



# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets






def  lowerCamelCase_	(      lowerCAmelCase__	:	Dict				)       ->							List[Any]:

							'''simple docstring'''






							return 1 / (1 + np.exp(-z				))






def  lowerCamelCase_	(      lowerCAmelCase__	:	List[Any]			,	lowerCAmelCase__	:	int				)       ->							Tuple:

							'''simple docstring'''






							return (-y * np.log(lowerCAmelCase__				) - (1 - y) * np.log(1 - h				)).mean()






def  lowerCamelCase_	(      lowerCAmelCase__	:	Any			,	lowerCAmelCase__	:	Union[str, Any]			,	lowerCAmelCase__	:	List[str]				)       ->							Tuple:

							'''simple docstring'''






							A      						=    np.dot(lowerCAmelCase__			,	lowerCAmelCase__				)
							return np.sum(y * scores - np.log(1 + np.exp(lowerCAmelCase__				)				)				)






def  lowerCamelCase_	(      lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	Dict			,	lowerCAmelCase__	:	Any			,	lowerCAmelCase__	:	str=70000				)       ->							int:

							'''simple docstring'''






							A      						=    np.zeros(x.shape[1]				)

							for iterations in range(lowerCAmelCase__				):
														A      						=    np.dot(lowerCAmelCase__			,	lowerCAmelCase__				)
														A      						=    sigmoid_function(lowerCAmelCase__				)
														A      						=    np.dot(x.T			,	h - y				) / y.size
														A      						=    theta - alpha * gradient  # updating the weights
														A      						=    np.dot(lowerCAmelCase__			,	lowerCAmelCase__				)
														A      						=    sigmoid_function(lowerCAmelCase__				)
														A      						=    cost_function(lowerCAmelCase__			,	lowerCAmelCase__				)
														if iterations % 100 == 0:
																					print(F'''loss: {j} \t'''				)  # printing the loss after every 100 iterations
							return theta


# In[68]:

if __name__ == "__main__":
				__snake_case     :Union[str, Any]			       =datasets.load_iris()
				__snake_case     :Optional[int]			       =iris.data[:, :2]
				__snake_case     :Any			       =(iris.target != 0) * 1

				__snake_case     :List[Any]			       =0.1
				__snake_case     :Union[str, Any]			       =logistic_reg(alpha, x, y, max_iterations=70000)
				print('theta: ', theta)  # printing the theta i.e our weights vector






				def  lowerCamelCase_	(      lowerCAmelCase__	:	Union[str, Any]				)       ->							str:

											'''simple docstring'''






											return sigmoid_function(
											    np.dot(lowerCAmelCase__			,	lowerCAmelCase__				)				)  # predicting the value of probability from the logistic regression algorithm

				plt.figure(figsize=(10, 6))
				plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='b', label='0')
				plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='r', label='1')
				((__snake_case)       ,				(__snake_case))     :int			       =(x[:, 0].min(), x[:, 0].max())
				((__snake_case)       ,				(__snake_case))     :Tuple			       =(x[:, 1].min(), x[:, 1].max())
				((__snake_case)       ,				(__snake_case))     :Optional[Any]			       =np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
				__snake_case     :List[str]			       =np.c_[xxa.ravel(), xxa.ravel()]
				__snake_case     :List[Any]			       =predict_prob(grid).reshape(xxa.shape)
				plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='black')

				plt.legend()



				plt.show()







"""simple docstring"""



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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase	:	Optional[Any]				=      logging.get_logger(__name__)


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Any			,					snake_case       : Any=False			,					snake_case       : Any=False				)->		str:






     '''simple docstring'''



     UpperCAmelCase__						:    Dict							=							"backbone." if is_semantic else ""

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

     # projection layer + position embeddings
     rename_keys.extend(
         [
             (f'{prefix}cls_token', "beit.embeddings.cls_token"),
             (f'{prefix}patch_embed.proj.weight', "beit.embeddings.patch_embeddings.projection.weight"),
             (f'{prefix}patch_embed.proj.bias', "beit.embeddings.patch_embeddings.projection.bias"),
             (f'{prefix}pos_embed', "beit.embeddings.position_embeddings"),
         ]				)

     if has_lm_head:
          # mask token + layernorm
          rename_keys.extend(
              [
                  ("mask_token", "beit.embeddings.mask_token"),
                  ("norm.weight", "layernorm.weight"),
                  ("norm.bias", "layernorm.bias"),
              ]				)
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ("fc_norm.weight", "beit.pooler.layernorm.weight"),
                  ("fc_norm.bias", "beit.pooler.layernorm.bias"),
                  ("head.weight", "classifier.weight"),
                  ("head.bias", "classifier.bias"),
              ]				)

     return rename_keys


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : str			,					snake_case       : List[str]			,					snake_case       : List[Any]=False			,					snake_case       : int=False				)->		Any:






     '''simple docstring'''



     for i in range(config.num_hidden_layers				):
          UpperCAmelCase__						:    Dict							=							"backbone." if is_semantic else ""
          # queries, keys and values
          UpperCAmelCase__						:    List[str]							=							state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight'				)
          UpperCAmelCase__						:    int							=							state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias'				)
          UpperCAmelCase__						:    Optional[int]							=							state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias'				)

