



'''simple docstring'''
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 				lowercase__      :





	def __init__(					self   :							Union[str, Any]					,lowerCamelCase__   :							Optional[Any]					,lowerCamelCase__   :							int=100					,lowerCamelCase__   :							List[str]=13					,lowerCamelCase__   :							Optional[Any]=30					,lowerCamelCase__   :							List[str]=2					,lowerCamelCase__   :							Tuple=3					,lowerCamelCase__   :							Optional[int]=True					,lowerCamelCase__   :							int=True					,lowerCamelCase__   :							Dict=32					,lowerCamelCase__   :							Optional[Any]=4					,lowerCamelCase__   :							List[str]=4					,lowerCamelCase__   :							Dict=37					,lowerCamelCase__   :							int="gelu"					,lowerCamelCase__   :							List[Any]=0.1					,lowerCamelCase__   :							int=0.1					,lowerCamelCase__   :							Any=10					,lowerCamelCase__   :							Union[str, Any]=0.0_2					,lowerCamelCase__   :							str=3					,lowerCamelCase__   :							int=None					,lowerCamelCase__   :							Optional[Any]=[0, 1, 2, 3]					,):







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

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





	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

							_UpperCamelCase   :						List[str]												=  self.get_config()

							return config, pixel_values, labels, pixel_labels





	def       UpperCamelCase_    (					self   :							Tuple					):







							'''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=lowerCamelCase__					,initializer_range=self.initializer_range					,out_indices=self.out_indices					,)





	def       UpperCamelCase_    (					self   :							str					,lowerCamelCase__   :							Optional[int]					,lowerCamelCase__   :							Optional[Any]					,lowerCamelCase__   :							int					,lowerCamelCase__   :							Optional[int]					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[int]												=  BeitModel(config=lowerCamelCase__					)
							model.to(lowerCamelCase__					)
							model.eval()
							_UpperCamelCase   :						Optional[Any]												=  model(lowerCamelCase__					)
							self.parent.assertEqual(result.last_hidden_state.shape					,(self.batch_size, self.seq_length, self.hidden_size)					)





	def       UpperCamelCase_    (					self   :							Union[str, Any]					,lowerCamelCase__   :							Optional[Any]					,lowerCamelCase__   :							Union[str, Any]					,lowerCamelCase__   :							Union[str, Any]					,lowerCamelCase__   :							Dict					):







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





	def       UpperCamelCase_    (					self   :							Optional[int]					,lowerCamelCase__   :							List[Any]					,lowerCamelCase__   :							Dict					,lowerCamelCase__   :							List[Any]					,lowerCamelCase__   :							int					):







							'''simple docstring'''
							_UpperCamelCase   :						str												=  self.type_sequence_label_size
							_UpperCamelCase   :						Tuple												=  BeitForImageClassification(lowerCamelCase__					)
							model.to(lowerCamelCase__					)
							model.eval()
							_UpperCamelCase   :						Any												=  model(lowerCamelCase__					,labels=lowerCamelCase__					)
							self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.type_sequence_label_size)					)

							# test greyscale images
							_UpperCamelCase   :						Tuple												=  1
							_UpperCamelCase   :						Dict												=  BeitForImageClassification(lowerCamelCase__					)
							model.to(lowerCamelCase__					)
							model.eval()

							_UpperCamelCase   :						Tuple												=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
							_UpperCamelCase   :						Optional[int]												=  model(lowerCamelCase__					,labels=lowerCamelCase__					)
							self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.type_sequence_label_size)					)





	def       UpperCamelCase_    (					self   :							Optional[int]					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							Any					,lowerCamelCase__   :							Union[str, Any]					,lowerCamelCase__   :							Optional[int]					):







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





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  self.prepare_config_and_inputs()
							_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase   :						Any												=  config_and_inputs
							_UpperCamelCase   :						int												=  {'pixel_values': pixel_values}
							return config, inputs_dict






@require_torch
class 				lowercase__      (				lowercase		,   lowercase		,   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       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							_UpperCamelCase   :						Union[str, Any]												=  BeitModelTester(self					)
							_UpperCamelCase   :						Dict												=  ConfigTester(self					,config_class=lowerCamelCase__					,has_text_modality=lowerCamelCase__					,hidden_size=37					)





	def       UpperCamelCase_    (					self   :							Dict					):







