import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Any				,  __UpperCamelCase					: List[str]				,  __UpperCamelCase					: Tuple				,  __UpperCamelCase					: List[Any]			)							->      Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   original_name.split("""."""			)[0]
  SCREAMING_SNAKE_CASE__    							=   key.split("""."""			)
  SCREAMING_SNAKE_CASE__    							=   int(key_list[key_list.index(__UpperCamelCase			) - 2]			)
  SCREAMING_SNAKE_CASE__    							=   int(key_list[key_list.index(__UpperCamelCase			) - 1]			)
  SCREAMING_SNAKE_CASE__    							=   orig_block_num - offset

  SCREAMING_SNAKE_CASE__    							=   key.replace(f"""{orig_block_num}.{layer_num}.{original_name}"""				,  f"""block.{new_block_num}.{layer_num}.{new_name}"""			)
  return key



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]			)							->      str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   OrderedDict()
  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   0, 0
  for key, value in state_dict.items():
    if key.startswith("""network"""			):
      SCREAMING_SNAKE_CASE__    							=   key.replace("""network"""				,  """poolformer.encoder"""			)
    if "proj" in key:
      # Works for the first embedding as well as the internal embedding layers
      if key.endswith("""bias"""			) and "patch_embed" not in key:
        patch_emb_offset += 1
      SCREAMING_SNAKE_CASE__    							=   key[: key.find("""proj"""			)]
      SCREAMING_SNAKE_CASE__    							=   key.replace(__UpperCamelCase				,  f"""patch_embeddings.{total_embed_found}."""			)
      SCREAMING_SNAKE_CASE__    							=   key.replace("""proj"""				,  """projection"""			)
      if key.endswith("""bias"""			):
        total_embed_found += 1
    if "patch_embeddings" in key:
      SCREAMING_SNAKE_CASE__    							=   """poolformer.encoder.""" + key
    if "mlp.fc1" in key:
      SCREAMING_SNAKE_CASE__    							=   replace_key_with_offset(__UpperCamelCase				,  __UpperCamelCase				,  """mlp.fc1"""				,  """output.conv1"""			)
    if "mlp.fc2" in key:
      SCREAMING_SNAKE_CASE__    							=   replace_key_with_offset(__UpperCamelCase				,  __UpperCamelCase				,  """mlp.fc2"""				,  """output.conv2"""			)
    if "norm1" in key:
      SCREAMING_SNAKE_CASE__    							=   replace_key_with_offset(__UpperCamelCase				,  __UpperCamelCase				,  """norm1"""				,  """before_norm"""			)
    if "norm2" in key:
      SCREAMING_SNAKE_CASE__    							=   replace_key_with_offset(__UpperCamelCase				,  __UpperCamelCase				,  """norm2"""				,  """after_norm"""			)
    if "layer_scale_1" in key:
      SCREAMING_SNAKE_CASE__    							=   replace_key_with_offset(__UpperCamelCase				,  __UpperCamelCase				,  """layer_scale_1"""				,  """layer_scale_1"""			)
    if "layer_scale_2" in key:
      SCREAMING_SNAKE_CASE__    							=   replace_key_with_offset(__UpperCamelCase				,  __UpperCamelCase				,  """layer_scale_2"""				,  """layer_scale_2"""			)
    if "head" in key:
      SCREAMING_SNAKE_CASE__    							=   key.replace("""head"""				,  """classifier"""			)
    SCREAMING_SNAKE_CASE__    							=   value
  return new_state_dict



def 			__SCREAMING_SNAKE_CASE						(      )							->      str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
  SCREAMING_SNAKE_CASE__    							=   Image.open(requests.get(__UpperCamelCase				,  stream=__UpperCamelCase			).raw			)

  return image



@torch.no_grad()
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]				,  __UpperCamelCase					: Any				,  __UpperCamelCase					: Tuple			)							->      Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   PoolFormerConfig()

