
import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

lowercase__						:str					    =       logging.getLogger()
lowercase__						:List[Any]					    =       torch.cuda.is_available()
lowercase__						:str					    =       {
    "max_tokens_per_batch": None,
    "supervise_forward": True,
    "normalize_hidden": True,
    "label_smoothing": 0.2,
    "eval_max_gen_length": None,
    "eval_beams": 1,
    "val_metric": "loss",
    "save_top_k": 1,
    "adafactor": True,
    "early_stopping_patience": 2,
    "logger_name": "default",
    "length_penalty": 0.5,
    "cache_dir": "",
    "task": "summarization",
    "num_workers": 2,
    "alpha_hid": 0,
    "freeze_embeds": True,
    "enc_only": False,
    "tgt_suffix": "",
    "resume_from_checkpoint": None,
    "sortish_sampler": True,
    "student_decoder_layers": 1,
    "val_check_interval": 1.0,
    "output_dir": "",
    "fp16": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    "no_teacher": False,
    "fp16_opt_level": "O1",
    "gpus": 1 if CUDA_AVAILABLE else 0,
    "n_tpu_cores": 0,
    "max_grad_norm": 1.0,
    "do_train": True,
    "do_predict": True,
    "accumulate_grad_batches": 1,
    "server_ip": "",
    "server_port": "",
    "seed": 42,
    "model_name_or_path": "sshleifer/bart-tiny-random",
    "config_name": "",
    "tokenizer_name": "facebook/bart-large",
    "do_lower_case": False,
    "learning_rate": 0.3,
    "lr_scheduler": "linear",
    "weight_decay": 0.0,
    "adam_epsilon": 1E-08,
    "warmup_steps": 0,
    "max_epochs": 1,
    "train_batch_size": 2,
    "eval_batch_size": 2,
    "max_source_length": 12,
    "max_target_length": 12,
    "val_max_target_length": 12,
    "test_max_target_length": 12,
    "fast_dev_run": False,
    "no_cache": False,
    "n_train": -1,
    "n_val": -1,
    "n_test": -1,
    "student_encoder_layers": 1,
    "freeze_encoder": False,
    "auto_scale_batch_size": False,
    "overwrite_output_dir": False,
    "student": None,
}



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__						):
					'''simple docstring'''
					lowercase		      =		'''\n'''.join(lowerCAmelCase__						)
					Path(lowerCAmelCase__						).open('''w'''						).writelines(lowerCAmelCase__						)


lowercase__						:List[Any]					    =       [" Sam ate lunch today.", "Sams lunch ingredients."]
lowercase__						:Optional[Any]					    =       ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
lowercase__						:Union[str, Any]					    =       "patrickvonplaten/t5-tiny-random"
lowercase__						:Union[str, Any]					    =       "sshleifer/t5-tinier-random"
lowercase__						:Dict					    =       "sshleifer/bart-tiny-random"
lowercase__						:Optional[Any]					    =       "sshleifer/tiny-mbart"
lowercase__						:int					    =       "sshleifer/tiny-marian-en-de"
lowercase__						:Optional[int]					    =       "stas/tiny-wmt19-en-de"


lowercase__						:List[Any]					    =       logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks



def 			UpperCamelCase       (				lowerCAmelCase__						):
					'''simple docstring'''
					for split in ["train", "val", "test"]:
										_dump_articles(os.path.join(lowerCAmelCase__					,     f'{split}.source'						)					,     lowerCAmelCase__						)
										_dump_articles(os.path.join(lowerCAmelCase__					,     f'{split}.target'						)					,     lowerCAmelCase__						)
					return tmp_dir







class 							lowercase						(   SCREAMING_SNAKE_CASE__						):




							@classmethod
							def 					A__ (							cls):
												logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
												return cls