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





	@unittest.skip(reason='BEiT does not use inputs_embeds'					)
	def       UpperCamelCase_    (					self   :							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       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							pass





	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							_UpperCamelCase							,     _UpperCamelCase   :						List[Any]												=  self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													_UpperCamelCase   :						Optional[int]												=  model_class(lowerCamelCase__					)
													self.assertIsInstance(model.get_input_embeddings()					,(nn.Module)					)
													_UpperCamelCase   :						Dict												=  model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(lowerCamelCase__					,nn.Linear					)					)





	def       UpperCamelCase_    (					self   :							Any					):







							'''simple docstring'''
							_UpperCamelCase							,     _UpperCamelCase   :						List[str]												=  self.model_tester.prepare_config_and_inputs_for_common()

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

													_UpperCamelCase   :						Optional[Any]												=  ['pixel_values']
													self.assertListEqual(arg_names[:1]					,lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							_UpperCamelCase   :						Tuple												=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							_UpperCamelCase   :						Union[str, Any]												=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__					)





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







							'''simple docstring'''
							_UpperCamelCase   :						Optional[int]												=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							Dict					):







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

							_UpperCamelCase							,     _UpperCamelCase   :						Optional[Any]												=  self.model_tester.prepare_config_and_inputs_for_common()
							_UpperCamelCase   :						List[Any]												=  True

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

													_UpperCamelCase   :						str												=  model_class(lowerCamelCase__					)
													model.to(lowerCamelCase__					)
													model.train()
													_UpperCamelCase   :						Tuple												=  self._prepare_for_class(lowerCamelCase__					,lowerCamelCase__					,return_labels=lowerCamelCase__					)
													_UpperCamelCase   :						Any												=  model(**lowerCamelCase__					).loss
													loss.backward()





	def       UpperCamelCase_    (					self   :							str					):







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

							_UpperCamelCase   :						Optional[int]												=  False
							_UpperCamelCase   :						str												=  True

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

													_UpperCamelCase   :						Any												=  model_class(lowerCamelCase__					)
													model.gradient_checkpointing_enable()
													model.to(lowerCamelCase__					)
													model.train()
													_UpperCamelCase   :						Any												=  self._prepare_for_class(lowerCamelCase__					,lowerCamelCase__					,return_labels=lowerCamelCase__					)
													_UpperCamelCase   :						Union[str, Any]												=  model(**lowerCamelCase__					).loss
													loss.backward()





	def       UpperCamelCase_    (					self   :							Any					):







							'''simple docstring'''
							_UpperCamelCase							,     _UpperCamelCase   :						List[str]												=  self.model_tester.prepare_config_and_inputs_for_common()

							_UpperCamelCase   :						List[str]												=  _config_zero_init(lowerCamelCase__					)
							for model_class in self.all_model_classes:
													_UpperCamelCase   :						str												=  model_class(config=lowerCamelCase__					)
													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   :							Dict					):







							'''simple docstring'''
							for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_UpperCamelCase   :						Tuple												=  BeitModel.from_pretrained(lowerCamelCase__					)
													self.assertIsNotNone(lowerCamelCase__					)






def A__       (	):
						_UpperCamelCase   :						str												=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
						return image






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





	@cached_property
	def       UpperCamelCase_    (					self   :							List[Any]					):







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





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







							'''simple docstring'''
							_UpperCamelCase   :						Any												=  BeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'					).to(lowerCamelCase__					)

							_UpperCamelCase   :						Tuple												=  self.default_image_processor
							_UpperCamelCase   :						Union[str, Any]												=  prepare_img()
							_UpperCamelCase   :						List[str]												=  image_processor(images=lowerCamelCase__					,return_tensors='pt'					).pixel_values.to(lowerCamelCase__					)

							# prepare bool_masked_pos
							_UpperCamelCase   :						Any												=  torch.ones((1, 196)					,dtype=torch.bool					).to(lowerCamelCase__					)

							# forward pass
							with torch.no_grad():
													_UpperCamelCase   :						Dict												=  model(pixel_values=lowerCamelCase__					,bool_masked_pos=lowerCamelCase__					)
							_UpperCamelCase   :						Dict												=  outputs.logits