  # set attributes based on model_name
  SCREAMING_SNAKE_CASE__    							=   """huggingface/label-files"""
  SCREAMING_SNAKE_CASE__    							=   model_name[-3:]
  SCREAMING_SNAKE_CASE__    							=   10_00
  SCREAMING_SNAKE_CASE__    							=   """imagenet-1k-id2label.json"""
  SCREAMING_SNAKE_CASE__    							=   (1, 10_00)

  # set config attributes
  SCREAMING_SNAKE_CASE__    							=   json.load(open(hf_hub_download(__UpperCamelCase				,  __UpperCamelCase				,  repo_type="""dataset"""			)				,  """r"""			)			)
  SCREAMING_SNAKE_CASE__    							=   {int(__UpperCamelCase			): v for k, v in idalabel.items()}
  SCREAMING_SNAKE_CASE__    							=   idalabel
  SCREAMING_SNAKE_CASE__    							=   {v: k for k, v in idalabel.items()}
  if size == "s12":
    SCREAMING_SNAKE_CASE__    							=   [2, 2, 6, 2]
    SCREAMING_SNAKE_CASE__    							=   [64, 1_28, 3_20, 5_12]
    SCREAMING_SNAKE_CASE__    							=   4.0
    SCREAMING_SNAKE_CASE__    							=   0.9
  elif size == "s24":
    SCREAMING_SNAKE_CASE__    							=   [4, 4, 12, 4]
    SCREAMING_SNAKE_CASE__    							=   [64, 1_28, 3_20, 5_12]
    SCREAMING_SNAKE_CASE__    							=   4.0
    SCREAMING_SNAKE_CASE__    							=   0.9
  elif size == "s36":
    SCREAMING_SNAKE_CASE__    							=   [6, 6, 18, 6]
    SCREAMING_SNAKE_CASE__    							=   [64, 1_28, 3_20, 5_12]
    SCREAMING_SNAKE_CASE__    							=   4.0
    SCREAMING_SNAKE_CASE__    							=   1E-6
    SCREAMING_SNAKE_CASE__    							=   0.9
  elif size == "m36":
    SCREAMING_SNAKE_CASE__    							=   [6, 6, 18, 6]
    SCREAMING_SNAKE_CASE__    							=   [96, 1_92, 3_84, 7_68]
    SCREAMING_SNAKE_CASE__    							=   4.0
    SCREAMING_SNAKE_CASE__    							=   1E-6
    SCREAMING_SNAKE_CASE__    							=   0.95
  elif size == "m48":
    SCREAMING_SNAKE_CASE__    							=   [8, 8, 24, 8]
    SCREAMING_SNAKE_CASE__    							=   [96, 1_92, 3_84, 7_68]
    SCREAMING_SNAKE_CASE__    							=   4.0
    SCREAMING_SNAKE_CASE__    							=   1E-6
    SCREAMING_SNAKE_CASE__    							=   0.95
  else:
    raise ValueError(f"""Size {size} not supported"""			)

  # load image processor
  SCREAMING_SNAKE_CASE__    							=   PoolFormerImageProcessor(crop_pct=__UpperCamelCase			)

  # Prepare image
  SCREAMING_SNAKE_CASE__    							=   prepare_img()
  SCREAMING_SNAKE_CASE__    							=   image_processor(images=__UpperCamelCase				,  return_tensors="""pt"""			).pixel_values

  logger.info(f"""Converting model {model_name}..."""			)

  # load original state dict
  SCREAMING_SNAKE_CASE__    							=   torch.load(__UpperCamelCase				,  map_location=torch.device("""cpu"""			)			)

  # rename keys
  SCREAMING_SNAKE_CASE__    							=   rename_keys(__UpperCamelCase			)

  # create HuggingFace model and load state dict
  SCREAMING_SNAKE_CASE__    							=   PoolFormerForImageClassification(__UpperCamelCase			)
  model.load_state_dict(__UpperCamelCase			)
  model.eval()

