






"""simple docstring"""






import pickle

import numpy as np
from matplotlib import pyplot as plt




class 							lowerCAmelCase__       :






      '''simple docstring'''

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




          '''simple docstring'''


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




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	{
              '''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 _SCREAMING_SNAKE_CASE				(		cls  :						Optional[int]		,			lowercase_  :						Optional[int]):




          '''simple docstring'''


          with open(lowercase_		,			'''rb''') as f:
              SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	pickle.load(lowercase_)  # noqa: S301

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




          '''simple docstring'''


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




          '''simple docstring'''


          return round(lowercase_		,			3)
      def _SCREAMING_SNAKE_CASE				(		self  :						int		,			lowercase_  :						List[str]		,			lowercase_  :						Tuple		,			lowercase_  :						Dict		,			lowercase_  :						Optional[Any]		,			lowercase_  :						List[str]):




          '''simple docstring'''


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

          # expanding the data slice to One dimenssion
          SCREAMING_SNAKE_CASE_   :						Dict							=	[]
          for each_focus in data_focus:
              focusa_list.extend(self.Expand_Mat(lowercase_))
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	np.asarray(lowercase_)
          return focus_list, data_featuremap
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple		,			lowercase_  :						str		,			lowercase_  :						str		,			lowercase_  :						Any="average_pool"):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	len(featuremaps[0])
          SCREAMING_SNAKE_CASE_   :						Any							=	int(size_map / size_pooling)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	[]
          for i_map in range(len(lowercase_)):
              SCREAMING_SNAKE_CASE_   :						int							=	featuremaps[i_map]
              SCREAMING_SNAKE_CASE_   :						List[str]							=	[]
              for i_focus in range(0		,			lowercase_		,			lowercase_):
                  for j_focus in range(0		,			lowercase_		,			lowercase_):
                      SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	feature_map[
                          i_focus : i_focus + size_pooling,
                          j_focus : j_focus + size_pooling,
                      ]
                      if pooling_type == "average_pool":
                          # average pooling
                          map_pooled.append(np.average(lowercase_))
                      elif pooling_type == "max_pooling":
                          # max pooling
                          map_pooled.append(np.max(lowercase_))
              SCREAMING_SNAKE_CASE_   :						str							=	np.asmatrix(lowercase_).reshape(lowercase_		,			lowercase_)
              featuremap_pooled.append(lowercase_)
          return featuremap_pooled
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]		,			lowercase_  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	[]
          for i in range(len(lowercase_)):
              SCREAMING_SNAKE_CASE_   :						List[str]							=	np.shape(data[i])
              SCREAMING_SNAKE_CASE_   :						Dict							=	data[i].reshape(1		,			shapes[0] * shapes[1])
              SCREAMING_SNAKE_CASE_   :						List[str]							=	data_listed.getA().tolist()[0]
              data_expanded.extend(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Any							=	np.asarray(lowercase_)
          return data_expanded
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]		,			lowercase_  :						Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[Any]							=	np.asarray(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	np.shape(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	data_mat.reshape(1		,			shapes[0] * shapes[1])
          return data_expanded
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]		,			lowercase_  :						Tuple		,			lowercase_  :						Union[str, Any]		,			lowercase_  :						List[str]		,			lowercase_  :						Optional[int]		,			lowercase_  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Any							=	[]
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	0
          for i_map in range(lowercase_):
              SCREAMING_SNAKE_CASE_   :						int							=	np.ones((size_map, size_map))
              for i in range(0		,			lowercase_		,			lowercase_):
                  for j in range(0		,			lowercase_		,			lowercase_):
                      SCREAMING_SNAKE_CASE_   :						Any							=	pd_pool[
                          i_pool
                      ]
                      SCREAMING_SNAKE_CASE_   :						List[str]							=	i_pool + 1
              SCREAMING_SNAKE_CASE_   :						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 _SCREAMING_SNAKE_CASE				(		self  :						Any		,			lowercase_  :						Any		,			lowercase_  :						Optional[int]		,			lowercase_  :						Union[str, Any]		,			lowercase_  :						Any		,			lowercase_  :						Optional[int]		,			lowercase_  :						Dict=bool):




