


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 		__a				:
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=100      ,   _SCREAMING_SNAKE_CASE=13      ,   _SCREAMING_SNAKE_CASE=30      ,   _SCREAMING_SNAKE_CASE=2      ,   _SCREAMING_SNAKE_CASE=3      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=32      ,   _SCREAMING_SNAKE_CASE=4      ,   _SCREAMING_SNAKE_CASE=4      ,   _SCREAMING_SNAKE_CASE=37      ,   _SCREAMING_SNAKE_CASE="gelu"      ,   _SCREAMING_SNAKE_CASE=0.1      ,   _SCREAMING_SNAKE_CASE=0.1      ,   _SCREAMING_SNAKE_CASE=10      ,   _SCREAMING_SNAKE_CASE=0.02      ,   _SCREAMING_SNAKE_CASE=3      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=[0, 1, 2, 3]      ,   )			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      parent
									_UpperCAmelCase					=      100
									_UpperCAmelCase					=      batch_size
									_UpperCAmelCase					=      image_size
									_UpperCAmelCase					=      patch_size
									_UpperCAmelCase					=      num_channels
									_UpperCAmelCase					=      is_training
									_UpperCAmelCase					=      use_labels
									_UpperCAmelCase					=      hidden_size
									_UpperCAmelCase					=      num_hidden_layers
									_UpperCAmelCase					=      num_attention_heads
									_UpperCAmelCase					=      intermediate_size
									_UpperCAmelCase					=      hidden_act
									_UpperCAmelCase					=      hidden_dropout_prob
									_UpperCAmelCase					=      attention_probs_dropout_prob
									_UpperCAmelCase					=      type_sequence_label_size
									_UpperCAmelCase					=      initializer_range
									_UpperCAmelCase					=      scope
									_UpperCAmelCase					=      out_indices
									_UpperCAmelCase					=      num_labels

									# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
									_UpperCAmelCase					=      (image_size // patch_size) ** 2
									_UpperCAmelCase					=      num_patches + 1
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





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

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

									_UpperCAmelCase					=      self.get_config()

									return config, pixel_values, labels, pixel_labels
		def      UpperCAmelCase__    (      self						)			->	Optional[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=_SCREAMING_SNAKE_CASE      ,   initializer_range=self.initializer_range      ,   out_indices=self.out_indices      ,   )
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      BeitModel(config=_SCREAMING_SNAKE_CASE						)
									model.to(_SCREAMING_SNAKE_CASE						)
									model.eval()
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE						)
									self.parent.assertEqual(result.last_hidden_state.shape      ,   (self.batch_size, self.seq_length, self.hidden_size)						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      BeitForMaskedImageModeling(config=_SCREAMING_SNAKE_CASE						)
									model.to(_SCREAMING_SNAKE_CASE						)
									model.eval()
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE						)
									self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.seq_length - 1, self.vocab_size)						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      self.type_sequence_label_size
									_UpperCAmelCase					=      BeitForImageClassification(_SCREAMING_SNAKE_CASE						)
									model.to(_SCREAMING_SNAKE_CASE						)
									model.eval()
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   labels=_SCREAMING_SNAKE_CASE						)
									self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.type_sequence_label_size)						)

									# test greyscale images
									_UpperCAmelCase					=      1
									_UpperCAmelCase					=      BeitForImageClassification(_SCREAMING_SNAKE_CASE						)
									model.to(_SCREAMING_SNAKE_CASE						)
									model.eval()

									_UpperCAmelCase					=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   labels=_SCREAMING_SNAKE_CASE						)
									self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.type_sequence_label_size)						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.num_labels
									_UpperCAmelCase					=      BeitForSemanticSegmentation(_SCREAMING_SNAKE_CASE						)
									model.to(_SCREAMING_SNAKE_CASE						)
									model.eval()
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE						)
									self.parent.assertEqual(
									    result.logits.shape      ,   (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)						)
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   labels=_SCREAMING_SNAKE_CASE						)
									self.parent.assertEqual(
									    result.logits.shape      ,   (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)						)






		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      self.prepare_config_and_inputs()
									_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      config_and_inputs
									_UpperCAmelCase					=      {'pixel_values': pixel_values}
									return config, inputs_dict



@require_torch
class 		__a				(    UpperCAmelCase   ,				UpperCAmelCase   ,				unittest.TestCase      ):
		_a     :  List[str]           =     (
		    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
		    if is_torch_available()
		    else ()
		)
		_a     :  Optional[int]           =     (
		    {
		        'feature-extraction': BeitModel,
		        'image-classification': BeitForImageClassification,
		        'image-segmentation': BeitForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		_a     :  Tuple           =     False
		_a     :  str           =     False
		_a     :  List[Any]           =     False
		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      BeitModelTester(self						)
									_UpperCAmelCase					=      ConfigTester(self      ,   config_class=_SCREAMING_SNAKE_CASE      ,   has_text_modality=_SCREAMING_SNAKE_CASE      ,   hidden_size=37						)
		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





									self.config_tester.run_common_tests()
		@unittest.skip(reason='BEiT does not use inputs_embeds'						)
		def      UpperCAmelCase__    (      self						)			->	Optional[int]:




