
import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


lowercase__		:List[Any]			  =   logging.get_logger(__name__)



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__=False		):
					'''simple docstring'''
					try:
										import torch  # noqa: F401
					except ImportError:
										logger.error(
										    '''Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see'''
										    ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
										    ''' instructions.'''		)
										raise

					if not is_sharded:
										lowercase						     =						os.path.abspath(lowerCAmelCase__		)
										logger.info(f'Loading PyTorch weights from {pt_path}'		)

										lowercase						     =						torch.load(lowerCAmelCase__			,      map_location='''cpu'''		)
										logger.info(f'PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()		):,} parameters.'		)

										lowercase						     =						convert_pytorch_state_dict_to_flax(lowerCAmelCase__			,      lowerCAmelCase__		)
					else:
										# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
										lowercase						     =						convert_pytorch_sharded_state_dict_to_flax(lowerCAmelCase__			,      lowerCAmelCase__		)
					return flax_state_dict



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      ):
					'''simple docstring'''

					def is_key_or_prefix_key_in_dict(lowerCAmelCase__		) -> bool:
										return len(set(lowerCAmelCase__		) & {key, (model_prefix,) + key}		) > 0

					# layer norm
					lowercase						     =						pt_tuple_key[:-1] + ('''scale''',)
					if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(lowerCAmelCase__		):
										return renamed_pt_tuple_key, pt_tensor

					# batch norm layer mean
					lowercase						     =						pt_tuple_key[:-1] + ('''mean''',)
					if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(lowerCAmelCase__		):
										return renamed_pt_tuple_key, pt_tensor

					# batch norm layer var
					lowercase						     =						pt_tuple_key[:-1] + ('''var''',)
					if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(lowerCAmelCase__		):
										return renamed_pt_tuple_key, pt_tensor

					# embedding
					lowercase						     =						pt_tuple_key[:-1] + ('''embedding''',)
					if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(lowerCAmelCase__		):
										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 and not is_key_or_prefix_key_in_dict(lowerCAmelCase__		):
										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" and not is_key_or_prefix_key_in_dict(lowerCAmelCase__		):
										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

					# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
					lowercase						     =						None
					if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
										lowercase						     =						pt_tuple_key[-2] + '''_g'''
					elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
										lowercase						     =						pt_tuple_key[-2] + '''_v'''
					if name is not None:
										lowercase						     =						pt_tuple_key[:-3] + (name,)
										return renamed_pt_tuple_key, pt_tensor

					return pt_tuple_key, pt_tensor



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					# convert pytorch tensor to numpy
					lowercase						     =						{k: v.numpy() for k, v in pt_state_dict.items()}

					lowercase						     =						flax_model.base_model_prefix

					# use params dict if the model contains batch norm layers
					if "params" in flax_model.params:
										lowercase						     =						flax_model.params['''params''']
					else:
										lowercase						     =						flax_model.params
					lowercase						     =						flatten_dict(lowerCAmelCase__		)

					# add batch_stats keys,values to dict
					if "batch_stats" in flax_model.params:
										lowercase						     =						flatten_dict(flax_model.params['''batch_stats''']		)
										random_flax_state_dict.update(lowerCAmelCase__		)

					lowercase						     =						{}

					lowercase						     =						(model_prefix not in flax_model_params) and (
					    model_prefix in {k.split('''.'''		)[0] for k in pt_state_dict.keys()}
					)
					lowercase						     =						(model_prefix in flax_model_params) and (
					    model_prefix not in {k.split('''.'''		)[0] for k in pt_state_dict.keys()}
					)

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

										# remove base model prefix if necessary
										lowercase						     =						pt_tuple_key[0] == model_prefix
										if load_model_with_head_into_base_model and has_base_model_prefix:
															lowercase						     =						pt_tuple_key[1:]

										# Correctly rename weight parameters
										lowercase     ,						lowercase						     =						rename_key_and_reshape_tensor(
										    lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		)

										# add model prefix if necessary
										lowercase						     =						(model_prefix,) + flax_key in random_flax_state_dict
										if load_base_model_into_model_with_head and require_base_model_prefix:
															lowercase						     =						(model_prefix,) + flax_key

										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}.'		)

        # add batch stats if the model contains batchnorm layers
										if "batch_stats" in flax_model.params:
															if "mean" in flax_key[-1] or "var" in flax_key[-1]:
																				lowercase						     =						jnp.asarray(lowerCAmelCase__		)
																				continue
															# remove num_batches_tracked key
															if "num_batches_tracked" in flax_key[-1]:
																				flax_state_dict.pop(lowerCAmelCase__			,      lowerCAmelCase__		)
																				continue

