






"""simple docstring"""


import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
		from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
		raise ImportError('You need to install the version of fairseq from the Mega repo!')




class 	__UpperCamelCase    (    nn.Module	):
					def __init__(       self       :  Optional[int]    ,   UpperCAmelCase       :  List[Any]    ,   UpperCAmelCase       :  Dict    ,   UpperCAmelCase       :  List[Any]      )  ->					Union[str, Any]:
											super().__init__()
											lowerCAmelCase      :List[str]				  = mega_args
											lowerCAmelCase      :List[str]				  = nn.Embedding(UpperCAmelCase    ,   self.mega_args.encoder_embed_dim      )
											lowerCAmelCase      :List[Any]				  = nn.ModuleList([MegaEncoderLayer(self.mega_args      ) for _ in range(UpperCAmelCase      )]      )
											lowerCAmelCase      :int				  = depth
					def 					UpperCAmelCase__   (       self       :  str    ,   UpperCAmelCase       :  Optional[int]    ,   UpperCAmelCase       :  List[str]    ,   UpperCAmelCase       :  int=True    ,   UpperCAmelCase       :  Tuple=0      )  ->					Dict:

											# Mega expects embeddings to be (time, batch, embedding size), but
											# Hugging Face returns tokens as (batch, time)
											if batch_first:
																	lowerCAmelCase      :Any				  = input_ids.T

											# to make things more confusing, Mega expects the attention mask to
											# be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
											# which is the opposite of what HF returns
											if ignore_mask_value == 0:
																	lowerCAmelCase      :str				  = 1 - attention_mask

											# get token embeddings from IDs
											lowerCAmelCase      :Union[str, Any]				  = self.embedding_layer(UpperCAmelCase      )

											# pass through the Mega layers
											# input is (time, batch, encoder dim) and output is the same
											for encoder in self.encoders:
																	lowerCAmelCase      :Optional[Any]				  = encoder(UpperCAmelCase    ,   UpperCAmelCase      )

											# return according to the shape specified
											if batch_first:
																	# (T, B, H) --> (B, T, H)
																	return torch.transpose(UpperCAmelCase    ,   0    ,   1      )
											else:
																	return embeds




class 	__UpperCamelCase    (    nn.Module	):
					def __init__(       self       :  List[Any]    ,   UpperCAmelCase       :  Tuple    ,   UpperCAmelCase       :  Optional[Any]    ,   UpperCAmelCase       :  Optional[int]      )  ->					List[str]:
											super().__init__()
											lowerCAmelCase      :str				  = MegaLM(UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase      )
											lowerCAmelCase      :str				  = nn.Linear(mega_args.encoder_embed_dim    ,   UpperCAmelCase      )
											lowerCAmelCase      :int				  = nn.Dropout(p=0.1      )
					def 					UpperCAmelCase__   (       self       :  str    ,   UpperCAmelCase       :  Any    ,   UpperCAmelCase       :  Optional[Any]    ,   UpperCAmelCase       :  List[str]=True    ,   UpperCAmelCase       :  Union[str, Any]=0      )  ->					Any:
											lowerCAmelCase      :Union[str, Any]				  = self.mega(UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase      )
											return self.mlm_head(self.dropout(UpperCAmelCase      )      )


def 					UpperCAmelCase			(				a__    ,			a__    ,			a__   ):





						'''simple docstring'''






						with open(os.path.join(a__    ,			'model_args.pkl'   )    ,			'rb'   ) as f:
												lowerCAmelCase      :Tuple				  = pkl.load(a__   )

						# load the original encoder
						lowerCAmelCase      :Dict				  = OriginalMegaForMaskedLM(**a__   ).eval()

						# load its weights
						print(
						    'Original Mega encoder:'    ,			original_mlm.mega.load_state_dict(
						        torch.load(os.path.join(a__    ,			'encoder_weights.pt'   )    ,			map_location='cpu'   )   )    ,			)
						print(
						    'Original Mega MLM layer:'    ,			original_mlm.mlm_head.load_state_dict(
						        torch.load(os.path.join(a__    ,			'mlm_head_weights.pt'   )    ,			map_location='cpu'   )   )    ,			)