							@slow
							@require_torch_gpu
							def 					A__ (							self):
												lowercase		      =		list_models()
												lowercase		      =		'''sshleifer'''
												lowercase		      =		[x.modelId for x in model_list if x.modelId.startswith(A__)]
												lowercase		      =		['''sshleifer/blenderbot-3B''', '''sshleifer/blenderbot-90M''']
												lowercase		      =		[]
												for m in model_ids:
																	if m in allowed_to_be_broken:
																						continue
																	try:
																						AutoConfig.from_pretrained(A__)
																	except Exception:
																						failures.append(A__)
												assert not failures, f'The following models could not be loaded through AutoConfig: {failures}'




							def 					A__ (							self):
												lowercase		      =		{'''student_encoder_layers''': 2, '''student_decoder_layers''': 1, '''no_teacher''': True}
												self._test_distiller_cli(A__)




							def 					A__ (							self):
												lowercase		      =		{
												    '''student_encoder_layers''': 2,
												    '''student_decoder_layers''': 1,
												    '''max_epochs''': 4,
												    '''val_check_interval''': 0.25,
												    '''alpha_hid''': 2.0,
												    '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
												}
												lowercase		      =		self._test_distiller_cli(A__  ,check_contents=A__)

												lowercase		      =		list(Path(model.output_dir).glob('''*.ckpt'''))
												self.assertEqual(1  ,len(A__))
												lowercase		      =		list(Path(model.output_dir).glob('''**/*.bin'''))
												self.assertEqual(len(A__)  ,2)
												lowercase		      =		lmap(str.strip  ,Path(model.hparams.data_dir).joinpath('''test.source''').open().readlines())
												lowercase		      =		tempfile.mktemp()  # XXX: not being cleaned up
												generate_summaries_or_translations(A__  ,A__  ,str(model.output_dir / '''best_tfmr'''))
												self.assertTrue(Path(A__).exists())

												lowercase		      =		self.get_auto_remove_tmp_dir()
												convert_pl_to_hf(ckpts[0]  ,transformer_ckpts[0].parent  ,A__)
												assert os.path.exists(os.path.join(A__  ,'''pytorch_model.bin'''))




							def 					A__ (							self):
												lowercase		      =		AutoModelForSeqaSeqLM.from_pretrained(A__)
												lowercase      ,		lowercase		      =		model.dummy_inputs['''input_ids'''], model.dummy_inputs['''attention_mask''']
												lowercase		      =		torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]  ,dtype=torch.long  ,device=model.device)
												lowercase		      =		target_ids[:, :-1].contiguous()  # Why this line?
												lowercase		      =		target_ids[:, 1:].clone()  # why clone?
												lowercase		      =		model(
												    A__  ,attention_mask=A__  ,decoder_input_ids=A__  ,labels=A__  ,use_cache=A__).loss

												lowercase		      =		model(A__  ,attention_mask=A__  ,decoder_input_ids=A__  ,use_cache=A__).logits

												lowercase		      =		nn.functional.log_softmax(A__  ,dim=-1)
												lowercase      ,		lowercase		      =		label_smoothed_nll_loss(
												    A__  ,A__  ,0.1  ,ignore_index=model.config.pad_token_id)
												with self.assertRaises(A__):
																	# TODO: understand why this breaks
																	self.assertEqual(A__  ,A__)




							def 					A__ (							self):
												lowercase		      =		{
												    '''student_encoder_layers''': 2,
												    '''student_decoder_layers''': 1,
												    '''num_train_epochs''': 4,
												    '''val_check_interval''': 0.25,
												    '''alpha_hid''': 2.0,
												    '''task''': '''translation''',
												    '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
												    '''tokenizer_name''': MBART_TINY,
												    '''teacher''': MBART_TINY,
												    '''src_lang''': '''en_XX''',
												    '''tgt_lang''': '''ro_RO''',
												}
												lowercase		      =		self._test_distiller_cli(A__  ,check_contents=A__)
												assert model.model.config.model_type == "mbart"

												lowercase		      =		list(Path(model.output_dir).glob('''*.ckpt'''))
												self.assertEqual(1  ,len(A__))
												lowercase		      =		list(Path(model.output_dir).glob('''**/*.bin'''))
												lowercase		      =		list(Path(model.output_dir).glob('''best_tfmr/*'''))
												assert len(A__) > 2
												self.assertEqual(len(A__)  ,2)