															# also add unexpected weight so that warning is thrown
															lowercase						     =						jnp.asarray(lowerCAmelCase__		)

										else:
															# also add unexpected weight so that warning is thrown
															lowercase						     =						jnp.asarray(lowerCAmelCase__		)

					return unflatten_dict(lowerCAmelCase__		)



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					import torch

					# Load the index
					lowercase						     =						{}
					for shard_file in shard_filenames:
										# load using msgpack utils
										lowercase						     =						torch.load(lowerCAmelCase__		)
										lowercase						     =						{k: v.numpy() for k, v in pt_state_dict.items()}

										lowercase						     =						flax_model.base_model_prefix

										# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
										if "batch_stats" in flax_model.params:
															lowercase						     =						flax_model.params['''params''']

															lowercase						     =						flatten_dict(lowerCAmelCase__		)
															random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats''']		)		)
										else:
															lowercase						     =						flax_model.params
															lowercase						     =						flatten_dict(lowerCAmelCase__		)

										lowercase						     =						(model_prefix not in flax_model_params) and (
										    model_prefix in {k.split('''.'''		)[0] for k in pt_state_dict.keys()}
										)
										lowercase						     =						(model_prefix in flax_model_params) and (
										    model_prefix not in {k.split('''.'''		)[0] for k in pt_state_dict.keys()}
										)
										# Need to change some parameters name to match Flax names
										for pt_key, pt_tensor in pt_state_dict.items():
															lowercase						     =						tuple(pt_key.split('''.'''		)		)

															# remove base model prefix if necessary
															lowercase						     =						pt_tuple_key[0] == model_prefix
															if load_model_with_head_into_base_model and has_base_model_prefix:
																				lowercase						     =						pt_tuple_key[1:]

															# Correctly rename weight parameters
															lowercase     ,						lowercase						     =						rename_key_and_reshape_tensor(
															    lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		)
															# add model prefix if necessary
															lowercase						     =						(model_prefix,) + flax_key in random_flax_state_dict
															if load_base_model_into_model_with_head and require_base_model_prefix:
																				lowercase						     =						(model_prefix,) + flax_key

															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}.'		)

            # add batch stats if the model contains batchnorm layers
															if "batch_stats" in flax_model.params:
																				if "mean" in flax_key[-1]:
																									lowercase						     =						jnp.asarray(lowerCAmelCase__		)
																									continue
																				if "var" in flax_key[-1]:
																									lowercase						     =						jnp.asarray(lowerCAmelCase__		)
																									continue
																				# remove num_batches_tracked key
																				if "num_batches_tracked" in flax_key[-1]:
																									flax_state_dict.pop(lowerCAmelCase__			,      lowerCAmelCase__		)
																									continue

																				# also add unexpected weight so that warning is thrown
																				lowercase						     =						jnp.asarray(lowerCAmelCase__		)

															else:
																				# also add unexpected weight so that warning is thrown
																				lowercase						     =						jnp.asarray(lowerCAmelCase__		)
					return unflatten_dict(lowerCAmelCase__		)



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						os.path.abspath(lowerCAmelCase__		)
					logger.info(f'Loading Flax weights from {flax_checkpoint_path}'		)

					# import correct flax class
					lowercase						     =						getattr(lowerCAmelCase__			,      '''Flax''' + model.__class__.__name__		)

					# load flax weight dict
					with open(lowerCAmelCase__			,      '''rb'''		) as state_f:
										try:
															lowercase						     =						from_bytes(lowerCAmelCase__			,      state_f.read()		)
										except UnpicklingError:
															raise EnvironmentError(f'Unable to convert {flax_checkpoint_path} to Flax deserializable object. '		)

					return load_flax_weights_in_pytorch_model(lowerCAmelCase__			,      lowerCAmelCase__		)



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''

					try:
										import torch  # noqa: F401
					except ImportError:
										logger.error(
										    '''Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see'''
										    ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
										    ''' instructions.'''		)
										raise

					# check if we have bf16 weights
					lowercase						     =						flatten_dict(jax.tree_util.tree_map(lambda lowerCAmelCase__		: x.dtype == jnp.bfloataa			,      lowerCAmelCase__		)		).values()
					if any(lowerCAmelCase__		):
										# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
										# and bf16 is not fully supported in PT yet.
										logger.warning(
										    '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
										    '''before loading those in PyTorch model.'''		)
										lowercase						     =						jax.tree_util.tree_map(
										    lambda lowerCAmelCase__		: params.astype(np.floataa		) if params.dtype == jnp.bfloataa else params			,      lowerCAmelCase__		)

					lowercase						     =						flatten_dict(lowerCAmelCase__		)
					lowercase						     =						pt_model.state_dict()

					lowercase						     =						(pt_model.base_model_prefix in flax_state) and (
					    pt_model.base_model_prefix not in {k.split('''.'''		)[0] for k in pt_model_dict.keys()}
					)
					lowercase						     =						(pt_model.base_model_prefix not in flax_state) and (
					    pt_model.base_model_prefix in {k.split('''.'''		)[0] for k in pt_model_dict.keys()}
					)

