'''simple docstring'''




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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_mbartaa import MBartaaTokenizer
else:
 _A										= None


_A										= logging.get_logger(__name__)

_A										= {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_A										= {
    'vocab_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'
        ),
    },
}

_A										= {
    'facebook/mbart-large-50-one-to-many-mmt': 1_0_2_4,
}

# fmt: off
_A										= ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']


class    UpperCAmelCase__	(							_snake_case      ):






   """simple docstring"""
   A				:							str			     =     VOCAB_FILES_NAMES
   A				:							List[str]			     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A				:							List[Any]			     =     PRETRAINED_VOCAB_FILES_MAP
   A				:							Optional[Any]			     =     ['''input_ids''', '''attention_mask''']
   A				:							Optional[int]			     =     MBartaaTokenizer

   A				:							List[int]			     =     []
   A				:							List[int]			     =     []
   def __init__(self							,					_a=None							,					_a=None							,					_a=None							,					_a=None							,					_a="</s>"							,					_a="</s>"							,					_a="<s>"							,					_a="<unk>"							,					_a="<pad>"							,					_a="<mask>"							,					**_a							,					)     ->  Optional[Any]:
        # Mask token behave like a normal word, i.e. include the space before it
        lowercase_       :							Tuple  					=       AddedToken(_a							,					lstrip=_a							,					rstrip=_a					) if isinstance(_a							,					_a					) else mask_token

        lowercase_       :							List[str]  					=       kwargs.get('additional_special_tokens'							,					[]					)
        kwargs["additional_special_tokens"] += [
            code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
        ]

        super().__init__(
            _a							,					src_lang=_a							,					tgt_lang=_a							,					tokenizer_file=_a							,					eos_token=_a							,					sep_token=_a							,					cls_token=_a							,					unk_token=_a							,					pad_token=_a							,					mask_token=_a							,					**_a							,					)

        lowercase_       :							Union[str, Any]  					=       vocab_file
        lowercase_       :							List[str]  					=       False if not self.vocab_file else True

        lowercase_       :							str  					=       {
            lang_code: self.convert_tokens_to_ids(_a					) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        lowercase_       :							str  					=       src_lang if src_lang is not None else 'en_XX'
        lowercase_       :							List[str]  					=       tgt_lang
        lowercase_       :							str  					=       self.lang_code_to_id[self._src_lang]
        self.set_src_lang_special_tokens(self._src_lang					)
   @property
   def 		_lowerCamelCase			(self					)     ->  str:
        return self._src_lang
   @src_lang.setter
   def 		_lowerCamelCase			(self							,					_a					)     ->  None:
        lowercase_       :							Optional[Any]  					=       new_src_lang
        self.set_src_lang_special_tokens(self._src_lang					)
   def 		_lowerCamelCase			(self							,					_a							,					_a = None					)     ->  List[int]:
        if token_ids_a is None:
             return self.prefix_tokens + token_ids_a + self.suffix_tokens
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
   def 		_lowerCamelCase			(self							,					_a							,					_a = "en_XX"							,					_a = None							,					_a = "ro_RO"							,					**_a							,					)     ->  BatchEncoding:
        lowercase_       :							List[str]  					=       src_lang
        lowercase_       :							str  					=       tgt_lang
        return super().prepare_seqaseq_batch(_a							,					_a							,					**_a					)
   def 		_lowerCamelCase			(self					)     ->  List[Any]:
        return self.set_src_lang_special_tokens(self.src_lang					)
   def 		_lowerCamelCase			(self					)     ->  Any:
        return self.set_tgt_lang_special_tokens(self.tgt_lang					)
   def 		_lowerCamelCase			(self							,					_a					)     ->  None:
        lowercase_       :							List[str]  					=       self.convert_tokens_to_ids(_a					)
        lowercase_       :							int  					=       [self.cur_lang_code_id]
        lowercase_       :							List[str]  					=       [self.eos_token_id]

        lowercase_       :							List[str]  					=       self.convert_ids_to_tokens(self.prefix_tokens					)
        lowercase_       :							List[str]  					=       self.convert_ids_to_tokens(self.suffix_tokens					)

        lowercase_       :							Tuple  					=       processors.TemplateProcessing(
            single=prefix_tokens_str + ['$A'] + suffix_tokens_str							,					pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str							,					special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str							,					self.prefix_tokens + self.suffix_tokens					)					)							,					)
   def 		_lowerCamelCase			(self							,					_a					)     ->  None:
        lowercase_       :							str  					=       self.convert_tokens_to_ids(_a					)
        lowercase_       :							Optional[int]  					=       [self.cur_lang_code_id]
        lowercase_       :							Optional[Any]  					=       [self.eos_token_id]

        lowercase_       :							Optional[Any]  					=       self.convert_ids_to_tokens(self.prefix_tokens					)
        lowercase_       :							str  					=       self.convert_ids_to_tokens(self.suffix_tokens					)

