



import argparse
import json
from pathlib import Path

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

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


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



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_=False  ):
			lowercase__					  =				[]
			for i in range(config.num_hidden_layers  ):
						# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
						rename_keys.append((f'''blocks.{i}.norm1.weight''', f'''deit.encoder.layer.{i}.layernorm_before.weight''')  )
						rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''deit.encoder.layer.{i}.layernorm_before.bias''')  )
						rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''deit.encoder.layer.{i}.attention.output.dense.weight''')  )
						rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''deit.encoder.layer.{i}.attention.output.dense.bias''')  )
						rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''deit.encoder.layer.{i}.layernorm_after.weight''')  )
						rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''deit.encoder.layer.{i}.layernorm_after.bias''')  )
						rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''deit.encoder.layer.{i}.intermediate.dense.weight''')  )
						rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''deit.encoder.layer.{i}.intermediate.dense.bias''')  )
						rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''deit.encoder.layer.{i}.output.dense.weight''')  )
						rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''deit.encoder.layer.{i}.output.dense.bias''')  )

			# projection layer + position embeddings
			rename_keys.extend(
			    [
			        ("cls_token", "deit.embeddings.cls_token"),
			        ("dist_token", "deit.embeddings.distillation_token"),
			        ("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
			        ("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
			        ("pos_embed", "deit.embeddings.position_embeddings"),
			    ]  )

			if base_model:
						# layernorm + pooler
						rename_keys.extend(
						    [
						        ("norm.weight", "layernorm.weight"),
						        ("norm.bias", "layernorm.bias"),
						        ("pre_logits.fc.weight", "pooler.dense.weight"),
						        ("pre_logits.fc.bias", "pooler.dense.bias"),
						    ]  )

						# if just the base model, we should remove "deit" from all keys that start with "deit"
						lowercase__					  =				[(pair[0], pair[1][4:]) if pair[1].startswith("deit"  ) else pair for pair in rename_keys]
			else:
						# layernorm + classification heads
						rename_keys.extend(
						    [
						        ("norm.weight", "deit.layernorm.weight"),
						        ("norm.bias", "deit.layernorm.bias"),
						        ("head.weight", "cls_classifier.weight"),
						        ("head.bias", "cls_classifier.bias"),
						        ("head_dist.weight", "distillation_classifier.weight"),
						        ("head_dist.bias", "distillation_classifier.bias"),
						    ]  )

			return rename_keys



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_=False  ):
			for i in range(config.num_hidden_layers  ):
						if base_model:
									lowercase__					  =				""
						else:
									lowercase__					  =				"deit."
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						lowercase__					  =				state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''  )
						lowercase__					  =				state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''  )
						# next, add query, keys and values (in that order) to the state dict
						lowercase__					  =				in_proj_weight[
						    : config.hidden_size, :
						]
						lowercase__					  =				in_proj_bias[: config.hidden_size]
						lowercase__					  =				in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						lowercase__					  =				in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						lowercase__					  =				in_proj_weight[
						    -config.hidden_size :, :
						]
						lowercase__					  =				in_proj_bias[-config.hidden_size :]



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				dct.pop(SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				val



def __lowerCAmelCase (     ):
			lowercase__					  =				"http://images.cocodataset.org/val2017/000000039769.jpg"
			lowercase__					  =				Image.open(requests.get(SCREAMING_SNAKE_CASE_						,  stream=SCREAMING_SNAKE_CASE_  ).raw  )
			return im



