

"""simple docstring"""


import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor






def     a__    (    __SCREAMING_SNAKE_CASE )	->	str:
		__lowerCAmelCase:       Tuple          =			SwinaSRConfig()

		if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
				__lowerCAmelCase:       Any          =			4
		elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
				__lowerCAmelCase:       Dict          =			4
				__lowerCAmelCase:       Optional[Any]          =			4_8
				__lowerCAmelCase:       List[str]          =			"pixelshuffle_aux"
		elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
				__lowerCAmelCase:       Tuple          =			[6, 6, 6, 6]
				__lowerCAmelCase:       int          =			6_0
				__lowerCAmelCase:       List[str]          =			[6, 6, 6, 6]
				__lowerCAmelCase:       Union[str, Any]          =			"pixelshuffledirect"
		elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
				__lowerCAmelCase:       Tuple          =			4
				__lowerCAmelCase:       Optional[Any]          =			"nearest+conv"
		elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
				__lowerCAmelCase:       Optional[int]          =			1
				__lowerCAmelCase:       Tuple          =			1
				__lowerCAmelCase:       str          =			1_2_6
				__lowerCAmelCase:       Any          =			7
				__lowerCAmelCase:       Any          =			255.0
				__lowerCAmelCase:       Tuple          =			""

		return config






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	Tuple:
		if "patch_embed.proj" in name and "layers" not in name:
				__lowerCAmelCase:       Any          =			name.replace("patch_embed.proj"					, "embeddings.patch_embeddings.projection" )
		if "patch_embed.norm" in name:
				__lowerCAmelCase:       int          =			name.replace("patch_embed.norm"					, "embeddings.patch_embeddings.layernorm" )
		if "layers" in name:
				__lowerCAmelCase:       List[str]          =			name.replace("layers"					, "encoder.stages" )
		if "residual_group.blocks" in name:
				__lowerCAmelCase:       Dict          =			name.replace("residual_group.blocks"					, "layers" )
		if "attn.proj" in name:
				__lowerCAmelCase:       Tuple          =			name.replace("attn.proj"					, "attention.output.dense" )
		if "attn" in name:
				__lowerCAmelCase:       Dict          =			name.replace("attn"					, "attention.self" )
		if "norm1" in name:
				__lowerCAmelCase:       Tuple          =			name.replace("norm1"					, "layernorm_before" )
		if "norm2" in name:
				__lowerCAmelCase:       Optional[int]          =			name.replace("norm2"					, "layernorm_after" )
		if "mlp.fc1" in name:
				__lowerCAmelCase:       Optional[int]          =			name.replace("mlp.fc1"					, "intermediate.dense" )
		if "mlp.fc2" in name:
				__lowerCAmelCase:       List[Any]          =			name.replace("mlp.fc2"					, "output.dense" )
		if "q_bias" in name:
				__lowerCAmelCase:       Dict          =			name.replace("q_bias"					, "query.bias" )
		if "k_bias" in name:
				__lowerCAmelCase:       Tuple          =			name.replace("k_bias"					, "key.bias" )
		if "v_bias" in name:
				__lowerCAmelCase:       Union[str, Any]          =			name.replace("v_bias"					, "value.bias" )
		if "cpb_mlp" in name:
				__lowerCAmelCase:       str          =			name.replace("cpb_mlp"					, "continuous_position_bias_mlp" )
		if "patch_embed.proj" in name:
				__lowerCAmelCase:       List[Any]          =			name.replace("patch_embed.proj"					, "patch_embed.projection" )

		if name == "norm.weight":
				__lowerCAmelCase:       List[Any]          =			"layernorm.weight"
		if name == "norm.bias":
				__lowerCAmelCase:       List[str]          =			"layernorm.bias"

		if "conv_first" in name:
				__lowerCAmelCase:       int          =			name.replace("conv_first"					, "first_convolution" )

