



'''simple docstring'''

import pickle

import numpy as np
from matplotlib import pyplot as plt

class      __magic_name__						:







       def __init__(							self :	List[Any]     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Optional[Any]     ,_UpperCAmelCase :	Optional[Any]     ,_UpperCAmelCase :	int=0.2     ,_UpperCAmelCase :	int=0.2		):
         _a					:				List[Any]				=							bp_numa
         _a					:				str				=							bp_numa
         _a					:				int				=							bp_numa
         _a					:				int				=							conva_get[:2]
         _a					:				Tuple				=							conva_get[2]
         _a					:				Optional[int]				=							size_pa
         _a					:				List[Any]				=							rate_w
         _a					:				Tuple				=							rate_t
         _a					:				Tuple				=							[
             np.mat(-1 * np.random.rand(self.conva[0]     ,self.conva[0]		) + 0.5		)
             for i in range(self.conva[1]		)
         ]
         _a					:				int				=							np.mat(-1 * np.random.rand(self.num_bpa     ,self.num_bpa		) + 0.5		)
         _a					:				int				=							np.mat(-1 * np.random.rand(self.num_bpa     ,self.num_bpa		) + 0.5		)
         _a					:				List[Any]				=							-2 * np.random.rand(self.conva[1]		) + 1
         _a					:				Dict				=							-2 * np.random.rand(self.num_bpa		) + 1
         _a					:				str				=							-2 * np.random.rand(self.num_bpa		) + 1







       def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	str		):
         # save model dict with pickle
         _a					:				int				=							{
             '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 :	Optional[Any]     ,_UpperCAmelCase :	Any		):
         # read saved model
         with open(_UpperCAmelCase     ,'rb'		) as f:
           _a					:				str				=							pickle.load(_UpperCAmelCase		)  # noqa: S301

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







       def 							__lowercase      (							self :	List[Any]     ,_UpperCAmelCase :	str		):
         return 1 / (1 + np.exp(-1 * x		))







       def 							__lowercase      (							self :	Optional[Any]     ,_UpperCAmelCase :	Any		):
         return round(_UpperCAmelCase     ,3		)







       def 							__lowercase      (							self :	Optional[Any]     ,_UpperCAmelCase :	List[str]     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	Tuple		):
         # convolution process
         _a					:				Any				=							convs[0]
         _a					:				Tuple				=							convs[1]
         _a					:				Dict				=							np.shape(_UpperCAmelCase		)[0]
         # get the data slice of original image data, data_focus
         _a					:				List[str]				=							[]
         for i_focus in range(0     ,size_data - size_conv + 1     ,_UpperCAmelCase		):
           for j_focus in range(0     ,size_data - size_conv + 1     ,_UpperCAmelCase		):
             _a					:				str				=							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
         _a					:				int				=							[]
         _a					:				Any				=							int((size_data - size_conv) / conv_step + 1		)
         for i_map in range(_UpperCAmelCase		):
           _a					:				Union[str, Any]				=							[]
           for i_focus in range(len(_UpperCAmelCase		)		):
             _a					:				Union[str, Any]				=							(
                 np.sum(np.multiply(data_focus[i_focus]     ,w_convs[i_map]		)		)
                 - thre_convs[i_map]
             )
             featuremap.append(self.sig(_UpperCAmelCase		)		)
           _a					:				int				=							np.asmatrix(_UpperCAmelCase		).reshape(
               _UpperCAmelCase     ,_UpperCAmelCase		)
           data_featuremap.append(_UpperCAmelCase		)

         # expanding the data slice to One dimenssion
         _a					:				Tuple				=							[]
         for each_focus in data_focus:
           focusa_list.extend(self.Expand_Mat(_UpperCAmelCase		)		)
         _a					:				Tuple				=							np.asarray(_UpperCAmelCase		)
         return focus_list, data_featuremap







