'''simple docstring'''

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: Union[str, Any]          =   logging.get_logger(__name__)
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 lowercase__				:    str  =					SwinConfig(
     embed_dim=192		,     depths=(2, 2, 18, 2)		,     num_heads=(6, 12, 24, 48)		,     window_size=12		,     out_features=['''stage2''', '''stage3''', '''stage4''']		,     )

 lowercase__				:    Union[str, Any]  =					DetaConfig(
     backbone_config=lowerCamelCase__		,     num_queries=900		,     encoder_ffn_dim=2048		,     decoder_ffn_dim=2048		,     num_feature_levels=5		,     assign_first_stage=lowerCamelCase__		,     with_box_refine=lowerCamelCase__		,     two_stage=lowerCamelCase__		,     )

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

 lowercase__				:    Dict  =					num_labels
 lowercase__				:    str  =					json.load(open(cached_download(hf_hub_url(lowerCamelCase__		,     lowerCamelCase__		,     repo_type='''dataset'''     )     )		,     '''r'''     )     )
 lowercase__				:    Tuple  =					{int(lowerCamelCase__     ): v for k, v in idalabel.items()}
 lowercase__				:    int  =					idalabel
 lowercase__				:    Any  =					{v: k for k, v in idalabel.items()}

 return config
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 lowercase__				:    Dict  =					[]

 # 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 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    List[Any]  =					dct.pop(lowerCamelCase__     )
 lowercase__				:    Optional[Any]  =					val
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Optional[int]  =					[int(backbone_config.embed_dim * 2**i     ) for i in range(len(backbone_config.depths     )     )]
 for i in range(len(backbone_config.depths     )     ):
  lowercase__				:    Union[str, Any]  =					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)
   lowercase__				:    int  =					state_dict.pop(F"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"""     )
   lowercase__				:    str  =					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
   lowercase__				:    Any  =					in_proj_weight[:dim, :]
   lowercase__				:    Tuple  =					in_proj_bias[: dim]
   lowercase__				:    int  =					in_proj_weight[
       dim : dim * 2, :
   ]
   lowercase__				:    Union[str, Any]  =					in_proj_bias[
       dim : dim * 2
   ]
   lowercase__				:    Union[str, Any]  =					in_proj_weight[
       -dim :, :
   ]
   lowercase__				:    Union[str, Any]  =					in_proj_bias[-dim :]
   # fmt: on
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Union[str, Any]  =					config.d_model
 for i in range(config.decoder_layers     ):
  # read in weights + bias of input projection layer of self-attention
  lowercase__				:    List[Any]  =					state_dict.pop(F"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""     )
  lowercase__				:    Optional[Any]  =					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
  lowercase__				:    List[Any]  =					in_proj_weight[:hidden_size, :]
  lowercase__				:    Any  =					in_proj_bias[:hidden_size]
  lowercase__				:    List[Any]  =					in_proj_weight[
      hidden_size : hidden_size * 2, :
  ]
  lowercase__				:    str  =					in_proj_bias[hidden_size : hidden_size * 2]
  lowercase__				:    Dict  =					in_proj_weight[-hidden_size:, :]
  lowercase__				:    List[Any]  =					in_proj_bias[-hidden_size:]
def 			__UpperCamelCase       (   ):
 lowercase__				:    List[str]  =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 lowercase__				:    List[Any]  =					Image.open(requests.get(lowerCamelCase__		,     stream=lowerCamelCase__     ).raw     )

 return im
@torch.no_grad()
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    int  =					get_deta_config(lowerCamelCase__     )

 # load original state dict
 if model_name == "deta-swin-large":
  lowercase__				:    Optional[Any]  =					hf_hub_download(repo_id='''nielsr/deta-checkpoints'''		,     filename='''adet_swin_ft.pth'''     )
 elif model_name == "deta-swin-large-o365":
  lowercase__				:    Union[str, 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"""     )

 lowercase__				:    Dict  =					torch.load(lowerCamelCase__		,     map_location='''cpu'''     )["""model"""]

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

 # rename keys
 lowercase__				:    List[str]  =					create_rename_keys(lowerCamelCase__     )
 for src, dest in rename_keys:
  rename_key(lowerCamelCase__		,     lowerCamelCase__		,     lowerCamelCase__     )
 read_in_swin_q_k_v(lowerCamelCase__		,     config.backbone_config     )
 read_in_decoder_q_k_v(lowerCamelCase__		,     lowerCamelCase__     )

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

    # finally, create HuggingFace model and load state dict
 lowercase__				:    List[str]  =					DetaForObjectDetection(lowerCamelCase__     )
 model.load_state_dict(lowerCamelCase__     )
 model.eval()

 lowercase__				:    List[Any]  =					"""cuda""" if torch.cuda.is_available() else """cpu"""
 model.to(lowerCamelCase__     )

 # load image processor
 lowercase__				:    Dict  =					DetaImageProcessor(format='''coco_detection'''     )

 # verify our conversion on image
 lowercase__				:    List[str]  =					prepare_img()
 lowercase__				:    List[Any]  =					processor(images=lowerCamelCase__		,     return_tensors='''pt'''     )
 lowercase__				:    int  =					encoding["""pixel_values"""]
 lowercase__				:    Optional[Any]  =					model(pixel_values.to(lowerCamelCase__     )     )