							def 					A__ (							self):
												lowercase		      =		{
												    '''student_encoder_layers''': 1,
												    '''student_decoder_layers''': 1,
												    '''alpha_hid''': 2.0,
												    '''teacher''': T5_TINY,
												    '''model_name_or_path''': T5_TINY,
												    '''tokenizer_name''': T5_TINY,
												}
												self._test_distiller_cli(A__)




							def 					A__ (							self):
												lowercase		      =		{
												    '''teacher''': T5_TINY,
												    '''student''': T5_TINIER,
												    '''model_name_or_path''': T5_TINIER,
												    '''tokenizer_name''': T5_TINIER,
												}
												self._test_distiller_cli(A__)





							def 					A__ (							self  ,A__  ,A__=True):
												lowercase		      =		{
												    '''label_smoothing''': 0.0,
												    '''early_stopping_patience''': -1,
												    '''train_batch_size''': 1,
												    '''eval_batch_size''': 2,
												    '''max_epochs''': 2,
												    '''alpha_mlm''': 0.2,
												    '''alpha_ce''': 0.8,
												    '''do_predict''': True,
												    '''model_name_or_path''': '''sshleifer/tinier_bart''',
												    '''teacher''': CHEAP_ARGS['''model_name_or_path'''],
												    '''val_check_interval''': 0.5,
												}
												default_updates.update(A__)
												lowercase		      =		CHEAP_ARGS.copy()
												lowercase		      =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
												lowercase		      =		self.get_auto_remove_tmp_dir()

												args_d.update(data_dir=A__  ,output_dir=A__  ,**A__)
												lowercase		      =		distill_main(argparse.Namespace(**A__))
												if not check_contents:
																	return model
												lowercase		      =		os.listdir(A__)
												lowercase		      =		{os.path.basename(A__) for p in contents}
												lowercase		      =		[p for p in contents if p.endswith('''ckpt''')]
												assert len(A__) > 0

												self.assertIn('''test_generations.txt'''  ,A__)
												self.assertIn('''test_results.txt'''  ,A__)

												lowercase		      =		load_json(model.metrics_save_path)
												lowercase		      =		metrics['''val'''][-1]
												self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']  ,0.01)
												self.assertGreaterEqual(1.0  ,last_step_stats['''val_avg_gen_time'''])
												self.assertIsInstance(last_step_stats[f'val_avg_{model.val_metric}']  ,A__)
												lowercase		      =		int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) + 1)
												self.assertEqual(len(metrics['''val'''])  ,A__)
												self.assertEqual(len(metrics['''test'''])  ,1)
												return model







class 							lowercase						(   SCREAMING_SNAKE_CASE__						):




							@parameterized.expand(
							    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]  ,)
							def 					A__ (							self  ,A__):
												lowercase		      =		CHEAP_ARGS.copy()
												lowercase		      =		'''translation''' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else '''summarization'''
												lowercase		      =		0.1 if task == '''translation''' else 0

												lowercase		      =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
												lowercase		      =		self.get_auto_remove_tmp_dir()
												args_d.update(
												    data_dir=A__  ,model_name_or_path=A__  ,tokenizer_name=A__  ,train_batch_size=2  ,eval_batch_size=2  ,output_dir=A__  ,do_predict=A__  ,task=A__  ,src_lang='''en_XX'''  ,tgt_lang='''ro_RO'''  ,freeze_encoder=A__  ,freeze_embeds=A__  ,)
												assert "n_train" in args_d
												lowercase		      =		argparse.Namespace(**A__)
												lowercase		      =		main(A__)