       def 							__lowercase      (							self :	List[Any]     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	Dict="average_pool"		):
         # pooling process
         _a					:				int				=							len(featuremaps[0]		)
         _a					:				Tuple				=							int(size_map / size_pooling		)
         _a					:				int				=							[]
         for i_map in range(len(_UpperCAmelCase		)		):
           _a					:				Union[str, Any]				=							featuremaps[i_map]
           _a					:				int				=							[]
           for i_focus in range(0     ,_UpperCAmelCase     ,_UpperCAmelCase		):
             for j_focus in range(0     ,_UpperCAmelCase     ,_UpperCAmelCase		):
               _a					:				List[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		)		)
           _a					:				Any				=							np.asmatrix(_UpperCAmelCase		).reshape(_UpperCAmelCase     ,_UpperCAmelCase		)
           featuremap_pooled.append(_UpperCAmelCase		)
         return featuremap_pooled







       def 							__lowercase      (							self :	Dict     ,_UpperCAmelCase :	Any		):
         # expanding three dimension data to one dimension list
         _a					:				Optional[int]				=							[]
         for i in range(len(_UpperCAmelCase		)		):
           _a					:				List[str]				=							np.shape(data[i]		)
           _a					:				List[Any]				=							data[i].reshape(1     ,shapes[0] * shapes[1]		)
           _a					:				List[str]				=							data_listed.getA().tolist()[0]
           data_expanded.extend(_UpperCAmelCase		)
         _a					:				List[str]				=							np.asarray(_UpperCAmelCase		)
         return data_expanded







       def 							__lowercase      (							self :	List[Any]     ,_UpperCAmelCase :	Optional[int]		):
         # expanding matrix to one dimension list
         _a					:				List[str]				=							np.asarray(_UpperCAmelCase		)
         _a					:				str				=							np.shape(_UpperCAmelCase		)
         _a					:				List[str]				=							data_mat.reshape(1     ,shapes[0] * shapes[1]		)
         return data_expanded







       def 							__lowercase      (							self :	Dict     ,_UpperCAmelCase :	List[str]     ,_UpperCAmelCase :	Optional[int]     ,_UpperCAmelCase :	Optional[int]     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Tuple		):
         _a					:				List[str]				=							[]
         _a					:				Any				=							0
         for i_map in range(_UpperCAmelCase		):
           _a					:				Union[str, Any]				=							np.ones((size_map, size_map)		)
           for i in range(0     ,_UpperCAmelCase     ,_UpperCAmelCase		):
             for j in range(0     ,_UpperCAmelCase     ,_UpperCAmelCase		):
               _a					:				Tuple				=							pd_pool[
                   i_pool
               ]
               _a					:				Dict				=							i_pool + 1
           _a					:				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 :	Union[str, Any]     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	List[Any]     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	List[str]     ,_UpperCAmelCase :	str=bool		):
         # model traning
         print('----------------------Start Training-------------------------'		)
         print((' - - Shape: Train_Data  ', np.shape(_UpperCAmelCase		))		)
         print((' - - Shape: Teach_Data  ', np.shape(_UpperCAmelCase		))		)
         _a					:				int				=							0
         _a					:				List[Any]				=							[]
         _a					:				Tuple				=							10000
         while rp < n_repeat and mse >= error_accuracy:
           _a					:				Dict				=							0
           print(F"""-------------Learning Time {rp}--------------"""		)
           for p in range(len(_UpperCAmelCase		)		):
             # print('------------Learning Image: %d--------------'%p)
             _a					:				Union[str, Any]				=							np.asmatrix(datas_train[p]		)
             _a					:				List[str]				=							np.asarray(datas_teach[p]		)
             _a					:				str				=							self.convolute(
                 _UpperCAmelCase     ,self.conva     ,self.w_conva     ,self.thre_conva     ,conv_step=self.step_conva     ,)
             _a					:				int				=							self.pooling(_UpperCAmelCase     ,self.size_poolinga		)
             _a					:				str				=							np.shape(_UpperCAmelCase		)
             _a					:				Optional[Any]				=							self._expand(_UpperCAmelCase		)
             _a					:				List[str]				=							data_bp_input

             _a					:				Optional[Any]				=							np.dot(_UpperCAmelCase     ,self.vji.T		) - self.thre_bpa
             _a					:				Union[str, Any]				=							self.sig(_UpperCAmelCase		)
             _a					:				Optional[int]				=							np.dot(_UpperCAmelCase     ,self.wkj.T		) - self.thre_bpa
             _a					:				Dict				=							self.sig(_UpperCAmelCase		)

