





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


UpperCAmelCase__						:			Dict									=logging.get_logger(__name__)





def      _lowercase					(						_UpperCAmelCase				)						->  List[str]:
				lowerCamelCase	 =r"""\w+[.]\d+"""
				lowerCamelCase	 =re.findall(_UpperCAmelCase			,      _UpperCAmelCase				)
				for pat in pats:
								lowerCamelCase	 =key.replace(_UpperCAmelCase			,      """_""".join(pat.split("""."""				)				)				)
				return key





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Any:
				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      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=42				)						->  int:
				# Step 1: Convert pytorch tensor to numpy
				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 ,  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				)












from abc import ABC, abstractmethod
from argparse import ArgumentParser

class       __A     (						a    ):


				@staticmethod
				@abstractmethod
				def _snake_case       (		UpperCAmelCase_							):
								raise NotImplementedError()


				@abstractmethod
				def _snake_case       (		self							):
								raise NotImplementedError()





