


import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)




def  a   (lowerCAmelCase__   ):
   __a					=  VideoMAEConfig()

   set_architecture_configs(lowerCAmelCase__		,				lowerCAmelCase__   )

   if "finetuned" not in model_name:
      __a					=  False

   if "finetuned" in model_name:
      __a					=  """huggingface/label-files"""
      if "kinetics" in model_name:
         __a					=  400
         __a					=  """kinetics400-id2label.json"""
      elif "ssv2" in model_name:
         __a					=  174
         __a					=  """something-something-v2-id2label.json"""
      else:
         raise ValueError("""Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned."""   )
      __a					=  json.load(open(hf_hub_download(lowerCAmelCase__		,				lowerCAmelCase__		,				repo_type="""dataset"""   )		,				"""r"""   )   )
      __a					=  {int(lowerCAmelCase__   ): v for k, v in idalabel.items()}
      __a					=  idalabel
      __a					=  {v: k for k, v in idalabel.items()}

   return config




def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   if "small" in model_name:
      __a					=  384
      __a					=  1_536
      __a					=  12
      __a					=  16
      __a					=  12
      __a					=  3
      __a					=  192
      __a					=  768
   elif "large" in model_name:
      __a					=  1_024
      __a					=  4_096
      __a					=  24
      __a					=  16
      __a					=  12
      __a					=  8
      __a					=  512
      __a					=  2_048
   elif "huge" in model_name:
      __a					=  1_280
      __a					=  5_120
      __a					=  32
      __a					=  16
      __a					=  12
      __a					=  8
      __a					=  640
      __a					=  2_560
   elif "base" not in model_name:
      raise ValueError("""Model name should include either \"small\", \"base\", \"large\", or \"huge\""""   )




def  a   (lowerCAmelCase__   ):
   if "encoder." in name:
      __a					=  name.replace("""encoder."""		,				""""""   )
   if "cls_token" in name:
      __a					=  name.replace("""cls_token"""		,				"""videomae.embeddings.cls_token"""   )
   if "decoder_pos_embed" in name:
      __a					=  name.replace("""decoder_pos_embed"""		,				"""decoder.decoder_pos_embed"""   )
   if "pos_embed" in name and "decoder" not in name:
      __a					=  name.replace("""pos_embed"""		,				"""videomae.embeddings.position_embeddings"""   )
   if "patch_embed.proj" in name:
      __a					=  name.replace("""patch_embed.proj"""		,				"""videomae.embeddings.patch_embeddings.projection"""   )
   if "patch_embed.norm" in name:
      __a					=  name.replace("""patch_embed.norm"""		,				"""videomae.embeddings.norm"""   )
   if "decoder.blocks" in name:
      __a					=  name.replace("""decoder.blocks"""		,				"""decoder.decoder_layers"""   )
   if "blocks" in name:
      __a					=  name.replace("""blocks"""		,				"""videomae.encoder.layer"""   )
   if "attn.proj" in name:
      __a					=  name.replace("""attn.proj"""		,				"""attention.output.dense"""   )
   if "attn" in name and "bias" not in name:
      __a					=  name.replace("""attn"""		,				"""attention.self"""   )
   if "attn" in name:
      __a					=  name.replace("""attn"""		,				"""attention.attention"""   )
   if "norm1" in name:
      __a					=  name.replace("""norm1"""		,				"""layernorm_before"""   )
   if "norm2" in name:
      __a					=  name.replace("""norm2"""		,				"""layernorm_after"""   )
   if "mlp.fc1" in name:
      __a					=  name.replace("""mlp.fc1"""		,				"""intermediate.dense"""   )
   if "mlp.fc2" in name:
      __a					=  name.replace("""mlp.fc2"""		,				"""output.dense"""   )
   if "decoder_embed" in name:
      __a					=  name.replace("""decoder_embed"""		,				"""decoder.decoder_embed"""   )
   if "decoder_norm" in name:
      __a					=  name.replace("""decoder_norm"""		,				"""decoder.decoder_norm"""   )
   if "decoder_pred" in name:
      __a					=  name.replace("""decoder_pred"""		,				"""decoder.decoder_pred"""   )
   if "norm.weight" in name and "decoder" not in name and "fc" not in name:
      __a					=  name.replace("""norm.weight"""		,				"""videomae.layernorm.weight"""   )
   if "norm.bias" in name and "decoder" not in name and "fc" not in name:
      __a					=  name.replace("""norm.bias"""		,				"""videomae.layernorm.bias"""   )
   if "head" in name and "decoder" not in name:
      __a					=  name.replace("""head"""		,				"""classifier"""   )

