



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


UpperCAmelCase__     = logging.get_logger(__name__)

UpperCAmelCase__     = '▁'

UpperCAmelCase__     = {'vocab_file': 'prophetnet.tokenizer'}

UpperCAmelCase__     = {
    'vocab_file': {
        'microsoft/xprophetnet-large-wiki100-cased': (
            'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
        ),
    }
}

UpperCAmelCase__     = {
    'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}

UpperCAmelCase__     = {
    'microsoft/xprophetnet-large-wiki100-cased': 512,
}





def 							A			(  _UpperCAmelCase       :     Any					)       ->				int:







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







class     __lowerCAmelCase					(    A		):
			UpperCamelCase           =  VOCAB_FILES_NAMES
			UpperCamelCase           =  PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase           =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase           =  ['input_ids', 'attention_mask']



			def __init__(		self     :						Tuple				,	A     :						str				,	A     :						Optional[int]="[SEP]"				,	A     :						Any="[SEP]"				,	A     :						Optional[Any]="[SEP]"				,	A     :						int="[UNK]"				,	A     :						List[str]="[PAD]"				,	A     :						Tuple="[CLS]"				,	A     :						List[str]="[MASK]"				,	A     :						Optional[Dict[str, Any]] = None				,	**A     :						int				,	)     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     {} if sp_model_kwargs is None else sp_model_kwargs

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

						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

						_UpperCAmelCase	 =     spm.SentencePieceProcessor(**self.sp_model_kwargs)
						self.sp_model.Load(str(UpperCAmelCase__))
						_UpperCAmelCase	 =     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
						_UpperCAmelCase	 =     {'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

						for i in range(10):
									_UpperCAmelCase	 =     F"[unused{i}]"
									_UpperCAmelCase	 =     5 + i

						# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
						_UpperCAmelCase	 =     12
						_UpperCAmelCase	 =     {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(UpperCAmelCase__)



			def __getstate__(		self     :						int)     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.__dict__.copy()
						_UpperCAmelCase	 =     None
						return state



			def __setstate__(		self     :						Dict				,	A     :						Tuple)     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     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'):
									_UpperCAmelCase	 =     {}

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



			def 					_lowerCamelCase (		self     :						Dict				,	A     :						List[int]				,	A     :						Optional[List[int]] = None				,	A     :						bool = False)     ->					Any:



						"""simple docstring"""






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

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



			def 					_lowerCamelCase (		self     :						Optional[int]				,	A     :						List[int]				,	A     :						Optional[List[int]] = None)     ->					Any:



						"""simple docstring"""






						_UpperCAmelCase	 =     [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 					_lowerCamelCase (		self     :						Any)     ->					List[str]:



						"""simple docstring"""






						return len(self.sp_model) + self.fairseq_offset



			def 					_lowerCamelCase (		self     :						Tuple)     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     {self.convert_ids_to_tokens(UpperCAmelCase__): i for i in range(self.vocab_size)}
						vocab.update(self.added_tokens_encoder)
						return vocab



			def 					_lowerCamelCase (		self     :						List[str]				,	A     :						str)     ->					Optional[Any]:



						"""simple docstring"""






						return self.sp_model.encode(UpperCAmelCase__				,	out_type=UpperCAmelCase__)



			def 					_lowerCamelCase (		self     :						Optional[Any]				,	A     :						Optional[Any])     ->					Any:



						"""simple docstring"""






						if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
						_UpperCAmelCase	 =     self.sp_model.PieceToId(UpperCAmelCase__)

						# 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 					_lowerCamelCase (		self     :						int				,	A     :						int)     ->					int:



						"""simple docstring"""






						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 					_lowerCamelCase (		self     :						Optional[Any]				,	A     :						Dict)     ->					Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     ''''''.join(UpperCAmelCase__).replace(UpperCAmelCase__				,	' ').strip()
						return out_string



			def 					_lowerCamelCase (		self     :						int				,	A     :						str				,	A     :						Optional[str] = None)     ->					Optional[int]:



						"""simple docstring"""






						if not os.path.isdir(UpperCAmelCase__):
									logger.error(F"Vocabulary path ({save_directory}) should be a directory")
									return
						_UpperCAmelCase	 =     os.path.join(
						    UpperCAmelCase__				,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

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

						return (out_vocab_file,)



			def 					_lowerCamelCase (		self     :						int				,	A     :						List[int]				,	A     :						Optional[List[int]] = None)     ->					Tuple:



						"""simple docstring"""






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





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 							A			(  _UpperCAmelCase       :     List[Any]					)       ->				int:







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


UpperCAmelCase__     = logging.getLogger(__name__)





def 							A			(  _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     Union[str, Any]					)       ->				Any:







			'''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 							A			(  _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     int					)       ->				str:







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







class     __lowerCAmelCase					(    pl.Callback		):



			def 					_lowerCamelCase (		self     :						Optional[int]				,	A     :						List[Any]				,	A     :						int)     ->					Dict:



						"""simple docstring"""






						_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 					_lowerCamelCase (		self     :						Optional[Any]				,	A     :						pl.Trainer				,	A     :						pl.LightningModule				,	A     :						str				,	A     :						int=True)     ->					None:



						"""simple docstring"""






						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 					_lowerCamelCase (		self     :						str				,	A     :						Optional[int]				,	A     :						List[str])     ->					Optional[Any]:



						"""simple docstring"""






						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 					_lowerCamelCase (		self     :						Dict				,	A     :						pl.Trainer				,	A     :						pl.LightningModule)     ->					int:



						"""simple docstring"""






						save_json(pl_module.metrics				,	pl_module.metrics_save_path)
						return self._write_logs(A				,	A				,	'test')



			@rank_zero_only
			def 					_lowerCamelCase (		self     :						Tuple				,	A     :						pl.Trainer				,	A     :						str)     ->					Dict:



						"""simple docstring"""






						save_json(pl_module.metrics				,	pl_module.metrics_save_path)
						# Uncommenting this will save val generations
						# return self._write_logs(trainer, pl_module, "valid")