 # verify logits
 print('''Logits:'''		,     outputs.logits[0, :3, :3]     )
 print('''Boxes:'''		,     outputs.pred_boxes[0, :3, :3]     )
 if model_name == "deta-swin-large":
  lowercase__				:    str  =					torch.tensor(
      [[-7.6_3_0_8, -2.8_4_8_5, -5.3_7_3_7], [-7.2_0_3_7, -4.5_5_0_5, -4.8_0_2_7], [-7.2_9_4_3, -4.2_6_1_1, -4.6_6_1_7]]     )
  lowercase__				:    List[str]  =					torch.tensor([[0.4_9_8_7, 0.4_9_6_9, 0.9_9_9_9], [0.2_5_4_9, 0.5_4_9_8, 0.4_8_0_5], [0.5_4_9_8, 0.2_7_5_7, 0.0_5_6_9]]     )
 elif model_name == "deta-swin-large-o365":
  lowercase__				:    Dict  =					torch.tensor(
      [[-8.0_1_2_2, -3.5_7_2_0, -4.9_7_1_7], [-8.1_5_4_7, -3.6_8_8_6, -4.6_3_8_9], [-7.6_6_1_0, -3.6_1_9_4, -5.0_1_3_4]]     )
  lowercase__				:    Union[str, Any]  =					torch.tensor([[0.2_5_2_3, 0.5_5_4_9, 0.4_8_8_1], [0.7_7_1_5, 0.4_1_4_9, 0.4_6_0_1], [0.5_5_0_3, 0.2_7_5_3, 0.0_5_7_5]]     )
 assert torch.allclose(outputs.logits[0, :3, :3]		,     expected_logits.to(lowerCamelCase__     )		,     atol=1E-4     )
 assert torch.allclose(outputs.pred_boxes[0, :3, :3]		,     expected_boxes.to(lowerCamelCase__     )		,     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(lowerCamelCase__     ).mkdir(exist_ok=lowerCamelCase__     )
  model.save_pretrained(lowerCamelCase__     )
  processor.save_pretrained(lowerCamelCase__     )

 # 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: Tuple          =   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: List[Any]          =   parser.parse_args()
  convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''




import pickle

import numpy as np
from matplotlib import pyplot as plt







class 	_lowerCAmelCase							:


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


	def     _a	(self       ,      lowercase							):
				# save model dict with pickle
				A_	:						Union[str, 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(lowercase       ,      """wb"""							) as f:
							pickle.dump(lowercase       ,      lowercase							)

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


	@classmethod
	def     _a	(cls       ,      lowercase							):
				# read saved model
				with open(lowercase       ,      """rb"""							) as f:
							A_	:						Optional[int]	=     pickle.load(lowercase							)  # noqa: S301

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


	def     _a	(self       ,      lowercase							):
				return 1 / (1 + np.exp(-1 * x							))


	def     _a	(self       ,      lowercase							):
				return round(lowercase       ,      3							)


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

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


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


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


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


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase							):
				A_	:						List[str]	=     []
				A_	:						Union[str, Any]	=     0
				for i_map in range(lowercase							):
							A_	:						Union[str, Any]	=     np.ones((size_map, size_map)							)
							for i in range(0       ,      lowercase       ,      lowercase							):
										for j in range(0       ,      lowercase       ,      lowercase							):
													A_	:						str	=     pd_pool[
													    i_pool
													]
													A_	:						Optional[Any]	=     i_pool + 1
							A_	:						Union[str, Any]	=     np.multiply(
							    lowercase       ,      np.multiply(out_map[i_map]       ,      (1 - out_map[i_map])							)							)
							pd_all.append(lowercase							)
				return pd_all


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

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

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

										A_	:						int	=     pd_i_all / (self.size_poolinga * self.size_poolinga)
										A_	:						str	=     pd_conva_pooled.T.getA().tolist()
										A_	:						List[Any]	=     self._calculate_gradient_from_pool(
										    lowercase       ,      lowercase       ,      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_	:						Any	=     self.rate_weight * np.dot(lowercase       ,      lowercase							)

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

													A_	:						Tuple	=     (
													    self.thre_conva[k_conv]
													    - np.sum(pd_conva_all[k_conv]							) * self.rate_thre
													)
										# all connected layer
										A_	:						Optional[int]	=     self.wkj + pd_k_all.T * bp_outa * self.rate_weight
										A_	:						List[str]	=     self.vji + pd_j_all.T * bp_outa * self.rate_weight
										A_	:						List[Any]	=     self.thre_bpa - pd_k_all * self.rate_thre
										A_	:						Optional[Any]	=     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_	:						List[Any]	=     rp + 1
							A_	:						Union[str, Any]	=     error_count / patterns
							all_mse.append(lowercase							)

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

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


	def     _a	(self       ,      lowercase							):
				# model predict
				A_	:						Tuple	=     []
				print("""-------------------Start Testing-------------------------"""							)
				print((""" - - Shape: Test_Data  """, np.shape(lowercase							))							)
				for p in range(len(lowercase							)							):
							A_	:						int	=     np.asmatrix(datas_test[p]							)
							A_,    A_	:						str	=     self.convolute(
							    lowercase       ,      self.conva       ,      self.w_conva       ,      self.thre_conva       ,      conv_step=self.step_conva       ,      )
							A_	:						Dict	=     self.pooling(lowercase       ,      self.size_poolinga							)
							A_	:						Tuple	=     self._expand(lowercase							)

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


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

				return data_conveda, data_pooleda


if __name__ == "__main__":
						pass