						# create a new config from the old one
						lowerCAmelCase      :Optional[int]				  = MegaConfig(
						    num_hidden_layers=mega_original_args['depth']    ,			vocab_size=mega_original_args['vocab_size']    ,			hidden_size=mega_original_args['mega_args'].encoder_embed_dim    ,			shared_representation_size=mega_original_args['mega_args'].encoder_z_dim    ,			intermediate_size=mega_original_args['mega_args'].encoder_hidden_dim    ,			ema_projection_size=mega_original_args['mega_args'].encoder_n_dim    ,			dropout_prob=mega_original_args['mega_args'].dropout    ,			attention_probs_dropout_prob=mega_original_args['mega_args'].attention_dropout    ,			hidden_dropout_prob=mega_original_args['mega_args'].hidden_dropout    ,			activation=mega_original_args['mega_args'].activation_fn    ,			attention_activation=mega_original_args['mega_args'].attention_activation_fn    ,			bidirectional=mega_original_args['mega_args'].bidirectional    ,			use_chunking=mega_original_args['mega_args'].encoder_chunk_size > 0    ,			chunk_size=mega_original_args['mega_args'].encoder_chunk_size    ,			truncation=mega_original_args['mega_args'].truncation_length    ,			normalization_type=mega_original_args['mega_args'].normalization_type    ,			normalize_before_mega=a__    ,			norm_affine=a__    ,			use_feature_dropout=mega_original_args['mega_args'].feature_dropout    ,			relative_positional_bias=mega_original_args['mega_args'].rel_pos_bias    ,			max_positions=mega_original_args['mega_args'].max_source_positions    ,			nffn_hidden_size=mega_original_args['mega_args'].encoder_ffn_embed_dim    ,			normalize_before_ffn=mega_original_args['mega_args'].normalize_before    ,			nffn_activation_dropout_prob=0.0    ,			add_token_type_embeddings=a__    ,			add_lm_hidden_dense_layer=a__    ,			)

						lowerCAmelCase      :Dict				  = MegaForMaskedLM(a__   ).eval()

						# the originl checkpoint just uses nn.Embedding for the word embeddings
						# we use a wrapper module for embeddings to add support for positional embeddings
						lowerCAmelCase      :Optional[int]				  = original_mlm.mega.embedding_layer.weight

						# modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
						# ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
						# also renaming previously confusing parameter names
						lowerCAmelCase      :List[str]				  = original_mlm.mega.encoders.state_dict()
						lowerCAmelCase      :str				  = {}
						for module_name in original_state_dict.keys():
												lowerCAmelCase      :Any				  = None
												# have to handle gamma, beta, and alpha differently due to their use
												# in multiple modules within the original repository;
												# beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
												# the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
												if "beta" in module_name:
																		# EMA sub-layers were always called "move" in the original repo
																		if "move.beta" in module_name:
																								lowerCAmelCase      :Optional[Any]				  = module_name.replace('move.beta'    ,			'ema_gate.ema_expansion_matrix'   )
																		elif "mega_layer.beta" in module_name:
																								lowerCAmelCase      :List[str]				  = module_name.replace('beta'    ,			'qk_bias'   )
																		else:
																								lowerCAmelCase      :Any				  = module_name.replace('beta'    ,			'b_param'   )
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
												elif "gamma" in module_name:
																		if "move.gamma" in module_name:
																								lowerCAmelCase      :int				  = module_name.replace('move.gamma'    ,			'ema_gate.kernel_projection_matrix'   )
																		elif "mega_layer.gamma" in module_name:
																								lowerCAmelCase      :List[str]				  = module_name.replace('gamma'    ,			'qk_weight'   )
																		else:
																								lowerCAmelCase      :List[Any]				  = module_name.replace('gamma'    ,			'g_param'   )
        # alpha is used in EMA and positional bias; renaming to improve readability
												elif "move.alpha" in module_name:
																		lowerCAmelCase      :List[Any]				  = module_name.replace('move.alpha'    ,			'ema_gate.decay_factor'   )
												# delta is only used in EMA; renaming to improve readability
												elif "move.delta" in module_name:
																		lowerCAmelCase      :Any				  = module_name.replace('move.delta'    ,			'ema_gate.damping_factor'   )
												# omega is only used in EMA; renaming to improve readability
												elif "omega" in module_name:
																		lowerCAmelCase      :List[str]				  = module_name.replace('move.omega'    ,			'ema_gate.residual_weight'   )

												if new_module_name:
																		lowerCAmelCase      :Dict				  = new_module_name

						if len(a__   ) != 0:
												print(F"""Renaming these keys: {updated_keys.keys()}"""   )
						else:
												print('No need to rename state dict entries'   )
						for old, new in updated_keys.items():
												lowerCAmelCase      :Dict				  = original_state_dict.pop(a__   )

