

import argparse
import json
from pathlib import Path

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

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


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

def        lowerCamelCase__       (    _a):
	SCREAMING_SNAKE_CASE      :					int    =				SwinConfig(
	    embed_dim=192							,	depths=(2, 2, 18, 2)							,	num_heads=(6, 12, 24, 48)							,	window_size=12							,	out_features=["stage2", "stage3", "stage4"]							,	)

	SCREAMING_SNAKE_CASE      :					Optional[int]    =				DetaConfig(
	    backbone_config=_a							,	num_queries=900							,	encoder_ffn_dim=2048							,	decoder_ffn_dim=2048							,	num_feature_levels=5							,	assign_first_stage=_a							,	with_box_refine=_a							,	two_stage=_a							,	)

	# set labels
	SCREAMING_SNAKE_CASE      :					Tuple    =				"huggingface/label-files"
	if "o365" in model_name:
		SCREAMING_SNAKE_CASE      :					Optional[int]    =				366
		SCREAMING_SNAKE_CASE      :					Optional[Any]    =				"object365-id2label.json"
	else:
		SCREAMING_SNAKE_CASE      :					str    =				91
		SCREAMING_SNAKE_CASE      :					Tuple    =				"coco-detection-id2label.json"

	SCREAMING_SNAKE_CASE      :					int    =				num_labels
	SCREAMING_SNAKE_CASE      :					str    =				json.load(open(cached_download(hf_hub_url(_a							,	_a							,	repo_type="dataset"))							,	"r"))
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				{int(_a): v for k, v in idalabel.items()}
	SCREAMING_SNAKE_CASE      :					List[str]    =				idalabel
	SCREAMING_SNAKE_CASE      :					List[Any]    =				{v: k for k, v in idalabel.items()}

	return config

def        lowerCamelCase__       (    _a):
	SCREAMING_SNAKE_CASE      :					List[Any]    =				[]

	# stem
	# fmt: off
	rename_keys.append(("backbone.0.body.patch_embed.proj.weight", "model.backbone.model.embeddings.patch_embeddings.projection.weight"))
	rename_keys.append(("backbone.0.body.patch_embed.proj.bias", "model.backbone.model.embeddings.patch_embeddings.projection.bias"))
	rename_keys.append(("backbone.0.body.patch_embed.norm.weight", "model.backbone.model.embeddings.norm.weight"))
	rename_keys.append(("backbone.0.body.patch_embed.norm.bias", "model.backbone.model.embeddings.norm.bias"))
	# stages
	for i in range(len(config.backbone_config.depths)):
		for j in range(config.backbone_config.depths[i]):
			rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm1.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight"))
			rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm1.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias"))
			rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table"))
			rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index"))
			rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight"))
			rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias"))
			rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm2.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight"))
			rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm2.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias"))
			rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight"))
			rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias"))
			rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight"))
			rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias"))

		if i < 3:
			rename_keys.append((f"backbone.0.body.layers.{i}.downsample.reduction.weight", f"model.backbone.model.encoder.layers.{i}.downsample.reduction.weight"))
			rename_keys.append((f"backbone.0.body.layers.{i}.downsample.norm.weight", f"model.backbone.model.encoder.layers.{i}.downsample.norm.weight"))
			rename_keys.append((f"backbone.0.body.layers.{i}.downsample.norm.bias", f"model.backbone.model.encoder.layers.{i}.downsample.norm.bias"))

	rename_keys.append(("backbone.0.body.norm1.weight", "model.backbone.model.hidden_states_norms.stage2.weight"))
	rename_keys.append(("backbone.0.body.norm1.bias", "model.backbone.model.hidden_states_norms.stage2.bias"))
	rename_keys.append(("backbone.0.body.norm2.weight", "model.backbone.model.hidden_states_norms.stage3.weight"))
	rename_keys.append(("backbone.0.body.norm2.bias", "model.backbone.model.hidden_states_norms.stage3.bias"))
	rename_keys.append(("backbone.0.body.norm3.weight", "model.backbone.model.hidden_states_norms.stage4.weight"))
	rename_keys.append(("backbone.0.body.norm3.bias", "model.backbone.model.hidden_states_norms.stage4.bias"))

