

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__  :      int			=    logging.get_logger(__name__)


def   _UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		str       ):





    """simple docstring"""


    if "resnet-50" in model_name:
        SCREAMING_SNAKE_CASE_									=						ResNetConfig.from_pretrained('microsoft/resnet-50'       )
    elif "resnet-101" in model_name:
        SCREAMING_SNAKE_CASE_									=						ResNetConfig.from_pretrained('microsoft/resnet-101'       )
    else:
        raise ValueError('Model name should include either resnet50 or resnet101'       )

    SCREAMING_SNAKE_CASE_									=						DetrConfig(use_timm_backbone=_SCREAMING_SNAKE_CASE ,   backbone_config=_SCREAMING_SNAKE_CASE       )

    # set label attributes
    SCREAMING_SNAKE_CASE_									=						'panoptic' in model_name
    if is_panoptic:
        SCREAMING_SNAKE_CASE_									=						250
    else:
        SCREAMING_SNAKE_CASE_									=						91
        SCREAMING_SNAKE_CASE_									=						'huggingface/label-files'
        SCREAMING_SNAKE_CASE_									=						'coco-detection-id2label.json'
        SCREAMING_SNAKE_CASE_									=						json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   repo_type='dataset'       ) ,   'r'       )       )
        SCREAMING_SNAKE_CASE_									=						{int(_SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE_									=						idalabel
        SCREAMING_SNAKE_CASE_									=						{v: k for k, v in idalabel.items()}

    return config, is_panoptic


def   _UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[]

    # stem
    # fmt: off
    rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight')       )
    rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight')       )
    rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias')       )
    rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean')       )
    rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var')       )
    # stages
    for stage_idx in range(len(config.backbone_config.depths       )       ):
        for layer_idx in range(config.backbone_config.depths[stage_idx]       ):
            # shortcut
            if layer_idx == 0:
                rename_keys.append(
                    (
                        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
                        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
                    )       )
                rename_keys.append(
                    (
                        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
                        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
                    )       )
                rename_keys.append(
                    (
                        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
                        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
                    )       )
                rename_keys.append(
                    (
                        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
                        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
                    )       )
                rename_keys.append(
                    (
                        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
                        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
                    )       )
            # 3 convs
            for i in range(3       ):
                rename_keys.append(
                    (
                        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
                        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
                    )       )
                rename_keys.append(
                    (
                        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
                        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
                    )       )
                rename_keys.append(
                    (
                        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
                        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
                    )       )
                rename_keys.append(
                    (
                        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
                        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
                    )       )
                rename_keys.append(
                    (
                        f"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
                        f"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
                    )       )
    # fmt: on

    for i in range(config.encoder_layers       ):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append(
            (
                f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""",
                f"""encoder.layers.{i}.self_attn.out_proj.weight""",
            )       )
        rename_keys.append(
            (f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias""")       )
        rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight""")       )
        rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias""")       )
        rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight""")       )
        rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias""")       )
        rename_keys.append(
            (f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight""")       )
        rename_keys.append(
            (f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias""")       )
        rename_keys.append(
            (f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight""")       )
        rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias""")       )
        # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
        rename_keys.append(
            (
                f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""",
                f"""decoder.layers.{i}.self_attn.out_proj.weight""",
            )       )
        rename_keys.append(
            (f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias""")       )
        rename_keys.append(
            (
                f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
                f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
            )       )
        rename_keys.append(
            (
                f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
                f"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
            )       )
        rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""decoder.layers.{i}.fc1.weight""")       )
        rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""decoder.layers.{i}.fc1.bias""")       )
        rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""decoder.layers.{i}.fc2.weight""")       )
        rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""decoder.layers.{i}.fc2.bias""")       )
        rename_keys.append(
            (f"""transformer.decoder.layers.{i}.norm1.weight""", f"""decoder.layers.{i}.self_attn_layer_norm.weight""")       )
        rename_keys.append(
            (f"""transformer.decoder.layers.{i}.norm1.bias""", f"""decoder.layers.{i}.self_attn_layer_norm.bias""")       )
        rename_keys.append(
            (f"""transformer.decoder.layers.{i}.norm2.weight""", f"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")       )
        rename_keys.append(
            (f"""transformer.decoder.layers.{i}.norm2.bias""", f"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")       )
        rename_keys.append(
            (f"""transformer.decoder.layers.{i}.norm3.weight""", f"""decoder.layers.{i}.final_layer_norm.weight""")       )
        rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""decoder.layers.{i}.final_layer_norm.bias""")       )

    # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
    rename_keys.extend(
        [
            ('input_proj.weight', 'input_projection.weight'),
            ('input_proj.bias', 'input_projection.bias'),
            ('query_embed.weight', 'query_position_embeddings.weight'),
            ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
            ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
            ('class_embed.weight', 'class_labels_classifier.weight'),
            ('class_embed.bias', 'class_labels_classifier.bias'),
            ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
            ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
            ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
            ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
            ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
            ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
        ]       )

    return rename_keys


def   _UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		Dict ,   _SCREAMING_SNAKE_CASE					:		List[Any]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						state_dict.pop(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						val


def   _UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Tuple ,   _SCREAMING_SNAKE_CASE					:		int=False       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						''
    if is_panoptic:
        SCREAMING_SNAKE_CASE_									=						'detr.'

    # first: transformer encoder
    for i in range(6       ):
        # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
        SCREAMING_SNAKE_CASE_									=						state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""       )
        SCREAMING_SNAKE_CASE_									=						state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""       )
        # next, add query, keys and values (in that order) to the state dict
        SCREAMING_SNAKE_CASE_									=						in_proj_weight[:256, :]
        SCREAMING_SNAKE_CASE_									=						in_proj_bias[:256]
        SCREAMING_SNAKE_CASE_									=						in_proj_weight[256:512, :]
        SCREAMING_SNAKE_CASE_									=						in_proj_bias[256:512]
        SCREAMING_SNAKE_CASE_									=						in_proj_weight[-256:, :]
        SCREAMING_SNAKE_CASE_									=						in_proj_bias[-256:]
    # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
    for i in range(6       ):
        # read in weights + bias of input projection layer of self-attention
        SCREAMING_SNAKE_CASE_									=						state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""       )
        SCREAMING_SNAKE_CASE_									=						state_dict.pop(f"""{prefix}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_									=						in_proj_weight[:256, :]
        SCREAMING_SNAKE_CASE_									=						in_proj_bias[:256]
        SCREAMING_SNAKE_CASE_									=						in_proj_weight[256:512, :]
        SCREAMING_SNAKE_CASE_									=						in_proj_bias[256:512]
        SCREAMING_SNAKE_CASE_									=						in_proj_weight[-256:, :]
        SCREAMING_SNAKE_CASE_									=						in_proj_bias[-256:]
        # read in weights + bias of input projection layer of cross-attention
        SCREAMING_SNAKE_CASE_									=						state_dict.pop(
            f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""       )
        SCREAMING_SNAKE_CASE_									=						state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""       )
        # next, add query, keys and values (in that order) of cross-attention to the state dict
        SCREAMING_SNAKE_CASE_									=						in_proj_weight_cross_attn[:256, :]
        SCREAMING_SNAKE_CASE_									=						in_proj_bias_cross_attn[:256]
        SCREAMING_SNAKE_CASE_									=						in_proj_weight_cross_attn[256:512, :]
        SCREAMING_SNAKE_CASE_									=						in_proj_bias_cross_attn[256:512]
        SCREAMING_SNAKE_CASE_									=						in_proj_weight_cross_attn[-256:, :]
        SCREAMING_SNAKE_CASE_									=						in_proj_bias_cross_attn[-256:]


def   _UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						'http://images.cocodataset.org/val2017/000000039769.jpg'
    SCREAMING_SNAKE_CASE_									=						Image.open(requests.get(_SCREAMING_SNAKE_CASE ,   stream=_SCREAMING_SNAKE_CASE       ).raw       )

    return im


@torch.no_grad()
def   _UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		List[Any]=None ,   _SCREAMING_SNAKE_CASE					:		Any=False       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						get_detr_config(_SCREAMING_SNAKE_CASE       )