						# now attempt to load the state dictionary with updated names
						# note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
						print('HF Mega encoder:'    ,			hf_mlm.mega.layers.load_state_dict(a__   )   )

						# load the MLM head weights directly
						print(
						    'HF Mega MLM layer:'    ,			hf_mlm.mlm_head.load_state_dict(
						        torch.load(os.path.join(a__    ,			'mlm_head_weights.pt'   )    ,			map_location='cpu'   )   )    ,			)

						# test on a randomly generated input sequence
						lowerCAmelCase      :int				  = torch.randint(0    ,			hf_config.vocab_size    ,			size=(4, 2_56)   )
						lowerCAmelCase      :str				  = torch.ones_like(a__   )
						# mask a few tokens to make sure masking is applied appropriately :)
						lowerCAmelCase      :Optional[int]				  = 0

						# run forward passes
						lowerCAmelCase      :List[Any]				  = original_mlm(a__    ,			a__    ,			batch_first=a__    ,			ignore_mask_value=0   )
						lowerCAmelCase      :List[str]				  = hf_mlm(a__    ,			a__   )[0]

						# print shapes and diff
						print(F"""original output {original_output.shape}"""   )
						print(F"""hf output {hf_output.shape}"""   )
						print(F"""max diff: {(original_output - hf_output).max()}"""   )  # 0.0
						lowerCAmelCase      :Optional[Any]				  = torch.allclose(a__    ,			a__    ,			atol=1e-3   )

						if success:
												print('Yay!'   )
												hf_mlm.save_pretrained(a__   )
						else:
												raise RuntimeError(F"""Something's broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}"""   )

						if includes_tokenizer:
												print('Transferring tokenizer'   )
												lowerCAmelCase      :Any				  = AutoTokenizer.from_pretrained(a__   )
												tokenizer.save_pretrained(a__   )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE      =      argparse.ArgumentParser()

		parser.add_argument(
		    '--pretrained_checkpoint_path',
		    default=None,
		    type=str,
		    required=True,
		    help='Point to the directory containing your model weights using the official Mega repo',
		)

		parser.add_argument(
		    '--output_path', default=None, type=str, required=True, help='Location to save the Hugging Face version'
		)

		parser.add_argument(
		    '--includes_tokenizer',
		    action='store_true',
		    help='Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo',
		)

		__SCREAMING_SNAKE_CASE      =      parser.parse_args()

		convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)







"""simple docstring"""


import json
import os
import re
import unicodedata
from typing import Dict, List, Optional, Tuple

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


__SCREAMING_SNAKE_CASE      =      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE      =      {
    'src_vocab_file': 'vocab-src.json',
    'tgt_vocab_file': 'vocab-tgt.json',
    'merges_file': 'merges.txt',
}

__SCREAMING_SNAKE_CASE      =      {
    'src_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-src.json'
    },
    'tgt_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-tgt.json'
    },
    'merges_file': {'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/merges.txt'},
}

__SCREAMING_SNAKE_CASE      =      {'stas/tiny-wmt19-en-de': 10_24}
__SCREAMING_SNAKE_CASE      =      {
    'stas/tiny-wmt19-en-de': {
        'langs': ['en', 'de'],
        'model_max_length': 10_24,
        'special_tokens_map_file': None,
        'full_tokenizer_file': None,
    }
}


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						lowerCAmelCase      :int				  = set()
						lowerCAmelCase      :Optional[Any]				  = word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)   )
												lowerCAmelCase      :int				  = char
						return pairs


