
"""simple docstring"""



import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[int]		)     ->					str:
  # vision encoder
  if "img_encoder.pos_embed" in name:
    _snake_case					     =       name.replace('''img_encoder.pos_embed''' ,					'''vision_model.embeddings.position_embeddings'''		)
  if "img_encoder.patch_embed.proj" in name:
    _snake_case					     =       name.replace('''img_encoder.patch_embed.proj''' ,					'''vision_model.embeddings.patch_embeddings.projection'''		)
  if "img_encoder.patch_embed.norm" in name:
    _snake_case					     =       name.replace('''img_encoder.patch_embed.norm''' ,					'''vision_model.embeddings.layernorm'''		)
  if "img_encoder.layers" in name:
    _snake_case					     =       name.replace('''img_encoder.layers''' ,					'''vision_model.encoder.stages'''		)
  if "blocks" in name and "res" not in name:
    _snake_case					     =       name.replace('''blocks''' ,					'''layers'''		)
  if "attn" in name and "pre_assign" not in name:
    _snake_case					     =       name.replace('''attn''' ,					'''self_attn'''		)
  if "proj" in name and "self_attn" in name and "text" not in name:
    _snake_case					     =       name.replace('''proj''' ,					'''out_proj'''		)
  if "pre_assign_attn.attn.proj" in name:
    _snake_case					     =       name.replace('''pre_assign_attn.attn.proj''' ,					'''pre_assign_attn.attn.out_proj'''		)
  if "norm1" in name:
    _snake_case					     =       name.replace('''norm1''' ,					'''layer_norm1'''		)
  if "norm2" in name and "pre_assign" not in name:
    _snake_case					     =       name.replace('''norm2''' ,					'''layer_norm2'''		)
  if "img_encoder.norm" in name:
    _snake_case					     =       name.replace('''img_encoder.norm''' ,					'''vision_model.layernorm'''		)
  # text encoder
  if "text_encoder.token_embedding" in name:
    _snake_case					     =       name.replace('''text_encoder.token_embedding''' ,					'''text_model.embeddings.token_embedding'''		)
  if "text_encoder.positional_embedding" in name:
    _snake_case					     =       name.replace('''text_encoder.positional_embedding''' ,					'''text_model.embeddings.position_embedding.weight'''		)
  if "text_encoder.transformer.resblocks." in name:
    _snake_case					     =       name.replace('''text_encoder.transformer.resblocks.''' ,					'''text_model.encoder.layers.'''		)
  if "ln_1" in name:
    _snake_case					     =       name.replace('''ln_1''' ,					'''layer_norm1'''		)
  if "ln_2" in name:
    _snake_case					     =       name.replace('''ln_2''' ,					'''layer_norm2'''		)
  if "c_fc" in name:
    _snake_case					     =       name.replace('''c_fc''' ,					'''fc1'''		)
  if "c_proj" in name:
    _snake_case					     =       name.replace('''c_proj''' ,					'''fc2'''		)
  if "text_encoder" in name:
    _snake_case					     =       name.replace('''text_encoder''' ,					'''text_model'''		)
  if "ln_final" in name:
    _snake_case					     =       name.replace('''ln_final''' ,					'''final_layer_norm'''		)
  # projection layers
  if "img_projector.linear_hidden." in name:
    _snake_case					     =       name.replace('''img_projector.linear_hidden.''' ,					'''visual_projection.'''		)
  if "img_projector.linear_out." in name:
    _snake_case					     =       name.replace('''img_projector.linear_out.''' ,					'''visual_projection.3.'''		)
  if "text_projector.linear_hidden" in name:
    _snake_case					     =       name.replace('''text_projector.linear_hidden''' ,					'''text_projection'''		)
  if "text_projector.linear_out" in name:
    _snake_case					     =       name.replace('''text_projector.linear_out''' ,					'''text_projection.3'''		)

  return name
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       Union[str, Any]		)     ->					Tuple:
  for key in orig_state_dict.copy().keys():
    _snake_case					     =       orig_state_dict.pop(__lowerCamelCase		)

    if "qkv" in key:
      # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
      # we need to split them up into separate matrices/vectors
      _snake_case					     =       key.split('''.'''		)
      _snake_case     ,					_snake_case					     =       int(key_split[2]		), int(key_split[4]		)
      _snake_case					     =       config.vision_config.hidden_size
      if "weight" in key:
        _snake_case					     =       val[:dim, :]
        _snake_case					     =       val[dim : dim * 2, :]
        _snake_case					     =       val[-dim:, :]
      else:
        _snake_case					     =       val[:dim]
        _snake_case					     =       val[dim : dim * 2]
        _snake_case					     =       val[-dim:]
    elif "in_proj" in key:
      # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
      # we need to split them up into separate matrices/vectors
      _snake_case					     =       key.split('''.'''		)
      _snake_case					     =       int(key_split[3]		)
      _snake_case					     =       config.text_config.hidden_size
      if "weight" in key:
        _snake_case					     =       val[:dim, :]
        _snake_case					     =       val[
            dim : dim * 2, :
        ]
        _snake_case					     =       val[-dim:, :]
      else:
        _snake_case					     =       val[:dim]
        _snake_case					     =       val[dim : dim * 2]
        _snake_case					     =       val[-dim:]
    else:
      _snake_case					     =       rename_key(__lowerCamelCase		)
      # squeeze if necessary
      if (
          "text_projection.0" in new_name
          or "text_projection.3" in new_name
          or "visual_projection.0" in new_name
          or "visual_projection.3" in new_name
      ):
        _snake_case					     =       val.squeeze_()
      else:
        _snake_case					     =       val

  return orig_state_dict
def 					_UpperCAmelCase      ( )     ->					List[str]:
  _snake_case					     =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _snake_case					     =       Image.open(requests.get(__lowerCamelCase ,					stream=__lowerCamelCase		).raw		)
  return im
@torch.no_grad()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       List[Any] ,					__lowerCamelCase   :       Optional[Any]="groupvit-gcc-yfcc" ,					__lowerCamelCase   :       str=False		)     ->					int:
  _snake_case					     =       GroupViTConfig()
  _snake_case					     =       GroupViTModel(__lowerCamelCase		).eval()