@torch.no_grad()
def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				DeiTConfig()
			# all deit models have fine-tuned heads
			lowercase__					  =				False
			# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
			lowercase__					  =				1000
			lowercase__					  =				"huggingface/label-files"
			lowercase__					  =				"imagenet-1k-id2label.json"
			lowercase__					  =				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  repo_type="dataset"  )						,  "r"  )  )
			lowercase__					  =				{int(SCREAMING_SNAKE_CASE_  ): v for k, v in idalabel.items()}
			lowercase__					  =				idalabel
			lowercase__					  =				{v: k for k, v in idalabel.items()}
			lowercase__					  =				int(deit_name[-6:-4]  )
			lowercase__					  =				int(deit_name[-3:]  )
			# size of the architecture
			if deit_name[9:].startswith("tiny"  ):
						lowercase__					  =				192
						lowercase__					  =				768
						lowercase__					  =				12
						lowercase__					  =				3
			elif deit_name[9:].startswith("small"  ):
						lowercase__					  =				384
						lowercase__					  =				1536
						lowercase__					  =				12
						lowercase__					  =				6
			if deit_name[9:].startswith("base"  ):
						pass
			elif deit_name[4:].startswith("large"  ):
						lowercase__					  =				1024
						lowercase__					  =				4096
						lowercase__					  =				24
						lowercase__					  =				16

			# load original model from timm
			lowercase__					  =				timm.create_model(SCREAMING_SNAKE_CASE_						,  pretrained=SCREAMING_SNAKE_CASE_  )
			timm_model.eval()

			# load state_dict of original model, remove and rename some keys
			lowercase__					  =				timm_model.state_dict()
			lowercase__					  =				create_rename_keys(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
			for src, dest in rename_keys:
						rename_key(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
			read_in_q_k_v(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )

			# load HuggingFace model
			lowercase__					  =				DeiTForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE_  ).eval()
			model.load_state_dict(SCREAMING_SNAKE_CASE_  )

			# Check outputs on an image, prepared by DeiTImageProcessor
			lowercase__					  =				int(
			    (256 / 224) * config.image_size  )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
			lowercase__					  =				DeiTImageProcessor(size=SCREAMING_SNAKE_CASE_						,  crop_size=config.image_size  )
			lowercase__					  =				image_processor(images=prepare_img()						,  return_tensors="pt"  )
			lowercase__					  =				encoding["pixel_values"]
			lowercase__					  =				model(SCREAMING_SNAKE_CASE_  )

			lowercase__					  =				timm_model(SCREAMING_SNAKE_CASE_  )
			assert timm_logits.shape == outputs.logits.shape
			assert torch.allclose(SCREAMING_SNAKE_CASE_						,  outputs.logits						,  atol=1e-3  )

			Path(SCREAMING_SNAKE_CASE_  ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_  )
			print(f'''Saving model {deit_name} to {pytorch_dump_folder_path}'''  )
			model.save_pretrained(SCREAMING_SNAKE_CASE_  )
			print(f'''Saving image processor to {pytorch_dump_folder_path}'''  )
			image_processor.save_pretrained(SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
							lowercase_								=     argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--deit_name""",
							    default="""vit_deit_base_distilled_patch16_224""",
							    type=str,
							    help="""Name of the DeiT timm model you'd like to convert.""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
							)

							lowercase_								=     parser.parse_args()
							convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)









import pickle

import numpy as np
from matplotlib import pyplot as plt
class 				_snake_case			:




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




				def 							A__      (						self					:						Any,					__lowercase					:						List[str]						):
							# save model dict with pickle
							lowercase__					  =				{
							    "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					:						Dict,					__lowercase					:						Union[str, Any]						):
							# read saved model
							with open(__lowercase,					"rb"						) as f:
										lowercase__					  =				pickle.load(__lowercase						)  # noqa: S301

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




				def 							A__      (						self					:						str,					__lowercase					:						List[Any]						):
							return 1 / (1 + np.exp(-1 * x						))




				def 							A__      (						self					:						List[str],					__lowercase					:						Optional[Any]						):
							return round(__lowercase,					3						)




				def 							A__      (						self					:						Optional[Any],					__lowercase					:						Dict,					__lowercase					:						Optional[int],					__lowercase					:						Optional[int],					__lowercase					:						Optional[Any],					__lowercase					:						str						):
							# convolution process
							lowercase__					  =				convs[0]
							lowercase__					  =				convs[1]
							lowercase__					  =				np.shape(__lowercase						)[0]
							# get the data slice of original image data, data_focus
							lowercase__					  =				[]
							for i_focus in range(0,					size_data - size_conv + 1,					__lowercase						):
										for j_focus in range(0,					size_data - size_conv + 1,					__lowercase						):
													lowercase__					  =				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
							lowercase__					  =				[]
							lowercase__					  =				int((size_data - size_conv) / conv_step + 1						)
							for i_map in range(__lowercase						):
										lowercase__					  =				[]
										for i_focus in range(len(__lowercase						)						):
													lowercase__					  =				(
													    np.sum(np.multiply(data_focus[i_focus],					w_convs[i_map]						)						)
													    - thre_convs[i_map]
													)
													featuremap.append(self.sig(__lowercase						)						)
										lowercase__					  =				np.asmatrix(__lowercase						).reshape(
										    __lowercase,					__lowercase						)
										data_featuremap.append(__lowercase						)