          '''simple docstring'''


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

                  SCREAMING_SNAKE_CASE_   :						int							=	np.dot(lowercase_		,			self.vji.T) - self.thre_bpa
                  SCREAMING_SNAKE_CASE_   :						Dict							=	self.sig(lowercase_)
                  SCREAMING_SNAKE_CASE_   :						str							=	np.dot(lowercase_		,			self.wkj.T) - self.thre_bpa
                  SCREAMING_SNAKE_CASE_   :						List[str]							=	self.sig(lowercase_)

                  # --------------Model Leaning ------------------------
                  # calculate error and gradient---------------
                  SCREAMING_SNAKE_CASE_   :						Tuple							=	np.multiply(
                      (data_teach - bp_outa)		,			np.multiply(lowercase_		,			(1 - bp_outa)))
                  SCREAMING_SNAKE_CASE_   :						Tuple							=	np.multiply(
                      np.dot(lowercase_		,			self.wkj)		,			np.multiply(lowercase_		,			(1 - bp_outa)))
                  SCREAMING_SNAKE_CASE_   :						Optional[int]							=	np.dot(lowercase_		,			self.vji)

                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	pd_i_all / (self.size_poolinga * self.size_poolinga)
                  SCREAMING_SNAKE_CASE_   :						Tuple							=	pd_conva_pooled.T.getA().tolist()
                  SCREAMING_SNAKE_CASE_   :						Optional[int]							=	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]):
                      SCREAMING_SNAKE_CASE_   :						List[Any]							=	self._expand_mat(pd_conva_all[k_conv])
                      SCREAMING_SNAKE_CASE_   :						int							=	self.rate_weight * np.dot(lowercase_		,			lowercase_)

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

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

          def draw_error():
              SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	[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 _SCREAMING_SNAKE_CASE				(		self  :						Tuple		,			lowercase_  :						List[str]):




          '''simple docstring'''


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

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



      def _SCREAMING_SNAKE_CASE				(		self  :						str		,			lowercase_  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	np.asmatrix(lowercase_)
          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.convolute(
              lowercase_		,			self.conva		,			self.w_conva		,			self.thre_conva		,			conv_step=self.step_conva		,			)
          SCREAMING_SNAKE_CASE_   :						int							=	self.pooling(lowercase_		,			self.size_poolinga)

          return data_conveda, data_pooleda


if __name__ == "__main__":
  pass










"""simple docstring"""






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_     :				List[Any]         =				logging.get_logger(__name__)



def       _A    (__a							)				-> Union[str, Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	DPTConfig(embedding_type='''hybrid'''							)

    if "large" in checkpoint_url:
        SCREAMING_SNAKE_CASE_   :						int							=	10_24
        SCREAMING_SNAKE_CASE_   :						Dict							=	40_96
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	24
        SCREAMING_SNAKE_CASE_   :						Any							=	16
        SCREAMING_SNAKE_CASE_   :						int							=	[5, 11, 17, 23]
        SCREAMING_SNAKE_CASE_   :						List[str]							=	[2_56, 5_12, 10_24, 10_24]
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	(1, 3_84, 3_84)

    if "nyu" or "midas" in checkpoint_url:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	7_68
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	[1, 1, 1, 0.5]
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	[2_56, 5_12, 7_68, 7_68]
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	1_50
        SCREAMING_SNAKE_CASE_   :						str							=	16
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	(1, 3_84, 3_84)
        SCREAMING_SNAKE_CASE_   :						int							=	False
        SCREAMING_SNAKE_CASE_   :						Tuple							=	'''project'''

    if "ade" in checkpoint_url:
        SCREAMING_SNAKE_CASE_   :						str							=	True
        SCREAMING_SNAKE_CASE_   :						Dict							=	7_68
        SCREAMING_SNAKE_CASE_   :						Tuple							=	[1, 1, 1, 0.5]
        SCREAMING_SNAKE_CASE_   :						List[str]							=	1_50
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	16
        SCREAMING_SNAKE_CASE_   :						Tuple							=	'''huggingface/label-files'''
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	'''ade20k-id2label.json'''
        SCREAMING_SNAKE_CASE_   :						List[str]							=	json.load(open(cached_download(hf_hub_url(__a					,       __a					,       repo_type='''dataset'''							)							)					,       '''r'''							)							)
        SCREAMING_SNAKE_CASE_   :						Dict							=	{int(__a							): v for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	idalabel
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	{v: k for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE_   :						Any							=	[1, 1_50, 4_80, 4_80]

