



'''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 	a__     :


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

     """simple docstring"""

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

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


 def 				lowercase__  (self : Any      )     ->			str:

     """simple docstring"""

     SCREAMING_SNAKE_CASE  :						Optional[int]      					= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

     SCREAMING_SNAKE_CASE  :						Dict      					= None
     SCREAMING_SNAKE_CASE  :						List[Any]      					= None
     if self.use_labels:
         SCREAMING_SNAKE_CASE  :						List[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  :						List[Any]      					= self.get_config()

     return config, pixel_values, labels, pixel_labels


 def 				lowercase__  (self : Tuple      )     ->			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=__UpperCAmelCase,					initializer_range=self.initializer_range,					out_indices=self.out_indices,					)


 def 				lowercase__  (self : List[str],					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : List[str],					__UpperCAmelCase : Optional[int]      )     ->			Union[str, Any]:

     """simple docstring"""

     SCREAMING_SNAKE_CASE  :						int      					= BeitModel(config=__UpperCAmelCase      )
     model.to(__UpperCAmelCase      )
     model.eval()
     SCREAMING_SNAKE_CASE  :						Optional[int]      					= model(__UpperCAmelCase      )
     self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size)      )


 def 				lowercase__  (self : List[Any],					__UpperCAmelCase : Dict,					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : Union[str, Any]      )     ->			int:

     """simple docstring"""

     SCREAMING_SNAKE_CASE  :						Optional[int]      					= BeitForMaskedImageModeling(config=__UpperCAmelCase      )
     model.to(__UpperCAmelCase      )
     model.eval()
     SCREAMING_SNAKE_CASE  :						Any      					= model(__UpperCAmelCase      )
     self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length - 1, self.vocab_size)      )


 def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : Dict,					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : List[Any]      )     ->			str:

     """simple docstring"""

     SCREAMING_SNAKE_CASE  :						List[Any]      					= self.type_sequence_label_size
     SCREAMING_SNAKE_CASE  :						int      					= BeitForImageClassification(__UpperCAmelCase      )
     model.to(__UpperCAmelCase      )
     model.eval()
     SCREAMING_SNAKE_CASE  :						List[Any]      					= model(__UpperCAmelCase,					labels=__UpperCAmelCase      )
     self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.type_sequence_label_size)      )

     # test greyscale images
     SCREAMING_SNAKE_CASE  :						str      					= 1
     SCREAMING_SNAKE_CASE  :						Any      					= BeitForImageClassification(__UpperCAmelCase      )
     model.to(__UpperCAmelCase      )
     model.eval()

     SCREAMING_SNAKE_CASE  :						Optional[int]      					= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
     SCREAMING_SNAKE_CASE  :						List[str]      					= model(__UpperCAmelCase,					labels=__UpperCAmelCase      )
     self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.type_sequence_label_size)      )


 def 				lowercase__  (self : List[Any],					__UpperCAmelCase : str,					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : int,					__UpperCAmelCase : List[Any]      )     ->			Union[str, Any]:

     """simple docstring"""

     SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.num_labels
     SCREAMING_SNAKE_CASE  :						Optional[Any]      					= BeitForSemanticSegmentation(__UpperCAmelCase      )
     model.to(__UpperCAmelCase      )
     model.eval()
     SCREAMING_SNAKE_CASE  :						Tuple      					= model(__UpperCAmelCase      )
     self.parent.assertEqual(
         result.logits.shape,					(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )
     SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model(__UpperCAmelCase,					labels=__UpperCAmelCase      )
     self.parent.assertEqual(
         result.logits.shape,					(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )






 def 				lowercase__  (self : Union[str, Any]      )     ->			Optional[Any]:

     """simple docstring"""

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



@require_torch
class 	a__     (				_lowercase,			_lowercase,			unittest.TestCase	):
 __magic_name__		:    Union[str, Any]	    =     (
     (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
     if is_torch_available()
     else ()
 )
 __magic_name__		:    Tuple	    =     (
     {
         "feature-extraction": BeitModel,
         "image-classification": BeitForImageClassification,
         "image-segmentation": BeitForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 __magic_name__		:    int	    =     False
 __magic_name__		:    Tuple	    =     False
 __magic_name__		:    int	    =     False


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

     """simple docstring"""