	# transformer encoder
	for i in range(config.encoder_layers):
		rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight", f"model.encoder.layers.{i}.self_attn.sampling_offsets.weight"))
		rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias", f"model.encoder.layers.{i}.self_attn.sampling_offsets.bias"))
		rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.attention_weights.weight", f"model.encoder.layers.{i}.self_attn.attention_weights.weight"))
		rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.attention_weights.bias", f"model.encoder.layers.{i}.self_attn.attention_weights.bias"))
		rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.value_proj.weight", f"model.encoder.layers.{i}.self_attn.value_proj.weight"))
		rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.value_proj.bias", f"model.encoder.layers.{i}.self_attn.value_proj.bias"))
		rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.output_proj.weight", f"model.encoder.layers.{i}.self_attn.output_proj.weight"))
		rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.output_proj.bias", f"model.encoder.layers.{i}.self_attn.output_proj.bias"))
		rename_keys.append((f"transformer.encoder.layers.{i}.norm1.weight", f"model.encoder.layers.{i}.self_attn_layer_norm.weight"))
		rename_keys.append((f"transformer.encoder.layers.{i}.norm1.bias", f"model.encoder.layers.{i}.self_attn_layer_norm.bias"))
		rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"model.encoder.layers.{i}.fc1.weight"))
		rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"model.encoder.layers.{i}.fc1.bias"))
		rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"model.encoder.layers.{i}.fc2.weight"))
		rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"model.encoder.layers.{i}.fc2.bias"))
		rename_keys.append((f"transformer.encoder.layers.{i}.norm2.weight", f"model.encoder.layers.{i}.final_layer_norm.weight"))
		rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"model.encoder.layers.{i}.final_layer_norm.bias"))

	# transformer decoder
	for i in range(config.decoder_layers):
		rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight", f"model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight"))
		rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias", f"model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias"))
		rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.attention_weights.weight", f"model.decoder.layers.{i}.encoder_attn.attention_weights.weight"))
		rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.attention_weights.bias", f"model.decoder.layers.{i}.encoder_attn.attention_weights.bias"))
		rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.value_proj.weight", f"model.decoder.layers.{i}.encoder_attn.value_proj.weight"))
		rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.value_proj.bias", f"model.decoder.layers.{i}.encoder_attn.value_proj.bias"))
		rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.output_proj.weight", f"model.decoder.layers.{i}.encoder_attn.output_proj.weight"))
		rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.output_proj.bias", f"model.decoder.layers.{i}.encoder_attn.output_proj.bias"))
		rename_keys.append((f"transformer.decoder.layers.{i}.norm1.weight", f"model.decoder.layers.{i}.encoder_attn_layer_norm.weight"))
		rename_keys.append((f"transformer.decoder.layers.{i}.norm1.bias", f"model.decoder.layers.{i}.encoder_attn_layer_norm.bias"))
		rename_keys.append((f"transformer.decoder.layers.{i}.self_attn.out_proj.weight", f"model.decoder.layers.{i}.self_attn.out_proj.weight"))
		rename_keys.append((f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"model.decoder.layers.{i}.self_attn.out_proj.bias"))
		rename_keys.append((f"transformer.decoder.layers.{i}.norm2.weight", f"model.decoder.layers.{i}.self_attn_layer_norm.weight"))
		rename_keys.append((f"transformer.decoder.layers.{i}.norm2.bias", f"model.decoder.layers.{i}.self_attn_layer_norm.bias"))
		rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"model.decoder.layers.{i}.fc1.weight"))
		rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"model.decoder.layers.{i}.fc1.bias"))
		rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"model.decoder.layers.{i}.fc2.weight"))
		rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"model.decoder.layers.{i}.fc2.bias"))
		rename_keys.append((f"transformer.decoder.layers.{i}.norm3.weight", f"model.decoder.layers.{i}.final_layer_norm.weight"))
		rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"model.decoder.layers.{i}.final_layer_norm.bias"))