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						lowerCAmelCase      :List[str]				  = text.replace('，'    ,			','   )
						lowerCAmelCase      :str				  = re.sub(R'。\s*'    ,			'. '    ,			a__   )
						lowerCAmelCase      :int				  = text.replace('、'    ,			','   )
						lowerCAmelCase      :str				  = text.replace('”'    ,			'"'   )
						lowerCAmelCase      :Union[str, Any]				  = text.replace('“'    ,			'"'   )
						lowerCAmelCase      :List[Any]				  = text.replace('∶'    ,			':'   )
						lowerCAmelCase      :List[Any]				  = text.replace('：'    ,			':'   )
						lowerCAmelCase      :Optional[Any]				  = text.replace('？'    ,			'?'   )
						lowerCAmelCase      :Tuple				  = text.replace('《'    ,			'"'   )
						lowerCAmelCase      :Dict				  = text.replace('》'    ,			'"'   )
						lowerCAmelCase      :List[str]				  = text.replace('）'    ,			')'   )
						lowerCAmelCase      :Dict				  = text.replace('！'    ,			'!'   )
						lowerCAmelCase      :Optional[Any]				  = text.replace('（'    ,			'('   )
						lowerCAmelCase      :Optional[Any]				  = text.replace('；'    ,			';'   )
						lowerCAmelCase      :Any				  = text.replace('１'    ,			'1'   )
						lowerCAmelCase      :Union[str, Any]				  = text.replace('」'    ,			'"'   )
						lowerCAmelCase      :Any				  = text.replace('「'    ,			'"'   )
						lowerCAmelCase      :str				  = text.replace('０'    ,			'0'   )
						lowerCAmelCase      :Dict				  = text.replace('３'    ,			'3'   )
						lowerCAmelCase      :Union[str, Any]				  = text.replace('２'    ,			'2'   )
						lowerCAmelCase      :str				  = text.replace('５'    ,			'5'   )
						lowerCAmelCase      :Tuple				  = text.replace('６'    ,			'6'   )
						lowerCAmelCase      :Optional[int]				  = text.replace('９'    ,			'9'   )
						lowerCAmelCase      :Optional[Any]				  = text.replace('７'    ,			'7'   )
						lowerCAmelCase      :Union[str, Any]				  = text.replace('８'    ,			'8'   )
						lowerCAmelCase      :Optional[Any]				  = text.replace('４'    ,			'4'   )
						lowerCAmelCase      :int				  = re.sub(R'．\s*'    ,			'. '    ,			a__   )
						lowerCAmelCase      :List[Any]				  = text.replace('～'    ,			'~'   )
						lowerCAmelCase      :int				  = text.replace('’'    ,			'\''   )
						lowerCAmelCase      :Dict				  = text.replace('…'    ,			'...'   )
						lowerCAmelCase      :Any				  = text.replace('━'    ,			'-'   )
						lowerCAmelCase      :Union[str, Any]				  = text.replace('〈'    ,			'<'   )
						lowerCAmelCase      :Any				  = text.replace('〉'    ,			'>'   )
						lowerCAmelCase      :str				  = text.replace('【'    ,			'['   )
						lowerCAmelCase      :Any				  = text.replace('】'    ,			']'   )
						lowerCAmelCase      :int				  = text.replace('％'    ,			'%'   )
						return text


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						lowerCAmelCase      :Optional[int]				  = []
						for char in text:
												lowerCAmelCase      :Optional[Any]				  = unicodedata.category(a__   )
												if cat.startswith('C'   ):
																		continue
												output.append(a__   )
						return "".join(a__   )







class 	__UpperCamelCase    (    UpperCamelCase	):
					lowercase_						:							Dict			=     VOCAB_FILES_NAMES
					lowercase_						:							Any			=     PRETRAINED_VOCAB_FILES_MAP
					lowercase_						:							Dict			=     PRETRAINED_INIT_CONFIGURATION
					lowercase_						:							Optional[Any]			=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase_						:							Union[str, Any]			=     ["""input_ids""", """attention_mask"""]
					def __init__(       self       :  int    ,   UpperCAmelCase       :  Tuple=None    ,   UpperCAmelCase       :  Dict=None    ,   UpperCAmelCase       :  Dict=None    ,   UpperCAmelCase       :  Tuple=None    ,   UpperCAmelCase       :  str=False    ,   UpperCAmelCase       :  Optional[int]="<unk>"    ,   UpperCAmelCase       :  List[Any]="<s>"    ,   UpperCAmelCase       :  List[str]="</s>"    ,   UpperCAmelCase       :  Optional[int]="<pad>"    ,   **UpperCAmelCase       :  int    ,   )  ->					int:
											super().__init__(
											    langs=UpperCAmelCase    ,   src_vocab_file=UpperCAmelCase    ,   tgt_vocab_file=UpperCAmelCase    ,   merges_file=UpperCAmelCase    ,   do_lower_case=UpperCAmelCase    ,   unk_token=UpperCAmelCase    ,   bos_token=UpperCAmelCase    ,   sep_token=UpperCAmelCase    ,   pad_token=UpperCAmelCase    ,   **UpperCAmelCase    ,   )

											try:
																	import sacremoses
											except ImportError:
																	raise ImportError(
																	    'You need to install sacremoses to use XLMTokenizer. '
																	    'See https://pypi.org/project/sacremoses/ for installation.'      )

											lowerCAmelCase      :str				  = sacremoses

											lowerCAmelCase      :str				  = src_vocab_file
											lowerCAmelCase      :List[str]				  = tgt_vocab_file
											lowerCAmelCase      :str				  = merges_file
											lowerCAmelCase      :Optional[Any]				  = do_lower_case