  # Define image processor
  SCREAMING_SNAKE_CASE__    							=   PoolFormerImageProcessor(crop_pct=__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   image_processor(images=prepare_img()				,  return_tensors="""pt"""			).pixel_values

  # forward pass
  SCREAMING_SNAKE_CASE__    							=   model(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   outputs.logits

  # define expected logit slices for different models
  if size == "s12":
    SCREAMING_SNAKE_CASE__    							=   torch.tensor([-0.3045, -0.6758, -0.4869]			)
  elif size == "s24":
    SCREAMING_SNAKE_CASE__    							=   torch.tensor([0.4402, -0.1374, -0.8045]			)
  elif size == "s36":
    SCREAMING_SNAKE_CASE__    							=   torch.tensor([-0.6080, -0.5133, -0.5898]			)
  elif size == "m36":
    SCREAMING_SNAKE_CASE__    							=   torch.tensor([0.3952, 0.2263, -1.2668]			)
  elif size == "m48":
    SCREAMING_SNAKE_CASE__    							=   torch.tensor([0.1167, -0.0656, -0.3423]			)
  else:
    raise ValueError(f"""Size {size} not supported"""			)

  # verify logits
  assert logits.shape == expected_shape
  assert torch.allclose(logits[0, :3]				,  __UpperCamelCase				,  atol=1E-2			)

  # finally, save model and image processor
  logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""			)
  Path(__UpperCamelCase			).mkdir(exist_ok=__UpperCamelCase			)
  model.save_pretrained(__UpperCamelCase			)
  print(f"""Saving image processor to {pytorch_dump_folder_path}"""			)
  image_processor.save_pretrained(__UpperCamelCase			)


if __name__ == "__main__":
   __lowerCamelCase    :				int							     =	argparse.ArgumentParser()

   parser.add_argument(
       '''--model_name''',
       default='''poolformer_s12''',
       type=str,
       help='''Name of the model you\'d like to convert.''',
   )
   parser.add_argument(
       '''--checkpoint_path''', default=None, type=str, help='''Path 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.'''
   )
   __lowerCamelCase    :				List[Any]							     =	parser.parse_args()
   convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)

# 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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple			)							->      int:







  """simple docstring"""





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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Union[str, Any]			)							->      Optional[Any]:







  """simple docstring"""





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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Union[str, Any]			)							->      int:







  """simple docstring"""





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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Any=7_00_00			)							->      Optional[int]:







  """simple docstring"""





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

  for iterations in range(__UpperCamelCase			):
    SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   sigmoid_function(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   np.dot(x.T				,  h - y			) / y.size
    SCREAMING_SNAKE_CASE__    							=   theta - alpha * gradient  # updating the weights
    SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   sigmoid_function(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   cost_function(__UpperCamelCase				,  __UpperCamelCase			)
    if iterations % 1_00 == 0:
      print(f"""loss: {j} \t"""			)  # printing the loss after every 100 iterations
  return theta


# In[68]:

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

   __lowerCamelCase    :				List[str]							     =	0.1
   __lowerCamelCase    :				str							     =	logistic_reg(alpha, x, y, max_iterations=7_0000)
   print('''theta: ''', theta)  # printing the theta i.e our weights vector



   def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]			)							->      Tuple:







     """simple docstring"""





     return sigmoid_function(
         np.dot(__UpperCamelCase				,  __UpperCamelCase			)			)  # 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''')
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				int							     =	(x[:, 0].min(), x[:, 0].max())
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				List[Any]							     =	(x[:, 1].min(), x[:, 1].max())
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				List[Any]							     =	np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
   __lowerCamelCase    :				Optional[int]							     =	np.c_[xxa.ravel(), xxa.ravel()]
   __lowerCamelCase    :				Optional[int]							     =	predict_prob(grid).reshape(xxa.shape)
   plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')

   plt.legend()






   plt.show()