	# fmt: on

	return rename_keys

def        lowerCamelCase__       (    _a							,	_a							,	_a):
	SCREAMING_SNAKE_CASE      :					Tuple    =				dct.pop(_a)
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				val

def        lowerCamelCase__       (    _a							,	_a):
	SCREAMING_SNAKE_CASE      :					Any    =				[int(backbone_config.embed_dim * 2**i) for i in range(len(backbone_config.depths))]
	for i in range(len(backbone_config.depths)):
		SCREAMING_SNAKE_CASE      :					Optional[int]    =				num_features[i]
		for j in range(backbone_config.depths[i]):
			# fmt: off
			# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
			SCREAMING_SNAKE_CASE      :					str    =				state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight")
			SCREAMING_SNAKE_CASE      :					Dict    =				state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias")
			# next, add query, keys and values (in that order) to the state dict
			SCREAMING_SNAKE_CASE      :					int    =				in_proj_weight[:dim, :]
			SCREAMING_SNAKE_CASE      :					Dict    =				in_proj_bias[: dim]
			SCREAMING_SNAKE_CASE      :					Any    =				in_proj_weight[
			    dim : dim * 2, :
			]
			SCREAMING_SNAKE_CASE      :					Tuple    =				in_proj_bias[
			    dim : dim * 2
			]
			SCREAMING_SNAKE_CASE      :					int    =				in_proj_weight[
			    -dim :, :
			]
			SCREAMING_SNAKE_CASE      :					Any    =				in_proj_bias[-dim :]
			# fmt: on

def        lowerCamelCase__       (    _a							,	_a):
	# transformer decoder self-attention layers
	SCREAMING_SNAKE_CASE      :					List[str]    =				config.d_model
	for i in range(config.decoder_layers):
		# read in weights + bias of input projection layer of self-attention
		SCREAMING_SNAKE_CASE      :					Tuple    =				state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_weight")
		SCREAMING_SNAKE_CASE      :					str    =				state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_bias")
		# next, add query, keys and values (in that order) to the state dict
		SCREAMING_SNAKE_CASE      :					int    =				in_proj_weight[:hidden_size, :]
		SCREAMING_SNAKE_CASE      :					str    =				in_proj_bias[:hidden_size]
		SCREAMING_SNAKE_CASE      :					int    =				in_proj_weight[
		    hidden_size : hidden_size * 2, :
		]
		SCREAMING_SNAKE_CASE      :					Tuple    =				in_proj_bias[hidden_size : hidden_size * 2]
		SCREAMING_SNAKE_CASE      :					Any    =				in_proj_weight[-hidden_size:, :]
		SCREAMING_SNAKE_CASE      :					Dict    =				in_proj_bias[-hidden_size:]

def        lowerCamelCase__       (    ):
	SCREAMING_SNAKE_CASE      :					Any    =				"http://images.cocodataset.org/val2017/000000039769.jpg"
	SCREAMING_SNAKE_CASE      :					Tuple    =				Image.open(requests.get(_a							,	stream=_a).raw)

	return im

@torch.no_grad()
def        lowerCamelCase__       (    _a							,	_a							,	_a):
	SCREAMING_SNAKE_CASE      :					int    =				get_deta_config(_a)

	# load original state dict
	if model_name == "deta-swin-large":
		SCREAMING_SNAKE_CASE      :					Any    =				hf_hub_download(repo_id="nielsr/deta-checkpoints"							,	filename="adet_swin_ft.pth")
	elif model_name == "deta-swin-large-o365":
		SCREAMING_SNAKE_CASE      :					Any    =				hf_hub_download(repo_id="jozhang97/deta-swin-l-o365"							,	filename="deta_swin_pt_o365.pth")
	else:
		raise ValueError(f"Model name {model_name} not supported")