							# verify the logits
							_UpperCamelCase   :						str												=  torch.Size((1, 196, 8192)					)
							self.assertEqual(logits.shape					,lowerCamelCase__					)

							_UpperCamelCase   :						Optional[int]												=  torch.tensor(
							    [[-3.2_4_3_7, 0.5_0_7_2, -1_3.9_1_7_4], [-3.2_4_5_6, 0.4_9_4_8, -1_3.9_4_0_1], [-3.2_0_3_3, 0.5_1_2_1, -1_3.8_5_5_0]]					).to(lowerCamelCase__					)

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





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







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

							_UpperCamelCase   :						str												=  self.default_image_processor
							_UpperCamelCase   :						Any												=  prepare_img()
							_UpperCamelCase   :						Optional[Any]												=  image_processor(images=lowerCamelCase__					,return_tensors='pt'					).to(lowerCamelCase__					)

							# forward pass
							with torch.no_grad():
													_UpperCamelCase   :						Any												=  model(**lowerCamelCase__					)
							_UpperCamelCase   :						Dict												=  outputs.logits

							# verify the logits
							_UpperCamelCase   :						List[Any]												=  torch.Size((1, 1000)					)
							self.assertEqual(logits.shape					,lowerCamelCase__					)

							_UpperCamelCase   :						Optional[Any]												=  torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]					).to(lowerCamelCase__					)

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

							_UpperCamelCase   :						List[str]												=  281
							self.assertEqual(logits.argmax(-1					).item()					,lowerCamelCase__					)





	@slow
	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[int]												=  BeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'					).to(
							    lowerCamelCase__					)

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

							# forward pass
							with torch.no_grad():
													_UpperCamelCase   :						Optional[int]												=  model(**lowerCamelCase__					)
							_UpperCamelCase   :						Any												=  outputs.logits

							# verify the logits
							_UpperCamelCase   :						Any												=  torch.Size((1, 21841)					)
							self.assertEqual(logits.shape					,lowerCamelCase__					)

							_UpperCamelCase   :						List[str]												=  torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]					).to(lowerCamelCase__					)

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

							_UpperCamelCase   :						Optional[Any]												=  2396
							self.assertEqual(logits.argmax(-1					).item()					,lowerCamelCase__					)





	@slow
	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'					)
							_UpperCamelCase   :						List[Any]												=  model.to(lowerCamelCase__					)

							_UpperCamelCase   :						str												=  BeitImageProcessor(do_resize=lowerCamelCase__					,size=640					,do_center_crop=lowerCamelCase__					)

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

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

							# verify the logits
							_UpperCamelCase   :						Optional[Any]												=  torch.Size((1, 150, 160, 160)					)
							self.assertEqual(logits.shape					,lowerCamelCase__					)

							_UpperCamelCase   :						Tuple												=  version.parse(PIL.__version__					) < version.parse('9.0.0'					)

							if is_pillow_less_than_a:
													_UpperCamelCase   :						List[Any]												=  torch.tensor(
													    [
													        [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
													        [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
													        [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
													    ]					,device=lowerCamelCase__					,)
							else:
													_UpperCamelCase   :						List[str]												=  torch.tensor(
													    [
													        [[-4.8_9_6_0, -2.3_6_8_8, -3.0_3_5_5], [-2.8_4_7_8, -0.9_8_3_6, -1.7_4_1_8], [-2.9_4_4_9, -1.3_3_3_2, -2.1_4_5_6]],
													        [[-5.8_0_8_1, -3.4_1_2_4, -4.1_0_0_6], [-3.8_5_6_1, -2.2_0_8_1, -3.0_3_2_3], [-3.8_3_6_5, -2.4_6_0_1, -3.3_6_6_9]],
													        [[-0.0_3_0_9, 3.9_8_6_8, 4.0_5_4_0], [2.9_6_4_0, 4.6_8_7_7, 4.9_9_7_6], [3.2_0_8_1, 4.7_6_9_0, 4.9_9_4_2]],
													    ]					,device=lowerCamelCase__					,)

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





	@slow
	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							_UpperCamelCase   :						List[str]												=  BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'					)
							_UpperCamelCase   :						Tuple												=  model.to(lowerCamelCase__					)

							_UpperCamelCase   :						Dict												=  BeitImageProcessor(do_resize=lowerCamelCase__					,size=640					,do_center_crop=lowerCamelCase__					)