     SCREAMING_SNAKE_CASE  :						List[Any]      					= BeitModelTester(self      )
     SCREAMING_SNAKE_CASE  :						Any      					= ConfigTester(self,					config_class=__UpperCAmelCase,					has_text_modality=__UpperCAmelCase,					hidden_size=37      )


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

     """simple docstring"""

     self.config_tester.run_common_tests()


 @unittest.skip(reason='''BEiT does not use inputs_embeds'''      )
 def 				lowercase__  (self : Tuple      )     ->			Tuple:

     """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 : str      )     ->			Any:

     """simple docstring"""

     pass


 def 				lowercase__  (self : Any      )     ->			Dict:

     """simple docstring"""

     SCREAMING_SNAKE_CASE  :						Any      					= self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE  :						Dict      					= model_class(__UpperCAmelCase      )
         self.assertIsInstance(model.get_input_embeddings(),					(nn.Module)      )
         SCREAMING_SNAKE_CASE  :						str      					= model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(__UpperCAmelCase,					nn.Linear      )      )


 def 				lowercase__  (self : str      )     ->			Dict:

     """simple docstring"""

     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  :						Optional[Any]      					= model_class(__UpperCAmelCase      )
         SCREAMING_SNAKE_CASE  :						Optional[int]      					= inspect.signature(model.forward      )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         SCREAMING_SNAKE_CASE  :						List[Any]      					= [*signature.parameters.keys()]

         SCREAMING_SNAKE_CASE  :						Any      					= ['''pixel_values''']
         self.assertListEqual(arg_names[:1],					__UpperCAmelCase      )


 def 				lowercase__  (self : Optional[int]      )     ->			Dict:

     """simple docstring"""

     SCREAMING_SNAKE_CASE  :						Tuple      					= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__UpperCAmelCase      )


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

     """simple docstring"""

     SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase      )


 def 				lowercase__  (self : Tuple      )     ->			Tuple:

     """simple docstring"""

     SCREAMING_SNAKE_CASE  :						List[Any]      					= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase      )


 def 				lowercase__  (self : List[Any]      )     ->			Dict:

     """simple docstring"""

     SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCAmelCase      )


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

     """simple docstring"""

     if not self.model_tester.is_training:
         return

     SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE  :						Dict      					= True

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

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


 def 				lowercase__  (self : Any      )     ->			List[str]:

     """simple docstring"""

     SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.model_tester.prepare_config_and_inputs_for_common()
     if not self.model_tester.is_training:
         return

     SCREAMING_SNAKE_CASE  :						Optional[int]      					= False
     SCREAMING_SNAKE_CASE  :						int      					= True

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

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


 def 				lowercase__  (self : Dict      )     ->			Union[str, Any]:

     """simple docstring"""

     SCREAMING_SNAKE_CASE  :						str      					= self.model_tester.prepare_config_and_inputs_for_common()

     SCREAMING_SNAKE_CASE  :						Any      					= _config_zero_init(__UpperCAmelCase      )
     for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE  :						Optional[int]      					= model_class(config=__UpperCAmelCase      )
         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 : Dict      )     ->			Union[str, Any]:

     """simple docstring"""

     for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         SCREAMING_SNAKE_CASE  :						Optional[Any]      					= BeitModel.from_pretrained(__UpperCAmelCase      )
         self.assertIsNotNone(__UpperCAmelCase      )






def       __lowercase  ():
    SCREAMING_SNAKE_CASE  :						int      					= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
    return image




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


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

     """simple docstring"""

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


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

     """simple docstring"""

     SCREAMING_SNAKE_CASE  :						Any      					= BeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''      ).to(__UpperCAmelCase      )

     SCREAMING_SNAKE_CASE  :						Dict      					= self.default_image_processor
     SCREAMING_SNAKE_CASE  :						Optional[int]      					= prepare_img()
     SCREAMING_SNAKE_CASE  :						int      					= image_processor(images=__UpperCAmelCase,					return_tensors='''pt'''      ).pixel_values.to(__UpperCAmelCase      )

     # prepare bool_masked_pos
     SCREAMING_SNAKE_CASE  :						str      					= torch.ones((1, 196),					dtype=torch.bool      ).to(__UpperCAmelCase      )