             # --------------Model Leaning ------------------------
             # calculate error and gradient---------------
             _a					:				Union[str, Any]				=							np.multiply(
                 (data_teach - bp_outa)     ,np.multiply(_UpperCAmelCase     ,(1 - bp_outa)		)		)
             _a					:				Any				=							np.multiply(
                 np.dot(_UpperCAmelCase     ,self.wkj		)     ,np.multiply(_UpperCAmelCase     ,(1 - bp_outa)		)		)
             _a					:				Optional[int]				=							np.dot(_UpperCAmelCase     ,self.vji		)

             _a					:				str				=							pd_i_all / (self.size_poolinga * self.size_poolinga)
             _a					:				Dict				=							pd_conva_pooled.T.getA().tolist()
             _a					:				Dict				=							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]		):
               _a					:				Optional[Any]				=							self._expand_mat(pd_conva_all[k_conv]		)
               _a					:				Optional[Any]				=							self.rate_weight * np.dot(_UpperCAmelCase     ,_UpperCAmelCase		)

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

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

         def draw_error():
           _a					:				int				=							[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 :	Dict     ,_UpperCAmelCase :	Dict		):
         # model predict
         _a					:				str				=							[]
         print('-------------------Start Testing-------------------------'		)
         print((' - - Shape: Test_Data  ', np.shape(_UpperCAmelCase		))		)
         for p in range(len(_UpperCAmelCase		)		):
           _a					:				List[Any]				=							np.asmatrix(datas_test[p]		)
           _a					:				Any				=							self.convolute(
               _UpperCAmelCase     ,self.conva     ,self.w_conva     ,self.thre_conva     ,conv_step=self.step_conva     ,)
           _a					:				Optional[Any]				=							self.pooling(_UpperCAmelCase     ,self.size_poolinga		)
           _a					:				Union[str, Any]				=							self._expand(_UpperCAmelCase		)

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







       def 							__lowercase      (							self :	List[str]     ,_UpperCAmelCase :	Any		):
         # return the data of image after convoluting process so we can check it out
         _a					:				Dict				=							np.asmatrix(_UpperCAmelCase		)
         _a					:				List[str]				=							self.convolute(
             _UpperCAmelCase     ,self.conva     ,self.w_conva     ,self.thre_conva     ,conv_step=self.step_conva     ,)
         _a					:				Dict				=							self.pooling(_UpperCAmelCase     ,self.size_poolinga		)

         return data_conveda, data_pooleda


if __name__ == "__main__":
       pass









'''simple docstring'''

import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
							from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import DPTImageProcessor

class      __magic_name__						:







							def __init__(							self :	Any     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	Optional[int]=2     ,_UpperCAmelCase :	Tuple=32     ,_UpperCAmelCase :	Dict=16     ,_UpperCAmelCase :	Optional[Any]=3     ,_UpperCAmelCase :	Tuple=True     ,_UpperCAmelCase :	Dict=True     ,_UpperCAmelCase :	str=32     ,_UpperCAmelCase :	Tuple=4     ,_UpperCAmelCase :	List[Any]=[0, 1, 2, 3]     ,_UpperCAmelCase :	List[str]=4     ,_UpperCAmelCase :	Any=37     ,_UpperCAmelCase :	List[Any]="gelu"     ,_UpperCAmelCase :	Optional[int]=0.1     ,_UpperCAmelCase :	Tuple=0.1     ,_UpperCAmelCase :	Optional[Any]=0.02     ,_UpperCAmelCase :	Optional[Any]=3     ,_UpperCAmelCase :	List[str]=[1, 384, 24, 24]     ,_UpperCAmelCase :	Union[str, Any]=True     ,_UpperCAmelCase :	List[str]=None     ,):
									_a					:				int				=							parent
									_a					:				Optional[Any]				=							batch_size
									_a					:				str				=							image_size
									_a					:				str				=							patch_size
									_a					:				Any				=							num_channels
									_a					:				Optional[Any]				=							is_training
									_a					:				str				=							use_labels
									_a					:				Union[str, Any]				=							hidden_size
									_a					:				int				=							num_hidden_layers
									_a					:				str				=							backbone_out_indices
									_a					:				List[Any]				=							num_attention_heads
									_a					:				Any				=							intermediate_size
									_a					:				List[Any]				=							hidden_act
									_a					:				int				=							hidden_dropout_prob
									_a					:				Optional[int]				=							attention_probs_dropout_prob
									_a					:				Optional[int]				=							initializer_range
									_a					:				Tuple				=							num_labels
									_a					:				Tuple				=							backbone_featmap_shape
									_a					:				Optional[int]				=							scope
									_a					:				Dict				=							is_hybrid
									# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
									_a					:				str				=							(image_size // patch_size) ** 2
									_a					:				Optional[int]				=							num_patches + 1







							def 							__lowercase      (							self :	Dict		):
									_a					:				str				=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