					# keep track of unexpected & missing keys
					lowercase						     =						[]
					lowercase						     =						set(pt_model_dict.keys()		)

					for flax_key_tuple, flax_tensor in flax_state_dict.items():
										lowercase						     =						flax_key_tuple[0] == pt_model.base_model_prefix
										lowercase						     =						'''.'''.join((pt_model.base_model_prefix,) + flax_key_tuple		) in pt_model_dict

										# adapt flax_key to prepare for loading from/to base model only
										if load_model_with_head_into_base_model and has_base_model_prefix:
															lowercase						     =						flax_key_tuple[1:]
										elif load_base_model_into_model_with_head and require_base_model_prefix:
															lowercase						     =						(pt_model.base_model_prefix,) + flax_key_tuple

										# rename flax weights to PyTorch format
										if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(lowerCAmelCase__		) not in pt_model_dict:
															# conv layer
															lowercase						     =						flax_key_tuple[:-1] + ('''weight''',)
															lowercase						     =						jnp.transpose(lowerCAmelCase__			,      (3, 2, 0, 1)		)
										elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCAmelCase__		) not in pt_model_dict:
															# linear layer
															lowercase						     =						flax_key_tuple[:-1] + ('''weight''',)
															lowercase						     =						flax_tensor.T
										elif flax_key_tuple[-1] in ["scale", "embedding"]:
															lowercase						     =						flax_key_tuple[:-1] + ('''weight''',)

										# adding batch stats from flax batch norm to pt
										elif "mean" in flax_key_tuple[-1]:
															lowercase						     =						flax_key_tuple[:-1] + ('''running_mean''',)
										elif "var" in flax_key_tuple[-1]:
															lowercase						     =						flax_key_tuple[:-1] + ('''running_var''',)

										if "batch_stats" in flax_state:
															lowercase						     =						'''.'''.join(flax_key_tuple[1:]		)  # Remove the params/batch_stats header
										else:
															lowercase						     =						'''.'''.join(lowerCAmelCase__		)

										# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
										lowercase						     =						{}
										# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
										for key in pt_model_dict:
															lowercase						     =						key.split('''.'''		)
															lowercase						     =						None
															if key_components[-3::2] == ["parametrizations", "original0"]:
																				lowercase						     =						key_components[-2] + '''_g'''
															elif key_components[-3::2] == ["parametrizations", "original1"]:
																				lowercase						     =						key_components[-2] + '''_v'''
															if name is not None:
																				lowercase						     =						key_components[:-3] + [name]
																				lowercase						     =						'''.'''.join(lowerCAmelCase__		)
																				lowercase						     =						key

										if flax_key in special_pt_names:
															lowercase						     =						special_pt_names[flax_key]

										if flax_key in pt_model_dict:
															if flax_tensor.shape != pt_model_dict[flax_key].shape:
																				raise ValueError(
																				    f'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
																				    f'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'		)
															else:
																				# add weight to pytorch dict
																				lowercase						     =						np.asarray(lowerCAmelCase__		) if not isinstance(lowerCAmelCase__			,      np.ndarray		) else flax_tensor
																				lowercase						     =						torch.from_numpy(lowerCAmelCase__		)
																				# remove from missing keys
																				missing_keys.remove(lowerCAmelCase__		)
										else:
															# weight is not expected by PyTorch model
															unexpected_keys.append(lowerCAmelCase__		)

					pt_model.load_state_dict(lowerCAmelCase__		)

					# re-transform missing_keys to list
					lowercase						     =						list(lowerCAmelCase__		)

					if len(lowerCAmelCase__		) > 0:
										logger.warning(
										    '''Some weights of the Flax model were not used when initializing the PyTorch model'''
										    f' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
										    f' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
										    ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
										    f' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
										    ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
										    ''' FlaxBertForSequenceClassification model).'''		)
					else:
										logger.warning(f'All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n'		)
					if len(lowerCAmelCase__		) > 0:
										logger.warning(
										    f'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
										    f' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
										    ''' use it for predictions and inference.'''		)
					else:
										logger.warning(
										    f'All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'
										    '''If your task is similar to the task the model of the checkpoint was trained on, '''
										    f'you can already use {pt_model.__class__.__name__} for predictions without further training.'		)

					return pt_model






import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase__		:Dict			  =   logging.get_logger(__name__)

lowercase__		:Optional[Any]			  =   "▁"

lowercase__		:str			  =   {"vocab_file": "prophetnet.tokenizer"}

lowercase__		:List[str]			  =   {
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

lowercase__		:Optional[Any]			  =   {
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

lowercase__		:List[Any]			  =   {
    "microsoft/xprophetnet-large-wiki100-cased": 512,
}



def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						collections.OrderedDict()
					with open(lowerCAmelCase__			,      '''r'''			,      encoding='''utf-8'''		) as reader:
										lowercase						     =						reader.readlines()
					for index, token in enumerate(lowerCAmelCase__		):
										lowercase						     =						token.rstrip('''\n'''		)
										lowercase						     =						index
					return vocab