	SCREAMING_SNAKE_CASE      :					List[Any]    =				torch.load(_a							,	map_location="cpu")["model"]

	# original state dict
	for name, param in state_dict.items():
		print(_a							,	param.shape)

	# rename keys
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				create_rename_keys(_a)
	for src, dest in rename_keys:
		rename_key(_a							,	_a							,	_a)
	read_in_swin_q_k_v(_a							,	config.backbone_config)
	read_in_decoder_q_k_v(_a							,	_a)

	# fix some prefixes
	for key in state_dict.copy().keys():
		if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
			SCREAMING_SNAKE_CASE      :					List[str]    =				state_dict.pop(_a)
			SCREAMING_SNAKE_CASE      :					Optional[int]    =				val
		if "input_proj" in key:
			SCREAMING_SNAKE_CASE      :					Any    =				state_dict.pop(_a)
			SCREAMING_SNAKE_CASE      :					str    =				val
		if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
			SCREAMING_SNAKE_CASE      :					Any    =				state_dict.pop(_a)
			SCREAMING_SNAKE_CASE      :					str    =				val

    # finally, create HuggingFace model and load state dict
	SCREAMING_SNAKE_CASE      :					str    =				DetaForObjectDetection(_a)
	model.load_state_dict(_a)
	model.eval()

	SCREAMING_SNAKE_CASE      :					str    =				"cuda" if torch.cuda.is_available() else "cpu"
	model.to(_a)

	# load image processor
	SCREAMING_SNAKE_CASE      :					int    =				DetaImageProcessor(format="coco_detection")

	# verify our conversion on image
	SCREAMING_SNAKE_CASE      :					List[str]    =				prepare_img()
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				processor(images=_a							,	return_tensors="pt")
	SCREAMING_SNAKE_CASE      :					List[Any]    =				encoding["pixel_values"]
	SCREAMING_SNAKE_CASE      :					Dict    =				model(pixel_values.to(_a))

	# verify logits
	print("Logits:"							,	outputs.logits[0, :3, :3])
	print("Boxes:"							,	outputs.pred_boxes[0, :3, :3])
	if model_name == "deta-swin-large":
		SCREAMING_SNAKE_CASE      :					Any    =				torch.tensor(
		    [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]])
		SCREAMING_SNAKE_CASE      :					Dict    =				torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]])
	elif model_name == "deta-swin-large-o365":
		SCREAMING_SNAKE_CASE      :					Tuple    =				torch.tensor(
		    [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]])
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]])
	assert torch.allclose(outputs.logits[0, :3, :3]							,	expected_logits.to(_a)							,	atol=1E-4)
	assert torch.allclose(outputs.pred_boxes[0, :3, :3]							,	expected_boxes.to(_a)							,	atol=1E-4)
	print("Everything ok!")

	if pytorch_dump_folder_path:
		# Save model and processor
		logger.info(f"Saving PyTorch model and processor to {pytorch_dump_folder_path}...")
		Path(_a).mkdir(exist_ok=_a)
		model.save_pretrained(_a)
		processor.save_pretrained(_a)

	# Push to hub
	if push_to_hub:
		print("Pushing model and processor to hub...")
		model.push_to_hub(f"jozhang97/{model_name}")
		processor.push_to_hub(f"jozhang97/{model_name}")


if __name__ == "__main__":
			a_     =			argparse.ArgumentParser()

			parser.add_argument(
			    '--model_name',
			    type=str,
			    default='deta-swin-large',
			    choices=['deta-swin-large', 'deta-swin-large-o365'],
			    help='Name of the model you\'d like to convert.',
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path',
			    default=None,
			    type=str,
			    help='Path to the folder to output PyTorch model.',
			)
			parser.add_argument(
			    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
			)
			a_     =			parser.parse_args()
			convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



import pickle

import numpy as np
from matplotlib import pyplot as plt







class      lowerCAmelCase__       :




