



'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
snake_case_			=     logging.get_logger(__name__)






def       __lowercase  (_SCREAMING_SNAKE_CASE      :str       ):
    SCREAMING_SNAKE_CASE  :						Tuple      					= DPTConfig()

    if "large" in checkpoint_url:
        SCREAMING_SNAKE_CASE  :						Dict      					= 10_24
        SCREAMING_SNAKE_CASE  :						Tuple      					= 40_96
        SCREAMING_SNAKE_CASE  :						Optional[Any]      					= 24
        SCREAMING_SNAKE_CASE  :						List[Any]      					= 16
        SCREAMING_SNAKE_CASE  :						int      					= [5, 11, 17, 23]
        SCREAMING_SNAKE_CASE  :						Optional[int]      					= [2_56, 5_12, 10_24, 10_24]
        SCREAMING_SNAKE_CASE  :						List[Any]      					= (1, 3_84, 3_84)

    if "ade" in checkpoint_url:
        SCREAMING_SNAKE_CASE  :						List[str]      					= True

        SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= 1_50
        SCREAMING_SNAKE_CASE  :						List[str]      					= '''huggingface/label-files'''
        SCREAMING_SNAKE_CASE  :						int      					= '''ade20k-id2label.json'''
        SCREAMING_SNAKE_CASE  :						int      					= json.load(open(cached_download(hf_hub_url(A__    ,	A__    ,	repo_type='''dataset'''       )       )    ,	'''r'''       )       )
        SCREAMING_SNAKE_CASE  :						Dict      					= {int(A__       ): v for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE  :						Dict      					= idalabel
        SCREAMING_SNAKE_CASE  :						Optional[Any]      					= {v: k for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE  :						List[str]      					= [1, 1_50, 4_80, 4_80]

    return config, expected_shape






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Dict       ):
    SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
    for k in ignore_keys:
        state_dict.pop(A__    ,	A__       )






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Dict       ):