		if (
		    "upsample" in name
		    or "conv_before_upsample" in name
		    or "conv_bicubic" in name
		    or "conv_up" in name
		    or "conv_hr" in name
		    or "conv_last" in name
		    or "aux" in name
		):
				# heads
				if "conv_last" in name:
						__lowerCAmelCase:       Optional[Any]          =			name.replace("conv_last"					, "final_convolution" )
				if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
						if "conv_before_upsample.0" in name:
								__lowerCAmelCase:       Dict          =			name.replace("conv_before_upsample.0"					, "conv_before_upsample" )
						if "upsample.0" in name:
								__lowerCAmelCase:       int          =			name.replace("upsample.0"					, "upsample.convolution_0" )
						if "upsample.2" in name:
								__lowerCAmelCase:       Any          =			name.replace("upsample.2"					, "upsample.convolution_1" )
						__lowerCAmelCase:       str          =			"upsample." + name
				elif config.upsampler == "pixelshuffledirect":
						__lowerCAmelCase:       Tuple          =			name.replace("upsample.0.weight"					, "upsample.conv.weight" )
						__lowerCAmelCase:       int          =			name.replace("upsample.0.bias"					, "upsample.conv.bias" )
				else:
						pass
		else:
				__lowerCAmelCase:       Tuple          =			"swin2sr." + name

		return name






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	Optional[Any]:
		for key in orig_state_dict.copy().keys():
				__lowerCAmelCase:       str          =			orig_state_dict.pop(__SCREAMING_SNAKE_CASE )

				if "qkv" in key:
						__lowerCAmelCase:       Union[str, Any]          =			key.split("." )
						__lowerCAmelCase:       int          =			int(key_split[1] )
						__lowerCAmelCase:       Dict          =			int(key_split[4] )
						__lowerCAmelCase:       int          =			config.embed_dim

						if "weight" in key:
								__lowerCAmelCase:       List[str]          =			val[:dim, :]
								__lowerCAmelCase:       int          =			val[dim : dim * 2, :]
								__lowerCAmelCase:       Optional[Any]          =			val[-dim:, :]
						else:
								__lowerCAmelCase:       str          =			val[:dim]
								__lowerCAmelCase:       Dict          =			val[dim : dim * 2]
								__lowerCAmelCase:       List[str]          =			val[-dim:]
						pass
				else:
						__lowerCAmelCase:       Any          =			val

		return orig_state_dict






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	int:
		__lowerCAmelCase:       List[Any]          =			get_config(__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       int          =			SwinaSRForImageSuperResolution(__SCREAMING_SNAKE_CASE )
		model.eval()

		__lowerCAmelCase:       Any          =			torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE					, map_location="cpu" )
		__lowerCAmelCase:       int          =			convert_state_dict(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )
		__lowerCAmelCase      , __lowerCAmelCase:       int          =			model.load_state_dict(__SCREAMING_SNAKE_CASE					, strict=__SCREAMING_SNAKE_CASE )

		if len(__SCREAMING_SNAKE_CASE ) > 0:
				raise ValueError("Missing keys when converting: {}".format(__SCREAMING_SNAKE_CASE ) )
		for key in unexpected_keys:
				if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
						raise ValueError(F"Unexpected key {key} in state_dict" )

    # verify values
		__lowerCAmelCase:       Union[str, Any]          =			"https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"
		__lowerCAmelCase:       Tuple          =			Image.open(requests.get(__SCREAMING_SNAKE_CASE					, stream=__SCREAMING_SNAKE_CASE ).raw ).convert("RGB" )
		__lowerCAmelCase:       Any          =			SwinaSRImageProcessor()
		# pixel_values = processor(image, return_tensors="pt").pixel_values

		__lowerCAmelCase:       Dict          =			1_2_6 if "Jpeg" in checkpoint_url else 2_5_6
		__lowerCAmelCase:       Dict          =			Compose(
		    [
		        Resize((image_size, image_size) ),
		        ToTensor(),
		        Normalize(mean=[0.485, 0.456, 0.406]					, std=[0.229, 0.224, 0.225] ),
		    ] )
		__lowerCAmelCase:       Optional[Any]          =			transforms(__SCREAMING_SNAKE_CASE ).unsqueeze(0 )

		if config.num_channels == 1:
				__lowerCAmelCase:       Optional[int]          =			pixel_values[:, 0, :, :].unsqueeze(1 )