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase=0.2 ,						__lowerCamelCase=0.2)			->				str:
            _A      :       Optional[int]				    = bp_numa
            _A      :       Dict				    = bp_numa
            _A      :       Tuple				    = bp_numa
            _A      :       List[str]				    = conva_get[:2]
            _A      :       Tuple				    = conva_get[2]
            _A      :       Optional[int]				    = size_pa
            _A      :       Optional[Any]				    = rate_w
            _A      :       Optional[Any]				    = rate_t
            _A      :       Union[str, Any]				    = [
                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      :       Dict				    = np.mat(-1 * np.random.rand(self.num_bpa ,						self.num_bpa) + 0.5)
            _A      :       Any				    = -2 * np.random.rand(self.conva[1]) + 1
            _A      :       Optional[int]				    = -2 * np.random.rand(self.num_bpa) + 1
            _A      :       Optional[Any]				    = -2 * np.random.rand(self.num_bpa) + 1


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Dict:
            # save model dict with pickle
            _A      :       Dict				    = {
                "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(__lowerCamelCase ,						"wb") as f:
                   pickle.dump(__lowerCamelCase ,						__lowerCamelCase)

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


     @classmethod
     def  _lowerCamelCase (						cls ,						__lowerCamelCase)			->				Any:
            # read saved model
            with open(__lowerCamelCase ,						"rb") as f:
                   _A      :       Any				    = pickle.load(__lowerCamelCase)  # noqa: S301

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


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Dict:
            return 1 / (1 + np.exp(-1 * x))


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Union[str, Any]:
            return round(__lowerCamelCase ,						3)


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

            # expanding the data slice to One dimenssion
            _A      :       Optional[Any]				    = []
            for each_focus in data_focus:
                   focusa_list.extend(self.Expand_Mat(__lowerCamelCase))
            _A      :       Dict				    = np.asarray(__lowerCamelCase)
            return focus_list, data_featuremap


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase="average_pool")			->				Dict:
            # pooling process
            _A      :       Optional[Any]				    = len(featuremaps[0])
            _A      :       str				    = int(size_map / size_pooling)
            _A      :       Optional[int]				    = []
            for i_map in range(len(__lowerCamelCase)):
                   _A      :       int				    = featuremaps[i_map]
                   _A      :       Optional[int]				    = []
                   for i_focus in range(0 ,						__lowerCamelCase ,						__lowerCamelCase):
                          for j_focus in range(0 ,						__lowerCamelCase ,						__lowerCamelCase):
                                 _A      :       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(__lowerCamelCase))
                                 elif pooling_type == "max_pooling":
                                        # max pooling
                                        map_pooled.append(np.max(__lowerCamelCase))
                   _A      :       Tuple				    = np.asmatrix(__lowerCamelCase).reshape(__lowerCamelCase ,						__lowerCamelCase)
                   featuremap_pooled.append(__lowerCamelCase)
            return featuremap_pooled


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Tuple:
            # expanding three dimension data to one dimension list
            _A      :       Tuple				    = []
            for i in range(len(__lowerCamelCase)):
                   _A      :       Union[str, Any]				    = np.shape(data[i])
                   _A      :       List[Any]				    = data[i].reshape(1 ,						shapes[0] * shapes[1])
                   _A      :       Optional[Any]				    = data_listed.getA().tolist()[0]
                   data_expanded.extend(__lowerCamelCase)
            _A      :       Optional[Any]				    = np.asarray(__lowerCamelCase)
            return data_expanded