   return name




def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   for key in orig_state_dict.copy().keys():
      __a					=  orig_state_dict.pop(lowerCAmelCase__   )

      if key.startswith("""encoder."""   ):
         __a					=  key.replace("""encoder."""		,				""""""   )

      if "qkv" in key:
         __a					=  key.split("""."""   )
         if key.startswith("""decoder.blocks"""   ):
            __a					=  config.decoder_hidden_size
            __a					=  int(key_split[2]   )
            __a					=  """decoder.decoder_layers."""
            if "weight" in key:
               __a					=  val[:dim, :]
               __a					=  val[dim : dim * 2, :]
               __a					=  val[-dim:, :]
         else:
            __a					=  config.hidden_size
            __a					=  int(key_split[1]   )
            __a					=  """videomae.encoder.layer."""
            if "weight" in key:
               __a					=  val[:dim, :]
               __a					=  val[dim : dim * 2, :]
               __a					=  val[-dim:, :]
      else:
         __a					=  val

   return orig_state_dict




def  a   ():
   __a					=  hf_hub_download(
       repo_id="""hf-internal-testing/spaghetti-video"""		,				filename="""eating_spaghetti.npy"""		,				repo_type="""dataset"""   )
   __a					=  np.load(lowerCAmelCase__   )
   return list(lowerCAmelCase__   )




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  get_videomae_config(lowerCAmelCase__   )

   if "finetuned" in model_name:
      __a					=  VideoMAEForVideoClassification(lowerCAmelCase__   )
   else:
      __a					=  VideoMAEForPreTraining(lowerCAmelCase__   )

   # download original checkpoint, hosted on Google Drive
   __a					=  """pytorch_model.bin"""
   gdown.cached_download(lowerCAmelCase__		,				lowerCAmelCase__		,				quiet=lowerCAmelCase__   )
   __a					=  torch.load(lowerCAmelCase__		,				map_location="""cpu"""   )
   if "model" in files:
      __a					=  files["""model"""]
   else:
      __a					=  files["""module"""]
   __a					=  convert_state_dict(lowerCAmelCase__		,				lowerCAmelCase__   )

   model.load_state_dict(lowerCAmelCase__   )
   model.eval()

   # verify model on basic input
   __a					=  VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]		,				image_std=[0.5, 0.5, 0.5]   )
   __a					=  prepare_video()
   __a					=  image_processor(lowerCAmelCase__		,				return_tensors="""pt"""   )

   if "finetuned" not in model_name:
      __a					=  hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos"""		,				filename="""bool_masked_pos.pt"""   )
      __a					=  torch.load(lowerCAmelCase__   )

   __a					=  model(**lowerCAmelCase__   )
   __a					=  outputs.logits

   __a					=  [
       """videomae-small-finetuned-kinetics""",
       """videomae-small-finetuned-ssv2""",
       # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
       """videomae-base-short""",
       """videomae-base-short-finetuned-kinetics""",
       """videomae-base""",
       """videomae-base-finetuned-kinetics""",
       """videomae-large""",
       """videomae-large-finetuned-kinetics""",
       """videomae-huge-finetuned-kinetics""",
       # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
       """videomae-base-short-ssv2""",
       """videomae-base-short-finetuned-ssv2""",
       """videomae-base-ssv2""",
       """videomae-base-finetuned-ssv2""",
   ]

   # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
   if model_name == "videomae-small-finetuned-kinetics":
      __a					=  torch.Size([1, 400]   )
      __a					=  torch.tensor([-0.9_2_9_1, -0.4_0_6_1, -0.9_3_0_7]   )
   elif model_name == "videomae-small-finetuned-ssv2":
      __a					=  torch.Size([1, 174]   )
      __a					=  torch.tensor([0.2_6_7_1, -0.4_6_8_9, -0.8_2_3_5]   )
   elif model_name == "videomae-base":
      __a					=  torch.Size([1, 1_408, 1_536]   )
      __a					=  torch.tensor([[0.7_7_3_9, 0.7_9_6_8, 0.7_0_8_9], [0.6_7_0_1, 0.7_4_8_7, 0.6_2_0_9], [0.4_2_8_7, 0.5_1_5_8, 0.4_7_7_3]]   )
   elif model_name == "videomae-base-short":
      __a					=  torch.Size([1, 1_408, 1_536]   )
      __a					=  torch.tensor([[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]]   )
      # we verified the loss both for normalized and unnormalized targets for this one
      __a					=  torch.tensor([0.5_1_4_2]   ) if config.norm_pix_loss else torch.tensor([0.6_4_6_9]   )
   elif model_name == "videomae-large":
      __a					=  torch.Size([1, 1_408, 1_536]   )
      __a					=  torch.tensor([[0.7_1_4_9, 0.7_9_9_7, 0.6_9_6_6], [0.6_7_6_8, 0.7_8_6_9, 0.6_9_4_8], [0.5_1_3_9, 0.6_2_2_1, 0.5_6_0_5]]   )
   elif model_name == "videomae-large-finetuned-kinetics":
      __a					=  torch.Size([1, 400]   )
      __a					=  torch.tensor([0.0_7_7_1, 0.0_0_1_1, -0.3_6_2_5]   )
   elif model_name == "videomae-huge-finetuned-kinetics":
      __a					=  torch.Size([1, 400]   )
      __a					=  torch.tensor([0.2_4_3_3, 0.1_6_3_2, -0.4_8_9_4]   )
   elif model_name == "videomae-base-short-finetuned-kinetics":
      __a					=  torch.Size([1, 400]   )
      __a					=  torch.tensor([0.6_5_8_8, 0.0_9_9_0, -0.2_4_9_3]   )
   elif model_name == "videomae-base-finetuned-kinetics":
      __a					=  torch.Size([1, 400]   )
      __a					=  torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1]   )
   elif model_name == "videomae-base-short-ssv2":
      __a					=  torch.Size([1, 1_408, 1_536]   )
      __a					=  torch.tensor([[0.4_7_1_2, 0.5_2_9_6, 0.5_7_8_6], [0.2_2_7_8, 0.2_7_2_9, 0.4_0_2_6], [0.0_3_5_2, 0.0_7_3_0, 0.2_5_0_6]]   )
   elif model_name == "videomae-base-short-finetuned-ssv2":
      __a					=  torch.Size([1, 174]   )
      __a					=  torch.tensor([-0.0_5_3_7, -0.1_5_3_9, -0.3_2_6_6]   )
   elif model_name == "videomae-base-ssv2":
      __a					=  torch.Size([1, 1_408, 1_536]   )
      __a					=  torch.tensor([[0.8_1_3_1, 0.8_7_2_7, 0.8_5_4_6], [0.7_3_6_6, 0.9_3_7_7, 0.8_8_7_0], [0.5_9_3_5, 0.8_8_7_4, 0.8_5_6_4]]   )
   elif model_name == "videomae-base-finetuned-ssv2":
      __a					=  torch.Size([1, 174]   )
      __a					=  torch.tensor([0.1_9_6_1, -0.8_3_3_7, -0.6_3_8_9]   )
   else:
      raise ValueError(f'''Model name not supported. Should be one of {model_names}'''   )

