import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
      import torch
      from torch import nn

      from transformers import (
          MODEL_MAPPING,
          BeitForImageClassification,
          BeitForMaskedImageModeling,
          BeitForSemanticSegmentation,
          BeitModel,
      )
      from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      import PIL
      from PIL import Image

      from transformers import BeitImageProcessor

class    snake_case       :




  def __init__(   self  :						Optional[int]  ,			a_  :						Any  ,			a_  :						List[str]=100  ,			a_  :						Dict=13  ,			a_  :						str=30  ,			a_  :						str=2  ,			a_  :						Any=3  ,			a_  :						int=True  ,			a_  :						str=True  ,			a_  :						Tuple=32  ,			a_  :						Optional[Any]=4  ,			a_  :						Optional[int]=4  ,			a_  :						List[Any]=37  ,			a_  :						str="gelu"  ,			a_  :						str=0.1  ,			a_  :						Dict=0.1  ,			a_  :						Union[str, Any]=10  ,			a_  :						int=0.02  ,			a_  :						Optional[Any]=3  ,			a_  :						Tuple=None  ,			a_  :						Optional[int]=[0, 1, 2, 3]  ,			)->							List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					int      =		parent
       SCREAMING_SNAKE_CASE__					:					int      =		100
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		batch_size
       SCREAMING_SNAKE_CASE__					:					Any      =		image_size
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		patch_size
       SCREAMING_SNAKE_CASE__					:					str      =		num_channels
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		is_training
       SCREAMING_SNAKE_CASE__					:					Tuple      =		use_labels
       SCREAMING_SNAKE_CASE__					:					Any      =		hidden_size
       SCREAMING_SNAKE_CASE__					:					int      =		num_hidden_layers
       SCREAMING_SNAKE_CASE__					:					Any      =		num_attention_heads
       SCREAMING_SNAKE_CASE__					:					str      =		intermediate_size
       SCREAMING_SNAKE_CASE__					:					str      =		hidden_act
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		hidden_dropout_prob
       SCREAMING_SNAKE_CASE__					:					Tuple      =		attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__					:					Dict      =		type_sequence_label_size
       SCREAMING_SNAKE_CASE__					:					List[str]      =		initializer_range
       SCREAMING_SNAKE_CASE__					:					str      =		scope
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		out_indices
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		num_labels

       # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		(image_size // patch_size) ** 2
       SCREAMING_SNAKE_CASE__					:					Any      =		num_patches + 1




  def 						__lowercase(   self  :						List[str]					)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Dict      =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

       SCREAMING_SNAKE_CASE__					:					Any      =		None
       SCREAMING_SNAKE_CASE__					:					int      =		None
       if self.use_labels:
            SCREAMING_SNAKE_CASE__					:					Any      =		ids_tensor([self.batch_size]  ,			self.type_sequence_label_size					)
            SCREAMING_SNAKE_CASE__					:					Dict      =		ids_tensor([self.batch_size, self.image_size, self.image_size]  ,			self.num_labels					)

       SCREAMING_SNAKE_CASE__					:					int      =		self.get_config()

       return config, pixel_values, labels, pixel_labels




  def 						__lowercase(   self  :						List[Any]					)->							Union[str, Any]:




       """simple docstring"""
       return BeitConfig(
           vocab_size=self.vocab_size  ,			image_size=self.image_size  ,			patch_size=self.patch_size  ,			num_channels=self.num_channels  ,			hidden_size=self.hidden_size  ,			num_hidden_layers=self.num_hidden_layers  ,			num_attention_heads=self.num_attention_heads  ,			intermediate_size=self.intermediate_size  ,			hidden_act=self.hidden_act  ,			hidden_dropout_prob=self.hidden_dropout_prob  ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,			is_decoder=a_  ,			initializer_range=self.initializer_range  ,			out_indices=self.out_indices  ,			)




  def 						__lowercase(   self  :						List[str]  ,			a_  :						str  ,			a_  :						Any  ,			a_  :						Tuple  ,			a_  :						Tuple					)->							List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[str]      =		BeitModel(config=a_					)
       model.to(a_					)
       model.eval()
       SCREAMING_SNAKE_CASE__					:					List[str]      =		model(a_					)
       self.parent.assertEqual(result.last_hidden_state.shape  ,			(self.batch_size, self.seq_length, self.hidden_size)					)




  def 						__lowercase(   self  :						List[str]  ,			a_  :						List[str]  ,			a_  :						List[Any]  ,			a_  :						int  ,			a_  :						Optional[Any]					)->							str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[str]      =		BeitForMaskedImageModeling(config=a_					)
       model.to(a_					)
       model.eval()
       SCREAMING_SNAKE_CASE__					:					Dict      =		model(a_					)
       self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.seq_length - 1, self.vocab_size)					)




  def 						__lowercase(   self  :						Any  ,			a_  :						Any  ,			a_  :						Optional[int]  ,			a_  :						str  ,			a_  :						Any					)->							Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.type_sequence_label_size
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		BeitForImageClassification(a_					)
       model.to(a_					)
       model.eval()
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		model(a_  ,			labels=a_					)
       self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.type_sequence_label_size)					)

