

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  :      str			 =logging.get_logger(__name__)



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	int									= R"\w+[.]\d+"
	UpperCAmelCase__:	Optional[int]									= re.findall(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
	for pat in pats:
		UpperCAmelCase__:	int									= key.replace(SCREAMING_SNAKE_CASE	,"_".join(pat.split("."      )      )      )
	return key



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Dict									= 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)
	):
		UpperCAmelCase__:	List[str]									= 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:
		UpperCAmelCase__:	Union[str, Any]									= 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:
		UpperCAmelCase__:	List[str]									= pt_tuple_key[:-1] + ("embedding",)
		return renamed_pt_tuple_key, pt_tensor

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

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

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

	# old PyTorch layer norm bias
	UpperCAmelCase__:	List[str]									= 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 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE=4_2      ):
	# Step 1: Convert pytorch tensor to numpy
	UpperCAmelCase__:	Optional[Any]									= {k: v.numpy() for k, v in pt_state_dict.items()}

	# Step 2: Since the model is stateless, get random Flax params
	UpperCAmelCase__:	List[Any]									= flax_model.init_weights(PRNGKey(SCREAMING_SNAKE_CASE      )      )

	UpperCAmelCase__:	List[Any]									= flatten_dict(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Tuple									= {}

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

		# Correctly rename weight parameters
		UpperCAmelCase__							,						UpperCAmelCase__:	Tuple									= rename_key_and_reshape_tensor(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )

		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
		UpperCAmelCase__:	str									= jnp.asarray(SCREAMING_SNAKE_CASE      )

	return unflatten_dict(SCREAMING_SNAKE_CASE      )


import fire

from utils import calculate_rouge, save_json



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE=None	,**SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Tuple									= [x.strip() for x in open(SCREAMING_SNAKE_CASE      ).readlines()]
	UpperCAmelCase__:	Dict									= [x.strip() for x in open(SCREAMING_SNAKE_CASE      ).readlines()][: len(SCREAMING_SNAKE_CASE      )]
	UpperCAmelCase__:	Any									= calculate_rouge(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,**SCREAMING_SNAKE_CASE      )
	if save_path is not None:
		save_json(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,indent=SCREAMING_SNAKE_CASE      )
	return metrics  # these print nicely


if __name__ == "__main__":
					fire.Fire(calculate_rouge_path)