



"""simple docstring"""






import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


__snake_case				:  Optional[Any] =       logging.get_logger(__name__)

__snake_case				:  List[str] =       "▁"

__snake_case				:  int =       {
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

__snake_case				:  List[str] =       {
    "vocab_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
    },
    "spm_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_config_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
    },
}

__snake_case				:  Union[str, Any] =       {
    "facebook/m2m100_418M": 1_024,
}

# fmt: off
__snake_case				:  List[str] =       {
    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
    "wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE        =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE        =      PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE        =      ['input_ids', 'attention_mask']

	SCREAMING_SNAKE_CASE        =      []
	SCREAMING_SNAKE_CASE        =      []

	def __init__(	self:						int			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						List[Any]=None			,     _SCREAMING_SNAKE_CASE:						Optional[int]=None			,     _SCREAMING_SNAKE_CASE:						List[Any]="<s>"			,     _SCREAMING_SNAKE_CASE:						int="</s>"			,     _SCREAMING_SNAKE_CASE:						Dict="</s>"			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]="<pad>"			,     _SCREAMING_SNAKE_CASE:						int="<unk>"			,     _SCREAMING_SNAKE_CASE:						List[Any]="m2m100"			,     _SCREAMING_SNAKE_CASE:						Optional[Dict[str, Any]] = None			,     _SCREAMING_SNAKE_CASE:						Dict=8			,     **_SCREAMING_SNAKE_CASE:						int			,     )							->			None:

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			{} if sp_model_kwargs is None else sp_model_kwargs

				__lowerCAmelCase :    str				=			language_codes
				__lowerCAmelCase :    Any				=			FAIRSEQ_LANGUAGE_CODES[language_codes]
				__lowerCAmelCase :    Any				=			{lang_code: F"""__{lang_code}__""" for lang_code in fairseq_language_code}

				__lowerCAmelCase :    int				=			kwargs.get("additional_special_tokens"			,     [])
				kwargs["additional_special_tokens"] += [
				    self.get_lang_token(__a)
				    for lang_code in fairseq_language_code
				    if self.get_lang_token(__a) not in kwargs["additional_special_tokens"]
				]

				super().__init__(
				    src_lang=__a			,     tgt_lang=__a			,     bos_token=__a			,     eos_token=__a			,     sep_token=__a			,     unk_token=__a			,     pad_token=__a			,     language_codes=__a			,     sp_model_kwargs=self.sp_model_kwargs			,     num_madeup_words=__a			,     **__a			,     )

				__lowerCAmelCase :    str				=			vocab_file
				__lowerCAmelCase :    Any				=			load_json(__a)
				__lowerCAmelCase :    Dict				=			{v: k for k, v in self.encoder.items()}
				__lowerCAmelCase :    Dict				=			spm_file
				__lowerCAmelCase :    Optional[int]				=			load_spm(__a			,     self.sp_model_kwargs)

				__lowerCAmelCase :    int				=			len(self.encoder)

				__lowerCAmelCase :    Any				=			{
				    self.get_lang_token(__a): self.encoder_size + i for i, lang_code in enumerate(__a)
				}
				__lowerCAmelCase :    int				=			{lang_code: self.encoder_size + i for i, lang_code in enumerate(__a)}
				__lowerCAmelCase :    Optional[int]				=			{v: k for k, v in self.lang_token_to_id.items()}

				__lowerCAmelCase :    Any				=			src_lang if src_lang is not None else "en"
				__lowerCAmelCase :    Dict				=			tgt_lang
				__lowerCAmelCase :    Tuple				=			self.get_lang_id(self._src_lang)
				self.set_src_lang_special_tokens(self._src_lang)

				__lowerCAmelCase :    Union[str, Any]				=			num_madeup_words

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						List[str])							->			int:

				"""simple docstring"""

				return len(self.encoder) + len(self.lang_token_to_id)

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			str:

				"""simple docstring"""

				return self._src_lang

	@src_lang.setter
	def 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						str)							->			None:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			new_src_lang
				self.set_src_lang_special_tokens(self._src_lang)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						str)							->			List[str]:

				"""simple docstring"""

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

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						Union[str, Any])							->			Optional[Any]:

				"""simple docstring"""

				if token in self.lang_token_to_id:
							return self.lang_token_to_id[token]
				return self.encoder.get(__a			,     self.encoder[self.unk_token])

	def 				_SCREAMING_SNAKE_CASE  (	self:						str			,     _SCREAMING_SNAKE_CASE:						int)							->			str:

				"""simple docstring"""

				if index in self.id_to_lang_token:
							return self.id_to_lang_token[index]
				return self.decoder.get(__a			,     self.unk_token)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Any			,     _SCREAMING_SNAKE_CASE:						Optional[int])							->			Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			[]
				__lowerCAmelCase :    str				=			""
				for token in tokens:
							# make sure that special tokens are not decoded using sentencepiece model
							if token in self.all_special_tokens:
										out_string += self.sp_model.decode(__a) + token
										__lowerCAmelCase :    List[Any]				=			[]
							else:
										current_sub_tokens.append(__a)
				out_string += self.sp_model.decode(__a)
				return out_string.strip()

	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple			,     _SCREAMING_SNAKE_CASE:						List[int]			,     _SCREAMING_SNAKE_CASE:						Optional[List[int]] = None			,     _SCREAMING_SNAKE_CASE:						bool = False)							->			List[int]:

				"""simple docstring"""