												lowercase		      =		module.model.get_input_embeddings()
												assert not input_embeds.weight.requires_grad
												if model == T5_TINY:
																	lowercase		      =		module.model.lm_head
																	assert not lm_head.weight.requires_grad
																	assert (lm_head.weight == input_embeds.weight).all().item()
												elif model == FSMT_TINY:
																	lowercase		      =		module.model.model
																	lowercase		      =		fsmt.decoder.embed_positions
																	assert not embed_pos.weight.requires_grad
																	assert not fsmt.decoder.embed_tokens.weight.requires_grad
																	# check that embeds are not the same
																	assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
												else:
																	lowercase		      =		module.model.model
																	lowercase		      =		bart.decoder.embed_positions
																	assert not embed_pos.weight.requires_grad
																	assert not bart.shared.weight.requires_grad
																	# check that embeds are the same
																	assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
																	assert bart.decoder.embed_tokens == bart.shared

												lowercase		      =		load_json(module.output_dir / '''text_batch.json''')
												assert isinstance(A__  ,A__)
												assert len(A__) >= 4




							def 					A__ (							self):
												lowercase		      =		CHEAP_ARGS.copy()

												lowercase		      =		'''summarization'''
												lowercase		      =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())

												args_d.update(
												    data_dir=A__  ,tokenizer_name=A__  ,train_batch_size=2  ,eval_batch_size=2  ,do_predict=A__  ,task=A__  ,src_lang='''en_XX'''  ,tgt_lang='''ro_RO'''  ,freeze_encoder=A__  ,freeze_embeds=A__  ,)

												# test models whose config includes the extra_model_args
												lowercase		      =		BART_TINY
												lowercase		      =		self.get_auto_remove_tmp_dir()
												lowercase		      =		args_d.copy()
												args_da.update(
												    model_name_or_path=A__  ,output_dir=A__  ,)
												lowercase		      =		('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
												for p in extra_model_params:
																	lowercase		      =		0.5
												lowercase		      =		argparse.Namespace(**A__)
												lowercase		      =		main(A__)
												for p in extra_model_params:
																	assert getattr(model.config  ,A__) == 0.5, f'failed to override the model config for param {p}'

												# test models whose config doesn't include the extra_model_args
												lowercase		      =		T5_TINY
												lowercase		      =		self.get_auto_remove_tmp_dir()
												lowercase		      =		args_d.copy()
												args_da.update(
												    model_name_or_path=A__  ,output_dir=A__  ,)
												lowercase		      =		'''encoder_layerdrop'''
												lowercase		      =		0.5
												lowercase		      =		argparse.Namespace(**A__)
												with pytest.raises(A__) as excinfo:
																	lowercase		      =		main(A__)
												assert str(excinfo.value) == f'model config doesn\'t have a `{unsupported_param}` attribute'





							def 					A__ (							self):
												lowercase		      =		CHEAP_ARGS.copy()

												lowercase		      =		'''summarization'''
												lowercase		      =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())

												lowercase		      =		BART_TINY
												lowercase		      =		self.get_auto_remove_tmp_dir()

												args_d.update(
												    data_dir=A__  ,model_name_or_path=A__  ,output_dir=A__  ,tokenizer_name=A__  ,train_batch_size=2  ,eval_batch_size=2  ,do_predict=A__  ,task=A__  ,src_lang='''en_XX'''  ,tgt_lang='''ro_RO'''  ,freeze_encoder=A__  ,freeze_embeds=A__  ,)

												# emulate finetune.py
												lowercase		      =		argparse.ArgumentParser()
												lowercase		      =		pl.Trainer.add_argparse_args(A__)
												lowercase		      =		SummarizationModule.add_model_specific_args(A__  ,os.getcwd())
												lowercase		      =		{'''--help''': True}

												# --help test
												with pytest.raises(A__) as excinfo:
																	with CaptureStdout() as cs:
																						lowercase		      =		parser.parse_args(A__)
																	assert False, "--help is expected to sys.exit"
												assert excinfo.type == SystemExit
												lowercase		      =		lightning_base.arg_to_scheduler_metavar
												assert expected in cs.out, "--help is expected to list the supported schedulers"