       # test greyscale images
       SCREAMING_SNAKE_CASE__					:					Any      =		1
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		BeitForImageClassification(a_					)
       model.to(a_					)
       model.eval()

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		model(a_  ,			labels=a_					)
       self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.type_sequence_label_size)					)




  def 						__lowercase(   self  :						Dict  ,			a_  :						Dict  ,			a_  :						Optional[int]  ,			a_  :						int  ,			a_  :						Optional[int]					)->							int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.num_labels
       SCREAMING_SNAKE_CASE__					:					Any      =		BeitForSemanticSegmentation(a_					)
       model.to(a_					)
       model.eval()
       SCREAMING_SNAKE_CASE__					:					List[str]      =		model(a_					)
       self.parent.assertEqual(
           result.logits.shape  ,			(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)					)
       SCREAMING_SNAKE_CASE__					:					int      =		model(a_  ,			labels=a_					)
       self.parent.assertEqual(
           result.logits.shape  ,			(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)					)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					str      =		self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					List[str]      =		config_and_inputs
       SCREAMING_SNAKE_CASE__					:					int      =		{'pixel_values': pixel_values}
       return config, inputs_dict




@require_torch
class    snake_case       (   UpperCamelCase_ ,    UpperCamelCase_ ,    unittest.TestCase    ):
  lowercase_								=  (
      (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
      if is_torch_available()
      else ()
  )
  lowercase_								=  (
      {
          'feature-extraction': BeitModel,
          'image-classification': BeitForImageClassification,
          'image-segmentation': BeitForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  lowercase_								=  False
  lowercase_								=  False
  lowercase_								=  False




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		BeitModelTester(self					)
       SCREAMING_SNAKE_CASE__					:					str      =		ConfigTester(self  ,			config_class=a_  ,			has_text_modality=a_  ,			hidden_size=37					)




  def 						__lowercase(   self  :						Any					)->							Dict:




       """simple docstring"""
       self.config_tester.run_common_tests()




  @unittest.skip(reason='BEiT does not use inputs_embeds'					)
  def 						__lowercase(   self  :						Optional[Any]					)->							Union[str, Any]:




       """simple docstring"""
       pass




  @require_torch_multi_gpu
  @unittest.skip(reason='BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'					)
  def 						__lowercase(   self  :						int					)->							Optional[Any]:




       """simple docstring"""
       pass




  def 						__lowercase(   self  :						int					)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		model_class(a_					)
            self.assertIsInstance(model.get_input_embeddings()  ,			(nn.Module)					)
            SCREAMING_SNAKE_CASE__					:					Tuple      =		model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(a_  ,			nn.Linear					)					)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		self.model_tester.prepare_config_and_inputs_for_common()

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

            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		['pixel_values']
            self.assertListEqual(arg_names[:1]  ,			a_					)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a_					)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*a_					)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*a_					)




  def 						__lowercase(   self  :						Optional[int]					)->							Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*a_					)




  def 						__lowercase(   self  :						int					)->							str:




       """simple docstring"""
       if not self.model_tester.is_training:
            return

       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Tuple      =		self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE__					:					str      =		True

       for model_class in self.all_model_classes:
            # we don't test BeitForMaskedImageModeling
            if model_class in [*get_values(a_					), BeitForMaskedImageModeling]:
                 continue

            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		model_class(a_					)
            model.to(a_					)
            model.train()
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		self._prepare_for_class(a_  ,			a_  ,			return_labels=a_					)
            SCREAMING_SNAKE_CASE__					:					Dict      =		model(**a_					).loss
            loss.backward()




  def 						__lowercase(   self  :						Optional[Any]					)->							Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		self.model_tester.prepare_config_and_inputs_for_common()
       if not self.model_tester.is_training:
            return

