






import numpy as np
from matplotlib import pyplot as plt

def 			__lowerCamelCase     (						snake_case__						)				->    Optional[Any]:






						"""simple docstring"""





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



class  __UpperCAmelCase						:







			def __init__(      self:    List[Any] ,      UpperCAmelCase_:    Union[str, Any] ,      UpperCAmelCase_:    Any=None ,      UpperCAmelCase_:    Optional[int]=None ,      UpperCAmelCase_:    List[Any]=False	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    units
									_SCREAMING_SNAKE_CASE       =    None
									_SCREAMING_SNAKE_CASE       =    None
									_SCREAMING_SNAKE_CASE       =    activation
									if learning_rate is None:
															_SCREAMING_SNAKE_CASE       =    0.3
									_SCREAMING_SNAKE_CASE       =    learning_rate
									_SCREAMING_SNAKE_CASE       =    is_input_layer







			def 				UpperCamelCase (      self:    List[str] ,      UpperCAmelCase_:    Optional[int]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    np.asmatrix(np.random.normal(0 ,      0.5 ,      (self.units, back_units)	)	)
									_SCREAMING_SNAKE_CASE       =    np.asmatrix(np.random.normal(0 ,      0.5 ,      self.units	)	).T
									if self.activation is None:
															_SCREAMING_SNAKE_CASE       =    sigmoid







			def 				UpperCamelCase (      self:    Any	):







									'''simple docstring'''




									if self.activation == sigmoid:
															_SCREAMING_SNAKE_CASE       =    np.dot(self.output ,      (1 - self.output).T	)
															_SCREAMING_SNAKE_CASE       =    np.diag(np.diag(UpperCAmelCase_	)	)
									else:
															_SCREAMING_SNAKE_CASE       =    1
									return gradient_activation







			def 				UpperCamelCase (      self:    str ,      UpperCAmelCase_:    List[Any]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    xdata
									if self.is_input_layer:
															# input layer
															_SCREAMING_SNAKE_CASE       =    xdata
															_SCREAMING_SNAKE_CASE       =    xdata
															return xdata
									else:
															_SCREAMING_SNAKE_CASE       =    np.dot(self.weight ,      self.xdata	) - self.bias
															_SCREAMING_SNAKE_CASE       =    self.activation(self.wx_plus_b	)
															return self.output







			def 				UpperCamelCase (      self:    List[str] ,      UpperCAmelCase_:    Tuple	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.cal_gradient()  # i * i 维
									_SCREAMING_SNAKE_CASE       =    np.asmatrix(np.dot(gradient.T ,      UpperCAmelCase_	)	)

									_SCREAMING_SNAKE_CASE       =    np.asmatrix(self.xdata	)
									_SCREAMING_SNAKE_CASE       =    -1
									_SCREAMING_SNAKE_CASE       =    self.weight

									_SCREAMING_SNAKE_CASE       =    np.dot(gradient.T ,      self._gradient_weight.T	)
									_SCREAMING_SNAKE_CASE       =    gradient * self._gradient_bias
									_SCREAMING_SNAKE_CASE       =    np.dot(UpperCAmelCase_ ,      self._gradient_x	).T
									# upgrade: the Negative gradient direction
									_SCREAMING_SNAKE_CASE       =    self.weight - self.learn_rate * self.gradient_weight
									_SCREAMING_SNAKE_CASE       =    self.bias - self.learn_rate * self.gradient_bias.T
									# updates the weights and bias according to learning rate (0.3 if undefined)
									return self.gradient



class  __UpperCAmelCase						:







			def __init__(      self:    int	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    []
									_SCREAMING_SNAKE_CASE       =    []
									_SCREAMING_SNAKE_CASE       =    plt.figure()
									_SCREAMING_SNAKE_CASE       =    self.fig_loss.add_subplot(1 ,      1 ,      1	)







			def 				UpperCamelCase (      self:    Any ,      UpperCAmelCase_:    int	):







									'''simple docstring'''




									self.layers.append(UpperCAmelCase_	)







			def 				UpperCamelCase (      self:    Optional[Any]	):







									'''simple docstring'''




									for i, layer in enumerate(self.layers[:]	):
															if i < 1:
																					_SCREAMING_SNAKE_CASE       =    True
															else:
																					layer.initializer(self.layers[i - 1].units	)







			def 				UpperCamelCase (      self:    Dict	):