     # forward pass
     with torch.no_grad():
         SCREAMING_SNAKE_CASE  :						int      					= model(pixel_values=__UpperCAmelCase,					bool_masked_pos=__UpperCAmelCase      )
     SCREAMING_SNAKE_CASE  :						List[Any]      					= outputs.logits

     # verify the logits
     SCREAMING_SNAKE_CASE  :						Dict      					= torch.Size((1, 196, 8192)      )
     self.assertEqual(logits.shape,					__UpperCAmelCase      )

     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(__UpperCAmelCase      )

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


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

     """simple docstring"""

     SCREAMING_SNAKE_CASE  :						List[Any]      					= BeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''      ).to(__UpperCAmelCase      )

     SCREAMING_SNAKE_CASE  :						List[str]      					= self.default_image_processor
     SCREAMING_SNAKE_CASE  :						Any      					= prepare_img()
     SCREAMING_SNAKE_CASE  :						int      					= image_processor(images=__UpperCAmelCase,					return_tensors='''pt'''      ).to(__UpperCAmelCase      )

     # forward pass
     with torch.no_grad():
         SCREAMING_SNAKE_CASE  :						Any      					= model(**__UpperCAmelCase      )
     SCREAMING_SNAKE_CASE  :						List[str]      					= outputs.logits

     # verify the logits
     SCREAMING_SNAKE_CASE  :						List[str]      					= torch.Size((1, 1000)      )
     self.assertEqual(logits.shape,					__UpperCAmelCase      )

     SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= torch.tensor([-1.2385, -1.0987, -1.0108]      ).to(__UpperCAmelCase      )

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

     SCREAMING_SNAKE_CASE  :						Optional[int]      					= 281
     self.assertEqual(logits.argmax(-1      ).item(),					__UpperCAmelCase      )


 @slow
 def 				lowercase__  (self : Tuple      )     ->			str:

     """simple docstring"""

     SCREAMING_SNAKE_CASE  :						Optional[Any]      					= BeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''      ).to(
         __UpperCAmelCase      )

     SCREAMING_SNAKE_CASE  :						Any      					= self.default_image_processor
     SCREAMING_SNAKE_CASE  :						Optional[Any]      					= prepare_img()
     SCREAMING_SNAKE_CASE  :						Dict      					= image_processor(images=__UpperCAmelCase,					return_tensors='''pt'''      ).to(__UpperCAmelCase      )

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

     # verify the logits
     SCREAMING_SNAKE_CASE  :						Dict      					= torch.Size((1, 21841)      )
     self.assertEqual(logits.shape,					__UpperCAmelCase      )

     SCREAMING_SNAKE_CASE  :						Tuple      					= torch.tensor([1.6881, -0.2787, 0.5901]      ).to(__UpperCAmelCase      )

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

     SCREAMING_SNAKE_CASE  :						int      					= 2396
     self.assertEqual(logits.argmax(-1      ).item(),					__UpperCAmelCase      )


 @slow
 def 				lowercase__  (self : str      )     ->			str:

     """simple docstring"""

     SCREAMING_SNAKE_CASE  :						Any      					= BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''      )
     SCREAMING_SNAKE_CASE  :						Optional[int]      					= model.to(__UpperCAmelCase      )

     SCREAMING_SNAKE_CASE  :						Tuple      					= BeitImageProcessor(do_resize=__UpperCAmelCase,					size=640,					do_center_crop=__UpperCAmelCase      )

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

     # forward pass
     with torch.no_grad():
         SCREAMING_SNAKE_CASE  :						Tuple      					= model(**__UpperCAmelCase      )
     SCREAMING_SNAKE_CASE  :						Tuple      					= outputs.logits

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

     SCREAMING_SNAKE_CASE  :						Dict      					= version.parse(PIL.__version__      ) < version.parse('''9.0.0'''      )

     if is_pillow_less_than_a:
         SCREAMING_SNAKE_CASE  :						Tuple      					= 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=__UpperCAmelCase,					)
     else:
         SCREAMING_SNAKE_CASE  :						Optional[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=__UpperCAmelCase,					)

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






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

     """simple docstring"""

     SCREAMING_SNAKE_CASE  :						int      					= BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''      )
     SCREAMING_SNAKE_CASE  :						str      					= model.to(__UpperCAmelCase      )

