
from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
				from fairscale.optim import OSS


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

snake_case :		Union[str, Any]							      =  {
    "linear": get_linear_schedule_with_warmup,
    "cosine": get_cosine_schedule_with_warmup,
    "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
    "polynomial": get_polynomial_decay_schedule_with_warmup,
    "constant": get_constant_schedule,
    "constant_w_warmup": get_constant_schedule_with_warmup,
}





class       _snake_case					(     snake_case				):







			def __init__(				self						,   _a=None						,   _a=None						,   *_a						,   **_a		):
				super().__init__(*_a						,   **_a		)

				if config is None:
					assert isinstance(self.model						,   _a		), (
					    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
					    f''' {self.model.__class__}'''
					)
					__magic_name__		:		List[Any]	   =     self.model.config
				else:
					__magic_name__		:		Optional[Any]	   =     config

				__magic_name__		:		str	   =     data_args
				__magic_name__		:		int	   =     self.config.tgt_vocab_size if isinstance(self.config						,   _a		) else self.config.vocab_size

				if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
					assert self.config.pad_token_id is not None, (
					    "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
					    " calculation or doing label smoothing."
					)

				if self.config.pad_token_id is None and self.config.eos_token_id is not None:
					logger.warning(
					    f'''The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'''
					    " padding.."		)

				if self.args.label_smoothing == 0:
					__magic_name__		:		Tuple	   =     torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id		)
				else:
					# dynamically import label_smoothed_nll_loss
					from utils import label_smoothed_nll_loss

					__magic_name__		:		Optional[Any]	   =     label_smoothed_nll_loss







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				if self.optimizer is None:
					__magic_name__		:		Optional[Any]	   =     ["bias", "LayerNorm.weight"]
					__magic_name__		:		Optional[int]	   =     [
					    {
					        "params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay		)],
					        "weight_decay": self.args.weight_decay,
					    },
					    {
					        "params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay		)],
					        "weight_decay": 0.0,
					    },
					]
					__magic_name__		:		List[str]	   =     Adafactor if self.args.adafactor else AdamW
					if self.args.adafactor:
						__magic_name__		:		Any	   =     Adafactor
						__magic_name__		:		Tuple	   =     {"scale_parameter": False, "relative_step": False}
					else:
						__magic_name__		:		Any	   =     AdamW
						__magic_name__		:		str	   =     {
						    "betas": (self.args.adam_betaa, self.args.adam_betaa),
						    "eps": self.args.adam_epsilon,
						}
					__magic_name__		:		Optional[Any]	   =     self.args.learning_rate
					if self.sharded_ddp:
						__magic_name__		:		Dict	   =     OSS(
						    params=_a						,   optim=_a						,   **_a						,   )
					else:
						__magic_name__		:		List[str]	   =     optimizer_cls(_a						,   **_a		)

				if self.lr_scheduler is None:
					__magic_name__		:		str	   =     self._get_lr_scheduler(_a		)
				else:  # ignoring --lr_scheduler
					logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		Dict	   =     arg_to_scheduler[self.args.lr_scheduler]
				if self.args.lr_scheduler == "constant":
					__magic_name__		:		int	   =     schedule_func(self.optimizer		)
				elif self.args.lr_scheduler == "constant_w_warmup":
					__magic_name__		:		Optional[Any]	   =     schedule_func(self.optimizer						,   num_warmup_steps=self.args.warmup_steps		)
				else:
					__magic_name__		:		Optional[Any]	   =     schedule_func(
					    self.optimizer						,   num_warmup_steps=self.args.warmup_steps						,   num_training_steps=_a		)
				return scheduler







			def 			SCREAMING_SNAKE_CASE    (				self		):
				if isinstance(self.train_dataset						,   torch.utils.data.IterableDataset		):
					return None
				elif is_torch_tpu_available():
					return get_tpu_sampler(self.train_dataset		)
				else:
					if self.args.sortish_sampler:
						self.train_dataset.make_sortish_sampler(
						    self.args.per_device_train_batch_size						,   distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)						,   )