												# --lr_scheduler=non_existing_scheduler test
												lowercase		      =		'''non_existing_scheduler'''
												lowercase		      =		{f'--lr_scheduler={unsupported_param}'}
												with pytest.raises(A__) as excinfo:
																	with CaptureStderr() as cs:
																						lowercase		      =		parser.parse_args(A__)
																	assert False, "invalid argument is expected to sys.exit"
												assert excinfo.type == SystemExit
												lowercase		      =		f'invalid choice: \'{unsupported_param}\''
												assert expected in cs.err, f'should have bailed on invalid choice of scheduler {unsupported_param}'

												# --lr_scheduler=existing_scheduler test
												lowercase		      =		'''cosine'''
												lowercase		      =		args_d.copy()
												lowercase		      =		supported_param
												lowercase		      =		argparse.Namespace(**A__)
												lowercase		      =		main(A__)
												assert (
												    getattr(model.hparams  ,'''lr_scheduler''') == supported_param
												), f'lr_scheduler={supported_param} shouldn\'t fail'






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

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


lowercase__						:Tuple					    =       logging.get_logger(__name__)

lowercase__						:List[str]					    =       {
    "src_vocab_file": "vocab-src.json",
    "tgt_vocab_file": "vocab-tgt.json",
    "merges_file": "merges.txt",
}

lowercase__						:List[str]					    =       {
    "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"},
}

lowercase__						:Dict					    =       {"stas/tiny-wmt19-en-de": 1024}
lowercase__						:str					    =       {
    "stas/tiny-wmt19-en-de": {
        "langs": ["en", "de"],
        "model_max_length": 1024,
        "special_tokens_map_file": None,
        "full_tokenizer_file": None,
    }
}



def 			UpperCamelCase       (				lowerCAmelCase__						):
					'''simple docstring'''
					lowercase		      =		set()
					lowercase		      =		word[0]
					for char in word[1:]:
										pairs.add((prev_char, char)						)
										lowercase		      =		char
					return pairs



def 			UpperCamelCase       (				lowerCAmelCase__						):
					'''simple docstring'''
					lowercase		      =		text.replace('''，'''					,     ''','''						)
					lowercase		      =		re.sub(R'''。\s*'''					,     '''. '''					,     lowerCAmelCase__						)
					lowercase		      =		text.replace('''、'''					,     ''','''						)
					lowercase		      =		text.replace('''”'''					,     '''"'''						)
					lowercase		      =		text.replace('''“'''					,     '''"'''						)
					lowercase		      =		text.replace('''∶'''					,     ''':'''						)
					lowercase		      =		text.replace('''：'''					,     ''':'''						)
					lowercase		      =		text.replace('''？'''					,     '''?'''						)
					lowercase		      =		text.replace('''《'''					,     '''"'''						)
					lowercase		      =		text.replace('''》'''					,     '''"'''						)
					lowercase		      =		text.replace('''）'''					,     ''')'''						)
					lowercase		      =		text.replace('''！'''					,     '''!'''						)
					lowercase		      =		text.replace('''（'''					,     '''('''						)
					lowercase		      =		text.replace('''；'''					,     ''';'''						)
					lowercase		      =		text.replace('''１'''					,     '''1'''						)
					lowercase		      =		text.replace('''」'''					,     '''"'''						)
					lowercase		      =		text.replace('''「'''					,     '''"'''						)
					lowercase		      =		text.replace('''０'''					,     '''0'''						)
					lowercase		      =		text.replace('''３'''					,     '''3'''						)
					lowercase		      =		text.replace('''２'''					,     '''2'''						)
					lowercase		      =		text.replace('''５'''					,     '''5'''						)
					lowercase		      =		text.replace('''６'''					,     '''6'''						)
					lowercase		      =		text.replace('''９'''					,     '''9'''						)
					lowercase		      =		text.replace('''７'''					,     '''7'''						)
					lowercase		      =		text.replace('''８'''					,     '''8'''						)
					lowercase		      =		text.replace('''４'''					,     '''4'''						)
					lowercase		      =		re.sub(R'''．\s*'''					,     '''. '''					,     lowerCAmelCase__						)
					lowercase		      =		text.replace('''～'''					,     '''~'''						)
					lowercase		      =		text.replace('''’'''					,     '''\''''						)
					lowercase		      =		text.replace('''…'''					,     '''...'''						)
					lowercase		      =		text.replace('''━'''					,     '''-'''						)
					lowercase		      =		text.replace('''〈'''					,     '''<'''						)
					lowercase		      =		text.replace('''〉'''					,     '''>'''						)
					lowercase		      =		text.replace('''【'''					,     '''['''						)
					lowercase		      =		text.replace('''】'''					,     ''']'''						)
					lowercase		      =		text.replace('''％'''					,     '''%'''						)
					return text