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	Optional[Any]	 =VOCAB_FILES_NAMES
							lowercase_	:	Any	 =PRETRAINED_VOCAB_FILES_MAP
							lowercase_	:	Any	 =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_	:	str	 =['''input_ids''', '''attention_mask''']




							def __init__(    self       ,A__       ,A__="[SEP]"       ,A__="[SEP]"       ,A__="[SEP]"       ,A__="[UNK]"       ,A__="[PAD]"       ,A__="[CLS]"       ,A__="[MASK]"       ,A__ = None       ,**A__       ,):
												lowercase						     =						{} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    bos_token=A__       ,eos_token=A__       ,sep_token=A__       ,unk_token=A__       ,pad_token=A__       ,cls_token=A__       ,mask_token=A__       ,sp_model_kwargs=self.sp_model_kwargs       ,**A__       ,)

												try:
																	import sentencepiece as spm
												except ImportError:
																	logger.warning(
																	    '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
																	    ''' pip install sentencepiece''')
																	raise

												lowercase						     =						spm.SentencePieceProcessor(**self.sp_model_kwargs)
												self.sp_model.Load(str(A__))
												lowercase						     =						vocab_file

												# Original fairseq vocab and spm vocab must be "aligned":
												# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
												# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
												# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
												# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

												# put special tokens and [unused] tokens into the vocab
												lowercase						     =						{'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

												for i in range(1_0):
																	lowercase						     =						f'[unused{i}]'
																	lowercase						     =						5 + i

												# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
												lowercase						     =						1_2
												lowercase						     =						{v: k for k, v in self.fairseq_tokens_to_ids.items()}
												for k in self.fairseq_tokens_to_ids.keys():
																	self.unique_no_split_tokens.append(A__)




							def __getstate__(    self):
												lowercase						     =						self.__dict__.copy()
												lowercase						     =						None
												return state




							def __setstate__(    self       ,A__):
												lowercase						     =						d
												try:
																	import sentencepiece as spm
												except ImportError:
																	logger.warning(
																	    '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
																	    ''' pip install sentencepiece''')
																	raise

												# for backward compatibility
												if not hasattr(self       ,'''sp_model_kwargs'''):
																	lowercase						     =						{}

												lowercase						     =						spm.SentencePieceProcessor(**self.sp_model_kwargs)
												self.sp_model.Load(self.vocab_file)




							def 			A__				(    self       ,A__       ,A__ = None       ,A__ = False):

												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=A__       ,token_ids_a=A__       ,already_has_special_tokens=A__)

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




							def 			A__				(    self       ,A__       ,A__ = None):
												lowercase						     =						[self.sep_token_id]

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




							@property
							def 			A__				(    self):
												return len(self.sp_model) + self.fairseq_offset




							def 			A__				(    self):
												lowercase						     =						{self.convert_ids_to_tokens(A__): i for i in range(self.vocab_size)}
												vocab.update(self.added_tokens_encoder)
												return vocab




							def 			A__				(    self       ,A__):
												return self.sp_model.encode(A__       ,out_type=A__)




							def 			A__				(    self       ,A__):
												if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
												lowercase						     =						self.sp_model.PieceToId(A__)

												# Need to return unknown token if the SP model returned 0
												return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




							def 			A__				(    self       ,A__):
												if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
												return self.sp_model.IdToPiece(index - self.fairseq_offset)




							def 			A__				(    self       ,A__):
												lowercase						     =						''''''.join(A__).replace(A__       ,''' ''').strip()
												return out_string




							def 			A__				(    self       ,A__       ,A__ = None):
												if not os.path.isdir(A__):
																	logger.error(f'Vocabulary path ({save_directory}) should be a directory')
																	return
												lowercase						     =						os.path.join(
												    A__       ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

												if os.path.abspath(self.vocab_file) != os.path.abspath(A__) and os.path.isfile(self.vocab_file):
																	copyfile(self.vocab_file       ,A__)
												elif not os.path.isfile(self.vocab_file):
																	with open(A__       ,'''wb''') as fi:
																						lowercase						     =						self.sp_model.serialized_model_proto()
																						fi.write(A__)

												return (out_vocab_file,)





							def 			A__				(    self       ,A__       ,A__ = None):

												if token_ids_a is None:
																	return token_ids_a + [self.sep_token_id]
												lowercase						     =						[self.sep_token_id]
												return token_ids_a + sep + token_ids_a + sep