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Union[str, Any]:
            # expanding matrix to one dimension list
            _A      :       List[Any]				    = np.asarray(__lowerCamelCase)
            _A      :       Union[str, Any]				    = np.shape(__lowerCamelCase)
            _A      :       Dict				    = data_mat.reshape(1 ,						shapes[0] * shapes[1])
            return data_expanded


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)			->				Optional[int]:
            _A      :       Dict				    = []
            _A      :       Any				    = 0
            for i_map in range(__lowerCamelCase):
                   _A      :       Union[str, Any]				    = np.ones((size_map, size_map))
                   for i in range(0 ,						__lowerCamelCase ,						__lowerCamelCase):
                          for j in range(0 ,						__lowerCamelCase ,						__lowerCamelCase):
                                 _A      :       List[Any]				    = pd_pool[
                                     i_pool
                                 ]
                                 _A      :       Tuple				    = i_pool + 1
                   _A      :       Optional[Any]				    = np.multiply(
                       __lowerCamelCase ,						np.multiply(out_map[i_map] ,						(1 - out_map[i_map])))
                   pd_all.append(__lowerCamelCase)
            return pd_all


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase=bool)			->				Union[str, Any]:
            # model traning
            print("----------------------Start Training-------------------------")
            print((" - - Shape: Train_Data  ", np.shape(__lowerCamelCase)))
            print((" - - Shape: Teach_Data  ", np.shape(__lowerCamelCase)))
            _A      :       Tuple				    = 0
            _A      :       Dict				    = []
            _A      :       Optional[Any]				    = 1_0_0_0_0
            while rp < n_repeat and mse >= error_accuracy:
                   _A      :       Union[str, Any]				    = 0
                   print(F"-------------Learning Time {rp}--------------")
                   for p in range(len(__lowerCamelCase)):
                          # print('------------Learning Image: %d--------------'%p)
                          _A      :       str				    = np.asmatrix(datas_train[p])
                          _A      :       Union[str, Any]				    = np.asarray(datas_teach[p])
                          _A    ,			_A      :       Any				    = self.convolute(
                              __lowerCamelCase ,						self.conva ,						self.w_conva ,						self.thre_conva ,						conv_step=self.step_conva ,						)
                          _A      :       Optional[Any]				    = self.pooling(__lowerCamelCase ,						self.size_poolinga)
                          _A      :       Optional[int]				    = np.shape(__lowerCamelCase)
                          _A      :       List[str]				    = self._expand(__lowerCamelCase)
                          _A      :       Tuple				    = data_bp_input

                          _A      :       int				    = np.dot(__lowerCamelCase ,						self.vji.T) - self.thre_bpa
                          _A      :       List[Any]				    = self.sig(__lowerCamelCase)
                          _A      :       Union[str, Any]				    = np.dot(__lowerCamelCase ,						self.wkj.T) - self.thre_bpa
                          _A      :       List[str]				    = self.sig(__lowerCamelCase)

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

                          _A      :       Any				    = pd_i_all / (self.size_poolinga * self.size_poolinga)
                          _A      :       Dict				    = pd_conva_pooled.T.getA().tolist()
                          _A      :       Optional[Any]				    = self._calculate_gradient_from_pool(
                              __lowerCamelCase ,						__lowerCamelCase ,						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      :       int				    = self._expand_mat(pd_conva_all[k_conv])
                                 _A      :       Optional[int]				    = self.rate_weight * np.dot(__lowerCamelCase ,						__lowerCamelCase)

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

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

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

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


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

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


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Dict:
            # return the data of image after convoluting process so we can check it out
            _A      :       Optional[int]				    = np.asmatrix(__lowerCamelCase)
            _A    ,			_A      :       Tuple				    = self.convolute(
                __lowerCamelCase ,						self.conva ,						self.w_conva ,						self.thre_conva ,						conv_step=self.step_conva ,						)
            _A      :       Union[str, Any]				    = self.pooling(__lowerCamelCase ,						self.size_poolinga)

            return data_conveda, data_pooleda


if __name__ == "__main__":
    pass