									'''simple docstring'''




									for i, layer in enumerate(self.layers[:]	):
															print(F'------- layer {i} -------'	)
															print("""weight.shape """ ,      np.shape(layer.weight	)	)
															print("""bias.shape """ ,      np.shape(layer.bias	)	)







			def 				UpperCamelCase (      self:    Optional[int] ,      UpperCAmelCase_:    str ,      UpperCAmelCase_:    int ,      UpperCAmelCase_:    Tuple ,      UpperCAmelCase_:    List[str]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    train_round
									_SCREAMING_SNAKE_CASE       =    accuracy

									self.ax_loss.hlines(self.accuracy ,      0 ,      self.train_round * 1.1	)

									_SCREAMING_SNAKE_CASE       =    np.shape(UpperCAmelCase_	)
									for _ in range(UpperCAmelCase_	):
															_SCREAMING_SNAKE_CASE       =    0
															for row in range(x_shape[0]	):
																					_SCREAMING_SNAKE_CASE       =    np.asmatrix(xdata[row, :]	).T
																					_SCREAMING_SNAKE_CASE       =    np.asmatrix(ydata[row, :]	).T

																					# forward propagation
																					for layer in self.layers:
																											_SCREAMING_SNAKE_CASE       =    layer.forward_propagation(_xdata	)

																					_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    self.cal_loss(_ydata ,      _xdata	)
																					_SCREAMING_SNAKE_CASE       =    all_loss + loss

																					# back propagation: the input_layer does not upgrade
																					for layer in self.layers[:0:-1]:
																											_SCREAMING_SNAKE_CASE       =    layer.back_propagation(UpperCAmelCase_	)

															_SCREAMING_SNAKE_CASE       =    all_loss / x_shape[0]
															self.train_mse.append(UpperCAmelCase_	)

															self.plot_loss()

															if mse < self.accuracy:
																					print("""----达到精度----"""	)
																					return mse
									return None







			def 				UpperCamelCase (      self:    Optional[Any] ,      UpperCAmelCase_:    List[str] ,      UpperCAmelCase_:    Optional[int]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    np.sum(np.power((ydata - ydata_) ,      2	)	)
									_SCREAMING_SNAKE_CASE       =    2 * (ydata_ - ydata)
									# vector (shape is the same as _ydata.shape)
									return self.loss, self.loss_gradient







			def 				UpperCamelCase (      self:    Optional[int]	):







									'''simple docstring'''




									if self.ax_loss.lines:
															self.ax_loss.lines.remove(self.ax_loss.lines[0]	)
									self.ax_loss.plot(self.train_mse ,      """r-"""	)
									plt.ion()
									plt.xlabel("""step"""	)
									plt.ylabel("""loss"""	)
									plt.show()
									plt.pause(0.1	)

def 			__lowerCamelCase     (						)				->    Optional[Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    np.random.randn(10			,10						)
						_SCREAMING_SNAKE_CASE       =    np.asarray(
						    [
						        [0.8, 0.4],
						        [0.4, 0.3],
						        [0.34, 0.45],
						        [0.67, 0.32],
						        [0.88, 0.67],
						        [0.78, 0.77],
						        [0.55, 0.66],
						        [0.55, 0.43],
						        [0.54, 0.1],
						        [0.1, 0.5],
						    ]						)
						_SCREAMING_SNAKE_CASE       =    BPNN()
						for i in (10, 20, 30, 2):
												model.add_layer(DenseLayer(snake_case__						)						)
						model.build()
						model.summary()
						model.train(xdata=snake_case__			,ydata=snake_case__			,train_round=1_00			,accuracy=0.01						)


if __name__ == "__main__":
		example()









import inspect
import unittest

from transformers import DataaVecVisionConfig
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,
		    DataaVecVisionForImageClassification,
		    DataaVecVisionForSemanticSegmentation,
		    DataaVecVisionModel,
		)
		from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import BeitImageProcessor



class  __UpperCAmelCase						:







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







									'''simple docstring'''




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

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







			def 				UpperCamelCase (      self:    Tuple	):







									'''simple docstring'''