    # load original model from torch hub
    SCREAMING_SNAKE_CASE_									=						{
        'detr-resnet-50': 'detr_resnet50',
        'detr-resnet-101': 'detr_resnet101',
    }
    logger.info(f"""Converting model {model_name}..."""       )
    SCREAMING_SNAKE_CASE_									=						torch.hub.load('facebookresearch/detr' ,   model_name_to_original_name[model_name] ,   pretrained=_SCREAMING_SNAKE_CASE       ).eval()
    SCREAMING_SNAKE_CASE_									=						detr.state_dict()
    # rename keys
    for src, dest in create_rename_keys(_SCREAMING_SNAKE_CASE       ):
        if is_panoptic:
            SCREAMING_SNAKE_CASE_									=						'detr.' + src
        rename_key(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
    # query, key and value matrices need special treatment
    read_in_q_k_v(_SCREAMING_SNAKE_CASE ,   is_panoptic=_SCREAMING_SNAKE_CASE       )
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    SCREAMING_SNAKE_CASE_									=						'detr.model.' if is_panoptic else 'model.'
    for key in state_dict.copy().keys():
        if is_panoptic:
            if (
                key.startswith('detr'       )
                and not key.startswith('class_labels_classifier'       )
                and not key.startswith('bbox_predictor'       )
            ):
                SCREAMING_SNAKE_CASE_									=						state_dict.pop(_SCREAMING_SNAKE_CASE       )
                SCREAMING_SNAKE_CASE_									=						val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                SCREAMING_SNAKE_CASE_									=						state_dict.pop(_SCREAMING_SNAKE_CASE       )
                SCREAMING_SNAKE_CASE_									=						val
            elif key.startswith('bbox_attention'       ) or key.startswith('mask_head'       ):
                continue
            else:
                SCREAMING_SNAKE_CASE_									=						state_dict.pop(_SCREAMING_SNAKE_CASE       )
                SCREAMING_SNAKE_CASE_									=						val
        else:
            if not key.startswith('class_labels_classifier'       ) and not key.startswith('bbox_predictor'       ):
                SCREAMING_SNAKE_CASE_									=						state_dict.pop(_SCREAMING_SNAKE_CASE       )
                SCREAMING_SNAKE_CASE_									=						val

    # finally, create HuggingFace model and load state dict
    SCREAMING_SNAKE_CASE_									=						DetrForSegmentation(_SCREAMING_SNAKE_CASE       ) if is_panoptic else DetrForObjectDetection(_SCREAMING_SNAKE_CASE       )
    model.load_state_dict(_SCREAMING_SNAKE_CASE       )
    model.eval()

    # verify our conversion on an image
    SCREAMING_SNAKE_CASE_									=						'coco_panoptic' if is_panoptic else 'coco_detection'
    SCREAMING_SNAKE_CASE_									=						DetrImageProcessor(format=_SCREAMING_SNAKE_CASE       )

    SCREAMING_SNAKE_CASE_									=						processor(images=prepare_img() ,   return_tensors='pt'       )
    SCREAMING_SNAKE_CASE_									=						encoding['pixel_values']

    SCREAMING_SNAKE_CASE_									=						detr(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						model(_SCREAMING_SNAKE_CASE       )

    assert torch.allclose(outputs.logits ,   original_outputs['pred_logits'] ,   atol=1E-3       )
    assert torch.allclose(outputs.pred_boxes ,   original_outputs['pred_boxes'] ,   atol=1E-3       )
    if is_panoptic:
        assert torch.allclose(outputs.pred_masks ,   original_outputs['pred_masks'] ,   atol=1E-4       )
    print('Looks ok!'       )

    if pytorch_dump_folder_path is not None:
        # Save model and image processor
        logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""       )
        Path(_SCREAMING_SNAKE_CASE       ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE       )
        model.save_pretrained(_SCREAMING_SNAKE_CASE       )
        processor.save_pretrained(_SCREAMING_SNAKE_CASE       )

    if push_to_hub:
        # Upload model and image processor to the hub
        logger.info('Uploading PyTorch model and image processor to the hub...'       )
        model.push_to_hub(f"""nielsr/{model_name}"""       )
        processor.push_to_hub(f"""nielsr/{model_name}"""       )


if __name__ == "__main__":
       UpperCamelCase__  :      Dict			=    argparse.ArgumentParser()

       parser.add_argument(
           "--model_name",
           default="detr-resnet-50",
           type=str,
           choices=["detr-resnet-50", "detr-resnet-101"],
           help="Name of the DETR 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 to push the model to the hub or not.")
       UpperCamelCase__  :      str			=    parser.parse_args()
       convert_detr_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       __snake_case  :