     SCREAMING_SNAKE_CASE  :						int      					= BeitImageProcessor(do_resize=__UpperCAmelCase,					size=640,					do_center_crop=__UpperCAmelCase      )

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

     # forward pass
     with torch.no_grad():
         SCREAMING_SNAKE_CASE  :						int      					= model(**__UpperCAmelCase      )

     SCREAMING_SNAKE_CASE  :						Dict      					= outputs.logits.detach().cpu()

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

     SCREAMING_SNAKE_CASE  :						Any      					= image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase      )
     SCREAMING_SNAKE_CASE  :						str      					= torch.Size((160, 160)      )
     self.assertEqual(segmentation[0].shape,					__UpperCAmelCase      )









'''simple docstring'''

import pickle

import numpy as np
from matplotlib import pyplot as plt


class 	a__     :


	def __init__(self : Any,					__UpperCAmelCase : int,					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : int,					__UpperCAmelCase : List[str]=0.2,					__UpperCAmelCase : Dict=0.2      )     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= bp_numa
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= bp_numa
					SCREAMING_SNAKE_CASE  :						str      					= bp_numa
					SCREAMING_SNAKE_CASE  :						Dict      					= conva_get[:2]
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= conva_get[2]
					SCREAMING_SNAKE_CASE  :						int      					= size_pa
					SCREAMING_SNAKE_CASE  :						int      					= rate_w
					SCREAMING_SNAKE_CASE  :						int      					= rate_t
					SCREAMING_SNAKE_CASE  :						int      					= [
					    np.mat(-1 * np.random.rand(self.conva[0],					self.conva[0]      ) + 0.5      )
					    for i in range(self.conva[1]      )
					]
					SCREAMING_SNAKE_CASE  :						List[Any]      					= np.mat(-1 * np.random.rand(self.num_bpa,					self.num_bpa      ) + 0.5      )
					SCREAMING_SNAKE_CASE  :						Dict      					= np.mat(-1 * np.random.rand(self.num_bpa,					self.num_bpa      ) + 0.5      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= -2 * np.random.rand(self.conva[1]      ) + 1
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= -2 * np.random.rand(self.num_bpa      ) + 1
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= -2 * np.random.rand(self.num_bpa      ) + 1


	def 				lowercase__  (self : Union[str, Any],					__UpperCAmelCase : Optional[Any]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= {
					    '''num_bp1''': self.num_bpa,
					    '''num_bp2''': self.num_bpa,
					    '''num_bp3''': self.num_bpa,
					    '''conv1''': self.conva,
					    '''step_conv1''': self.step_conva,
					    '''size_pooling1''': self.size_poolinga,
					    '''rate_weight''': self.rate_weight,
					    '''rate_thre''': self.rate_thre,
					    '''w_conv1''': self.w_conva,
					    '''wkj''': self.wkj,
					    '''vji''': self.vji,
					    '''thre_conv1''': self.thre_conva,
					    '''thre_bp2''': self.thre_bpa,
					    '''thre_bp3''': self.thre_bpa,
					}
					with open(__UpperCAmelCase,					'''wb'''      ) as f:
									pickle.dump(__UpperCAmelCase,					__UpperCAmelCase      )

					print(F'''Model saved： {save_path}'''      )


	@classmethod
	def 				lowercase__  (cls : str,					__UpperCAmelCase : List[str]      )     ->			int:

					"""simple docstring"""

					with open(__UpperCAmelCase,					'''rb'''      ) as f:
									SCREAMING_SNAKE_CASE  :						int      					= pickle.load(__UpperCAmelCase      )  # noqa: S301

					SCREAMING_SNAKE_CASE  :						List[str]      					= model_dic.get('''conv1'''      )
					conv_get.append(model_dic.get('''step_conv1'''      )      )
					SCREAMING_SNAKE_CASE  :						Dict      					= model_dic.get('''size_pooling1'''      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= model_dic.get('''num_bp1'''      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model_dic.get('''num_bp2'''      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= model_dic.get('''num_bp3'''      )
					SCREAMING_SNAKE_CASE  :						Any      					= model_dic.get('''rate_weight'''      )
					SCREAMING_SNAKE_CASE  :						Any      					= model_dic.get('''rate_thre'''      )
					# create model instance
					SCREAMING_SNAKE_CASE  :						List[Any]      					= CNN(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase      )
					# modify model parameter
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model_dic.get('''w_conv1'''      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= model_dic.get('''wkj'''      )
					SCREAMING_SNAKE_CASE  :						Any      					= model_dic.get('''vji'''      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model_dic.get('''thre_conv1'''      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= model_dic.get('''thre_bp2'''      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model_dic.get('''thre_bp3'''      )
					return conv_ins