		__lowerCAmelCase:       int          =			model(__SCREAMING_SNAKE_CASE )

		# assert values
		if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
				__lowerCAmelCase:       Optional[int]          =			torch.Size([1, 3, 5_1_2, 5_1_2] )
				__lowerCAmelCase:       List[Any]          =			torch.tensor(
				    [[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]] )
		elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
				__lowerCAmelCase:       Tuple          =			torch.Size([1, 3, 1_0_2_4, 1_0_2_4] )
				__lowerCAmelCase:       List[str]          =			torch.tensor(
				    [[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]] )
		elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
				# TODO values didn't match exactly here
				__lowerCAmelCase:       Optional[Any]          =			torch.Size([1, 3, 1_0_2_4, 1_0_2_4] )
				__lowerCAmelCase:       List[Any]          =			torch.tensor(
				    [[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]] )
		elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
				__lowerCAmelCase:       Optional[Any]          =			torch.Size([1, 3, 5_1_2, 5_1_2] )
				__lowerCAmelCase:       Optional[int]          =			torch.tensor(
				    [[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]] )
		elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
				__lowerCAmelCase:       Dict          =			torch.Size([1, 3, 1_0_2_4, 1_0_2_4] )
				__lowerCAmelCase:       Optional[Any]          =			torch.tensor(
				    [[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]] )

		assert (
		    outputs.reconstruction.shape == expected_shape
		), F"Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}"
		assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]					, __SCREAMING_SNAKE_CASE					, atol=1E-3 )
		print("Looks ok!" )

		__lowerCAmelCase:       int          =			{
		    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth": (
		        "swin2SR-classical-sr-x2-64"
		    ),
		    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth": (
		        "swin2SR-classical-sr-x4-64"
		    ),
		    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth": (
		        "swin2SR-compressed-sr-x4-48"
		    ),
		    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth": (
		        "swin2SR-lightweight-x2-64"
		    ),
		    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth": (
		        "swin2SR-realworld-sr-x4-64-bsrgan-psnr"
		    ),
		}
		__lowerCAmelCase:       List[str]          =			url_to_name[checkpoint_url]

		if pytorch_dump_folder_path is not None:
				print(F"Saving model {model_name} to {pytorch_dump_folder_path}" )
				model.save_pretrained(__SCREAMING_SNAKE_CASE )
				print(F"Saving image processor to {pytorch_dump_folder_path}" )
				processor.save_pretrained(__SCREAMING_SNAKE_CASE )

		if push_to_hub:
				model.push_to_hub(F"caidas/{model_name}" )
				processor.push_to_hub(F"caidas/{model_name}" )


if __name__ == "__main__":
			__A						   =			argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--checkpoint_url",
			    default="https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth",
			    type=str,
			    help="URL of the original Swin2SR checkpoint you'd like to convert.",
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
			)
			parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the converted model to the hub.")

			__A						   =			parser.parse_args()
			convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""


import pickle

import numpy as np
from matplotlib import pyplot as plt





class     snake_case	:

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



						'''simple docstring'''


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

				def 							lowercase_     (	self							:				Optional[int]     ,  UpperCamelCase__							:				int)->   List[str]:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			{
						    "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							:				Dict     ,  UpperCamelCase__							:				Union[str, Any])->   List[Any]:



						'''simple docstring'''


						with open(UpperCamelCase__     ,  "rb") as f:
								__lowerCAmelCase:       Dict          =			pickle.load(UpperCamelCase__)  # noqa: S301

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

				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				List[Any])->   List[Any]:



						'''simple docstring'''


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

				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				List[Any])->   Optional[Any]:



						'''simple docstring'''


						return round(UpperCamelCase__     ,  3)

				def 							lowercase_     (	self							:				Optional[int]     ,  UpperCamelCase__							:				Optional[int]     ,  UpperCamelCase__							:				Any     ,  UpperCamelCase__							:				List[str]     ,  UpperCamelCase__							:				Tuple     ,  UpperCamelCase__							:				int)->   Dict:



						'''simple docstring'''


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

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

				def 							lowercase_     (	self							:				Any     ,  UpperCamelCase__							:				List[str]     ,  UpperCamelCase__							:				List[Any]     ,  UpperCamelCase__							:				Optional[Any]="average_pool")->   str:



						'''simple docstring'''


						__lowerCAmelCase:       Tuple          =			len(featuremaps[0])
						__lowerCAmelCase:       List[Any]          =			int(size_map / size_pooling)
						__lowerCAmelCase:       int          =			[]
						for i_map in range(len(UpperCamelCase__)):
								__lowerCAmelCase:       str          =			featuremaps[i_map]
								__lowerCAmelCase:       List[Any]          =			[]
								for i_focus in range(0     ,  UpperCamelCase__     ,  UpperCamelCase__):
										for j_focus in range(0     ,  UpperCamelCase__     ,  UpperCamelCase__):
												__lowerCAmelCase:       Any          =			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__))
								__lowerCAmelCase:       Optional[int]          =			np.asmatrix(UpperCamelCase__).reshape(UpperCamelCase__     ,  UpperCamelCase__)
								featuremap_pooled.append(UpperCamelCase__)
						return featuremap_pooled

				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				str)->   int:



						'''simple docstring'''


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

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



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			np.asarray(UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			np.shape(UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			data_mat.reshape(1     ,  shapes[0] * shapes[1])
						return data_expanded

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



						'''simple docstring'''


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

				def 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				Union[str, Any]     ,  UpperCamelCase__							:				int     ,  UpperCamelCase__							:				Dict     ,  UpperCamelCase__							:				List[Any]     ,  UpperCamelCase__							:				Any     ,  UpperCamelCase__							:				str=bool)->   List[str]:



						'''simple docstring'''


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

										__lowerCAmelCase:       int          =			np.dot(UpperCamelCase__     ,  self.vji.T) - self.thre_bpa
										__lowerCAmelCase:       int          =			self.sig(UpperCamelCase__)
										__lowerCAmelCase:       Optional[Any]          =			np.dot(UpperCamelCase__     ,  self.wkj.T) - self.thre_bpa
										__lowerCAmelCase:       str          =			self.sig(UpperCamelCase__)

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

										__lowerCAmelCase:       Union[str, Any]          =			pd_i_all / (self.size_poolinga * self.size_poolinga)
										__lowerCAmelCase:       str          =			pd_conva_pooled.T.getA().tolist()
										__lowerCAmelCase:       str          =			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]):
												__lowerCAmelCase:       List[Any]          =			self._expand_mat(pd_conva_all[k_conv])
												__lowerCAmelCase:       int          =			self.rate_weight * np.dot(UpperCamelCase__     ,  UpperCamelCase__)

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

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

						def draw_error():
								__lowerCAmelCase:       Dict          =			[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							:				Union[str, Any]     ,  UpperCamelCase__							:				Tuple)->   List[str]:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			[]
						print("-------------------Start Testing-------------------------")
						print((" - - Shape: Test_Data  ", np.shape(UpperCamelCase__)))
						for p in range(len(UpperCamelCase__)):
								__lowerCAmelCase:       Dict          =			np.asmatrix(datas_test[p])
								__lowerCAmelCase      , __lowerCAmelCase:       Optional[int]          =			self.convolute(
								    UpperCamelCase__     ,  self.conva     ,  self.w_conva     ,  self.thre_conva     ,  conv_step=self.step_conva     ,  )
								__lowerCAmelCase:       Tuple          =			self.pooling(UpperCamelCase__     ,  self.size_poolinga)
								__lowerCAmelCase:       List[str]          =			self._expand(UpperCamelCase__)

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




				def 							lowercase_     (	self							:				int     ,  UpperCamelCase__							:				Any)->   Any:



						'''simple docstring'''


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

						return data_conveda, data_pooleda


if __name__ == "__main__":
			pass