    return config, expected_shape



def       _A    (__a							)				-> List[str]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						int							=	['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
    for k in ignore_keys:
        state_dict.pop(__a					,       __a							)



def       _A    (__a							)				-> Optional[int]:
    """simple docstring"""






    if (
        "pretrained.model" in name
        and "cls_token" not in name
        and "pos_embed" not in name
        and "patch_embed" not in name
    ):
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	name.replace('''pretrained.model'''					,       '''dpt.encoder'''							)
    if "pretrained.model" in name:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	name.replace('''pretrained.model'''					,       '''dpt.embeddings'''							)
    if "patch_embed" in name:
        SCREAMING_SNAKE_CASE_   :						str							=	name.replace('''patch_embed'''					,       ''''''							)
    if "pos_embed" in name:
        SCREAMING_SNAKE_CASE_   :						Any							=	name.replace('''pos_embed'''					,       '''position_embeddings'''							)
    if "attn.proj" in name:
        SCREAMING_SNAKE_CASE_   :						Tuple							=	name.replace('''attn.proj'''					,       '''attention.output.dense'''							)
    if "proj" in name and "project" not in name:
        SCREAMING_SNAKE_CASE_   :						Dict							=	name.replace('''proj'''					,       '''projection'''							)
    if "blocks" in name:
        SCREAMING_SNAKE_CASE_   :						str							=	name.replace('''blocks'''					,       '''layer'''							)
    if "mlp.fc1" in name:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	name.replace('''mlp.fc1'''					,       '''intermediate.dense'''							)
    if "mlp.fc2" in name:
        SCREAMING_SNAKE_CASE_   :						Tuple							=	name.replace('''mlp.fc2'''					,       '''output.dense'''							)
    if "norm1" in name and "backbone" not in name:
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	name.replace('''norm1'''					,       '''layernorm_before'''							)
    if "norm2" in name and "backbone" not in name:
        SCREAMING_SNAKE_CASE_   :						Tuple							=	name.replace('''norm2'''					,       '''layernorm_after'''							)
    if "scratch.output_conv" in name:
        SCREAMING_SNAKE_CASE_   :						Tuple							=	name.replace('''scratch.output_conv'''					,       '''head'''							)
    if "scratch" in name:
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	name.replace('''scratch'''					,       '''neck'''							)
    if "layer1_rn" in name:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	name.replace('''layer1_rn'''					,       '''convs.0'''							)
    if "layer2_rn" in name:
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	name.replace('''layer2_rn'''					,       '''convs.1'''							)
    if "layer3_rn" in name:
        SCREAMING_SNAKE_CASE_   :						int							=	name.replace('''layer3_rn'''					,       '''convs.2'''							)
    if "layer4_rn" in name:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	name.replace('''layer4_rn'''					,       '''convs.3'''							)
    if "refinenet" in name:
        SCREAMING_SNAKE_CASE_   :						Dict							=	int(name[len('''neck.refinenet'''							) : len('''neck.refinenet'''							) + 1]							)
        # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
        SCREAMING_SNAKE_CASE_   :						int							=	name.replace(f'refinenet{layer_idx}'					,       f'fusion_stage.layers.{abs(layer_idx-4							)}'							)
    if "out_conv" in name:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	name.replace('''out_conv'''					,       '''projection'''							)
    if "resConfUnit1" in name:
        SCREAMING_SNAKE_CASE_   :						str							=	name.replace('''resConfUnit1'''					,       '''residual_layer1'''							)
    if "resConfUnit2" in name:
        SCREAMING_SNAKE_CASE_   :						str							=	name.replace('''resConfUnit2'''					,       '''residual_layer2'''							)
    if "conv1" in name:
        SCREAMING_SNAKE_CASE_   :						Any							=	name.replace('''conv1'''					,       '''convolution1'''							)
    if "conv2" in name:
        SCREAMING_SNAKE_CASE_   :						int							=	name.replace('''conv2'''					,       '''convolution2'''							)
    # readout blocks
    if "pretrained.act_postprocess1.0.project.0" in name:
        SCREAMING_SNAKE_CASE_   :						Dict							=	name.replace('''pretrained.act_postprocess1.0.project.0'''					,       '''neck.reassemble_stage.readout_projects.0.0'''							)
    if "pretrained.act_postprocess2.0.project.0" in name:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	name.replace('''pretrained.act_postprocess2.0.project.0'''					,       '''neck.reassemble_stage.readout_projects.1.0'''							)
    if "pretrained.act_postprocess3.0.project.0" in name:
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	name.replace('''pretrained.act_postprocess3.0.project.0'''					,       '''neck.reassemble_stage.readout_projects.2.0'''							)
    if "pretrained.act_postprocess4.0.project.0" in name:
        SCREAMING_SNAKE_CASE_   :						Tuple							=	name.replace('''pretrained.act_postprocess4.0.project.0'''					,       '''neck.reassemble_stage.readout_projects.3.0'''							)

