


'''simple docstring'''


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()
__magic_name__			=      logging.get_logger(__name__)




def      lowerCamelCase					(      lowerCamelCase  :      Optional[int]							,						lowerCamelCase  :      int							,						lowerCamelCase  :      Tuple							,						lowerCamelCase  :      List[str]):
      A_						:  List[Any]          =				original_name.split(""".""")[0]
      A_						:  str          =				key.split(""".""")
      A_						:  List[Any]          =				int(key_list[key_list.index(lowerCamelCase) - 2])
      A_						:  List[Any]          =				int(key_list[key_list.index(lowerCamelCase) - 1])
      A_						:  List[Any]          =				orig_block_num - offset

      A_						:  Optional[Any]          =				key.replace(F'{orig_block_num}.{layer_num}.{original_name}'							,						F'block.{new_block_num}.{layer_num}.{new_name}')
      return key




def      lowerCamelCase					(      lowerCamelCase  :      Any):
      A_						:  str          =				OrderedDict()
      A_						:  Tuple          =				0, 0
      for key, value in state_dict.items():
            if key.startswith("""network"""):
                  A_						:  List[str]          =				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
                  A_						:  Optional[int]          =				key[: key.find("""proj""")]
                  A_						:  List[str]          =				key.replace(lowerCamelCase							,						F'patch_embeddings.{total_embed_found}.')
                  A_						:  Tuple          =				key.replace("""proj"""							,						"""projection""")
                  if key.endswith("""bias"""):
                        total_embed_found += 1
            if "patch_embeddings" in key:
                  A_						:  Tuple          =				"""poolformer.encoder.""" + key
            if "mlp.fc1" in key:
                  A_						:  Any          =				replace_key_with_offset(lowerCamelCase							,						lowerCamelCase							,						"""mlp.fc1"""							,						"""output.conv1""")
            if "mlp.fc2" in key:
                  A_						:  str          =				replace_key_with_offset(lowerCamelCase							,						lowerCamelCase							,						"""mlp.fc2"""							,						"""output.conv2""")
            if "norm1" in key:
                  A_						:  Dict          =				replace_key_with_offset(lowerCamelCase							,						lowerCamelCase							,						"""norm1"""							,						"""before_norm""")
            if "norm2" in key:
                  A_						:  Any          =				replace_key_with_offset(lowerCamelCase							,						lowerCamelCase							,						"""norm2"""							,						"""after_norm""")
            if "layer_scale_1" in key:
                  A_						:  Any          =				replace_key_with_offset(lowerCamelCase							,						lowerCamelCase							,						"""layer_scale_1"""							,						"""layer_scale_1""")
            if "layer_scale_2" in key:
                  A_						:  List[Any]          =				replace_key_with_offset(lowerCamelCase							,						lowerCamelCase							,						"""layer_scale_2"""							,						"""layer_scale_2""")
            if "head" in key:
                  A_						:  int          =				key.replace("""head"""							,						"""classifier""")
            A_						:  Optional[int]          =				value
      return new_state_dict




def      lowerCamelCase					(      ):
      A_						:  str          =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      A_						:  Dict          =				Image.open(requests.get(lowerCamelCase							,						stream=lowerCamelCase).raw)

      return image




@torch.no_grad()
def      lowerCamelCase					(      lowerCamelCase  :      int							,						lowerCamelCase  :      Any							,						lowerCamelCase  :      Dict):
      A_						:  List[str]          =				PoolFormerConfig()

      # set attributes based on model_name
      A_						:  Tuple          =				"""huggingface/label-files"""
      A_						:  List[Any]          =				model_name[-3:]
      A_						:  int          =				1000
      A_						:  Tuple          =				"""imagenet-1k-id2label.json"""
      A_						:  str          =				(1, 1000)

