self       ,							config_class=a				)

      def  A_				(			self				)					->      Dict:


        '''simple docstring'''

        self.config_tester.run_common_tests()

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


        '''simple docstring'''

        _UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*a				)

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


        '''simple docstring'''

        _UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_binary_image_segmentation(*a				)

      def  A_				(			self				)					->      Tuple:


        '''simple docstring'''

        _UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_segmentation(*a				)

      @unittest.skip("""SegFormer does not use inputs_embeds"""				)
      def  A_				(			self				)					->      int:


        '''simple docstring'''

        pass

      @unittest.skip("""SegFormer does not have get_input_embeddings method and get_output_embeddings methods"""				)
      def  A_				(			self				)					->      int:


        '''simple docstring'''

        pass

      def  A_				(			self				)					->      Dict:


        '''simple docstring'''

        _UpperCamelCase    ,	_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs_for_common()

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

          _UpperCamelCase						    =	["""pixel_values"""]
          self.assertListEqual(arg_names[:1]       ,							a				)

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


        '''simple docstring'''

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

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

          _UpperCamelCase						    =	sum(self.model_tester.depths				)
          self.assertEqual(len(a				)       ,							a				)

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

          self.assertEqual(len(a				)       ,							a				)

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

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

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

          _UpperCamelCase						    =	outputs.attentions

          self.assertEqual(len(a				)       ,							a				)
          # verify the first attentions (first block, first layer)
          _UpperCamelCase						    =	(self.model_tester.image_size // 4) ** 2
          _UpperCamelCase						    =	(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  A_				(			self				)					->      Tuple:


        '''simple docstring'''

        def check_hidden_states_output(a       ,							a       ,							a				):
          _UpperCamelCase						    =	model_class(a				)
          model.to(a				)
          model.eval()

          with torch.no_grad():
            _UpperCamelCase						    =	model(**self._prepare_for_class(a       ,							a				)				)

          _UpperCamelCase						    =	outputs.hidden_states

          _UpperCamelCase						    =	self.model_tester.num_encoder_blocks
          self.assertEqual(len(a				)       ,							a				)

          # 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,
              ]       ,							)

        _UpperCamelCase    ,	_UpperCamelCase						    =	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          _UpperCamelCase						    =	True
          check_hidden_states_output(a       ,							a       ,							a				)

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

          check_hidden_states_output(a       ,							a       ,							a				)

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


        '''simple docstring'''

        if not self.model_tester.is_training:
          return

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

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

          _UpperCamelCase						    =	model_class(a				)
          model.to(a				)
          model.train()
          _UpperCamelCase						    =	self._prepare_for_class(a       ,							a       ,							return_labels=a				)
          _UpperCamelCase						    =	model(**a				).loss
          loss.backward()

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


        '''simple docstring'''

        pass







      @slow
      def  A_				(			self				)					->      str:


        '''simple docstring'''

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




def 							__A()		->				Optional[Any]:

  """simple docstring"""






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







@require_torch
class        lowerCAmelCase__      (   unittest.TestCase	):

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


        '''simple docstring'''

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

        _UpperCamelCase						    =	prepare_img()
        _UpperCamelCase						    =	image_processor(images=a       ,							return_tensors="""pt"""				)
        _UpperCamelCase						    =	encoded_inputs.pixel_values.to(a				)

        with torch.no_grad():
          _UpperCamelCase						    =	model(a				)

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

        _UpperCamelCase						    =	torch.tensor(
            [
                [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
                [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
                [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
            ]				).to(a				)
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]       ,							a       ,							atol=1e-4				)				)

      @slow
      def  A_				(			self				)					->      Tuple:


        '''simple docstring'''

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

        _UpperCamelCase						    =	prepare_img()
        _UpperCamelCase						    =	image_processor(images=a       ,							return_tensors="""pt"""				)
        _UpperCamelCase						    =	encoded_inputs.pixel_values.to(a				)

        with torch.no_grad():
          _UpperCamelCase						    =	model(a				)

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

        _UpperCamelCase						    =	torch.tensor(
            [
                [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
                [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
                [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
            ]				).to(a				)
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]       ,							a       ,							atol=1e-1				)				)







      @slow
      def  A_				(			self				)					->      Optional[int]:


        '''simple docstring'''

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

        _UpperCamelCase						    =	prepare_img()
        _UpperCamelCase						    =	image_processor(images=a       ,							return_tensors="""pt"""				)
        _UpperCamelCase						    =	encoded_inputs.pixel_values.to(a				)

        with torch.no_grad():
          _UpperCamelCase						    =	model(a				)

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

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

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





import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


lowerCamelCase__	=							logging.get_logger(__name__)







class        lowerCAmelCase__      (   __lowercase	):

						def __init__(			self       ,							*a       ,							**a				)					->      None:


								'''simple docstring'''

								warnings.warn(
								    """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
								    """ use BeitImageProcessor instead."""       ,							a       ,							)
								super().__init__(*a       ,							**a				)