    # resize blocks
    if "pretrained.act_postprocess1.3" in name:
        SCREAMING_SNAKE_CASE_   :						Tuple							=	name.replace('''pretrained.act_postprocess1.3'''					,       '''neck.reassemble_stage.layers.0.projection'''							)
    if "pretrained.act_postprocess1.4" in name:
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	name.replace('''pretrained.act_postprocess1.4'''					,       '''neck.reassemble_stage.layers.0.resize'''							)
    if "pretrained.act_postprocess2.3" in name:
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	name.replace('''pretrained.act_postprocess2.3'''					,       '''neck.reassemble_stage.layers.1.projection'''							)
    if "pretrained.act_postprocess2.4" in name:
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	name.replace('''pretrained.act_postprocess2.4'''					,       '''neck.reassemble_stage.layers.1.resize'''							)
    if "pretrained.act_postprocess3.3" in name:
        SCREAMING_SNAKE_CASE_   :						str							=	name.replace('''pretrained.act_postprocess3.3'''					,       '''neck.reassemble_stage.layers.2.projection'''							)
    if "pretrained.act_postprocess4.3" in name:
        SCREAMING_SNAKE_CASE_   :						Dict							=	name.replace('''pretrained.act_postprocess4.3'''					,       '''neck.reassemble_stage.layers.3.projection'''							)
    if "pretrained.act_postprocess4.4" in name:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	name.replace('''pretrained.act_postprocess4.4'''					,       '''neck.reassemble_stage.layers.3.resize'''							)
    if "pretrained" in name:
        SCREAMING_SNAKE_CASE_   :						Any							=	name.replace('''pretrained'''					,       '''dpt'''							)
    if "bn" in name:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	name.replace('''bn'''					,       '''batch_norm'''							)
    if "head" in name:
        SCREAMING_SNAKE_CASE_   :						int							=	name.replace('''head'''					,       '''head.head'''							)
    if "encoder.norm" in name:
        SCREAMING_SNAKE_CASE_   :						Any							=	name.replace('''encoder.norm'''					,       '''layernorm'''							)
    if "auxlayer" in name:
        SCREAMING_SNAKE_CASE_   :						int							=	name.replace('''auxlayer'''					,       '''auxiliary_head.head'''							)
    if "backbone" in name:
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	name.replace('''backbone'''					,       '''backbone.bit.encoder'''							)

    if ".." in name:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	name.replace('''..'''					,       '''.'''							)

    if "stem.conv" in name:
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	name.replace('''stem.conv'''					,       '''bit.embedder.convolution'''							)
    if "blocks" in name:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	name.replace('''blocks'''					,       '''layers'''							)
    if "convolution" in name and "backbone" in name:
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	name.replace('''convolution'''					,       '''conv'''							)
    if "layer" in name and "backbone" in name:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	name.replace('''layer'''					,       '''layers'''							)
    if "backbone.bit.encoder.bit" in name:
        SCREAMING_SNAKE_CASE_   :						int							=	name.replace('''backbone.bit.encoder.bit'''					,       '''backbone.bit'''							)
    if "embedder.conv" in name:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	name.replace('''embedder.conv'''					,       '''embedder.convolution'''							)
    if "backbone.bit.encoder.stem.norm" in name:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	name.replace('''backbone.bit.encoder.stem.norm'''					,       '''backbone.bit.embedder.norm'''							)
    return name