       SCREAMING_SNAKE_CASE__					:					str      =		False
       SCREAMING_SNAKE_CASE__					:					Tuple      =		True

       for model_class in self.all_model_classes:
            # we don't test BeitForMaskedImageModeling
            if (
                model_class in [*get_values(a_					), BeitForMaskedImageModeling]
                or not model_class.supports_gradient_checkpointing
            ):
                 continue

            SCREAMING_SNAKE_CASE__					:					List[str]      =		model_class(a_					)
            model.gradient_checkpointing_enable()
            model.to(a_					)
            model.train()
            SCREAMING_SNAKE_CASE__					:					Any      =		self._prepare_for_class(a_  ,			a_  ,			return_labels=a_					)
            SCREAMING_SNAKE_CASE__					:					str      =		model(**a_					).loss
            loss.backward()




  def 						__lowercase(   self  :						List[Any]					)->							Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					str      =		self.model_tester.prepare_config_and_inputs_for_common()

       SCREAMING_SNAKE_CASE__					:					List[str]      =		_config_zero_init(a_					)
       for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__					:					Any      =		model_class(config=a_					)
            for name, param in model.named_parameters():
                 # we skip lambda parameters as these require special initial values
                 # determined by config.layer_scale_init_value
                 if "lambda" in name:
                      continue
                 if param.requires_grad:
                      self.assertIn(
                          ((param.data.mean() * 1e9).round() / 1e9).item()  ,			[0.0, 1.0]  ,			msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''  ,			)




  @slow
  def 						__lowercase(   self  :						Any					)->							str:




       """simple docstring"""
       for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__					:					List[str]      =		BeitModel.from_pretrained(a_					)
            self.assertIsNotNone(a_					)







def      _a					(   ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					List[Any]      =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
     return image







@require_torch
@require_vision
class    snake_case       (   unittest.TestCase    ):




  @cached_property
  def 						__lowercase(   self  :						str					)->							List[Any]:




       """simple docstring"""
       return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'					) if is_vision_available() else None




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		BeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'					).to(a_					)

       SCREAMING_SNAKE_CASE__					:					int      =		self.default_image_processor
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		prepare_img()
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		image_processor(images=a_  ,			return_tensors='pt'					).pixel_values.to(a_					)

       # prepare bool_masked_pos
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		torch.ones((1, 196)  ,			dtype=torch.bool					).to(a_					)

       # forward pass
       with torch.no_grad():
            SCREAMING_SNAKE_CASE__					:					str      =		model(pixel_values=a_  ,			bool_masked_pos=a_					)
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		outputs.logits

       # verify the logits
       SCREAMING_SNAKE_CASE__					:					Tuple      =		torch.Size((1, 196, 8192)					)
       self.assertEqual(logits.shape  ,			a_					)

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		torch.tensor(
           [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]					).to(a_					)

       self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]  ,			a_  ,			atol=1e-2					)					)




  @slow
  def 						__lowercase(   self  :						List[Any]					)->							Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'					).to(a_					)

       SCREAMING_SNAKE_CASE__					:					List[str]      =		self.default_image_processor
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		prepare_img()
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		image_processor(images=a_  ,			return_tensors='pt'					).to(a_					)

       # forward pass
       with torch.no_grad():
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		model(**a_					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		outputs.logits

       # verify the logits
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		torch.Size((1, 1000)					)
       self.assertEqual(logits.shape  ,			a_					)

       SCREAMING_SNAKE_CASE__					:					int      =		torch.tensor([-1.2385, -1.0987, -1.0108]					).to(a_					)

       self.assertTrue(torch.allclose(logits[0, :3]  ,			a_  ,			atol=1e-4					)					)

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		281
       self.assertEqual(logits.argmax(-1					).item()  ,			a_					)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		BeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'					).to(
           a_					)

       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.default_image_processor
       SCREAMING_SNAKE_CASE__					:					Dict      =		prepare_img()
       SCREAMING_SNAKE_CASE__					:					Tuple      =		image_processor(images=a_  ,			return_tensors='pt'					).to(a_					)

       # forward pass
       with torch.no_grad():
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		model(**a_					)
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		outputs.logits

       # verify the logits
       SCREAMING_SNAKE_CASE__					:					Dict      =		torch.Size((1, 2_1841)					)
       self.assertEqual(logits.shape  ,			a_					)

       SCREAMING_SNAKE_CASE__					:					str      =		torch.tensor([1.6881, -0.2787, 0.5901]					).to(a_					)

       self.assertTrue(torch.allclose(logits[0, :3]  ,			a_  ,			atol=1e-4					)					)

