import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


__lowerCamelCase			: Tuple							   =		logging.get_logger(__name__)



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







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      r"""\w+[.]\d+"""
  SCREAMING_SNAKE_CASE__										=      re.findall(__UpperCamelCase ,     __UpperCamelCase	)
  for pat in pats:
    SCREAMING_SNAKE_CASE__										=      key.replace(__UpperCamelCase ,     """_""".join(pat.split("""."""	)	)	)
  return key



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







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      pt_tuple_key[:-1] + ("""scale""",)
  if (
      any("""norm""" in str_ for str_ in pt_tuple_key	)
      and (pt_tuple_key[-1] == "bias")
      and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
      and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
  ):
    SCREAMING_SNAKE_CASE__										=      pt_tuple_key[:-1] + ("""scale""",)
    return renamed_pt_tuple_key, pt_tensor
  elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
    SCREAMING_SNAKE_CASE__										=      pt_tuple_key[:-1] + ("""scale""",)
    return renamed_pt_tuple_key, pt_tensor

  # embedding
  if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
    SCREAMING_SNAKE_CASE__										=      pt_tuple_key[:-1] + ("""embedding""",)
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  SCREAMING_SNAKE_CASE__										=      pt_tuple_key[:-1] + ("""kernel""",)
  if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
    SCREAMING_SNAKE_CASE__										=      pt_tensor.transpose(2 ,     3 ,     1 ,     0	)
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  SCREAMING_SNAKE_CASE__										=      pt_tuple_key[:-1] + ("""kernel""",)
  if pt_tuple_key[-1] == "weight":
    SCREAMING_SNAKE_CASE__										=      pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm weight
  SCREAMING_SNAKE_CASE__										=      pt_tuple_key[:-1] + ("""weight""",)
  if pt_tuple_key[-1] == "gamma":
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm bias
  SCREAMING_SNAKE_CASE__										=      pt_tuple_key[:-1] + ("""bias""",)
  if pt_tuple_key[-1] == "beta":
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor






def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       List[Any] ,     __UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       Optional[int]=42	)							->					Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      {k: v.numpy() for k, v in pt_state_dict.items()}

  # Step 2: Since the model is stateless, get random Flax params
  SCREAMING_SNAKE_CASE__										=      flax_model.init_weights(PRNGKey(__UpperCamelCase	)	)

  SCREAMING_SNAKE_CASE__										=      flatten_dict(__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      {}

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    SCREAMING_SNAKE_CASE__										=      rename_key(__UpperCamelCase	)
    SCREAMING_SNAKE_CASE__										=      tuple(renamed_pt_key.split("""."""	)	)

    # Correctly rename weight parameters
    SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      rename_key_and_reshape_tensor(__UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase	)

    if flax_key in random_flax_state_dict:
      if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
        raise ValueError(
            f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
            f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""	)

        # also add unexpected weight so that warning is thrown
    SCREAMING_SNAKE_CASE__										=      jnp.asarray(__UpperCamelCase	)

  return unflatten_dict(__UpperCamelCase	)

from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
   __lowerCamelCase			: str							   =		input('''Enter image url: ''').strip()
   print(F"""Downloading image from {url} ...""")
   __lowerCamelCase			: Any							   =		BeautifulSoup(requests.get(url).content, '''html.parser''')
   # The image URL is in the content field of the first meta tag with property og:image
   __lowerCamelCase			: List[Any]							   =		soup.find('''meta''', {'''property''': '''og:image'''})['''content''']
   __lowerCamelCase			: Tuple							   =		requests.get(image_url).content
   __lowerCamelCase			: Union[str, Any]							   =		F"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
   with open(file_name, '''wb''') as fp:
      fp.write(image_data)
   print(F"""Done. Image saved to disk as {file_name}.""")