	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : Dict      )     ->			Optional[Any]:

					"""simple docstring"""

					return 1 / (1 + np.exp(-1 * x      ))


	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : List[str]      )     ->			str:

					"""simple docstring"""

					return round(__UpperCAmelCase,					3      )


	def 				lowercase__  (self : Union[str, Any],					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Any,					__UpperCAmelCase : int      )     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= convs[0]
					SCREAMING_SNAKE_CASE  :						int      					= convs[1]
					SCREAMING_SNAKE_CASE  :						List[str]      					= np.shape(__UpperCAmelCase      )[0]
					# get the data slice of original image data, data_focus
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= []
					for i_focus in range(0,					size_data - size_conv + 1,					__UpperCAmelCase      ):
									for j_focus in range(0,					size_data - size_conv + 1,					__UpperCAmelCase      ):
													SCREAMING_SNAKE_CASE  :						Optional[Any]      					= data[
													    i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
													]
													data_focus.append(__UpperCAmelCase      )
        # calculate the feature map of every single kernel, and saved as list of matrix
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= []
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= int((size_data - size_conv) / conv_step + 1      )
					for i_map in range(__UpperCAmelCase      ):
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= []
									for i_focus in range(len(__UpperCAmelCase      )      ):
													SCREAMING_SNAKE_CASE  :						Any      					= (
													    np.sum(np.multiply(data_focus[i_focus],					w_convs[i_map]      )      )
													    - thre_convs[i_map]
													)
													featuremap.append(self.sig(__UpperCAmelCase      )      )
									SCREAMING_SNAKE_CASE  :						Dict      					= np.asmatrix(__UpperCAmelCase      ).reshape(
									    __UpperCAmelCase,					__UpperCAmelCase      )
									data_featuremap.append(__UpperCAmelCase      )

					# expanding the data slice to One dimenssion
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= []
					for each_focus in data_focus:
									focusa_list.extend(self.Expand_Mat(__UpperCAmelCase      )      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.asarray(__UpperCAmelCase      )
					return focus_list, data_featuremap


	def 				lowercase__  (self : Any,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Tuple,					__UpperCAmelCase : List[str]="average_pool"      )     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= len(featuremaps[0]      )
					SCREAMING_SNAKE_CASE  :						str      					= int(size_map / size_pooling      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= []
					for i_map in range(len(__UpperCAmelCase      )      ):
									SCREAMING_SNAKE_CASE  :						str      					= featuremaps[i_map]
									SCREAMING_SNAKE_CASE  :						Any      					= []
									for i_focus in range(0,					__UpperCAmelCase,					__UpperCAmelCase      ):
													for j_focus in range(0,					__UpperCAmelCase,					__UpperCAmelCase      ):
																	SCREAMING_SNAKE_CASE  :						str      					= feature_map[
																	    i_focus : i_focus + size_pooling,
																	    j_focus : j_focus + size_pooling,
																	]
																	if pooling_type == "average_pool":
																					# average pooling
																					map_pooled.append(np.average(__UpperCAmelCase      )      )
																	elif pooling_type == "max_pooling":
																					# max pooling
																					map_pooled.append(np.max(__UpperCAmelCase      )      )
									SCREAMING_SNAKE_CASE  :						Tuple      					= np.asmatrix(__UpperCAmelCase      ).reshape(__UpperCAmelCase,					__UpperCAmelCase      )
									featuremap_pooled.append(__UpperCAmelCase      )
					return featuremap_pooled


	def 				lowercase__  (self : Any,					__UpperCAmelCase : List[str]      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= []
					for i in range(len(__UpperCAmelCase      )      ):
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.shape(data[i]      )
									SCREAMING_SNAKE_CASE  :						str      					= data[i].reshape(1,					shapes[0] * shapes[1]      )
									SCREAMING_SNAKE_CASE  :						int      					= data_listed.getA().tolist()[0]
									data_expanded.extend(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= np.asarray(__UpperCAmelCase      )
					return data_expanded