											# cache of sm.MosesPunctNormalizer instance
											lowerCAmelCase      :str				  = {}
											# cache of sm.MosesTokenizer instance
											lowerCAmelCase      :Any				  = {}
											lowerCAmelCase      :List[str]				  = {}

											if langs and len(UpperCAmelCase      ) == 2:
																	lowerCAmelCase  ,   lowerCAmelCase      :List[Any]				  = langs
											else:
																	raise ValueError(
																	    f"""arg `langs` needs to be a list of 2 langs, e.g. ['en', 'ru'], but got {langs}. """
																	    'Usually that means that tokenizer can\'t find a mapping for the given model path '
																	    'in PRETRAINED_VOCAB_FILES_MAP, and other maps of this tokenizer.'      )

											with open(UpperCAmelCase    ,   encoding='utf-8'      ) as src_vocab_handle:
																	lowerCAmelCase      :Tuple				  = json.load(UpperCAmelCase      )
											with open(UpperCAmelCase    ,   encoding='utf-8'      ) as tgt_vocab_handle:
																	lowerCAmelCase      :str				  = json.load(UpperCAmelCase      )
																	lowerCAmelCase      :int				  = {v: k for k, v in tgt_vocab.items()}
											with open(UpperCAmelCase    ,   encoding='utf-8'      ) as merges_handle:
																	lowerCAmelCase      :Optional[int]				  = merges_handle.read().split('\n'      )[:-1]
											lowerCAmelCase      :List[Any]				  = [tuple(merge.split()[:2]      ) for merge in merges]
											lowerCAmelCase      :int				  = dict(zip(UpperCAmelCase    ,   range(len(UpperCAmelCase      )      )      )      )
											lowerCAmelCase      :str				  = {}
					def 					UpperCAmelCase__   (       self       :  Optional[Any]      )  ->					Dict[str, int]:
											return self.get_src_vocab()
					@property
					def 					UpperCAmelCase__   (       self       :  Dict      )  ->					int:
											return self.src_vocab_size
					def 					UpperCAmelCase__   (       self       :  int    ,   UpperCAmelCase       :  Tuple    ,   UpperCAmelCase       :  Tuple      )  ->					List[Any]:
											if lang not in self.cache_moses_punct_normalizer:
																	lowerCAmelCase      :str				  = self.sm.MosesPunctNormalizer(lang=UpperCAmelCase      )
																	lowerCAmelCase      :List[str]				  = punct_normalizer
											return self.cache_moses_punct_normalizer[lang].normalize(UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Tuple    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  Optional[int]      )  ->					str:
											if lang not in self.cache_moses_tokenizer:
																	lowerCAmelCase      :Optional[int]				  = self.sm.MosesTokenizer(lang=UpperCAmelCase      )
																	lowerCAmelCase      :Any				  = moses_tokenizer
											return self.cache_moses_tokenizer[lang].tokenize(
											    UpperCAmelCase    ,   aggressive_dash_splits=UpperCAmelCase    ,   return_str=UpperCAmelCase    ,   escape=UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Optional[Any]    ,   UpperCAmelCase       :  int    ,   UpperCAmelCase       :  str      )  ->					List[Any]:
											if lang not in self.cache_moses_detokenizer:
																	lowerCAmelCase      :Any				  = self.sm.MosesDetokenizer(lang=UpperCAmelCase      )
																	lowerCAmelCase      :str				  = moses_detokenizer
											return self.cache_moses_detokenizer[lang].detokenize(UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Optional[int]    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  List[Any]      )  ->					int:
											lowerCAmelCase      :Dict				  = replace_unicode_punct(UpperCAmelCase      )
											lowerCAmelCase      :Union[str, Any]				  = self.moses_punct_norm(UpperCAmelCase    ,   UpperCAmelCase      )
											lowerCAmelCase      :Dict				  = remove_non_printing_char(UpperCAmelCase      )
											return text
					@property
					def 					UpperCAmelCase__   (       self       :  str      )  ->					Union[str, Any]:
											return len(self.encoder      )
					@property
					def 					UpperCAmelCase__   (       self       :  int      )  ->					Optional[int]:
											return len(self.decoder      )
					def 					UpperCAmelCase__   (       self       :  List[Any]      )  ->					Tuple:
											return dict(self.encoder    ,   **self.added_tokens_encoder      )
					def 					UpperCAmelCase__   (       self       :  Any      )  ->					str:
											return dict(self.decoder    ,   **self.added_tokens_decoder      )
					def 					UpperCAmelCase__   (       self       :  List[str]    ,   UpperCAmelCase       :  List[str]      )  ->					Tuple:
											lowerCAmelCase      :List[str]				  = tuple(token[:-1]      ) + (token[-1] + '</w>',)
											if token in self.cache:
																	return self.cache[token]
											lowerCAmelCase      :Dict				  = get_pairs(UpperCAmelCase      )