    if (
        "pretrained.model" in name
        and "cls_token" not in name
        and "pos_embed" not in name
        and "patch_embed" not in name
    ):
        SCREAMING_SNAKE_CASE  :						Tuple      					= name.replace('''pretrained.model'''    ,	'''dpt.encoder'''       )
    if "pretrained.model" in name:
        SCREAMING_SNAKE_CASE  :						List[str]      					= name.replace('''pretrained.model'''    ,	'''dpt.embeddings'''       )
    if "patch_embed" in name:
        SCREAMING_SNAKE_CASE  :						Any      					= name.replace('''patch_embed'''    ,	'''patch_embeddings'''       )
    if "pos_embed" in name:
        SCREAMING_SNAKE_CASE  :						List[Any]      					= name.replace('''pos_embed'''    ,	'''position_embeddings'''       )
    if "attn.proj" in name:
        SCREAMING_SNAKE_CASE  :						Optional[int]      					= name.replace('''attn.proj'''    ,	'''attention.output.dense'''       )
    if "proj" in name and "project" not in name:
        SCREAMING_SNAKE_CASE  :						str      					= name.replace('''proj'''    ,	'''projection'''       )
    if "blocks" in name:
        SCREAMING_SNAKE_CASE  :						str      					= name.replace('''blocks'''    ,	'''layer'''       )
    if "mlp.fc1" in name:
        SCREAMING_SNAKE_CASE  :						Dict      					= name.replace('''mlp.fc1'''    ,	'''intermediate.dense'''       )
    if "mlp.fc2" in name:
        SCREAMING_SNAKE_CASE  :						Optional[Any]      					= name.replace('''mlp.fc2'''    ,	'''output.dense'''       )
    if "norm1" in name:
        SCREAMING_SNAKE_CASE  :						List[Any]      					= name.replace('''norm1'''    ,	'''layernorm_before'''       )
    if "norm2" in name:
        SCREAMING_SNAKE_CASE  :						Tuple      					= name.replace('''norm2'''    ,	'''layernorm_after'''       )
    if "scratch.output_conv" in name:
        SCREAMING_SNAKE_CASE  :						str      					= name.replace('''scratch.output_conv'''    ,	'''head'''       )
    if "scratch" in name:
        SCREAMING_SNAKE_CASE  :						List[str]      					= name.replace('''scratch'''    ,	'''neck'''       )
    if "layer1_rn" in name:
        SCREAMING_SNAKE_CASE  :						Dict      					= name.replace('''layer1_rn'''    ,	'''convs.0'''       )
    if "layer2_rn" in name:
        SCREAMING_SNAKE_CASE  :						List[str]      					= name.replace('''layer2_rn'''    ,	'''convs.1'''       )
    if "layer3_rn" in name:
        SCREAMING_SNAKE_CASE  :						Dict      					= name.replace('''layer3_rn'''    ,	'''convs.2'''       )
    if "layer4_rn" in name:
        SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= name.replace('''layer4_rn'''    ,	'''convs.3'''       )
    if "refinenet" in name:
        SCREAMING_SNAKE_CASE  :						Any      					= int(name[len('''neck.refinenet'''       ) : len('''neck.refinenet'''       ) + 1]       )
        # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
        SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= name.replace(F'''refinenet{layer_idx}'''    ,	F'''fusion_stage.layers.{abs(layer_idx-4       )}'''       )
    if "out_conv" in name:
        SCREAMING_SNAKE_CASE  :						List[Any]      					= name.replace('''out_conv'''    ,	'''projection'''       )
    if "resConfUnit1" in name:
        SCREAMING_SNAKE_CASE  :						List[Any]      					= name.replace('''resConfUnit1'''    ,	'''residual_layer1'''       )
    if "resConfUnit2" in name:
        SCREAMING_SNAKE_CASE  :						List[Any]      					= name.replace('''resConfUnit2'''    ,	'''residual_layer2'''       )
    if "conv1" in name:
        SCREAMING_SNAKE_CASE  :						Any      					= name.replace('''conv1'''    ,	'''convolution1'''       )
    if "conv2" in name:
        SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= name.replace('''conv2'''    ,	'''convolution2'''       )
    # readout blocks
    if "pretrained.act_postprocess1.0.project.0" in name:
        SCREAMING_SNAKE_CASE  :						Any      					= name.replace('''pretrained.act_postprocess1.0.project.0'''    ,	'''neck.reassemble_stage.readout_projects.0.0'''       )
    if "pretrained.act_postprocess2.0.project.0" in name:
        SCREAMING_SNAKE_CASE  :						List[Any]      					= name.replace('''pretrained.act_postprocess2.0.project.0'''    ,	'''neck.reassemble_stage.readout_projects.1.0'''       )
    if "pretrained.act_postprocess3.0.project.0" in name:
        SCREAMING_SNAKE_CASE  :						Tuple      					= name.replace('''pretrained.act_postprocess3.0.project.0'''    ,	'''neck.reassemble_stage.readout_projects.2.0'''       )
    if "pretrained.act_postprocess4.0.project.0" in name:
        SCREAMING_SNAKE_CASE  :						Optional[int]      					= name.replace('''pretrained.act_postprocess4.0.project.0'''    ,	'''neck.reassemble_stage.readout_projects.3.0'''       )
    # resize blocks
    if "pretrained.act_postprocess1.3" in name:
        SCREAMING_SNAKE_CASE  :						List[str]      					= name.replace('''pretrained.act_postprocess1.3'''    ,	'''neck.reassemble_stage.layers.0.projection'''       )
    if "pretrained.act_postprocess1.4" in name:
        SCREAMING_SNAKE_CASE  :						str      					= name.replace('''pretrained.act_postprocess1.4'''    ,	'''neck.reassemble_stage.layers.0.resize'''       )
    if "pretrained.act_postprocess2.3" in name:
        SCREAMING_SNAKE_CASE  :						Dict      					= name.replace('''pretrained.act_postprocess2.3'''    ,	'''neck.reassemble_stage.layers.1.projection'''       )
    if "pretrained.act_postprocess2.4" in name:
        SCREAMING_SNAKE_CASE  :						Any      					= name.replace('''pretrained.act_postprocess2.4'''    ,	'''neck.reassemble_stage.layers.1.resize'''       )
    if "pretrained.act_postprocess3.3" in name:
        SCREAMING_SNAKE_CASE  :						Optional[int]      					= name.replace('''pretrained.act_postprocess3.3'''    ,	'''neck.reassemble_stage.layers.2.projection'''       )
    if "pretrained.act_postprocess4.3" in name:
        SCREAMING_SNAKE_CASE  :						Optional[int]      					= name.replace('''pretrained.act_postprocess4.3'''    ,	'''neck.reassemble_stage.layers.3.projection'''       )
    if "pretrained.act_postprocess4.4" in name:
        SCREAMING_SNAKE_CASE  :						Optional[int]      					= name.replace('''pretrained.act_postprocess4.4'''    ,	'''neck.reassemble_stage.layers.3.resize'''       )
    if "pretrained" in name:
        SCREAMING_SNAKE_CASE  :						Any      					= name.replace('''pretrained'''    ,	'''dpt'''       )
    if "bn" in name:
        SCREAMING_SNAKE_CASE  :						int      					= name.replace('''bn'''    ,	'''batch_norm'''       )
    if "head" in name:
        SCREAMING_SNAKE_CASE  :						List[Any]      					= name.replace('''head'''    ,	'''head.head'''       )
    if "encoder.norm" in name:
        SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= name.replace('''encoder.norm'''    ,	'''layernorm'''       )
    if "auxlayer" in name:
        SCREAMING_SNAKE_CASE  :						Dict      					= name.replace('''auxlayer'''    ,	'''auxiliary_head.head'''       )