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

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

									_SCREAMING_SNAKE_CASE       =    self.get_config()

									return config, pixel_values, labels, pixel_labels







			def 				UpperCamelCase (      self:    Any	):







									'''simple docstring'''




									return DataaVecVisionConfig(
									    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 				UpperCamelCase (      self:    Optional[Any] ,      UpperCAmelCase_:    str ,      UpperCAmelCase_:    Optional[int] ,      UpperCAmelCase_:    Optional[Any] ,      UpperCAmelCase_:    List[Any]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    DataaVecVisionModel(config=UpperCAmelCase_	)
									model.to(UpperCAmelCase_	)
									model.eval()
									_SCREAMING_SNAKE_CASE       =    model(UpperCAmelCase_	)
									# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
									_SCREAMING_SNAKE_CASE       =    (self.image_size // self.patch_size) ** 2
									self.parent.assertEqual(result.last_hidden_state.shape ,      (self.batch_size, num_patches + 1, self.hidden_size)	)







			def 				UpperCamelCase (      self:    Union[str, Any] ,      UpperCAmelCase_:    Tuple ,      UpperCAmelCase_:    Optional[int] ,      UpperCAmelCase_:    Optional[int] ,      UpperCAmelCase_:    str	):







									'''simple docstring'''




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







			def 				UpperCamelCase (      self:    List[str] ,      UpperCAmelCase_:    str ,      UpperCAmelCase_:    List[Any] ,      UpperCAmelCase_:    Tuple ,      UpperCAmelCase_:    List[str]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.num_labels
									_SCREAMING_SNAKE_CASE       =    DataaVecVisionForSemanticSegmentation(UpperCAmelCase_	)
									model.to(UpperCAmelCase_	)
									model.eval()
									_SCREAMING_SNAKE_CASE       =    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       =    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 				UpperCamelCase (      self:    Optional[Any]	):







									'''simple docstring'''




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



@require_torch
class  __UpperCAmelCase						(_UpperCAmelCase			,_UpperCAmelCase			,unittest.TestCase       ):
			__snake_case							: Tuple							=				(
			    (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)
			__snake_case							: Optional[Any]							=				(
			    {
			        "feature-extraction": DataaVecVisionModel,
			        "image-classification": DataaVecVisionForImageClassification,
			        "image-segmentation": DataaVecVisionForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			__snake_case							: Dict							=				False
			__snake_case							: List[str]							=				False
			__snake_case							: Dict							=				False







			def 				UpperCamelCase (      self:    str	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    DataaVecVisionModelTester(self	)
									_SCREAMING_SNAKE_CASE       =    ConfigTester(
									    self ,      config_class=UpperCAmelCase_ ,      has_text_modality=UpperCAmelCase_ ,      hidden_size=37	)







			def 				UpperCamelCase (      self:    Optional[Any]	):







									'''simple docstring'''




									self.config_tester.run_common_tests()







			def 				UpperCamelCase (      self:    str	):







									'''simple docstring'''




									pass







			@require_torch_multi_gpu
			@unittest.skip(
			    reason="""Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""	)
			def 				UpperCamelCase (      self:    List[str]	):







									'''simple docstring'''




									pass







			def 				UpperCamelCase (      self:    Tuple	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    self.model_tester.prepare_config_and_inputs_for_common()

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







			def 				UpperCamelCase (      self:    Optional[int]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    self.model_tester.prepare_config_and_inputs_for_common()

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

															_SCREAMING_SNAKE_CASE       =    ["""pixel_values"""]
															self.assertListEqual(arg_names[:1] ,      UpperCAmelCase_	)







			def 				UpperCamelCase (      self:    Tuple	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*UpperCAmelCase_	)







			def 				UpperCamelCase (      self:    Tuple	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_segmentation(*UpperCAmelCase_	)







			def 				UpperCamelCase (      self:    Optional[int]	):







									'''simple docstring'''




									if not self.model_tester.is_training:
															return

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

									for model_class in self.all_model_classes:
															if model_class in [*get_values(UpperCAmelCase_	)]:
																					continue

