






'''simple docstring'''







def 						A__			(				__lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			int						):
						return [sentence[i : i + ngram_size] for i in range(len(__lowerCAmelCase						) - ngram_size + 1						)]


if __name__ == "__main__":
	from doctest import testmod

	testmod()









'''simple docstring'''







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    :      Union[str, Any]  			=		logging.get_logger(__name__)
def 						A__			(				__lowerCAmelCase		:			int						):
						lowerCamelCase__         =  R"""\w+[.]\d+"""
						lowerCamelCase__         =  re.findall(__lowerCAmelCase   ,    __lowerCAmelCase						)
						for pat in pats:
												lowerCamelCase__         =  key.replace(__lowerCAmelCase   ,    """_""".join(pat.split("""."""						)						)						)
						return key
def 						A__			(				__lowerCAmelCase		:			Union[str, Any]   ,    __lowerCAmelCase		:			Union[str, Any]   ,    __lowerCAmelCase		:			Tuple						):
						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 						A__			(				__lowerCAmelCase		:			int   ,    __lowerCAmelCase		:			Union[str, Any]   ,    __lowerCAmelCase		:			Tuple=42						):
						# 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(__lowerCAmelCase						)						)

						lowerCamelCase__         =  flatten_dict(__lowerCAmelCase						)
						lowerCamelCase__         =  {}

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

												# Correctly rename weight parameters
												lowerCamelCase__       ,			lowerCamelCase__         =  rename_key_and_reshape_tensor(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase						)

												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(__lowerCAmelCase						)

						return unflatten_dict(__lowerCAmelCase						)