									"""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      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									pass
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase		,	_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																_UpperCAmelCase					=      model_class(_SCREAMING_SNAKE_CASE						)
																self.assertIsInstance(model.get_input_embeddings()      ,   (nn.Module)						)
																_UpperCAmelCase					=      model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE      ,   nn.Linear						)						)
		def      UpperCAmelCase__    (      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(_SCREAMING_SNAKE_CASE						)
																_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]      ,   _SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	Union[str, 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:
																# we don't test BeitForMaskedImageModeling
																if model_class in [*get_values(_SCREAMING_SNAKE_CASE						), BeitForMaskedImageModeling]:
																							continue

																_UpperCAmelCase					=      model_class(_SCREAMING_SNAKE_CASE						)
																model.to(_SCREAMING_SNAKE_CASE						)
																model.train()
																_UpperCAmelCase					=      self._prepare_for_class(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   return_labels=_SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase					=      model(**_SCREAMING_SNAKE_CASE						).loss
																loss.backward()
		def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase		,	_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs_for_common()
									if not self.model_tester.is_training:
																return

									_UpperCAmelCase					=      False
									_UpperCAmelCase					=      True

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

																_UpperCAmelCase					=      model_class(_SCREAMING_SNAKE_CASE						)
																model.gradient_checkpointing_enable()
																model.to(_SCREAMING_SNAKE_CASE						)
																model.train()
																_UpperCAmelCase					=      self._prepare_for_class(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   return_labels=_SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase					=      model(**_SCREAMING_SNAKE_CASE						).loss
																loss.backward()
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase		,	_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs_for_common()

									_UpperCAmelCase					=      _config_zero_init(_SCREAMING_SNAKE_CASE						)
									for model_class in self.all_model_classes:
																_UpperCAmelCase					=      model_class(config=_SCREAMING_SNAKE_CASE						)
																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      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_UpperCAmelCase					=      BeitModel.from_pretrained(_SCREAMING_SNAKE_CASE						)
																self.assertIsNotNone(_SCREAMING_SNAKE_CASE						)



def 	lowerCAmelCase__			(      )  ->		Any:


							'''simple docstring'''





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







@require_torch
@require_vision
class 		__a				(    unittest.TestCase      ):
		@cached_property
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'						) if is_vision_available() else None
		@slow
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      BeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'						).to(_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      self.default_image_processor
									_UpperCAmelCase					=      prepare_img()
									_UpperCAmelCase					=      image_processor(images=_SCREAMING_SNAKE_CASE      ,   return_tensors='pt'						).pixel_values.to(_SCREAMING_SNAKE_CASE						)

									# prepare bool_masked_pos
									_UpperCAmelCase					=      torch.ones((1, 196)      ,   dtype=torch.bool						).to(_SCREAMING_SNAKE_CASE						)

									# forward pass
									with torch.no_grad():
																_UpperCAmelCase					=      model(pixel_values=_SCREAMING_SNAKE_CASE      ,   bool_masked_pos=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      outputs.logits

									# verify the logits
									_UpperCAmelCase					=      torch.Size((1, 196, 8192)						)
									self.assertEqual(logits.shape      ,   _SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      torch.tensor(
									    [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]						).to(_SCREAMING_SNAKE_CASE						)

									self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]      ,   _SCREAMING_SNAKE_CASE      ,   atol=1e-2						)						)
		@slow
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'						).to(_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      self.default_image_processor
									_UpperCAmelCase					=      prepare_img()
									_UpperCAmelCase					=      image_processor(images=_SCREAMING_SNAKE_CASE      ,   return_tensors='pt'						).to(_SCREAMING_SNAKE_CASE						)

									# forward pass
									with torch.no_grad():
																_UpperCAmelCase					=      model(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      outputs.logits

									# verify the logits
									_UpperCAmelCase					=      torch.Size((1, 1000)						)
									self.assertEqual(logits.shape      ,   _SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      torch.tensor([-1.2385, -1.0987, -1.0108]						).to(_SCREAMING_SNAKE_CASE						)

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

									_UpperCAmelCase					=      281
									self.assertEqual(logits.argmax(-1						).item()      ,   _SCREAMING_SNAKE_CASE						)
		@slow
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      BeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'						).to(
									    _SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      self.default_image_processor
									_UpperCAmelCase					=      prepare_img()
									_UpperCAmelCase					=      image_processor(images=_SCREAMING_SNAKE_CASE      ,   return_tensors='pt'						).to(_SCREAMING_SNAKE_CASE						)

									# forward pass
									with torch.no_grad():
																_UpperCAmelCase					=      model(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      outputs.logits

									# verify the logits
									_UpperCAmelCase					=      torch.Size((1, 21841)						)
									self.assertEqual(logits.shape      ,   _SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      torch.tensor([1.6881, -0.2787, 0.5901]						).to(_SCREAMING_SNAKE_CASE						)