											if not pairs:
																	return token + "</w>"

											while True:
																	lowerCAmelCase      :Optional[Any]				  = min(UpperCAmelCase    ,   key=lambda UpperCAmelCase      : self.bpe_ranks.get(UpperCAmelCase    ,   float('inf'      )      )      )
																	if bigram not in self.bpe_ranks:
																							break
																	lowerCAmelCase  ,   lowerCAmelCase      :Tuple				  = bigram
																	lowerCAmelCase      :int				  = []
																	lowerCAmelCase      :Union[str, Any]				  = 0
																	while i < len(UpperCAmelCase      ):
																							try:
																													lowerCAmelCase      :List[Any]				  = word.index(UpperCAmelCase    ,   UpperCAmelCase      )
																							except ValueError:
																													new_word.extend(word[i:]      )
																													break
																							else:
																													new_word.extend(word[i:j]      )
																													lowerCAmelCase      :Any				  = j

																							if word[i] == first and i < len(UpperCAmelCase      ) - 1 and word[i + 1] == second:
																													new_word.append(first + second      )
																													i += 2
																							else:
																													new_word.append(word[i]      )
																													i += 1
																	lowerCAmelCase      :Optional[Any]				  = tuple(UpperCAmelCase      )
																	lowerCAmelCase      :Dict				  = new_word
																	if len(UpperCAmelCase      ) == 1:
																							break
																	else:
																							lowerCAmelCase      :Any				  = get_pairs(UpperCAmelCase      )
											lowerCAmelCase      :Dict				  = ' '.join(UpperCAmelCase      )
											if word == "\n  </w>":
																	lowerCAmelCase      :List[Any]				  = '\n</w>'
											lowerCAmelCase      :Optional[Any]				  = word
											return word
					def 					UpperCAmelCase__   (       self       :  List[Any]    ,   UpperCAmelCase       :  int    ,   UpperCAmelCase       :  List[str]="en"    ,   UpperCAmelCase       :  int=False      )  ->					List[Any]:
											lowerCAmelCase      :Optional[Any]				  = self.src_lang

											if self.do_lower_case:
																	lowerCAmelCase      :Tuple				  = text.lower()

											if bypass_tokenizer:
																	lowerCAmelCase      :List[str]				  = text.split()
											else:
																	lowerCAmelCase      :Tuple				  = self.moses_pipeline(UpperCAmelCase    ,   lang=UpperCAmelCase      )
																	lowerCAmelCase      :Optional[int]				  = self.moses_tokenize(UpperCAmelCase    ,   lang=UpperCAmelCase      )

											lowerCAmelCase      :int				  = []
											for token in text:
																	if token:
																							split_tokens.extend(list(self.bpe(UpperCAmelCase      ).split(' '      )      )      )

											return split_tokens
					def 					UpperCAmelCase__   (       self       :  Dict    ,   UpperCAmelCase       :  Any      )  ->					List[Any]:
											return self.encoder.get(UpperCAmelCase    ,   self.encoder.get(self.unk_token      )      )
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]    ,   UpperCAmelCase       :  Any      )  ->					Dict:
											return self.decoder.get(UpperCAmelCase    ,   self.unk_token      )
					def 					UpperCAmelCase__   (       self       :  List[str]    ,   UpperCAmelCase       :  Optional[Any]      )  ->					int:
											lowerCAmelCase      :Optional[int]				  = [t.replace(' '    ,   ''      ).replace('</w>'    ,   ' '      ) for t in tokens]
											lowerCAmelCase      :Optional[int]				  = ''.join(UpperCAmelCase      ).split()
											# detokenize
											lowerCAmelCase      :Dict				  = self.moses_detokenize(UpperCAmelCase    ,   self.tgt_lang      )
											return text
					def 					UpperCAmelCase__   (       self       :  str    ,   UpperCAmelCase       :  List[int]    ,   UpperCAmelCase       :  Optional[List[int]] = None      )  ->					List[int]:
											lowerCAmelCase      :Union[str, Any]				  = [self.sep_token_id]