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		2396
       self.assertEqual(logits.argmax(-1					).item()  ,			a_					)




  @slow
  def 						__lowercase(   self  :						Tuple					)->							Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'					)
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		model.to(a_					)

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		BeitImageProcessor(do_resize=a_  ,			size=640  ,			do_center_crop=a_					)

       SCREAMING_SNAKE_CASE__					:					List[str]      =		load_dataset('hf-internal-testing/fixtures_ade20k'  ,			split='test'					)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		Image.open(ds[0]['file']					)
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		image_processor(images=a_  ,			return_tensors='pt'					).to(a_					)

       # forward pass
       with torch.no_grad():
            SCREAMING_SNAKE_CASE__					:					str      =		model(**a_					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		outputs.logits

       # verify the logits
       SCREAMING_SNAKE_CASE__					:					int      =		torch.Size((1, 150, 160, 160)					)
       self.assertEqual(logits.shape  ,			a_					)

       SCREAMING_SNAKE_CASE__					:					str      =		version.parse(PIL.__version__					) < version.parse('9.0.0'					)

       if is_pillow_less_than_a:
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		torch.tensor(
                [
                    [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
                    [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
                    [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
                ]  ,			device=a_  ,			)
       else:
            SCREAMING_SNAKE_CASE__					:					Any      =		torch.tensor(
                [
                    [[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
                    [[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
                    [[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
                ]  ,			device=a_  ,			)

       self.assertTrue(torch.allclose(logits[0, :3, :3, :3]  ,			a_  ,			atol=1e-4					)					)




  @slow
  def 						__lowercase(   self  :						Union[str, Any]					)->							Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					str      =		BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'					)
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		model.to(a_					)

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		BeitImageProcessor(do_resize=a_  ,			size=640  ,			do_center_crop=a_					)

       SCREAMING_SNAKE_CASE__					:					str      =		load_dataset('hf-internal-testing/fixtures_ade20k'  ,			split='test'					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		Image.open(ds[0]['file']					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		image_processor(images=a_  ,			return_tensors='pt'					).to(a_					)

       # forward pass
       with torch.no_grad():
            SCREAMING_SNAKE_CASE__					:					Dict      =		model(**a_					)

       SCREAMING_SNAKE_CASE__					:					List[str]      =		outputs.logits.detach().cpu()

       SCREAMING_SNAKE_CASE__					:					int      =		image_processor.post_process_semantic_segmentation(outputs=a_  ,			target_sizes=[(500, 300)]					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		torch.Size((500, 300)					)
       self.assertEqual(segmentation[0].shape  ,			a_					)

       SCREAMING_SNAKE_CASE__					:					Dict      =		image_processor.post_process_semantic_segmentation(outputs=a_					)
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		torch.Size((160, 160)					)
       self.assertEqual(segmentation[0].shape  ,			a_					)






"""simple docstring"""
import math




def 					a__						(						lowerCAmelCase    )	-> list[int]:
      UpperCAmelCase__			:							Optional[int]							=    []
      UpperCAmelCase__			:							Union[str, Any]							=    2
      UpperCAmelCase__			:							List[Any]							=    int(math.sqrt(lowerCAmelCase    )    )  # Size of every segment
      UpperCAmelCase__			:							Dict							=    [True] * (end + 1)
      UpperCAmelCase__			:							str							=    []

      while start <= end:
            if temp[start] is True:
                  in_prime.append(lowerCAmelCase    )
                  for i in range(start * start			,	end + 1			,	lowerCAmelCase    ):
                        UpperCAmelCase__			:							Optional[Any]							=    False
            start += 1
      prime += in_prime

      UpperCAmelCase__			:							Optional[Any]							=    end + 1
      UpperCAmelCase__			:							Dict							=    min(2 * end			,	lowerCAmelCase    )

      while low <= n:
            UpperCAmelCase__			:							Optional[int]							=    [True] * (high - low + 1)
            for each in in_prime:
                  UpperCAmelCase__			:							Optional[int]							=    math.floor(low / each    ) * each
                  if t < low:
                        t += each

                  for j in range(lowerCAmelCase			,	high + 1			,	lowerCAmelCase    ):
                        UpperCAmelCase__			:							int							=    False

            for j in range(len(lowerCAmelCase    )    ):
                  if temp[j] is True:
                        prime.append(j + low    )

            UpperCAmelCase__			:							Dict							=    high + 1
            UpperCAmelCase__			:							int							=    min(high + end			,	lowerCAmelCase    )

      return prime





print(sieve(10**6))