          UpperCAmelCase__						:    List[Any]							=							in_proj_weight[
              : config.hidden_size, :
          ]
          UpperCAmelCase__						:    Dict							=							q_bias
          UpperCAmelCase__						:    Optional[int]							=							in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          UpperCAmelCase__						:    Any							=							in_proj_weight[
              -config.hidden_size :, :
          ]
          UpperCAmelCase__						:    int							=							v_bias

          # gamma_1 and gamma_2
          # we call them lambda because otherwise they are renamed when using .from_pretrained
          UpperCAmelCase__						:    int							=							state_dict.pop(f'{prefix}blocks.{i}.gamma_1'				)
          UpperCAmelCase__						:    Tuple							=							state_dict.pop(f'{prefix}blocks.{i}.gamma_2'				)

          UpperCAmelCase__						:    Dict							=							gamma_a
          UpperCAmelCase__						:    Tuple							=							gamma_a


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : str			,					snake_case       : Dict			,					snake_case       : Tuple				)->		Any:






     '''simple docstring'''



     UpperCAmelCase__						:    str							=							dct.pop(snake_case				)
     UpperCAmelCase__						:    Tuple							=							val


def 	SCREAMING_SNAKE_CASE__       (  )->		Optional[Any]:






     '''simple docstring'''



     UpperCAmelCase__						:    Optional[int]							=							"http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCAmelCase__						:    List[str]							=							Image.open(requests.get(snake_case			,					stream=snake_case				).raw				)
     return im


@torch.no_grad()
def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Any			,					snake_case       : str			,					snake_case       : Optional[Any]=False				)->		str:






     '''simple docstring'''



     UpperCAmelCase__						:    Optional[Any]							=							False if "rvlcdip" in checkpoint_url else True
     UpperCAmelCase__						:    int							=							BeitConfig(use_absolute_position_embeddings=snake_case			,					use_mask_token=snake_case				)

     # size of the architecture
     if "large" in checkpoint_url or "dit-l" in checkpoint_url:
          UpperCAmelCase__						:    Optional[int]							=							1024
          UpperCAmelCase__						:    Optional[Any]							=							4096
          UpperCAmelCase__						:    List[str]							=							24
          UpperCAmelCase__						:    List[str]							=							16

     # labels
     if "rvlcdip" in checkpoint_url:
          UpperCAmelCase__						:    List[Any]							=							16
          UpperCAmelCase__						:    Optional[int]							=							"huggingface/label-files"
          UpperCAmelCase__						:    List[Any]							=							"rvlcdip-id2label.json"
          UpperCAmelCase__						:    List[str]							=							json.load(open(hf_hub_download(snake_case			,					snake_case			,					repo_type="dataset"				)			,					"r"				)				)
          UpperCAmelCase__						:    Dict							=							{int(snake_case				): v for k, v in idalabel.items()}
          UpperCAmelCase__						:    List[Any]							=							idalabel
          UpperCAmelCase__						:    Optional[Any]							=							{v: k for k, v in idalabel.items()}

     # load state_dict of original model, remove and rename some keys
     UpperCAmelCase__						:    List[Any]							=							torch.hub.load_state_dict_from_url(snake_case			,					map_location="cpu"				)["model"]

     UpperCAmelCase__						:    str							=							create_rename_keys(snake_case			,					has_lm_head=snake_case				)
     for src, dest in rename_keys:
          rename_key(snake_case			,					snake_case			,					snake_case				)
     read_in_q_k_v(snake_case			,					snake_case			,					has_lm_head=snake_case				)

     # load HuggingFace model
     UpperCAmelCase__						:    int							=							BeitForMaskedImageModeling(snake_case				) if has_lm_head else BeitForImageClassification(snake_case				)
     model.eval()
     model.load_state_dict(snake_case				)

     # Check outputs on an image
     UpperCAmelCase__						:    List[Any]							=							BeitImageProcessor(
         size=config.image_size			,					resample=PILImageResampling.BILINEAR			,					do_center_crop=snake_case				)
     UpperCAmelCase__						:    Any							=							prepare_img()

     UpperCAmelCase__						:    Any							=							image_processor(images=snake_case			,					return_tensors="pt"				)
     UpperCAmelCase__						:    List[str]							=							encoding["pixel_values"]

     UpperCAmelCase__						:    Optional[Any]							=							model(snake_case				)
     UpperCAmelCase__						:    Optional[Any]							=							outputs.logits

     # verify logits
     UpperCAmelCase__						:    Union[str, Any]							=							[1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192]
     assert logits.shape == torch.Size(snake_case				), "Shape of logits not as expected"

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

     if push_to_hub:
          if has_lm_head:
               UpperCAmelCase__						:    Any							=							"dit-base" if "base" in checkpoint_url else "dit-large"
          else:
               UpperCAmelCase__						:    int							=							"dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
          image_processor.push_to_hub(
              repo_path_or_name=Path(snake_case			,					snake_case				)			,					organization="nielsr"			,					commit_message="Add image processor"			,					use_temp_dir=snake_case			,					)
          model.push_to_hub(
              repo_path_or_name=Path(snake_case			,					snake_case				)			,					organization="nielsr"			,					commit_message="Add model"			,					use_temp_dir=snake_case			,					)


if __name__ == "__main__":
 _lowerCAmelCase	:	Tuple				=      argparse.ArgumentParser()

 parser.add_argument(
     """--checkpoint_url""",
     default="""https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth""",
     type=str,
     help="""URL to the original PyTorch checkpoint (.pth file).""",
 )
 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""",
 )
 _lowerCAmelCase	:	str				=      parser.parse_args()
 convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