	def 				lowercase__  (self : Any,					__UpperCAmelCase : Optional[Any]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= np.asarray(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= np.shape(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= data_mat.reshape(1,					shapes[0] * shapes[1]      )
					return data_expanded


	def 				lowercase__  (self : Tuple,					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Dict,					__UpperCAmelCase : Any,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Union[str, Any]      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= []
					SCREAMING_SNAKE_CASE  :						List[str]      					= 0
					for i_map in range(__UpperCAmelCase      ):
									SCREAMING_SNAKE_CASE  :						Dict      					= np.ones((size_map, size_map)      )
									for i in range(0,					__UpperCAmelCase,					__UpperCAmelCase      ):
													for j in range(0,					__UpperCAmelCase,					__UpperCAmelCase      ):
																	SCREAMING_SNAKE_CASE  :						str      					= pd_pool[
																	    i_pool
																	]
																	SCREAMING_SNAKE_CASE  :						Dict      					= i_pool + 1
									SCREAMING_SNAKE_CASE  :						List[Any]      					= np.multiply(
									    __UpperCAmelCase,					np.multiply(out_map[i_map],					(1 - out_map[i_map])      )      )
									pd_all.append(__UpperCAmelCase      )
					return pd_all


	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : List[str],					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Optional[int]=bool      )     ->			List[Any]:

					"""simple docstring"""

					print('''----------------------Start Training-------------------------'''      )
					print((''' - - Shape: Train_Data  ''', np.shape(__UpperCAmelCase      ))      )
					print((''' - - Shape: Teach_Data  ''', np.shape(__UpperCAmelCase      ))      )
					SCREAMING_SNAKE_CASE  :						Dict      					= 0
					SCREAMING_SNAKE_CASE  :						str      					= []
					SCREAMING_SNAKE_CASE  :						Any      					= 10000
					while rp < n_repeat and mse >= error_accuracy:
									SCREAMING_SNAKE_CASE  :						int      					= 0
									print(F'''-------------Learning Time {rp}--------------'''      )
									for p in range(len(__UpperCAmelCase      )      ):
													# print('------------Learning Image: %d--------------'%p)
													SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.asmatrix(datas_train[p]      )
													SCREAMING_SNAKE_CASE  :						Tuple      					= np.asarray(datas_teach[p]      )
													SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						int      					= self.convolute(
													    __UpperCAmelCase,					self.conva,					self.w_conva,					self.thre_conva,					conv_step=self.step_conva,					)
													SCREAMING_SNAKE_CASE  :						Dict      					= self.pooling(__UpperCAmelCase,					self.size_poolinga      )
													SCREAMING_SNAKE_CASE  :						Dict      					= np.shape(__UpperCAmelCase      )
													SCREAMING_SNAKE_CASE  :						str      					= self._expand(__UpperCAmelCase      )
													SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= data_bp_input

													SCREAMING_SNAKE_CASE  :						Tuple      					= np.dot(__UpperCAmelCase,					self.vji.T      ) - self.thre_bpa
													SCREAMING_SNAKE_CASE  :						List[Any]      					= self.sig(__UpperCAmelCase      )
													SCREAMING_SNAKE_CASE  :						Dict      					= np.dot(__UpperCAmelCase,					self.wkj.T      ) - self.thre_bpa
													SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.sig(__UpperCAmelCase      )

													# --------------Model Leaning ------------------------
													# calculate error and gradient---------------
													SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.multiply(
													    (data_teach - bp_outa),					np.multiply(__UpperCAmelCase,					(1 - bp_outa)      )      )
													SCREAMING_SNAKE_CASE  :						List[Any]      					= np.multiply(
													    np.dot(__UpperCAmelCase,					self.wkj      ),					np.multiply(__UpperCAmelCase,					(1 - bp_outa)      )      )
													SCREAMING_SNAKE_CASE  :						Dict      					= np.dot(__UpperCAmelCase,					self.vji      )