					return (
					    RandomSampler(self.train_dataset		)
					    if self.args.local_rank == -1
					    else DistributedSampler(self.train_dataset		)
					)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a		):
				if self.args.label_smoothing == 0:
					if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
						# force training to ignore pad token
						__magic_name__		:		Optional[int]	   =     model(**_a						,   use_cache=_a		)[0]
						__magic_name__		:		str	   =     self.loss_fn(logits.view(-1						,   logits.shape[-1]		)						,   labels.view(-1		)		)
					else:
						# compute usual loss via models
						__magic_name__   ,    __magic_name__		:		Union[str, Any]	   =     model(**_a						,   labels=_a						,   use_cache=_a		)[:2]
				else:
					# compute label smoothed loss
					__magic_name__		:		Any	   =     model(**_a						,   use_cache=_a		)[0]
					__magic_name__		:		str	   =     torch.nn.functional.log_softmax(_a						,   dim=-1		)
					__magic_name__   ,    __magic_name__		:		Tuple	   =     self.loss_fn(_a						,   _a						,   self.args.label_smoothing						,   ignore_index=self.config.pad_token_id		)
				return loss, logits







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a		):
				__magic_name__		:		Optional[int]	   =     inputs.pop("labels"		)
				__magic_name__   ,    __magic_name__		:		Optional[int]	   =     self._compute_loss(_a						,   _a						,   _a		)
				return loss







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a						,   _a = None						,   ):
				__magic_name__		:		List[str]	   =     self._prepare_inputs(_a		)

				__magic_name__		:		List[str]	   =     {
				    "max_length": self.data_args.val_max_target_length
				    if self.data_args is not None
				    else self.config.max_length,
				    "num_beams": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
				}

				if self.args.predict_with_generate and not self.args.prediction_loss_only:
					__magic_name__		:		Optional[int]	   =     self.model.generate(
					    inputs["input_ids"]						,   attention_mask=inputs["attention_mask"]						,   **_a						,   )
					# in case the batch is shorter than max length, the output should be padded
					if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
						__magic_name__		:		Dict	   =     self._pad_tensors_to_max_len(_a						,   gen_kwargs["max_length"]		)

				__magic_name__		:		List[str]	   =     inputs.pop("labels"		)
				with torch.no_grad():
					# compute loss on predict data
					__magic_name__   ,    __magic_name__		:		Optional[Any]	   =     self._compute_loss(_a						,   _a						,   _a		)

				__magic_name__		:		List[Any]	   =     loss.mean().detach()
				if self.args.prediction_loss_only:
					return (loss, None, None)

				__magic_name__		:		Dict	   =     generated_tokens if self.args.predict_with_generate else logits

				if labels.shape[-1] < gen_kwargs["max_length"]:
					__magic_name__		:		int	   =     self._pad_tensors_to_max_len(_a						,   gen_kwargs["max_length"]		)

				return (loss, logits, labels)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a		):
				# If PAD token is not defined at least EOS token has to be defined
				__magic_name__		:		int	   =     self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

				if pad_token_id is None:
					raise ValueError(
					    "Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"
					    f''' padded to `max_length`={max_length}'''		)

				__magic_name__		:		int	   =     pad_token_id * torch.ones(
				    (tensor.shape[0], max_length)						,   dtype=tensor.dtype						,   device=tensor.device		)
				__magic_name__		:		Optional[int]	   =     tensor
				return padded_tensor


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

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


snake_case :		Dict							      =  logging.get_logger(__name__)

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

snake_case :		Dict							      =  {
    "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",
    },
}

snake_case :		Union[str, Any]							      =  {
    "vinai/phobert-base": 256,
    "vinai/phobert-large": 256,
}






def   lowerCAmelCase_						(					_snake_case      :  str   )				->		Union[str, Any]:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     set()
	__magic_name__		:		Any	   =     word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)   )
		__magic_name__		:		int	   =     char