def       _A    (__a					,       __a							)				-> Dict:
    """simple docstring"""






    for i in range(config.num_hidden_layers							):
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        SCREAMING_SNAKE_CASE_   :						Dict							=	state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight'							)
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.bias'							)
        # next, add query, keys and values (in that order) to the state dict
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	in_proj_weight[: config.hidden_size, :]
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	in_proj_bias[: config.hidden_size]
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        SCREAMING_SNAKE_CASE_   :						int							=	in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        SCREAMING_SNAKE_CASE_   :						List[str]							=	in_proj_weight[
            -config.hidden_size :, :
        ]
        SCREAMING_SNAKE_CASE_   :						Any							=	in_proj_bias[-config.hidden_size :]



def       _A    ()				-> Dict:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Dict							=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	Image.open(requests.get(__a					,       stream=__a							).raw							)
    return im



@torch.no_grad()
def       _A    (__a					,       __a					,       __a					,       __a					,       __a							)				-> List[str]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						int							=	get_dpt_config(__a							)
    # load original state_dict from URL
    # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
    SCREAMING_SNAKE_CASE_   :						int							=	torch.load(__a					,       map_location='''cpu'''							)
    # remove certain keys
    remove_ignore_keys_(__a							)
    # rename keys
    for key in state_dict.copy().keys():
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	state_dict.pop(__a							)
        SCREAMING_SNAKE_CASE_   :						Dict							=	val
    # read in qkv matrices
    read_in_q_k_v(__a					,       __a							)

    # load HuggingFace model
    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	DPTForSemanticSegmentation(__a							) if '''ade''' in checkpoint_url else DPTForDepthEstimation(__a							)
    model.load_state_dict(__a							)
    model.eval()

    # Check outputs on an image
    SCREAMING_SNAKE_CASE_   :						Dict							=	4_80 if '''ade''' in checkpoint_url else 3_84
    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	DPTImageProcessor(size=__a							)

    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	prepare_img()
    SCREAMING_SNAKE_CASE_   :						Tuple							=	image_processor(__a					,       return_tensors='''pt'''							)

    # forward pass
    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	model(**__a							).logits if '''ade''' in checkpoint_url else model(**__a							).predicted_depth

    if show_prediction:
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	(
            torch.nn.functional.interpolate(
                outputs.unsqueeze(1							)					,       size=(image.size[1], image.size[0])					,       mode='''bicubic'''					,       align_corners=__a					,       )
            .squeeze()
            .cpu()
            .numpy()
        )

        Image.fromarray((prediction / prediction.max()) * 2_55							).show()

    if pytorch_dump_folder_path is not None:
        Path(__a							).mkdir(exist_ok=__a							)
        print(f'Saving model to {pytorch_dump_folder_path}'							)
        model.save_pretrained(__a							)
        print(f'Saving image processor to {pytorch_dump_folder_path}'							)
        image_processor.save_pretrained(__a							)

    if push_to_hub:
        model.push_to_hub('''ybelkada/dpt-hybrid-midas'''							)
        image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas'''							)


if __name__ == "__main__":
  UpperCAmelCase_     :				List[str]         =				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--checkpoint_url""",
      default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
      type=str,
      help="""URL of the original DPT checkpoint you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default=None,
      type=str,
      required=False,
      help="""Path to the output PyTorch model directory.""",
  )
  parser.add_argument(
      """--push_to_hub""",
      action="""store_true""",
  )
  parser.add_argument(
      """--model_name""",
      default="""dpt-large""",
      type=str,
      help="""Name of the model, in case you're pushing to the hub.""",
  )
  parser.add_argument(
      """--show_prediction""",
      action="""store_true""",
  )

  UpperCAmelCase_     :				List[Any]         =				parser.parse_args()
  convert_dpt_checkpoint(
      args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
  )