									_a					:				Any				=							None
									if self.use_labels:
											_a					:				Union[str, Any]				=							ids_tensor([self.batch_size, self.image_size, self.image_size]     ,self.num_labels		)

									_a					:				int				=							self.get_config()

									return config, pixel_values, labels







							def 							__lowercase      (							self :	int		):
									_a					:				Dict				=							{
									    'global_padding': 'same',
									    'layer_type': 'bottleneck',
									    'depths': [3, 4, 9],
									    'out_features': ['stage1', 'stage2', 'stage3'],
									    'embedding_dynamic_padding': True,
									    'hidden_sizes': [96, 192, 384, 768],
									    'num_groups': 2,
									}

									return DPTConfig(
									    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     ,backbone_out_indices=self.backbone_out_indices     ,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     ,is_hybrid=self.is_hybrid     ,backbone_config=_UpperCAmelCase     ,backbone_featmap_shape=self.backbone_featmap_shape     ,)







							def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	Optional[Any]		):
									_a					:				Optional[Any]				=							DPTModel(config=_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a					:				List[Any]				=							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 :	List[Any]     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Any		):
									_a					:				List[Any]				=							self.num_labels
									_a					:				Dict				=							DPTForDepthEstimation(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a					:				str				=							model(_UpperCAmelCase		)
									self.parent.assertEqual(result.predicted_depth.shape     ,(self.batch_size, self.image_size, self.image_size)		)







							def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	List[Any]		):
									_a					:				Optional[int]				=							self.num_labels
									_a					:				Optional[int]				=							DPTForSemanticSegmentation(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a					:				Optional[int]				=							model(_UpperCAmelCase     ,labels=_UpperCAmelCase		)
									self.parent.assertEqual(
									    result.logits.shape     ,(self.batch_size, self.num_labels, self.image_size, self.image_size)		)







							def 							__lowercase      (							self :	List[Any]		):
									_a					:				List[str]				=							self.prepare_config_and_inputs()
									_a	, _a	, _a					:				List[Any]				=							config_and_inputs
									_a					:				List[Any]				=							{'pixel_values': pixel_values}
									return config, inputs_dict

@require_torch
class      __magic_name__						(   _UpperCamelCase      ,						_UpperCamelCase      ,						unittest.TestCase       ):
							lowerCAmelCase	:   Optional[int]           = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
							lowerCAmelCase	:   int           = (
							    {
							        'depth-estimation': DPTForDepthEstimation,
							        'feature-extraction': DPTModel,
							        'image-segmentation': DPTForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							lowerCAmelCase	:   Union[str, Any]           = False
							lowerCAmelCase	:   Dict           = False
							lowerCAmelCase	:   List[str]           = False







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a					:				Union[str, Any]				=							DPTModelTester(self		)
									_a					:				int				=							ConfigTester(self     ,config_class=_UpperCAmelCase     ,has_text_modality=_UpperCAmelCase     ,hidden_size=37		)







							def 							__lowercase      (							self :	Any		):
									self.config_tester.run_common_tests()







							@unittest.skip(reason='DPT does not use inputs_embeds'		)
							def 							__lowercase      (							self :	Tuple		):
									pass







							def 							__lowercase      (							self :	List[str]		):
									_a	, _a					:				Any				=							self.model_tester.prepare_config_and_inputs_for_common()

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







							def 							__lowercase      (							self :	Any		):
									_a	, _a					:				List[Any]				=							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_a					:				Optional[int]				=							model_class(_UpperCAmelCase		)
											_a					:				Tuple				=							inspect.signature(model.forward		)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											_a					:				Union[str, Any]				=							[*signature.parameters.keys()]