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

							# forward pass
							with torch.no_grad():
													_UpperCamelCase   :						Union[str, Any]												=  model(**lowerCamelCase__					)

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

							_UpperCamelCase   :						List[Any]												=  image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase__					,target_sizes=[(500, 300)]					)
							_UpperCamelCase   :						Union[str, Any]												=  torch.Size((500, 300)					)
							self.assertEqual(segmentation[0].shape					,lowerCamelCase__					)

							_UpperCamelCase   :						Tuple												=  image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase__					)
							_UpperCamelCase   :						List[Any]												=  torch.Size((160, 160)					)
							self.assertEqual(segmentation[0].shape					,lowerCamelCase__					)







'''simple docstring'''
import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def A__       (	UpperCAmelCase_				):
						if "model" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('model.'    ,    ''				)
						if "norm1" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('norm1'    ,    'attention.output.LayerNorm'				)
						if "norm2" in orig_key:
												_UpperCamelCase   :						int												=  orig_key.replace('norm2'    ,    'output.LayerNorm'				)
						if "norm" in orig_key:
												_UpperCamelCase   :						str												=  orig_key.replace('norm'    ,    'LayerNorm'				)
						if "transformer" in orig_key:
												_UpperCamelCase   :						int												=  orig_key.split('.'				)[0].split('_'				)[-1]
												_UpperCamelCase   :						Tuple												=  orig_key.replace(f'transformer_{layer_num}'    ,    f'encoder.layer.{layer_num}'				)
						if "mha.attn" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('mha.attn'    ,    'attention.self'				)
						if "mha" in orig_key:
												_UpperCamelCase   :						Union[str, Any]												=  orig_key.replace('mha'    ,    'attention'				)
						if "W_q" in orig_key:
												_UpperCamelCase   :						Union[str, Any]												=  orig_key.replace('W_q'    ,    'self.query'				)
						if "W_k" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('W_k'    ,    'self.key'				)
						if "W_v" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('W_v'    ,    'self.value'				)
						if "ff1" in orig_key:
												_UpperCamelCase   :						Optional[Any]												=  orig_key.replace('ff1'    ,    'intermediate.dense'				)
						if "ff2" in orig_key:
												_UpperCamelCase   :						List[str]												=  orig_key.replace('ff2'    ,    'output.dense'				)
						if "ff" in orig_key:
												_UpperCamelCase   :						Union[str, Any]												=  orig_key.replace('ff'    ,    'output.dense'				)
						if "mlm_class" in orig_key:
												_UpperCamelCase   :						Tuple												=  orig_key.replace('mlm.mlm_class'    ,    'cls.predictions.decoder'				)
						if "mlm" in orig_key:
												_UpperCamelCase   :						int												=  orig_key.replace('mlm'    ,    'cls.predictions.transform'				)
						if "cls" not in orig_key:
												_UpperCamelCase   :						Optional[int]												=  'yoso.' + orig_key

						return orig_key






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						for key in orig_state_dict.copy().keys():
												_UpperCamelCase   :						Any												=  orig_state_dict.pop(UpperCAmelCase_				)

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		_UpperCamelCase   :						Union[str, Any]												=  val

						_UpperCamelCase   :						Any												=  orig_state_dict['cls.predictions.decoder.bias']
						_UpperCamelCase   :						Union[str, Any]												=  torch.arange(UpperCAmelCase_				).expand((1, -1)				) + 2

						return orig_state_dict






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						List[Any]												=  torch.load(UpperCAmelCase_    ,    map_location='cpu'				)['model_state_dict']
						_UpperCamelCase   :						List[str]												=  YosoConfig.from_json_file(UpperCAmelCase_				)
						_UpperCamelCase   :						Union[str, Any]												=  YosoForMaskedLM(UpperCAmelCase_				)

						_UpperCamelCase   :						Tuple												=  convert_checkpoint_helper(config.max_position_embeddings    ,    UpperCAmelCase_				)

						print(model.load_state_dict(UpperCAmelCase_				)				)
						model.eval()
						model.save_pretrained(UpperCAmelCase_				)

						print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'				)


if __name__ == "__main__":
					snake_case_					:      int    			=    argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for YOSO model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					snake_case_					:      List[Any]    			=    parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