        lowercase_       :							Tuple  					=       processors.TemplateProcessing(
            single=prefix_tokens_str + ['$A'] + suffix_tokens_str							,					pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str							,					special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str							,					self.prefix_tokens + self.suffix_tokens					)					)							,					)
   def 		_lowerCamelCase			(self							,					_a							,					_a							,					_a							,					_a							,					**_a					)     ->  Optional[int]:
        if src_lang is None or tgt_lang is None:
             raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'					)
        lowercase_       :							List[Any]  					=       src_lang
        lowercase_       :							Optional[Any]  					=       self(_a							,					add_special_tokens=_a							,					return_tensors=_a							,					**_a					)
        lowercase_       :							Any  					=       self.convert_tokens_to_ids(_a					)
        lowercase_       :							List[Any]  					=       tgt_lang_id
        return inputs





   def 		_lowerCamelCase			(self							,					_a							,					_a = None					)     ->  Tuple[str]:
        if not self.can_save_slow_tokenizer:
             raise ValueError(
                 'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                 'tokenizer.'					)

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

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

        return (out_vocab_file,)





'''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 import save_json

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							Optional[Any]  					=       filter(lambda SCREAMING_SNAKE_CASE_				: p.requires_grad	,							model.parameters()				)
     lowercase_       :							Tuple  					=       sum([np.prod(p.size()				) for p in model_parameters]				)
     return params


_A										= logging.getLogger(__name__)


class    UpperCAmelCase__	(							pl.Callback      ):






   """simple docstring"""
   def 		_lowerCamelCase			(self							,					_a							,					_a					)     ->  str:
        lowercase_       :							List[str]  					=       {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							,					_a							,					_a							,					_a							,					_a=True					)     ->  None:
        logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''					)
        lowercase_       :							Union[str, Any]  					=       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
        lowercase_       :							Optional[int]  					=       Path(pl_module.hparams.output_dir					)
        if type_path == "test":
             lowercase_       :							Union[str, Any]  					=       od / 'test_results.txt'
             lowercase_       :							Tuple  					=       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.
             lowercase_       :							List[str]  					=       od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
             lowercase_       :							int  					=       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
                  lowercase_       :							Any  					=       metrics[key]
                  if isinstance(_a							,					torch.Tensor					):
                       lowercase_       :							Any  					=       val.item()
                  lowercase_       :							List[str]  					=       f'''{key}: {val:.6f}\n'''
                  writer.write(_a					)

        if not save_generations:
             return

        if "preds" in metrics:
             lowercase_       :							Optional[Any]  					=       '\n'.join(metrics['preds']					)
             generations_file.open('w+'					).write(_a					)
   @rank_zero_only
   def 		_lowerCamelCase			(self							,					_a							,					_a					)     ->  Tuple:
        try:
             lowercase_       :							List[Any]  					=       pl_module.model.model.num_parameters()
        except AttributeError:
             lowercase_       :							Dict  					=       pl_module.model.num_parameters()

        lowercase_       :							Optional[Any]  					=       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							,					_a							,					_a					)     ->  int:
        save_json(pl_module.metrics							,					pl_module.metrics_save_path					)
        return self._write_logs(_a							,					_a							,					'test'					)





   @rank_zero_only
   def 		_lowerCamelCase			(self							,					_a							,					_a					)     ->  List[str]:
        save_json(pl_module.metrics							,					pl_module.metrics_save_path					)
        # Uncommenting this will save val generations
        # return self._write_logs(trainer, pl_module, "valid")



def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_=1	,							SCREAMING_SNAKE_CASE_=False				):
     if metric == "rouge2":
          lowercase_       :							Optional[int]  					=       '{val_avg_rouge2:.4f}-{step_count}'
     elif metric == "bleu":
          lowercase_       :							int  					=       '{val_avg_bleu:.4f}-{step_count}'
     elif metric == "loss":
          lowercase_       :							List[Any]  					=       '{val_avg_loss:.4f}-{step_count}'
     else:
          raise NotImplementedError(
              f'''seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to'''
              ' this function.'				)

     lowercase_       :							Tuple  					=       ModelCheckpoint(
         dirpath=SCREAMING_SNAKE_CASE_	,							filename=SCREAMING_SNAKE_CASE_	,							monitor=f'''val_{metric}'''	,							mode='min' if 'loss' in metric else 'max'	,							save_top_k=SCREAMING_SNAKE_CASE_	,							)
     return checkpoint_callback






def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     return EarlyStopping(
         monitor=f'''val_{metric}'''	,							mode='min' if 'loss' in metric else 'max'	,							patience=SCREAMING_SNAKE_CASE_	,							verbose=SCREAMING_SNAKE_CASE_	,							)