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

				__lowerCAmelCase :    List[Any]				=			[1] * len(self.prefix_tokens)
				__lowerCAmelCase :    str				=			[1] * len(self.suffix_tokens)
				if token_ids_a is None:
							return prefix_ones + ([0] * len(__a)) + suffix_ones
				return prefix_ones + ([0] * len(__a)) + ([0] * len(__a)) + suffix_ones

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

				"""simple docstring"""

				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 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any])							->			Dict:

				"""simple docstring"""

				__lowerCAmelCase :    Dict				=			{self.convert_ids_to_tokens(__a): i for i in range(self.vocab_size)}
				vocab.update(self.added_tokens_encoder)
				return vocab

	def __getstate__(	self:						Optional[int])							->			Dict:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			self.__dict__.copy()
				__lowerCAmelCase :    str				=			None
				return state

	def __setstate__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Dict)							->			None:

				"""simple docstring"""

				__lowerCAmelCase :    str				=			d

				# for backward compatibility
				if not hasattr(self			,     "sp_model_kwargs"):
							__lowerCAmelCase :    Tuple				=			{}

				__lowerCAmelCase :    List[Any]				=			load_spm(self.spm_file			,     self.sp_model_kwargs)

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						Optional[str] = None)							->			Tuple[str]:

				"""simple docstring"""

				__lowerCAmelCase :    int				=			Path(__a)
				if not save_dir.is_dir():
							raise OSError(F"""{save_directory} should be a directory""")
				__lowerCAmelCase :    Dict				=			save_dir / (
				    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
				)
				__lowerCAmelCase :    List[str]				=			save_dir / (
				    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
				)

				save_json(self.encoder			,     __a)

				if os.path.abspath(self.spm_file) != os.path.abspath(__a) and os.path.isfile(self.spm_file):
							copyfile(self.spm_file			,     __a)
				elif not os.path.isfile(self.spm_file):
							with open(__a			,     "wb") as fi:
										__lowerCAmelCase :    Dict				=			self.sp_model.serialized_model_proto()
										fi.write(__a)

				return (str(__a), str(__a))

	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple			,     _SCREAMING_SNAKE_CASE:						List[str]			,     _SCREAMING_SNAKE_CASE:						str = "en"			,     _SCREAMING_SNAKE_CASE:						Optional[List[str]] = None			,     _SCREAMING_SNAKE_CASE:						str = "ro"			,     **_SCREAMING_SNAKE_CASE:						Optional[int]			,     )							->			BatchEncoding:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			src_lang
				__lowerCAmelCase :    Optional[Any]				=			tgt_lang
				self.set_src_lang_special_tokens(self.src_lang)
				return super().prepare_seqaseq_batch(__a			,     __a			,     **__a)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Any			,     _SCREAMING_SNAKE_CASE:						List[str]			,     _SCREAMING_SNAKE_CASE:						Optional[str]			,     _SCREAMING_SNAKE_CASE:						Optional[str]			,     **_SCREAMING_SNAKE_CASE:						Tuple)							->			List[str]:

				"""simple docstring"""

				if src_lang is None or tgt_lang is None:
							raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
				__lowerCAmelCase :    Tuple				=			src_lang
				__lowerCAmelCase :    List[Any]				=			self(__a			,     add_special_tokens=__a			,     **__a)
				__lowerCAmelCase :    Optional[int]				=			self.get_lang_id(__a)
				__lowerCAmelCase :    Dict				=			tgt_lang_id
				return inputs

	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int])							->			str:

				"""simple docstring"""

				self.set_src_lang_special_tokens(self.src_lang)

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			List[str]:

				"""simple docstring"""

				self.set_tgt_lang_special_tokens(self.tgt_lang)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict			,     _SCREAMING_SNAKE_CASE:						str)							->			None:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			self.get_lang_token(__a)
				__lowerCAmelCase :    Tuple				=			self.lang_token_to_id[lang_token]
				__lowerCAmelCase :    List[Any]				=			[self.cur_lang_id]
				__lowerCAmelCase :    Dict				=			[self.eos_token_id]

	def 				_SCREAMING_SNAKE_CASE  (	self:						str			,     _SCREAMING_SNAKE_CASE:						str)							->			None:

				"""simple docstring"""

				__lowerCAmelCase :    int				=			self.get_lang_token(__a)
				__lowerCAmelCase :    Any				=			self.lang_token_to_id[lang_token]
				__lowerCAmelCase :    str				=			[self.cur_lang_id]
				__lowerCAmelCase :    int				=			[self.eos_token_id]

	def 				_SCREAMING_SNAKE_CASE  (	self:						int			,     _SCREAMING_SNAKE_CASE:						str)							->			str:

				"""simple docstring"""

				return self.lang_code_to_token[lang]







	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						str)							->			int:

				"""simple docstring"""

				__lowerCAmelCase :    Dict				=			self.get_lang_token(__a)
				return self.lang_token_to_id[lang_token]






def      _lowercase							(    __snake_case   ,__snake_case      )		->  str:
			__lowerCAmelCase :    Dict				=			sentencepiece.SentencePieceProcessor(**UpperCamelCase__      )
			spm.Load(str(UpperCamelCase__      )      )
			return spm


def      _lowercase							(    __snake_case      )		->  Any:
			with open(UpperCamelCase__   ,"r"      ) as f:
						return json.load(UpperCamelCase__      )





def      _lowercase							(    __snake_case   ,__snake_case      )		->  Union[str, Any]:
			with open(UpperCamelCase__   ,"w"      ) as f:
						json.dump(UpperCamelCase__   ,UpperCamelCase__   ,indent=2      )
'''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")
