



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_rembert import RemBertTokenizer
else:
							lowercase_						=       None

lowercase_						=       logging.get_logger(__name__)
lowercase_						=       {"""vocab_file""": """sentencepiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowercase_						=       {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
    """tokenizer_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/tokenizer.json""",
    },
}

lowercase_						=       {
    """google/rembert""": 256,
}

lowercase_						=       """▁"""
class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   Optional[int]       						=VOCAB_FILES_NAMES
				UpperCamelCase__			:   Dict       						=PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase__			:   Tuple       						=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase__			:   Dict       						=RemBertTokenizer




				def __init__(  self   :  Tuple,   __lowercase   :  Dict=None,   __lowercase   :  Tuple=None,   __lowercase   :  Any=True,   __lowercase   :  int=True,   __lowercase   :  Tuple=False,   __lowercase   :  List[str]="[CLS]",   __lowercase   :  Union[str, Any]="[SEP]",   __lowercase   :  Union[str, Any]="<unk>",   __lowercase   :  Optional[Any]="[SEP]",   __lowercase   :  Dict="<pad>",   __lowercase   :  List[Any]="[CLS]",   __lowercase   :  str="[MASK]",   **__lowercase   :  Dict,   ):
							# Mask token behave like a normal word, i.e. include the space before it
							lowercase__   					=			AddedToken(__lowercase,   lstrip=__lowercase,   rstrip=__lowercase  ) if isinstance(__lowercase,   __lowercase  ) else mask_token

							super().__init__(
							    __lowercase,   tokenizer_file=__lowercase,   do_lower_case=__lowercase,   remove_space=__lowercase,   keep_accents=__lowercase,   bos_token=__lowercase,   eos_token=__lowercase,   unk_token=__lowercase,   sep_token=__lowercase,   pad_token=__lowercase,   cls_token=__lowercase,   mask_token=__lowercase,   **__lowercase,   )

							lowercase__   					=			do_lower_case
							lowercase__   					=			remove_space
							lowercase__   					=			keep_accents
							lowercase__   					=			vocab_file
							lowercase__   					=			False if not self.vocab_file else True




				def 							A__ (  self   :  Dict,   __lowercase   :  List[int],   __lowercase   :  Optional[List[int]] = None  ):
							lowercase__   					=			[self.sep_token_id]
							lowercase__   					=			[self.cls_token_id]
							if token_ids_a is None:
										return cls + token_ids_a + sep
							return cls + token_ids_a + sep + token_ids_a + sep




				def 							A__ (  self   :  List[Any],   __lowercase   :  List[int],   __lowercase   :  Optional[List[int]] = None,   __lowercase   :  bool = False  ):

							if already_has_special_tokens:
										if token_ids_a is not None:
													raise ValueError(
													    "You should not supply a second sequence if the provided sequence of "
													    "ids is already formatted with special tokens for the model."  )
										return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

							if token_ids_a is not None:
										return [1] + ([0] * len(__lowercase  )) + [1] + ([0] * len(__lowercase  )) + [1]
							return [1] + ([0] * len(__lowercase  )) + [1]




				def 							A__ (  self   :  List[Any],   __lowercase   :  List[int],   __lowercase   :  Optional[List[int]] = None  ):
							lowercase__   					=			[self.sep_token_id]
							lowercase__   					=			[self.cls_token_id]

							if token_ids_a is None:
										return len(cls + token_ids_a + sep  ) * [0]
							return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]




				def 							A__ (  self   :  int,   __lowercase   :  str,   __lowercase   :  Optional[str] = None  ):
							if not os.path.isdir(__lowercase  ):
										logger.error("Vocabulary path ({}) should be a directory".format(__lowercase  )  )
										return
							lowercase__   					=			os.path.join(
							    __lowercase,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

							if os.path.abspath(self.vocab_file  ) != os.path.abspath(__lowercase  ):
										copyfile(self.vocab_file,   __lowercase  )

							return (out_vocab_file,)








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


lowercase_						=       logging.get_logger(__name__)



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



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			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)
			):
						lowercase__   					=			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:
						lowercase__   					=			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:
						lowercase__   					=			pt_tuple_key[:-1] + ("embedding",)
						return renamed_pt_tuple_key, pt_tensor

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

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

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

			# old PyTorch layer norm bias
			lowercase__   					=			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	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=42					):
			# Step 1: Convert pytorch tensor to numpy
			lowercase__   					=			{k: v.numpy() for k, v in pt_state_dict.items()}

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

			lowercase__   					=			flatten_dict(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			{}

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

						# Correctly rename weight parameters
						lowercase__					,    lowercase__   					=			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
						lowercase__   					=			jnp.asarray(SCREAMING_SNAKE_CASE_					)

			return unflatten_dict(SCREAMING_SNAKE_CASE_					)