   # verify logits
   assert logits.shape == expected_shape
   if "finetuned" in model_name:
      assert torch.allclose(logits[0, :3]		,				lowerCAmelCase__		,				atol=1E-4   )
   else:
      print("""Logits:"""		,				logits[0, :3, :3]   )
      assert torch.allclose(logits[0, :3, :3]		,				lowerCAmelCase__		,				atol=1E-4   )
   print("""Logits ok!"""   )

   # verify loss, if applicable
   if model_name == "videomae-base-short":
      __a					=  outputs.loss
      assert torch.allclose(lowerCAmelCase__		,				lowerCAmelCase__		,				atol=1E-4   )
      print("""Loss ok!"""   )

   if pytorch_dump_folder_path is not None:
      print(f'''Saving model and image processor to {pytorch_dump_folder_path}'''   )
      image_processor.save_pretrained(lowerCAmelCase__   )
      model.save_pretrained(lowerCAmelCase__   )

   if push_to_hub:
      print("""Pushing to the hub..."""   )
      model.push_to_hub(lowerCAmelCase__		,				organization="""nielsr"""   )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE							    =							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--checkpoint_url',
      default='https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4',
      type=str,
      help=(
          'URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'
          ' download link.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_folder_path',
      default='/Users/nielsrogge/Documents/VideoMAE/Test',
      type=str,
      help='Path to the output PyTorch model directory.',
  )
  parser.add_argument('--model_name', default='videomae-base', type=str, help='Name of the model.')
  parser.add_argument(
      '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
  )

  SCREAMING_SNAKE_CASE							    =							parser.parse_args()
  convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)










import pickle

import numpy as np
from matplotlib import pyplot as plt







class 	__lowerCAmelCase      :





   def __init__(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case=0.2       ,      snake_case=0.2   )					->	Any:
      """simple docstring"""





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





   def 						_snake_case			(			self       ,      snake_case   )					->	int:
      """simple docstring"""





      a__						:    str   		=	{
          "num_bp1": self.num_bpa,
          "num_bp2": self.num_bpa,
          "num_bp3": self.num_bpa,
          "conv1": self.conva,
          "step_conv1": self.step_conva,
          "size_pooling1": self.size_poolinga,
          "rate_weight": self.rate_weight,
          "rate_thre": self.rate_thre,
          "w_conv1": self.w_conva,
          "wkj": self.wkj,
          "vji": self.vji,
          "thre_conv1": self.thre_conva,
          "thre_bp2": self.thre_bpa,
          "thre_bp3": self.thre_bpa,
      }
      with open(snake_case       ,      "wb"   ) as f:
         pickle.dump(snake_case       ,      snake_case   )

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





   @classmethod
   def 						_snake_case			(			cls       ,      snake_case   )					->	List[Any]:
      """simple docstring"""





      with open(snake_case       ,      "rb"   ) as f:
         a__						:    List[str]   		=	pickle.load(snake_case   )  # noqa: S301

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





   def 						_snake_case			(			self       ,      snake_case   )					->	Optional[int]:
      """simple docstring"""





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





   def 						_snake_case			(			self       ,      snake_case   )					->	str:
      """simple docstring"""





      return round(snake_case       ,      3   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case   )					->	Optional[int]:
      """simple docstring"""





