






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

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


a_		:    str   							=     logging.get_logger(__name__)

a_		:    Union[str, Any]   							=     {
    'vocab_file': 'vocab.txt',
    'merges_file': 'bpe.codes',
}

a_		:    List[str]   							=     {
    'vocab_file': {
        'vinai/phobert-base': 'https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt',
        'vinai/phobert-large': 'https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt',
    },
    'merges_file': {
        'vinai/phobert-base': 'https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes',
        'vinai/phobert-large': 'https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes',
    },
}

a_		:    Tuple   							=     {
    'vinai/phobert-base': 2_56,
    'vinai/phobert-large': 2_56,
}






def 					lowerCamelCase__					(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE							=			set()
			SCREAMING_SNAKE_CASE							=			word[0]
			for char in word[1:]:
						pairs.add((prev_char, char))
						SCREAMING_SNAKE_CASE							=			char

			SCREAMING_SNAKE_CASE							=			set(_UpperCAmelCase)
			return pairs







class 		_snake_case	(	A__       ):
			_lowercase		:     Optional[Any]        =				VOCAB_FILES_NAMES
			_lowercase		:     Tuple        =				PRETRAINED_VOCAB_FILES_MAP
			_lowercase		:     Union[str, Any]        =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






			def __init__(							self		,       a		,       a		,       a="<s>"		,       a="</s>"		,       a="</s>"		,       a="<s>"		,       a="<unk>"		,       a="<pad>"		,       a="<mask>"		,       **a		,       )   ->							Dict:
						super().__init__(
						    bos_token=a		,       eos_token=a		,       unk_token=a		,       sep_token=a		,       cls_token=a		,       pad_token=a		,       mask_token=a		,       **a		,       )

						SCREAMING_SNAKE_CASE							=			vocab_file
						SCREAMING_SNAKE_CASE							=			merges_file

						SCREAMING_SNAKE_CASE							=			{}
						SCREAMING_SNAKE_CASE							=			0
						SCREAMING_SNAKE_CASE							=			1
						SCREAMING_SNAKE_CASE							=			2
						SCREAMING_SNAKE_CASE							=			3

						self.add_from_file(a)

						SCREAMING_SNAKE_CASE							=			{v: k for k, v in self.encoder.items()}

						with open(a		,       encoding='utf-8') as merges_handle:
									SCREAMING_SNAKE_CASE							=			merges_handle.read().split('\n')[:-1]
						SCREAMING_SNAKE_CASE							=			[tuple(merge.split()[:-1]) for merge in merges]
						SCREAMING_SNAKE_CASE							=			dict(zip(a		,       range(len(a))))
						SCREAMING_SNAKE_CASE							=			{}






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a = None)   ->							List[int]:

						if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						SCREAMING_SNAKE_CASE							=			[self.cls_token_id]
						SCREAMING_SNAKE_CASE							=			[self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a = None		,       a = False)   ->							List[int]:

						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 [1] + ([0] * len(a)) + [1]
						return [1] + ([0] * len(a)) + [1, 1] + ([0] * len(a)) + [1]






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a = None)   ->							List[int]:
						SCREAMING_SNAKE_CASE							=			[self.sep_token_id]
						SCREAMING_SNAKE_CASE							=			[self.cls_token_id]

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






			@property
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[str]:
						return len(self.encoder)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Union[str, Any]:
						return dict(self.encoder		,       **self.added_tokens_encoder)






			def  SCREAMING_SNAKE_CASE__     (							self		,       a)   ->							Dict:
						if token in self.cache:
									return self.cache[token]
						SCREAMING_SNAKE_CASE							=			tuple(a)
						SCREAMING_SNAKE_CASE							=			tuple(list(word[:-1]) + [word[-1] + '</w>'])
						SCREAMING_SNAKE_CASE							=			get_pairs(a)