													SCREAMING_SNAKE_CASE  :						Dict      					= pd_i_all / (self.size_poolinga * self.size_poolinga)
													SCREAMING_SNAKE_CASE  :						Optional[Any]      					= pd_conva_pooled.T.getA().tolist()
													SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self._calculate_gradient_from_pool(
													    __UpperCAmelCase,					__UpperCAmelCase,					shape_featuremapa[0],					shape_featuremapa[1],					self.size_poolinga,					)
													# weight and threshold learning process---------
													# convolution layer
													for k_conv in range(self.conva[1]      ):
																	SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self._expand_mat(pd_conva_all[k_conv]      )
																	SCREAMING_SNAKE_CASE  :						Any      					= self.rate_weight * np.dot(__UpperCAmelCase,					__UpperCAmelCase      )

																	SCREAMING_SNAKE_CASE  :						List[Any]      					= self.w_conva[k_conv] + delta_w.reshape(
																	    (self.conva[0], self.conva[0])      )

																	SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= (
																	    self.thre_conva[k_conv]
																	    - np.sum(pd_conva_all[k_conv]      ) * self.rate_thre
																	)
													# all connected layer
													SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.wkj + pd_k_all.T * bp_outa * self.rate_weight
													SCREAMING_SNAKE_CASE  :						List[str]      					= self.vji + pd_j_all.T * bp_outa * self.rate_weight
													SCREAMING_SNAKE_CASE  :						List[Any]      					= self.thre_bpa - pd_k_all * self.rate_thre
													SCREAMING_SNAKE_CASE  :						int      					= self.thre_bpa - pd_j_all * self.rate_thre
													# calculate the sum error of all single image
													SCREAMING_SNAKE_CASE  :						List[Any]      					= np.sum(abs(data_teach - bp_outa      )      )
													error_count += errors
													# print('   ----Teach      ',data_teach)
													# print('   ----BP_output  ',bp_out3)
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= rp + 1
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= error_count / patterns
									all_mse.append(__UpperCAmelCase      )

					def draw_error():
									SCREAMING_SNAKE_CASE  :						Tuple      					= [error_accuracy for i in range(int(n_repeat * 1.2      )      )]
									plt.plot(__UpperCAmelCase,					'''+-'''      )
									plt.plot(__UpperCAmelCase,					'''r--'''      )
									plt.xlabel('''Learning Times'''      )
									plt.ylabel('''All_mse'''      )
									plt.grid(__UpperCAmelCase,					alpha=0.5      )
									plt.show()

					print('''------------------Training Complished---------------------'''      )
					print((''' - - Training epoch: ''', rp, F'''     - - Mse: {mse:.6f}''')      )
					if draw_e:
									draw_error()
					return mse


	def 				lowercase__  (self : List[str],					__UpperCAmelCase : List[str]      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= []
					print('''-------------------Start Testing-------------------------'''      )
					print((''' - - Shape: Test_Data  ''', np.shape(__UpperCAmelCase      ))      )
					for p in range(len(__UpperCAmelCase      )      ):
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.asmatrix(datas_test[p]      )
									SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Any      					= self.convolute(
									    __UpperCAmelCase,					self.conva,					self.w_conva,					self.thre_conva,					conv_step=self.step_conva,					)
									SCREAMING_SNAKE_CASE  :						List[Any]      					= self.pooling(__UpperCAmelCase,					self.size_poolinga      )
									SCREAMING_SNAKE_CASE  :						str      					= self._expand(__UpperCAmelCase      )

									SCREAMING_SNAKE_CASE  :						Any      					= data_bp_input
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= bp_outa * self.vji.T - self.thre_bpa
									SCREAMING_SNAKE_CASE  :						Dict      					= self.sig(__UpperCAmelCase      )
									SCREAMING_SNAKE_CASE  :						Any      					= bp_outa * self.wkj.T - self.thre_bpa
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.sig(__UpperCAmelCase      )
									produce_out.extend(bp_outa.getA().tolist()      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= [list(map(self.do_round,					__UpperCAmelCase      )      ) for each in produce_out]
					return np.asarray(__UpperCAmelCase      )






	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : Union[str, Any]      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.asmatrix(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.convolute(
					    __UpperCAmelCase,					self.conva,					self.w_conva,					self.thre_conva,					conv_step=self.step_conva,					)
					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.pooling(__UpperCAmelCase,					self.size_poolinga      )

					return data_conveda, data_pooleda


if __name__ == "__main__":
	pass