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

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





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case="average_pool"   )					->	Tuple:
      """simple docstring"""





      a__						:    Dict   		=	len(featuremaps[0]   )
      a__						:    Optional[int]   		=	int(size_map / size_pooling   )
      a__						:    Any   		=	[]
      for i_map in range(len(snake_case   )   ):
         a__						:    List[str]   		=	featuremaps[i_map]
         a__						:    Optional[Any]   		=	[]
         for i_focus in range(0       ,      snake_case       ,      snake_case   ):
            for j_focus in range(0       ,      snake_case       ,      snake_case   ):
               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(snake_case   )   )
               elif pooling_type == "max_pooling":
                  # max pooling
                  map_pooled.append(np.max(snake_case   )   )
         a__						:    Dict   		=	np.asmatrix(snake_case   ).reshape(snake_case       ,      snake_case   )
         featuremap_pooled.append(snake_case   )
      return featuremap_pooled





   def 						_snake_case			(			self       ,      snake_case   )					->	Optional[Any]:
      """simple docstring"""





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





   def 						_snake_case			(			self       ,      snake_case   )					->	str:
      """simple docstring"""





      a__						:    Dict   		=	np.asarray(snake_case   )
      a__						:    List[Any]   		=	np.shape(snake_case   )
      a__						:    Optional[Any]   		=	data_mat.reshape(1       ,      shapes[0] * shapes[1]   )
      return data_expanded





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case   )					->	Any:
      """simple docstring"""





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





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case=bool   )					->	Optional[int]:
      """simple docstring"""





      print("----------------------Start Training-------------------------"   )
      print((" - - Shape: Train_Data  ", np.shape(snake_case   ))   )
      print((" - - Shape: Teach_Data  ", np.shape(snake_case   ))   )
      a__						:    List[Any]   		=	0
      a__						:    Any   		=	[]
      a__						:    Dict   		=	10_000
      while rp < n_repeat and mse >= error_accuracy:
         a__						:    Optional[Any]   		=	0
         print(F"""-------------Learning Time {rp}--------------"""   )
         for p in range(len(snake_case   )   ):
            # print('------------Learning Image: %d--------------'%p)
            a__						:    Optional[int]   		=	np.asmatrix(datas_train[p]   )
            a__						:    Optional[Any]   		=	np.asarray(datas_teach[p]   )
            a__		,  a__						:    Tuple   		=	self.convolute(
                snake_case       ,      self.conva       ,      self.w_conva       ,      self.thre_conva       ,      conv_step=self.step_conva       ,      )
            a__						:    List[Any]   		=	self.pooling(snake_case       ,      self.size_poolinga   )
            a__						:    str   		=	np.shape(snake_case   )
            a__						:    List[Any]   		=	self._expand(snake_case   )
            a__						:    Union[str, Any]   		=	data_bp_input

            a__						:    Optional[Any]   		=	np.dot(snake_case       ,      self.vji.T   ) - self.thre_bpa
            a__						:    Optional[int]   		=	self.sig(snake_case   )
            a__						:    Union[str, Any]   		=	np.dot(snake_case       ,      self.wkj.T   ) - self.thre_bpa
            a__						:    Dict   		=	self.sig(snake_case   )

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

            a__						:    List[str]   		=	pd_i_all / (self.size_poolinga * self.size_poolinga)
            a__						:    Tuple   		=	pd_conva_pooled.T.getA().tolist()
            a__						:    List[str]   		=	self._calculate_gradient_from_pool(
                snake_case       ,      snake_case       ,      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__						:    Dict   		=	self._expand_mat(pd_conva_all[k_conv]   )
               a__						:    Optional[int]   		=	self.rate_weight * np.dot(snake_case       ,      snake_case   )

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

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

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

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





   def 						_snake_case			(			self       ,      snake_case   )					->	Any:
      """simple docstring"""





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

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





   def 						_snake_case			(			self       ,      snake_case   )					->	Union[str, Any]:
      """simple docstring"""





      a__						:    Dict   		=	np.asmatrix(snake_case   )
      a__		,  a__						:    Optional[int]   		=	self.convolute(
          snake_case       ,      self.conva       ,      self.w_conva       ,      self.thre_conva       ,      conv_step=self.step_conva       ,      )
      a__						:    Any   		=	self.pooling(snake_case       ,      self.size_poolinga   )

      return data_conveda, data_pooleda


if __name__ == "__main__":
  pass