						if not pairs:
									return token

						while True:
									SCREAMING_SNAKE_CASE							=			min(a		,       key=lambda a: self.bpe_ranks.get(a		,       float('inf')))
									if bigram not in self.bpe_ranks:
												break
									SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			bigram
									SCREAMING_SNAKE_CASE							=			[]
									SCREAMING_SNAKE_CASE							=			0
									while i < len(a):
												try:
															SCREAMING_SNAKE_CASE							=			word.index(a		,       a)
												except ValueError:
															new_word.extend(word[i:])
															break
												else:
															new_word.extend(word[i:j])
															SCREAMING_SNAKE_CASE							=			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
									SCREAMING_SNAKE_CASE							=			tuple(a)
									SCREAMING_SNAKE_CASE							=			new_word
									if len(a) == 1:
												break
									else:
												SCREAMING_SNAKE_CASE							=			get_pairs(a)
						SCREAMING_SNAKE_CASE							=			'@@ '.join(a)
						SCREAMING_SNAKE_CASE							=			word[:-4]
						SCREAMING_SNAKE_CASE							=			word
						return word






			def  SCREAMING_SNAKE_CASE__     (							self		,       a)   ->							Tuple:
						SCREAMING_SNAKE_CASE							=			[]

						SCREAMING_SNAKE_CASE							=			re.findall(R'\S+\n?'		,       a)

						for token in words:
									split_tokens.extend(list(self.bpe(a).split(' ')))
						return split_tokens






			def  SCREAMING_SNAKE_CASE__     (							self		,       a)   ->							Any:
						return self.encoder.get(a		,       self.encoder.get(self.unk_token))






			def  SCREAMING_SNAKE_CASE__     (							self		,       a)   ->							List[str]:
						return self.decoder.get(a		,       self.unk_token)






			def  SCREAMING_SNAKE_CASE__     (							self		,       a)   ->							Tuple:
						SCREAMING_SNAKE_CASE							=			' '.join(a).replace('@@ '		,       '').strip()
						return out_string






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

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

						if os.path.abspath(self.merges_file) != os.path.abspath(a):
									copyfile(self.merges_file		,       a)

						return out_vocab_file, out_merge_file







			def  SCREAMING_SNAKE_CASE__     (							self		,       a)   ->							Optional[Any]:
						if isinstance(a		,       a):
									try:
												with open(a		,       'r'		,       encoding='utf-8') as fd:
															self.add_from_file(a)
									except FileNotFoundError as fnfe:
												raise fnfe
									except UnicodeError:
												raise Exception(f'''Incorrect encoding detected in {f}, please rebuild the dataset''')
									return

						SCREAMING_SNAKE_CASE							=			f.readlines()
						for lineTmp in lines:
									SCREAMING_SNAKE_CASE							=			lineTmp.strip()
									SCREAMING_SNAKE_CASE							=			line.rfind(' ')
									if idx == -1:
												raise ValueError('Incorrect dictionary format, expected \'<token> <cnt>\'')
									SCREAMING_SNAKE_CASE							=			line[:idx]
									SCREAMING_SNAKE_CASE							=			len(self.encoder)





'''simple docstring'''


import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json





def    lowerCAmelCase       (		UpperCamelCase__					: Tuple							):


	"""simple docstring"""
	__UpperCAmelCase      					=      filter(lambda UpperCamelCase__							: p.requires_grad     ,       model.parameters()							)
	__UpperCAmelCase      					=      sum([np.prod(p.size()							) for p in model_parameters]							)
	return params


__lowerCAmelCase						:							List[str]    							=						logging.getLogger(__name__)





def    lowerCAmelCase       (		UpperCamelCase__					: Any     ,       UpperCamelCase__					: str							):


	"""simple docstring"""
	if metric == "rouge2":
		__UpperCAmelCase      					=      '''{val_avg_rouge2:.4f}-{step_count}'''
	elif metric == "bleu":
		__UpperCAmelCase      					=      '''{val_avg_bleu:.4f}-{step_count}'''
	elif metric == "em":
		__UpperCAmelCase      					=      '''{val_avg_em:.4f}-{step_count}'''
	elif metric == "loss":
		__UpperCAmelCase      					=      '''{val_avg_loss:.4f}-{step_count}'''
	else:
		raise NotImplementedError(
		    f"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
		    ''' function.'''							)

