





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


_a 						=      logging.get_logger(__name__)





def lowerCAmelCase__(__snake_case							)				->      Dict:







	'''simple docstring'''







	lowerCamelCase__								=		R'\w+[.]\d+'
	lowerCamelCase__								=		re.findall(_UpperCAmelCase       ,_UpperCAmelCase							)
	for pat in pats:
		lowerCamelCase__								=		key.replace(_UpperCAmelCase       ,'''_'''.join(pat.split('''.'''							)							)							)
	return key





def lowerCAmelCase__(__snake_case       ,__snake_case       ,__snake_case							)				->      int:







	'''simple docstring'''







	lowerCamelCase__								=		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)
	):
		lowerCamelCase__								=		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:
		lowerCamelCase__								=		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:
		lowerCamelCase__								=		pt_tuple_key[:-1] + ('embedding',)
		return renamed_pt_tuple_key, pt_tensor

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

	# linear layer
	lowerCamelCase__								=		pt_tuple_key[:-1] + ('kernel',)
	if pt_tuple_key[-1] == "weight":
		lowerCamelCase__								=		pt_tensor.T
		return renamed_pt_tuple_key, pt_tensor

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

	# old PyTorch layer norm bias
	lowerCamelCase__								=		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 lowerCAmelCase__(__snake_case       ,__snake_case       ,__snake_case=42							)				->      List[str]:







	'''simple docstring'''







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

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

	lowerCamelCase__								=		flatten_dict(_UpperCAmelCase							)
	lowerCamelCase__								=		{}

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

		# Correctly rename weight parameters
		lowerCamelCase__								=		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
		lowerCamelCase__								=		jnp.asarray(_UpperCAmelCase							)

	return unflatten_dict(_UpperCAmelCase							)










"""simple docstring"""




import os





def 						UpperCAmelCase__		(     ):






  """simple docstring"""





  A_  :       Any										=  os.path.join(os.path.dirname(_UpperCAmelCase					) ,					'num.txt'					)
  with open(_UpperCAmelCase					) as file_hand:
    return str(sum(int(_UpperCAmelCase					) for line in file_hand					)					)[:10]


if __name__ == "__main__":
   print(solution())