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

									_UpperCAmelCase					=      2396
									self.assertEqual(logits.argmax(-1						).item()      ,   _SCREAMING_SNAKE_CASE						)
		@slow
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									_UpperCAmelCase					=      BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'						)
									_UpperCAmelCase					=      model.to(_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      BeitImageProcessor(do_resize=_SCREAMING_SNAKE_CASE      ,   size=640      ,   do_center_crop=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      load_dataset('hf-internal-testing/fixtures_ade20k'      ,   split='test'						)
									_UpperCAmelCase					=      Image.open(ds[0]['file']						)
									_UpperCAmelCase					=      image_processor(images=_SCREAMING_SNAKE_CASE      ,   return_tensors='pt'						).to(_SCREAMING_SNAKE_CASE						)

									# forward pass
									with torch.no_grad():
																_UpperCAmelCase					=      model(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      outputs.logits

									# verify the logits
									_UpperCAmelCase					=      torch.Size((1, 150, 160, 160)						)
									self.assertEqual(logits.shape      ,   _SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      version.parse(PIL.__version__						) < version.parse('9.0.0'						)

									if is_pillow_less_than_a:
																_UpperCAmelCase					=      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=_SCREAMING_SNAKE_CASE      ,   )
									else:
																_UpperCAmelCase					=      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=_SCREAMING_SNAKE_CASE      ,   )

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






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




									"""simple docstring"""





									_UpperCAmelCase					=      BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'						)
									_UpperCAmelCase					=      model.to(_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      BeitImageProcessor(do_resize=_SCREAMING_SNAKE_CASE      ,   size=640      ,   do_center_crop=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      load_dataset('hf-internal-testing/fixtures_ade20k'      ,   split='test'						)
									_UpperCAmelCase					=      Image.open(ds[0]['file']						)
									_UpperCAmelCase					=      image_processor(images=_SCREAMING_SNAKE_CASE      ,   return_tensors='pt'						).to(_SCREAMING_SNAKE_CASE						)

									# forward pass
									with torch.no_grad():
																_UpperCAmelCase					=      model(**_SCREAMING_SNAKE_CASE						)

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

									_UpperCAmelCase					=      image_processor.post_process_semantic_segmentation(outputs=_SCREAMING_SNAKE_CASE      ,   target_sizes=[(500, 300)]						)
									_UpperCAmelCase					=      torch.Size((500, 300)						)
									self.assertEqual(segmentation[0].shape      ,   _SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      image_processor.post_process_semantic_segmentation(outputs=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      torch.Size((160, 160)						)
									self.assertEqual(segmentation[0].shape      ,   _SCREAMING_SNAKE_CASE						)




from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def 	lowerCAmelCase__			(      a__:		NDArray[floataa]      ,  a__:		NDArray[floataa]      ,  a__:		list[int]      ,  a__:		int      ,  )  ->		list[float]:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      coefficient_matrix.shape
							_UpperCAmelCase		,	_UpperCAmelCase					=      constant_matrix.shape

							if rowsa != colsa:
														_UpperCAmelCase					=      F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
														raise ValueError(a__	)

							if colsa != 1:
														_UpperCAmelCase					=      F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
														raise ValueError(a__	)

							if rowsa != rowsa:
														_UpperCAmelCase					=      (
														    'Coefficient and constant matrices dimensions must be nxn and nx1 but '
														    F'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
														)
														raise ValueError(a__	)

							if len(a__	) != rowsa:
														_UpperCAmelCase					=      (
														    'Number of initial values must be equal to number of rows in coefficient '
														    F'''matrix but received {len(a__	)} and {rowsa}'''
														)
														raise ValueError(a__	)

							if iterations <= 0:
														raise ValueError('Iterations must be at least 1'	)

							_UpperCAmelCase					=      np.concatenate(
							    (coefficient_matrix, constant_matrix)      ,  axis=1	)

							_UpperCAmelCase		,	_UpperCAmelCase					=      table.shape

							strictly_diagonally_dominant(a__	)

							# Iterates the whole matrix for given number of times
							for _ in range(a__	):
														_UpperCAmelCase					=      []
														for row in range(a__	):
																					_UpperCAmelCase					=      0
																					for col in range(a__	):
																												if col == row:
																																			_UpperCAmelCase					=      table[row][col]
																												elif col == cols - 1:
																																			_UpperCAmelCase					=      table[row][col]
																												else:
																																			temp += (-1) * table[row][col] * init_val[col]
																					_UpperCAmelCase					=      (temp + val) / denom
																					new_val.append(a__	)
														_UpperCAmelCase					=      new_val

							return [float(a__	) for i in new_val]

def 	lowerCAmelCase__			(      a__:		NDArray[floataa]	)  ->		bool:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      table.shape

							_UpperCAmelCase					=      True

							for i in range(0      ,  a__	):
														_UpperCAmelCase					=      0
														for j in range(0      ,  cols - 1	):
																					if i == j:
																												continue
																					else:
																												total += table[i][j]

														if table[i][i] <= total:
																					raise ValueError('Coefficient matrix is not strictly diagonally dominant'	)

							return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
		import doctest

		doctest.testmod()