  _snake_case					     =       torch.load(__lowerCamelCase ,					map_location='''cpu'''		)['''model''']
  _snake_case					     =       convert_state_dict(__lowerCamelCase ,					__lowerCamelCase		)
  _snake_case     ,					_snake_case					     =       model.load_state_dict(__lowerCamelCase ,					strict=__lowerCamelCase		)
  assert missing_keys == ["text_model.embeddings.position_ids"]
  assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__lowerCamelCase		) == 0)

  # verify result
  _snake_case					     =       CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''		)
  _snake_case					     =       prepare_img()
  _snake_case					     =       processor(text=['''a photo of a cat''', '''a photo of a dog'''] ,					images=__lowerCamelCase ,					padding=__lowerCamelCase ,					return_tensors='''pt'''		)

  with torch.no_grad():
    _snake_case					     =       model(**__lowerCamelCase		)

  if model_name == "groupvit-gcc-yfcc":
    _snake_case					     =       torch.tensor([[13.3_523, 6.3_629]]		)
  elif model_name == "groupvit-gcc-redcaps":
    _snake_case					     =       torch.tensor([[16.1_873, 8.6_230]]		)
  else:
    raise ValueError(f'''Model name {model_name} not supported.'''		)
  assert torch.allclose(outputs.logits_per_image ,					__lowerCamelCase ,					atol=1E-3		)

  processor.save_pretrained(__lowerCamelCase		)
  model.save_pretrained(__lowerCamelCase		)
  print('''Successfully saved processor and model to''' ,					__lowerCamelCase		)

  if push_to_hub:
    print('''Pushing to the hub...'''		)
    processor.push_to_hub(__lowerCamelCase ,					organization='''nielsr'''		)
    model.push_to_hub(__lowerCamelCase ,					organization='''nielsr'''		)


if __name__ == "__main__":
 UpperCAmelCase__          = argparse.ArgumentParser()
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, help='Path to dump the processor and PyTorch model.'
 )
 parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to GroupViT checkpoint')
 parser.add_argument(
     '--model_name',
     default='groupvit-gccy-fcc',
     type=str,
     help='Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'',
 )
 parser.add_argument(
     '--push_to_hub',
     action='store_true',
     help='Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.',
 )
 UpperCAmelCase__          = parser.parse_args()

 convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)





"""simple docstring"""



# 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 					_UpperCAmelCase      ( __lowerCamelCase   :       str		)     ->					List[Any]:
  return 1 / (1 + np.exp(-z		))
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[Any] ,					__lowerCamelCase   :       List[str]		)     ->					Optional[Any]:
  return (-y * np.log(__lowerCamelCase		) - (1 - y) * np.log(1 - h		)).mean()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Dict		)     ->					List[str]:
  _snake_case					     =       np.dot(__lowerCamelCase ,					__lowerCamelCase		)
  return np.sum(y * scores - np.log(1 + np.exp(__lowerCamelCase		)		)		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       str=7_00_00		)     ->					Optional[Any]:
  _snake_case					     =       np.zeros(x.shape[1]		)

  for iterations in range(__lowerCamelCase		):
    _snake_case					     =       np.dot(__lowerCamelCase ,					__lowerCamelCase		)
    _snake_case					     =       sigmoid_function(__lowerCamelCase		)
    _snake_case					     =       np.dot(x.T ,					h - y		) / y.size
    _snake_case					     =       theta - alpha * gradient  # updating the weights
    _snake_case					     =       np.dot(__lowerCamelCase ,					__lowerCamelCase		)
    _snake_case					     =       sigmoid_function(__lowerCamelCase		)
    _snake_case					     =       cost_function(__lowerCamelCase ,					__lowerCamelCase		)
    if iterations % 1_00 == 0:
      print(f'''loss: {j} \t'''		)  # printing the loss after every 100 iterations
  return theta


# In[68]:

if __name__ == "__main__":
 UpperCAmelCase__          = datasets.load_iris()
 UpperCAmelCase__          = iris.data[:, :2]
 UpperCAmelCase__          = (iris.target != 0) * 1

 UpperCAmelCase__          = 0.1
 UpperCAmelCase__          = logistic_reg(alpha, x, y, max_iterations=70000)
 print('theta: ', theta)  # printing the theta i.e our weights vector
 def 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple		)     ->					Union[str, Any]:
   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')
 ((UpperCAmelCase__)      ,	(UpperCAmelCase__))          = (x[:, 0].min(), x[:, 0].max())
 ((UpperCAmelCase__)      ,	(UpperCAmelCase__))          = (x[:, 1].min(), x[:, 1].max())
 ((UpperCAmelCase__)      ,	(UpperCAmelCase__))          = np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
 UpperCAmelCase__          = np.c_[xxa.ravel(), xxa.ravel()]
 UpperCAmelCase__          = predict_prob(grid).reshape(xxa.shape)
 plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='black')

 plt.legend()

 plt.show()