															_SCREAMING_SNAKE_CASE       =    model_class(UpperCAmelCase_	)
															model.to(UpperCAmelCase_	)
															model.train()
															_SCREAMING_SNAKE_CASE       =    self._prepare_for_class(UpperCAmelCase_ ,      UpperCAmelCase_ ,      return_labels=UpperCAmelCase_	)
															_SCREAMING_SNAKE_CASE       =    model(**UpperCAmelCase_	).loss
															loss.backward()







			def 				UpperCamelCase (      self:    Dict	):







									'''simple docstring'''




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

									_SCREAMING_SNAKE_CASE       =    False
									_SCREAMING_SNAKE_CASE       =    True

									for model_class in self.all_model_classes:
															if model_class in [*get_values(UpperCAmelCase_	)] or not model_class.supports_gradient_checkpointing:
																					continue
															# TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
															# this can then be incorporated into _prepare_for_class in test_modeling_common.py
															elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
																					_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    inputs_dict["""pixel_values"""].shape
																					_SCREAMING_SNAKE_CASE       =    torch.zeros(
																					    [self.model_tester.batch_size, height, width] ,      device=UpperCAmelCase_	).long()
															_SCREAMING_SNAKE_CASE       =    model_class(UpperCAmelCase_	)
															model.gradient_checkpointing_enable()
															model.to(UpperCAmelCase_	)
															model.train()
															_SCREAMING_SNAKE_CASE       =    self._prepare_for_class(UpperCAmelCase_ ,      UpperCAmelCase_ ,      return_labels=UpperCAmelCase_	)
															_SCREAMING_SNAKE_CASE       =    model(**UpperCAmelCase_	).loss
															loss.backward()







			def 				UpperCamelCase (      self:    str	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    self.model_tester.prepare_config_and_inputs_for_common()

									_SCREAMING_SNAKE_CASE       =    _config_zero_init(UpperCAmelCase_	)
									for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE       =    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' ,      )







			def 				UpperCamelCase (      self:    int ,      UpperCAmelCase_:    Optional[Any] ,      UpperCAmelCase_:    int ,      UpperCAmelCase_:    Any ,      UpperCAmelCase_:    Union[str, Any]=2E-4 ,      UpperCAmelCase_:    Tuple="outputs" ,      UpperCAmelCase_:    Union[str, Any]=None	):







									'''simple docstring'''




									super().check_pt_tf_outputs(UpperCAmelCase_ ,      UpperCAmelCase_ ,      UpperCAmelCase_ ,      UpperCAmelCase_ ,      UpperCAmelCase_ ,      UpperCAmelCase_	)







			def 				UpperCamelCase (      self:    Tuple	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_	)







			@slow
			def 				UpperCamelCase (      self:    int	):







									'''simple docstring'''




									for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_SCREAMING_SNAKE_CASE       =    DataaVecVisionModel.from_pretrained(UpperCAmelCase_	)
															self.assertIsNotNone(UpperCAmelCase_	)

def 			__lowerCamelCase     (						)				->    int:






						"""simple docstring"""





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




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







			@cached_property
			def 				UpperCamelCase (      self:    Tuple	):







									'''simple docstring'''




									return (
									    BeitImageProcessor.from_pretrained("""facebook/data2vec-vision-base-ft1k"""	) if is_vision_available() else None
									)







			@slow
			def 				UpperCamelCase (      self:    Tuple	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    DataaVecVisionForImageClassification.from_pretrained("""facebook/data2vec-vision-base-ft1k"""	).to(
									    UpperCAmelCase_	)

									_SCREAMING_SNAKE_CASE       =    self.default_image_processor
									_SCREAMING_SNAKE_CASE       =    prepare_img()
									_SCREAMING_SNAKE_CASE       =    image_processor(images=UpperCAmelCase_ ,      return_tensors="""pt"""	).to(UpperCAmelCase_	)

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

									# verify the logits
									_SCREAMING_SNAKE_CASE       =    torch.Size((1, 1_000)	)
									self.assertEqual(logits.shape ,      UpperCAmelCase_	)

									_SCREAMING_SNAKE_CASE       =    torch.tensor([0.32_77, -0.13_95, 0.09_11]	).to(UpperCAmelCase_	)

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

									_SCREAMING_SNAKE_CASE       =    [model.config.labelaid[i] for i in ["""remote control, remote""", """tabby, tabby cat"""]]
									self.assertEqual(logits[0].topk(2	).indices.cpu().tolist() ,      UpperCAmelCase_	)