  def __init__(					self						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A=0.2						,		_A=0.2):
      SCREAMING_SNAKE_CASE_									=						bp_numa
      SCREAMING_SNAKE_CASE_									=						bp_numa
      SCREAMING_SNAKE_CASE_									=						bp_numa
      SCREAMING_SNAKE_CASE_									=						conva_get[:2]
      SCREAMING_SNAKE_CASE_									=						conva_get[2]
      SCREAMING_SNAKE_CASE_									=						size_pa
      SCREAMING_SNAKE_CASE_									=						rate_w
      SCREAMING_SNAKE_CASE_									=						rate_t
      SCREAMING_SNAKE_CASE_									=						[
          np.mat(-1 * np.random.rand(self.conva[0]						,		self.conva[0]) + 0.5)
          for i in range(self.conva[1])
      ]
      SCREAMING_SNAKE_CASE_									=						np.mat(-1 * np.random.rand(self.num_bpa						,		self.num_bpa) + 0.5)
      SCREAMING_SNAKE_CASE_									=						np.mat(-1 * np.random.rand(self.num_bpa						,		self.num_bpa) + 0.5)
      SCREAMING_SNAKE_CASE_									=						-2 * np.random.rand(self.conva[1]) + 1
      SCREAMING_SNAKE_CASE_									=						-2 * np.random.rand(self.num_bpa) + 1
      SCREAMING_SNAKE_CASE_									=						-2 * np.random.rand(self.num_bpa) + 1

  def 						lowerCAmelCase__     (					self						,		_A):
      # save model dict with pickle
      SCREAMING_SNAKE_CASE_									=						{
          '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(_A						,		'wb') as f:
          pickle.dump(_A						,		_A)

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

  @classmethod
  def 						lowerCAmelCase__     (					cls						,		_A):
      # read saved model
      with open(_A						,		'rb') as f:
          SCREAMING_SNAKE_CASE_									=						pickle.load(_A)  # noqa: S301

      SCREAMING_SNAKE_CASE_									=						model_dic.get('conv1')
      conv_get.append(model_dic.get('step_conv1'))
      SCREAMING_SNAKE_CASE_									=						model_dic.get('size_pooling1')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('num_bp1')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('num_bp2')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('num_bp3')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('rate_weight')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('rate_thre')
      # create model instance
      SCREAMING_SNAKE_CASE_									=						CNN(_A						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A)
      # modify model parameter
      SCREAMING_SNAKE_CASE_									=						model_dic.get('w_conv1')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('wkj')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('vji')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('thre_conv1')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('thre_bp2')
      SCREAMING_SNAKE_CASE_									=						model_dic.get('thre_bp3')
      return conv_ins

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

  def 						lowerCAmelCase__     (					self						,		_A):
      return round(_A						,		3)

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

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

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A="average_pool"):
      # pooling process
      SCREAMING_SNAKE_CASE_									=						len(featuremaps[0])
      SCREAMING_SNAKE_CASE_									=						int(size_map / size_pooling)
      SCREAMING_SNAKE_CASE_									=						[]
      for i_map in range(len(_A)):
          SCREAMING_SNAKE_CASE_									=						featuremaps[i_map]
          SCREAMING_SNAKE_CASE_									=						[]
          for i_focus in range(0						,		_A						,		_A):
              for j_focus in range(0						,		_A						,		_A):
                  SCREAMING_SNAKE_CASE_									=						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(_A))
                  elif pooling_type == "max_pooling":
                      # max pooling
                      map_pooled.append(np.max(_A))
          SCREAMING_SNAKE_CASE_									=						np.asmatrix(_A).reshape(_A						,		_A)
          featuremap_pooled.append(_A)
      return featuremap_pooled

  def 						lowerCAmelCase__     (					self						,		_A):
      # expanding three dimension data to one dimension list
      SCREAMING_SNAKE_CASE_									=						[]
      for i in range(len(_A)):
          SCREAMING_SNAKE_CASE_									=						np.shape(data[i])
          SCREAMING_SNAKE_CASE_									=						data[i].reshape(1						,		shapes[0] * shapes[1])
          SCREAMING_SNAKE_CASE_									=						data_listed.getA().tolist()[0]
          data_expanded.extend(_A)
      SCREAMING_SNAKE_CASE_									=						np.asarray(_A)
      return data_expanded

  def 						lowerCAmelCase__     (					self						,		_A):
      # expanding matrix to one dimension list
      SCREAMING_SNAKE_CASE_									=						np.asarray(_A)
      SCREAMING_SNAKE_CASE_									=						np.shape(_A)
      SCREAMING_SNAKE_CASE_									=						data_mat.reshape(1						,		shapes[0] * shapes[1])
      return data_expanded