    return name






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Tuple    ,	_SCREAMING_SNAKE_CASE      :Any       ):



    for i in range(config.num_hidden_layers       ):
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        SCREAMING_SNAKE_CASE  :						List[Any]      					= state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight'''       )
        SCREAMING_SNAKE_CASE  :						List[str]      					= state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias'''       )
        # next, add query, keys and values (in that order) to the state dict
        SCREAMING_SNAKE_CASE  :						Any      					= in_proj_weight[: config.hidden_size, :]
        SCREAMING_SNAKE_CASE  :						Tuple      					= in_proj_bias[: config.hidden_size]
        SCREAMING_SNAKE_CASE  :						List[Any]      					= in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        SCREAMING_SNAKE_CASE  :						Optional[int]      					= in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        SCREAMING_SNAKE_CASE  :						int      					= in_proj_weight[
            -config.hidden_size :, :
        ]
        SCREAMING_SNAKE_CASE  :						List[str]      					= in_proj_bias[-config.hidden_size :]






def       __lowercase  ():
    SCREAMING_SNAKE_CASE  :						str      					= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= Image.open(requests.get(A__    ,	stream=A__       ).raw       )
    return im






@torch.no_grad()
def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[int]    ,	_SCREAMING_SNAKE_CASE      :Union[str, Any]    ,	_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Union[str, Any]       ):
    SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= get_dpt_config(A__       )
    # load original state_dict from URL
    SCREAMING_SNAKE_CASE  :						Optional[Any]      					= torch.hub.load_state_dict_from_url(A__    ,	map_location='''cpu'''       )
    # remove certain keys
    remove_ignore_keys_(A__       )
    # rename keys
    for key in state_dict.copy().keys():
        SCREAMING_SNAKE_CASE  :						int      					= state_dict.pop(A__       )
        SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= val
    # read in qkv matrices
    read_in_q_k_v(A__    ,	A__       )

    # load HuggingFace model
    SCREAMING_SNAKE_CASE  :						List[str]      					= DPTForSemanticSegmentation(A__       ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(A__       )
    model.load_state_dict(A__       )
    model.eval()

    # Check outputs on an image
    SCREAMING_SNAKE_CASE  :						Optional[Any]      					= 4_80 if '''ade''' in checkpoint_url else 3_84
    SCREAMING_SNAKE_CASE  :						Any      					= DPTImageProcessor(size=A__       )

    SCREAMING_SNAKE_CASE  :						List[str]      					= prepare_img()
    SCREAMING_SNAKE_CASE  :						Any      					= image_processor(A__    ,	return_tensors='''pt'''       )

    # forward pass
    SCREAMING_SNAKE_CASE  :						str      					= model(**A__       ).logits if '''ade''' in checkpoint_url else model(**A__       ).predicted_depth

    # Assert logits
    SCREAMING_SNAKE_CASE  :						Tuple      					= torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]       )
    if "ade" in checkpoint_url:
        SCREAMING_SNAKE_CASE  :						Optional[Any]      					= torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]       )
    assert outputs.shape == torch.Size(A__       )
    assert (
        torch.allclose(outputs[0, 0, :3, :3]    ,	A__    ,	atol=1E-4       )
        if "ade" in checkpoint_url
        else torch.allclose(outputs[0, :3, :3]    ,	A__       )
    )

    Path(A__       ).mkdir(exist_ok=A__       )
    print(F'''Saving model to {pytorch_dump_folder_path}'''       )
    model.save_pretrained(A__       )
    print(F'''Saving image processor to {pytorch_dump_folder_path}'''       )
    image_processor.save_pretrained(A__       )

    if push_to_hub:
        print('''Pushing model to hub...'''       )
        model.push_to_hub(
            repo_path_or_name=Path(A__    ,	A__       )    ,	organization='''nielsr'''    ,	commit_message='''Add model'''    ,	use_temp_dir=A__    ,	)
        image_processor.push_to_hub(
            repo_path_or_name=Path(A__    ,	A__       )    ,	organization='''nielsr'''    ,	commit_message='''Add image processor'''    ,	use_temp_dir=A__    ,	)


if __name__ == "__main__":
 snake_case_			=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--checkpoint_url""",
     default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
     type=str,
     help="""URL of the original DPT checkpoint you\'d like to convert.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""",
     default=None,
     type=str,
     required=True,
     help="""Path to the output PyTorch model directory.""",
 )
 parser.add_argument(
     """--push_to_hub""",
     action="""store_true""",
 )
 parser.add_argument(
     """--model_name""",
     default="""dpt-large""",
     type=str,
     help="""Name of the model, in case you\'re pushing to the hub.""",
 )

 snake_case_			=     parser.parse_args()
 convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)









'''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