      # set config attributes
      A_						:  Tuple          =				json.load(open(hf_hub_download(lowerCamelCase							,						lowerCamelCase							,						repo_type="""dataset""")							,						"""r"""))
      A_						:  List[str]          =				{int(lowerCamelCase): v for k, v in idalabel.items()}
      A_						:  Tuple          =				idalabel
      A_						:  Union[str, Any]          =				{v: k for k, v in idalabel.items()}
      if size == "s12":
            A_						:  List[str]          =				[2, 2, 6, 2]
            A_						:  str          =				[64, 128, 320, 512]
            A_						:  Optional[Any]          =				4.0
            A_						:  Tuple          =				0.9
      elif size == "s24":
            A_						:  str          =				[4, 4, 12, 4]
            A_						:  Any          =				[64, 128, 320, 512]
            A_						:  Any          =				4.0
            A_						:  Union[str, Any]          =				0.9
      elif size == "s36":
            A_						:  Optional[int]          =				[6, 6, 18, 6]
            A_						:  List[Any]          =				[64, 128, 320, 512]
            A_						:  Optional[Any]          =				4.0
            A_						:  str          =				1E-6
            A_						:  Optional[int]          =				0.9
      elif size == "m36":
            A_						:  Optional[int]          =				[6, 6, 18, 6]
            A_						:  Optional[int]          =				[96, 192, 384, 768]
            A_						:  List[Any]          =				4.0
            A_						:  List[str]          =				1E-6
            A_						:  List[Any]          =				0.95
      elif size == "m48":
            A_						:  Optional[int]          =				[8, 8, 24, 8]
            A_						:  Optional[int]          =				[96, 192, 384, 768]
            A_						:  Dict          =				4.0
            A_						:  List[str]          =				1E-6
            A_						:  Tuple          =				0.95
      else:
            raise ValueError(F'Size {size} not supported')

      # load image processor
      A_						:  str          =				PoolFormerImageProcessor(crop_pct=lowerCamelCase)

      # Prepare image
      A_						:  Union[str, Any]          =				prepare_img()
      A_						:  Tuple          =				image_processor(images=lowerCamelCase							,						return_tensors="""pt""").pixel_values

      logger.info(F'Converting model {model_name}...')

      # load original state dict
      A_						:  Optional[int]          =				torch.load(lowerCamelCase							,						map_location=torch.device("""cpu"""))

      # rename keys
      A_						:  Union[str, Any]          =				rename_keys(lowerCamelCase)

      # create HuggingFace model and load state dict
      A_						:  Optional[int]          =				PoolFormerForImageClassification(lowerCamelCase)
      model.load_state_dict(lowerCamelCase)
      model.eval()

      # Define image processor
      A_						:  int          =				PoolFormerImageProcessor(crop_pct=lowerCamelCase)
      A_						:  List[Any]          =				image_processor(images=prepare_img()							,						return_tensors="""pt""").pixel_values

      # forward pass
      A_						:  str          =				model(lowerCamelCase)
      A_						:  Optional[int]          =				outputs.logits

      # define expected logit slices for different models
      if size == "s12":
            A_						:  List[str]          =				torch.tensor([-0.3045, -0.6758, -0.4869])
      elif size == "s24":
            A_						:  Optional[int]          =				torch.tensor([0.4402, -0.1374, -0.8045])
      elif size == "s36":
            A_						:  Tuple          =				torch.tensor([-0.6080, -0.5133, -0.5898])
      elif size == "m36":
            A_						:  str          =				torch.tensor([0.3952, 0.2263, -1.2668])
      elif size == "m48":
            A_						:  Union[str, Any]          =				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]							,						lowerCamelCase							,						atol=1E-2)

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


if __name__ == "__main__":
     __magic_name__			=      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.'
     )
     __magic_name__			=      parser.parse_args()
     convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)




'''simple docstring'''


from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403




def      lowerCamelCase					(      lowerCamelCase  :      str							,						lowerCamelCase  :      complex							,						lowerCamelCase  :      str = "x"							,						lowerCamelCase  :      float = 10**-10							,						lowerCamelCase  :      int = 1							,						):
      A_						:  int          =				symbols(lowerCamelCase)
      A_						:  List[Any]          =				lambdify(lowerCamelCase							,						lowerCamelCase)
      A_						:  List[str]          =				lambdify(lowerCamelCase							,						diff(lowerCamelCase							,						lowerCamelCase))

      A_						:  str          =				starting_point

      while True:
            if diff_function(lowerCamelCase) != 0:
                  A_						:  int          =				prev_guess - multiplicity * func(lowerCamelCase) / diff_function(
                      lowerCamelCase)
            else:
                  raise ZeroDivisionError("""Could not find root""") from None

            # Precision is checked by comparing the difference of consecutive guesses
            if abs(next_guess - prev_guess) < precision:
                  return next_guess

            A_						:  Union[str, Any]          =				next_guess


# Let's Execute
if __name__ == "__main__":
     # Find root of trigonometric function
     # Find value of pi
     print(f"""The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}""")

     # Find root of polynomial
     # Find fourth Root of 5
     print(f"""The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5j)}""")

     # Find value of e
     print(
         'The root of log(y) - 1 = 0 is ',
         f"""{newton_raphson('log(y) - 1', 2, variable='y')}""",
     )

     # Exponential Roots
     print(
         'The root of exp(x) - 1 = 0 is',
         f"""{newton_raphson('exp(x) - 1', 10, precision=0.0_0_5)}""",
     )

     # Find root of cos(x)
     print(f"""The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}""")