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A):
      SCREAMING_SNAKE_CASE_									=						[]
      SCREAMING_SNAKE_CASE_									=						0
      for i_map in range(_A):
          SCREAMING_SNAKE_CASE_									=						np.ones((size_map, size_map))
          for i in range(0						,		_A						,		_A):
              for j in range(0						,		_A						,		_A):
                  SCREAMING_SNAKE_CASE_									=						pd_pool[
                      i_pool
                  ]
                  SCREAMING_SNAKE_CASE_									=						i_pool + 1
          SCREAMING_SNAKE_CASE_									=						np.multiply(
              _A						,		np.multiply(out_map[i_map]						,		(1 - out_map[i_map])))
          pd_all.append(_A)
      return pd_all

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

              SCREAMING_SNAKE_CASE_									=						np.dot(_A						,		self.vji.T) - self.thre_bpa
              SCREAMING_SNAKE_CASE_									=						self.sig(_A)
              SCREAMING_SNAKE_CASE_									=						np.dot(_A						,		self.wkj.T) - self.thre_bpa
              SCREAMING_SNAKE_CASE_									=						self.sig(_A)

              # --------------Model Leaning ------------------------
              # calculate error and gradient---------------
              SCREAMING_SNAKE_CASE_									=						np.multiply(
                  (data_teach - bp_outa)						,		np.multiply(_A						,		(1 - bp_outa)))
              SCREAMING_SNAKE_CASE_									=						np.multiply(
                  np.dot(_A						,		self.wkj)						,		np.multiply(_A						,		(1 - bp_outa)))
              SCREAMING_SNAKE_CASE_									=						np.dot(_A						,		self.vji)

              SCREAMING_SNAKE_CASE_									=						pd_i_all / (self.size_poolinga * self.size_poolinga)
              SCREAMING_SNAKE_CASE_									=						pd_conva_pooled.T.getA().tolist()
              SCREAMING_SNAKE_CASE_									=						self._calculate_gradient_from_pool(
                  _A						,		_A						,		shape_featuremapa[0]						,		shape_featuremapa[1]						,		self.size_poolinga						,		)
              # weight and threshold learning process---------
              # convolution layer
              for k_conv in range(self.conva[1]):
                  SCREAMING_SNAKE_CASE_									=						self._expand_mat(pd_conva_all[k_conv])
                  SCREAMING_SNAKE_CASE_									=						self.rate_weight * np.dot(_A						,		_A)

                  SCREAMING_SNAKE_CASE_									=						self.w_conva[k_conv] + delta_w.reshape(
                      (self.conva[0], self.conva[0]))

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

      def draw_error():
          SCREAMING_SNAKE_CASE_									=						[error_accuracy for i in range(int(n_repeat * 1.2))]
          plt.plot(_A						,		'+-')
          plt.plot(_A						,		'r--')
          plt.xlabel('Learning Times')
          plt.ylabel('All_mse')
          plt.grid(_A						,		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						,		_A):
      # model predict
      SCREAMING_SNAKE_CASE_									=						[]
      print('-------------------Start Testing-------------------------')
      print((' - - Shape: Test_Data  ', np.shape(_A)))
      for p in range(len(_A)):
          SCREAMING_SNAKE_CASE_									=						np.asmatrix(datas_test[p])
          SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.convolute(
              _A						,		self.conva						,		self.w_conva						,		self.thre_conva						,		conv_step=self.step_conva						,		)
          SCREAMING_SNAKE_CASE_									=						self.pooling(_A						,		self.size_poolinga)
          SCREAMING_SNAKE_CASE_									=						self._expand(_A)

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


  def 						lowerCAmelCase__     (					self						,		_A):
      # return the data of image after convoluting process so we can check it out
      SCREAMING_SNAKE_CASE_									=						np.asmatrix(_A)
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.convolute(
          _A						,		self.conva						,		self.w_conva						,		self.thre_conva						,		conv_step=self.step_conva						,		)
      SCREAMING_SNAKE_CASE_									=						self.pooling(_A						,		self.size_poolinga)

      return data_conveda, data_pooleda


if __name__ == "__main__":
       pass