def 			UpperCamelCase       (				lowerCAmelCase__						):
					'''simple docstring'''
					lowercase		      =		[]
					for char in text:
										lowercase		      =		unicodedata.category(lowerCAmelCase__						)
										if cat.startswith('''C'''						):
															continue
										output.append(lowerCAmelCase__						)
					return "".join(lowerCAmelCase__						)







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




							def __init__(							self  ,A__=None  ,A__=None  ,A__=None  ,A__=None  ,A__=False  ,A__="<unk>"  ,A__="<s>"  ,A__="</s>"  ,A__="<pad>"  ,**A__  ,):
												super().__init__(
												    langs=A__  ,src_vocab_file=A__  ,tgt_vocab_file=A__  ,merges_file=A__  ,do_lower_case=A__  ,unk_token=A__  ,bos_token=A__  ,sep_token=A__  ,pad_token=A__  ,**A__  ,)

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

												lowercase		      =		sacremoses

												lowercase		      =		src_vocab_file
												lowercase		      =		tgt_vocab_file
												lowercase		      =		merges_file
												lowercase		      =		do_lower_case

												# cache of sm.MosesPunctNormalizer instance
												lowercase		      =		{}
												# cache of sm.MosesTokenizer instance
												lowercase		      =		{}
												lowercase		      =		{}

												if langs and len(A__) == 2:
																	lowercase      ,		lowercase		      =		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(A__  ,encoding='''utf-8''') as src_vocab_handle:
																	lowercase		      =		json.load(A__)
												with open(A__  ,encoding='''utf-8''') as tgt_vocab_handle:
																	lowercase		      =		json.load(A__)
																	lowercase		      =		{v: k for k, v in tgt_vocab.items()}
												with open(A__  ,encoding='''utf-8''') as merges_handle:
																	lowercase		      =		merges_handle.read().split('''\n''')[:-1]
												lowercase		      =		[tuple(merge.split()[:2]) for merge in merges]
												lowercase		      =		dict(zip(A__  ,range(len(A__))))
												lowercase		      =		{}




							def 					A__ (							self):
												return self.get_src_vocab()




							@property
							def 					A__ (							self):
												return self.src_vocab_size




							def 					A__ (							self  ,A__  ,A__):
												if lang not in self.cache_moses_punct_normalizer:
																	lowercase		      =		self.sm.MosesPunctNormalizer(lang=A__)
																	lowercase		      =		punct_normalizer
												return self.cache_moses_punct_normalizer[lang].normalize(A__)




							def 					A__ (							self  ,A__  ,A__):
												if lang not in self.cache_moses_tokenizer:
																	lowercase		      =		self.sm.MosesTokenizer(lang=A__)
																	lowercase		      =		moses_tokenizer
												return self.cache_moses_tokenizer[lang].tokenize(
												    A__  ,aggressive_dash_splits=A__  ,return_str=A__  ,escape=A__)




							def 					A__ (							self  ,A__  ,A__):
												if lang not in self.cache_moses_detokenizer:
																	lowercase		      =		self.sm.MosesDetokenizer(lang=A__)
																	lowercase		      =		moses_detokenizer
												return self.cache_moses_detokenizer[lang].detokenize(A__)




							def 					A__ (							self  ,A__  ,A__):
												lowercase		      =		replace_unicode_punct(A__)
												lowercase		      =		self.moses_punct_norm(A__  ,A__)
												lowercase		      =		remove_non_printing_char(A__)
												return text