	__UpperCAmelCase      					=      ModelCheckpoint(
	    dirpath=UpperCamelCase__     ,       filename=UpperCamelCase__     ,       monitor=f"""val_{metric}"""     ,       mode='''max'''     ,       save_top_k=1     ,       every_n_epochs=1     ,       )

	return checkpoint_callback





def    lowerCAmelCase       (		UpperCamelCase__					: Tuple     ,       UpperCamelCase__					: Optional[Any]							):


	"""simple docstring"""
	return EarlyStopping(
	    monitor=f"""val_{metric}"""     ,       mode='''min''' if '''loss''' in metric else '''max'''     ,       patience=UpperCamelCase__     ,       verbose=UpperCamelCase__     ,       )






class     A ( pl.Callback ):





							def    snake_case__    (   self					:				Union[str, Any]  ,		__a					:				int  ,		__a					:				Any						)				->						Optional[Any]:
								__UpperCAmelCase      					=      {f"""lr_group_{i}""": param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups						)}
								pl_module.logger.log_metrics(__a						)





							@rank_zero_only
							def    snake_case__    (   self					:				Union[str, Any]  ,		__a					:				pl.Trainer  ,		__a					:				pl.LightningModule  ,		__a					:				str  ,		__a					:				Any=True						)				->						None:
								logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""						)
								__UpperCAmelCase      					=      trainer.callback_metrics
								trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['''log''', '''progress_bar''', '''preds''']}						)
								# Log results
								__UpperCAmelCase      					=      Path(pl_module.hparams.output_dir						)
								if type_path == "test":
									__UpperCAmelCase      					=      od / '''test_results.txt'''
									__UpperCAmelCase      					=      od / '''test_generations.txt'''
								else:
									# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
									# If people want this it will be easy enough to add back.
									__UpperCAmelCase      					=      od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
									__UpperCAmelCase      					=      od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
									results_file.parent.mkdir(exist_ok=__a						)
									generations_file.parent.mkdir(exist_ok=__a						)
								with open(__a  ,		'''a+'''						) as writer:
									for key in sorted(__a						):
										if key in ["log", "progress_bar", "preds"]:
											continue
										__UpperCAmelCase      					=      metrics[key]
										if isinstance(__a  ,		torch.Tensor						):
											__UpperCAmelCase      					=      val.item()
										__UpperCAmelCase      					=      f"""{key}: {val:.6f}\n"""
										writer.write(__a						)

								if not save_generations:
									return

								if "preds" in metrics:
									__UpperCAmelCase      					=      '''\n'''.join(metrics['''preds''']						)
									generations_file.open('''w+'''						).write(__a						)





							@rank_zero_only
							def    snake_case__    (   self					:				List[Any]  ,		__a					:				Optional[int]  ,		__a					:				Union[str, Any]						)				->						List[Any]:
								try:
									__UpperCAmelCase      					=      pl_module.model.model.num_parameters()
								except AttributeError:
									__UpperCAmelCase      					=      pl_module.model.num_parameters()

								__UpperCAmelCase      					=      count_trainable_parameters(__a						)
								# mp stands for million parameters
								trainer.logger.log_metrics({'''n_params''': npars, '''mp''': npars / 1e6, '''grad_mp''': n_trainable_pars / 1e6}						)





							@rank_zero_only
							def    snake_case__    (   self					:				List[str]  ,		__a					:				pl.Trainer  ,		__a					:				pl.LightningModule						)				->						Optional[int]:
								save_json(pl_module.metrics  ,		pl_module.metrics_save_path						)
								return self._write_logs(__a  ,		__a  ,		'''test'''						)






							@rank_zero_only
							def    snake_case__    (   self					:				Any  ,		__a					:				pl.Trainer  ,		__a					:				Union[str, Any]						)				->						Tuple:
								save_json(pl_module.metrics  ,		pl_module.metrics_save_path						)
								# Uncommenting this will save val generations
								# return self._write_logs(trainer, pl_module, "valid")