	__magic_name__		:		List[str]	   =     set(_snake_case   )
	return pairs





class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    VOCAB_FILES_NAMES
			UpperCamelCase__          =    PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__          =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







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

				__magic_name__		:		Dict	   =     vocab_file
				__magic_name__		:		Tuple	   =     merges_file

				__magic_name__		:		List[Any]	   =     {}
				__magic_name__		:		List[Any]	   =     0
				__magic_name__		:		Tuple	   =     1
				__magic_name__		:		int	   =     2
				__magic_name__		:		Union[str, Any]	   =     3

				self.add_from_file(_a		)

				__magic_name__		:		Optional[int]	   =     {v: k for k, v in self.encoder.items()}

				with open(_a						,   encoding="utf-8"		) as merges_handle:
					__magic_name__		:		List[str]	   =     merges_handle.read().split("\n"		)[:-1]
				__magic_name__		:		Union[str, Any]	   =     [tuple(merge.split()[:-1]		) for merge in merges]
				__magic_name__		:		Union[str, Any]	   =     dict(zip(_a						,   range(len(_a		)		)		)		)
				__magic_name__		:		Optional[int]	   =     {}







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):

				if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				__magic_name__		:		Optional[Any]	   =     [self.cls_token_id]
				__magic_name__		:		Union[str, Any]	   =     [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		):

				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		):
				__magic_name__		:		Optional[Any]	   =     [self.sep_token_id]
				__magic_name__		:		Tuple	   =     [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		):
				return len(self.encoder		)







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







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				if token in self.cache:
					return self.cache[token]
				__magic_name__		:		List[Any]	   =     tuple(_a		)
				__magic_name__		:		List[Any]	   =     tuple(list(word[:-1]		) + [word[-1] + "</w>"]		)
				__magic_name__		:		Any	   =     get_pairs(_a		)

				if not pairs:
					return token

				while True:
					__magic_name__		:		str	   =     min(_a						,   key=lambda _a		: self.bpe_ranks.get(_a						,   float("inf"		)		)		)
					if bigram not in self.bpe_ranks:
						break
					__magic_name__   ,    __magic_name__		:		List[str]	   =     bigram
					__magic_name__		:		List[str]	   =     []
					__magic_name__		:		List[str]	   =     0
					while i < len(_a		):
						try:
							__magic_name__		:		Any	   =     word.index(_a						,   _a		)
						except ValueError:
							new_word.extend(word[i:]		)
							break
						else:
							new_word.extend(word[i:j]		)
							__magic_name__		:		Tuple	   =     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
					__magic_name__		:		Union[str, Any]	   =     tuple(_a		)
					__magic_name__		:		Optional[int]	   =     new_word
					if len(_a		) == 1:
						break
					else:
						__magic_name__		:		List[Any]	   =     get_pairs(_a		)
				__magic_name__		:		Optional[int]	   =     "@@ ".join(_a		)
				__magic_name__		:		Tuple	   =     word[:-4]
				__magic_name__		:		str	   =     word
				return word







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		Optional[Any]	   =     []

				__magic_name__		:		Dict	   =     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		):
				return self.encoder.get(_a						,   self.encoder.get(self.unk_token		)		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				return self.decoder.get(_a						,   self.unk_token		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		Tuple	   =     " ".join(_a		).replace("@@ "						,   ""		).strip()
				return out_string







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):
				if not os.path.isdir(_a		):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
					return
				__magic_name__		:		Optional[int]	   =     os.path.join(
				    _a						,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
				__magic_name__		:		Union[str, Any]	   =     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		):
				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

				__magic_name__		:		List[Any]	   =     f.readlines()
				for lineTmp in lines:
					__magic_name__		:		Optional[Any]	   =     lineTmp.strip()
					__magic_name__		:		Union[str, Any]	   =     line.rfind(" "		)
					if idx == -1:
						raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'"		)
					__magic_name__		:		Optional[int]	   =     line[:idx]
					__magic_name__		:		Dict	   =     len(self.encoder		)