											# no bos used in fairseq
											if token_ids_a is None:
																	return token_ids_a + sep
											return token_ids_a + sep + token_ids_a + sep
					def 					UpperCAmelCase__   (       self       :  Dict    ,   UpperCAmelCase       :  List[int]    ,   UpperCAmelCase       :  Optional[List[int]] = None    ,   UpperCAmelCase       :  bool = False      )  ->					List[int]:

											if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=UpperCAmelCase    ,   token_ids_a=UpperCAmelCase    ,   already_has_special_tokens=UpperCAmelCase      )
											# no bos used in fairseq
											if token_ids_a is not None:
																	return ([0] * len(UpperCAmelCase      )) + [1] + ([0] * len(UpperCAmelCase      )) + [1]
											return ([0] * len(UpperCAmelCase      )) + [1]
					def 					UpperCAmelCase__   (       self       :  str    ,   UpperCAmelCase       :  List[int]    ,   UpperCAmelCase       :  Optional[List[int]] = None      )  ->					List[int]:
											lowerCAmelCase      :Optional[Any]				  = [self.sep_token_id]

											# no bos used in fairseq
											if token_ids_a is None:
																	return len(token_ids_a + sep      ) * [0]
											return len(token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]
					def 					UpperCAmelCase__   (       self       :  Optional[int]    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  Optional[str] = None      )  ->					Tuple[str]:
											if not os.path.isdir(UpperCAmelCase      ):
																	logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""      )
																	return

											lowerCAmelCase      :Any				  = os.path.join(
											    UpperCAmelCase    ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['src_vocab_file']      )
											lowerCAmelCase      :Tuple				  = os.path.join(
											    UpperCAmelCase    ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['tgt_vocab_file']      )
											lowerCAmelCase      :Optional[int]				  = os.path.join(
											    UpperCAmelCase    ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']      )

											with open(UpperCAmelCase    ,   'w'    ,   encoding='utf-8'      ) as f:
																	f.write(json.dumps(self.encoder    ,   indent=2    ,   sort_keys=UpperCAmelCase    ,   ensure_ascii=UpperCAmelCase      ) + '\n'      )

											with open(UpperCAmelCase    ,   'w'    ,   encoding='utf-8'      ) as f:
																	lowerCAmelCase      :int				  = {v: k for k, v in self.decoder.items()}
																	f.write(json.dumps(UpperCAmelCase    ,   indent=2    ,   sort_keys=UpperCAmelCase    ,   ensure_ascii=UpperCAmelCase      ) + '\n'      )

											lowerCAmelCase      :Optional[Any]				  = 0
											with open(UpperCAmelCase    ,   'w'    ,   encoding='utf-8'      ) as writer:
																	for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,   key=lambda UpperCAmelCase      : kv[1]      ):
																							if index != token_index:
																													logger.warning(
																													    f"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
																													    ' Please check that the tokenizer is not corrupted!'      )
																													lowerCAmelCase      :List[Any]				  = token_index
																							writer.write(' '.join(UpperCAmelCase      ) + '\n'      )
																							index += 1

											return src_vocab_file, tgt_vocab_file, merges_file
					def __getstate__(       self       :  Union[str, Any]      )  ->					int:
											lowerCAmelCase      :Any				  = self.__dict__.copy()
											lowerCAmelCase      :Tuple				  = None
											return state
					def __setstate__(       self       :  Any    ,   UpperCAmelCase       :  Union[str, Any]      )  ->					List[Any]:
											lowerCAmelCase      :Tuple				  = d

											try:
																	import sacremoses
											except ImportError:
																	raise ImportError(
																	    'You need to install sacremoses to use XLMTokenizer. '
																	    'See https://pypi.org/project/sacremoses/ for installation.'      )

											lowerCAmelCase      :str				  = sacremoses