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




				def 							A__      (						self					:						List[Any],					__lowercase					:						Any,					__lowercase					:						List[Any],					__lowercase					:						Union[str, Any]="average_pool"						):
							# pooling process
							lowercase__					  =				len(featuremaps[0]						)
							lowercase__					  =				int(size_map / size_pooling						)
							lowercase__					  =				[]
							for i_map in range(len(__lowercase						)						):
										lowercase__					  =				featuremaps[i_map]
										lowercase__					  =				[]
										for i_focus in range(0,					__lowercase,					__lowercase						):
													for j_focus in range(0,					__lowercase,					__lowercase						):
																lowercase__					  =				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						)						)
										lowercase__					  =				np.asmatrix(__lowercase						).reshape(__lowercase,					__lowercase						)
										featuremap_pooled.append(__lowercase						)
							return featuremap_pooled




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




				def 							A__      (						self					:						Optional[int],					__lowercase					:						Optional[int]						):
							# expanding matrix to one dimension list
							lowercase__					  =				np.asarray(__lowercase						)
							lowercase__					  =				np.shape(__lowercase						)
							lowercase__					  =				data_mat.reshape(1,					shapes[0] * shapes[1]						)
							return data_expanded




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




				def 							A__      (						self					:						Tuple,					__lowercase					:						int,					__lowercase					:						Optional[Any],					__lowercase					:						List[Any],					__lowercase					:						Optional[Any],					__lowercase					:						List[Any],					__lowercase					:						List[str]=bool						):
							# model traning
							print("----------------------Start Training-------------------------"						)
							print((" - - Shape: Train_Data  ", np.shape(__lowercase						))						)
							print((" - - Shape: Teach_Data  ", np.shape(__lowercase						))						)
							lowercase__					  =				0
							lowercase__					  =				[]
							lowercase__					  =				1_0000
							while rp < n_repeat and mse >= error_accuracy:
										lowercase__					  =				0
										print(F'''-------------Learning Time {rp}--------------'''						)
										for p in range(len(__lowercase						)						):
													# print('------------Learning Image: %d--------------'%p)
													lowercase__					  =				np.asmatrix(datas_train[p]						)
													lowercase__					  =				np.asarray(datas_teach[p]						)
													lowercase__  ,       lowercase__					  =				self.convolute(
													    __lowercase,					self.conva,					self.w_conva,					self.thre_conva,					conv_step=self.step_conva,					)
													lowercase__					  =				self.pooling(__lowercase,					self.size_poolinga						)
													lowercase__					  =				np.shape(__lowercase						)
													lowercase__					  =				self._expand(__lowercase						)
													lowercase__					  =				data_bp_input

													lowercase__					  =				np.dot(__lowercase,					self.vji.T						) - self.thre_bpa
													lowercase__					  =				self.sig(__lowercase						)
													lowercase__					  =				np.dot(__lowercase,					self.wkj.T						) - self.thre_bpa
													lowercase__					  =				self.sig(__lowercase						)

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

													lowercase__					  =				pd_i_all / (self.size_poolinga * self.size_poolinga)
													lowercase__					  =				pd_conva_pooled.T.getA().tolist()
													lowercase__					  =				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]						):
																lowercase__					  =				self._expand_mat(pd_conva_all[k_conv]						)
																lowercase__					  =				self.rate_weight * np.dot(__lowercase,					__lowercase						)

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

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

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

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




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

							return data_conveda, data_pooleda


if __name__ == "__main__":
							pass