							@property
							def 					A__ (							self):
												return len(self.encoder)




							@property
							def 					A__ (							self):
												return len(self.decoder)




							def 					A__ (							self):
												return dict(self.encoder  ,**self.added_tokens_encoder)




							def 					A__ (							self):
												return dict(self.decoder  ,**self.added_tokens_decoder)




							def 					A__ (							self  ,A__):
												lowercase		      =		tuple(token[:-1]) + (token[-1] + '''</w>''',)
												if token in self.cache:
																	return self.cache[token]
												lowercase		      =		get_pairs(A__)

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

												while True:
																	lowercase		      =		min(A__  ,key=lambda A__: self.bpe_ranks.get(A__  ,float('''inf''')))
																	if bigram not in self.bpe_ranks:
																						break
																	lowercase      ,		lowercase		      =		bigram
																	lowercase		      =		[]
																	lowercase		      =		0
																	while i < len(A__):
																						try:
																											lowercase		      =		word.index(A__  ,A__)
																						except ValueError:
																											new_word.extend(word[i:])
																											break
																						else:
																											new_word.extend(word[i:j])
																											lowercase		      =		j

																						if word[i] == first and i < len(A__) - 1 and word[i + 1] == second:
																											new_word.append(first + second)
																											i += 2
																						else:
																											new_word.append(word[i])
																											i += 1
																	lowercase		      =		tuple(A__)
																	lowercase		      =		new_word
																	if len(A__) == 1:
																						break
																	else:
																						lowercase		      =		get_pairs(A__)
												lowercase		      =		''' '''.join(A__)
												if word == "\n  </w>":
																	lowercase		      =		'''\n</w>'''
												lowercase		      =		word
												return word




							def 					A__ (							self  ,A__  ,A__="en"  ,A__=False):
												lowercase		      =		self.src_lang

												if self.do_lower_case:
																	lowercase		      =		text.lower()

												if bypass_tokenizer:
																	lowercase		      =		text.split()
												else:
																	lowercase		      =		self.moses_pipeline(A__  ,lang=A__)
																	lowercase		      =		self.moses_tokenize(A__  ,lang=A__)

												lowercase		      =		[]
												for token in text:
																	if token:
																						split_tokens.extend(list(self.bpe(A__).split(''' ''')))

												return split_tokens




							def 					A__ (							self  ,A__):
												return self.encoder.get(A__  ,self.encoder.get(self.unk_token))




							def 					A__ (							self  ,A__):
												return self.decoder.get(A__  ,self.unk_token)




							def 					A__ (							self  ,A__):
												lowercase		      =		[t.replace(''' '''  ,'''''').replace('''</w>'''  ,''' ''') for t in tokens]
												lowercase		      =		''''''.join(A__).split()
												# detokenize
												lowercase		      =		self.moses_detokenize(A__  ,self.tgt_lang)
												return text




							def 					A__ (							self  ,A__  ,A__ = None):
												lowercase		      =		[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 					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__)
												# no bos used in fairseq
												if token_ids_a is not None:
																	return ([0] * len(A__)) + [1] + ([0] * len(A__)) + [1]
												return ([0] * len(A__)) + [1]




							def 					A__ (							self  ,A__  ,A__ = None):
												lowercase		      =		[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 					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['''src_vocab_file'''])
												lowercase		      =		os.path.join(
												    A__  ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''tgt_vocab_file'''])
												lowercase		      =		os.path.join(
												    A__  ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''])

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

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

												lowercase		      =		0
												with open(A__  ,'''w'''  ,encoding='''utf-8''') as writer:
																	for bpe_tokens, token_index in sorted(self.bpe_ranks.items()  ,key=lambda A__: 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!''')
																											lowercase		      =		token_index
																						writer.write(''' '''.join(A__) + '''\n''')
																						index += 1

												return src_vocab_file, tgt_vocab_file, merges_file




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





							def __setstate__(							self  ,A__):
												lowercase		      =		d

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

												lowercase		      =		sacremoses