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







							def 							__lowercase      (							self :	str		):
									_a					:				Tuple				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	int		):
									_a					:				int				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_depth_estimation(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	str		):
									_a					:				Optional[Any]				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	Union[str, Any]		):
									for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
													continue

											_a	, _a					:				Optional[Any]				=							self.model_tester.prepare_config_and_inputs_for_common()
											_a					:				Tuple				=							True

											if model_class in get_values(_UpperCAmelCase		):
													continue

											_a					:				str				=							model_class(_UpperCAmelCase		)
											model.to(_UpperCAmelCase		)
											model.train()
											_a					:				Optional[int]				=							self._prepare_for_class(_UpperCAmelCase     ,_UpperCAmelCase     ,return_labels=_UpperCAmelCase		)
											_a					:				Optional[Any]				=							model(**_UpperCAmelCase		).loss
											loss.backward()







							def 							__lowercase      (							self :	Union[str, Any]		):
									for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
													continue

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

											if model_class in get_values(_UpperCAmelCase		) or not model_class.supports_gradient_checkpointing:
													continue
											_a					:				str				=							model_class(_UpperCAmelCase		)
											model.to(_UpperCAmelCase		)
											model.gradient_checkpointing_enable()
											model.train()
											_a					:				int				=							self._prepare_for_class(_UpperCAmelCase     ,_UpperCAmelCase     ,return_labels=_UpperCAmelCase		)
											_a					:				Optional[Any]				=							model(**_UpperCAmelCase		).loss
											loss.backward()







							def 							__lowercase      (							self :	str		):
									_a	, _a					:				str				=							self.model_tester.prepare_config_and_inputs_for_common()

									_a					:				List[str]				=							_config_zero_init(_UpperCAmelCase		)
									for model_class in self.all_model_classes:
											_a					:				Tuple				=							model_class(config=_UpperCAmelCase		)
											# Skip the check for the backbone
											_a					:				Any				=							[]
											for name, module in model.named_modules():
													if module.__class__.__name__ == "DPTViTHybridEmbeddings":
															_a					:				Union[str, Any]				=							[F"""{name}.{key}""" for key in module.state_dict().keys()]
															break

											for name, param in model.named_parameters():
													if param.requires_grad:
															if name in backbone_params:
																	continue
															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"""     ,)







							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'		)
							def 							__lowercase      (							self :	Optional[Any]		):
									pass







							@slow
							def 							__lowercase      (							self :	Union[str, Any]		):
									for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
											_a					:				List[Any]				=							DPTModel.from_pretrained(_UpperCAmelCase		)
											self.assertIsNotNone(_UpperCAmelCase		)







							def 							__lowercase      (							self :	Any		):
									# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
									_a	, _a					:				List[Any]				=							self.model_tester.prepare_config_and_inputs_for_common()
									_a					:				Optional[Any]				=							'add'
									with self.assertRaises(_UpperCAmelCase		):
											_a					:				List[str]				=							DPTForDepthEstimation(_UpperCAmelCase		)



def 					__lowerCamelCase					(				)      ->				Optional[Any]:
		_a					:				Optional[Any]				=							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
		return image

@require_torch
@require_vision
@slow
class      __magic_name__						(   unittest.TestCase       ):







							def 							__lowercase      (							self :	Any		):
									_a					:				Optional[Any]				=							DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'		)
									_a					:				Union[str, Any]				=							DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'		).to(_UpperCAmelCase		)

									_a					:				Optional[int]				=							prepare_img()
									_a					:				Optional[Any]				=							image_processor(images=_UpperCAmelCase     ,return_tensors='pt'		).to(_UpperCAmelCase		)

									# forward pass
									with torch.no_grad():
											_a					:				Tuple				=							model(**_UpperCAmelCase		)
											_a					:				Union[str, Any]				=							outputs.predicted_depth

									# verify the predicted depth
									_a					:				str				=							torch.Size((1, 384, 384)		)
									self.assertEqual(predicted_depth.shape     ,_UpperCAmelCase		)

									_a					:				int				=							torch.tensor(
									    [[[5.64_37, 5.61_46, 5.65_11], [5.43_71, 5.56_49, 5.59_58], [5.52_15, 5.51_84, 5.52_93]]]		).to(_UpperCAmelCase		)

									self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100     ,_UpperCAmelCase     ,atol=1E-4		)		)




