
"""simple docstring"""




import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
					import jieba

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


UpperCAmelCase__      :    List[Any]    							= logging.get_logger(__name__)

UpperCAmelCase__      :    Union[str, Any]    							= {'vocab_file': 'vocab.txt'}

UpperCAmelCase__      :    Dict    							= {
    'vocab_file': {
        'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt',
    },
}

UpperCAmelCase__      :    Optional[Any]    							= {
    'openbmb/cpm-ant-10b': 1_0_2_4,
}

def lowercase_  (   _snake_case						):
							SCREAMING_SNAKE_CASE__						:       str           =   collections.OrderedDict()
							with open(_snake_case   ,"""r"""   ,encoding="""utf-8"""						) as reader:
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   reader.readlines()
							for index, token in enumerate(_snake_case						):
														SCREAMING_SNAKE_CASE__						:       List[str]           =   token.rstrip("""\n"""						)
														SCREAMING_SNAKE_CASE__						:       Tuple           =   index
							return vocab



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__="<unk>"   ,       SCREAMING_SNAKE_CASE__=2_00    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   vocab
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   unk_token
											SCREAMING_SNAKE_CASE__						:       Tuple           =   max_input_chars_per_word



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   list(SCREAMING_SNAKE_CASE__    )
											if len(SCREAMING_SNAKE_CASE__    ) > self.max_input_chars_per_word:
																		return [self.unk_token]

											SCREAMING_SNAKE_CASE__						:       str           =   0
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   []
											while start < len(SCREAMING_SNAKE_CASE__    ):
																		SCREAMING_SNAKE_CASE__						:       Any           =   len(SCREAMING_SNAKE_CASE__    )
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   None
																		while start < end:
																									SCREAMING_SNAKE_CASE__						:       Any           =   """""".join(chars[start:end]    )
																									if substr in self.vocab:
																																SCREAMING_SNAKE_CASE__						:       List[str]           =   substr
																																break
																									end -= 1
																		if cur_substr is None:
																									sub_tokens.append(self.unk_token    )
																									start += 1
																		else:
																									sub_tokens.append(SCREAMING_SNAKE_CASE__    )
																									SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   end

											return sub_tokens






class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase      :						Any					       =   VOCAB_FILES_NAMES
				__UpperCamelCase      :						str					       =   PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase      :						Optional[Any]					       =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase      :						Tuple					       =   ['''input_ids''', '''attention_mask''']
				__UpperCamelCase      :						Any					       =   False



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__="<d>"   ,       SCREAMING_SNAKE_CASE__="</d>"   ,       SCREAMING_SNAKE_CASE__="<s>"   ,       SCREAMING_SNAKE_CASE__="</s>"   ,       SCREAMING_SNAKE_CASE__="<pad>"   ,       SCREAMING_SNAKE_CASE__="<unk>"   ,       SCREAMING_SNAKE_CASE__="</n>"   ,       SCREAMING_SNAKE_CASE__="</_>"   ,       SCREAMING_SNAKE_CASE__="left"   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		int:



											"""simple docstring"""




											requires_backends(self   ,       ["""jieba"""]    )
											super().__init__(
											    bod_token=SCREAMING_SNAKE_CASE__   ,       eod_token=SCREAMING_SNAKE_CASE__   ,       bos_token=SCREAMING_SNAKE_CASE__   ,       eos_token=SCREAMING_SNAKE_CASE__   ,       pad_token=SCREAMING_SNAKE_CASE__   ,       unk_token=SCREAMING_SNAKE_CASE__   ,       line_token=SCREAMING_SNAKE_CASE__   ,       space_token=SCREAMING_SNAKE_CASE__   ,       padding_side=SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__   ,       )
											SCREAMING_SNAKE_CASE__						:       int           =   bod_token
											SCREAMING_SNAKE_CASE__						:       Any           =   eod_token
											SCREAMING_SNAKE_CASE__						:       str           =   load_vocab(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   self.encoder[space_token]
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.encoder[line_token]

											del self.encoder[space_token]
											del self.encoder[line_token]

											SCREAMING_SNAKE_CASE__						:       Dict           =   collections.OrderedDict(sorted(self.encoder.items()   ,       key=lambda SCREAMING_SNAKE_CASE__    : x[1]    )    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   {v: k for k, v in self.encoder.items()}

											SCREAMING_SNAKE_CASE__						:       Tuple           =   WordpieceTokenizer(vocab=self.encoder   ,       unk_token=self.unk_token    )



				@property
				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											return self.encoder[self.bod_token]



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



											"""simple docstring"""




											return self.encoder[self.eod_token]



				@property
				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											return self.encoder["\n"]



				@property
				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											return len(self.encoder    )



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											return dict(self.encoder   ,       **self.added_tokens_encoder    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       int           =   []
											for x in jieba.cut(SCREAMING_SNAKE_CASE__   ,       cut_all=SCREAMING_SNAKE_CASE__    ):
																		output_tokens.extend(self.wordpiece_tokenizer.tokenize(SCREAMING_SNAKE_CASE__    )    )
											return output_tokens



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       int           =   [i for i in token_ids if i >= 0]
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   [
											    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
											]
											return super()._decode(SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		List[str]:



											"""simple docstring"""




											return token in self.encoder



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		str:



											"""simple docstring"""




											return "".join(SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Optional[int]:



											"""simple docstring"""




											return self.encoder.get(SCREAMING_SNAKE_CASE__   ,       self.encoder.get(self.unk_token    )    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




											return self.decoder.get(SCREAMING_SNAKE_CASE__   ,       self.unk_token    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = None    )					->		Tuple[str]:



											"""simple docstring"""




											if os.path.isdir(SCREAMING_SNAKE_CASE__    ):
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   os.path.join(
																		    SCREAMING_SNAKE_CASE__   ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )
											else:
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   (filename_prefix + """-""" if filename_prefix else """""") + save_directory
											SCREAMING_SNAKE_CASE__						:       int           =   0
											if " " in self.encoder:
																		SCREAMING_SNAKE_CASE__						:       int           =   self.encoder[""" """]
																		del self.encoder[" "]
											if "\n" in self.encoder:
																		SCREAMING_SNAKE_CASE__						:       Dict           =   self.encoder["""\n"""]
																		del self.encoder["\n"]
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   collections.OrderedDict(sorted(self.encoder.items()   ,       key=lambda SCREAMING_SNAKE_CASE__    : x[1]    )    )
											with open(SCREAMING_SNAKE_CASE__   ,       """w"""   ,       encoding="""utf-8"""    ) as writer:
																		for token, token_index in self.encoder.items():
																									if index != token_index:
																																logger.warning(
																																    F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
																																    """ Please check that the vocabulary is not corrupted!"""    )
																																SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   token_index
																									writer.write(token + """\n"""    )
																									index += 1
											return (vocab_file,)



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



											"""simple docstring"""




											if token_ids_a is None:
																		return [self.bos_token_id] + token_ids_a
											return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a



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



											"""simple docstring"""




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

											if token_ids_a is not None:
																		return [1] + ([0] * len(SCREAMING_SNAKE_CASE__    )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE__    ))
											return [1] + ([0] * len(SCREAMING_SNAKE_CASE__    ))








"""simple docstring"""







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


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   {
    '''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       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       def __init__(						self		,     snake_case__=None		,     snake_case__=None		,     *snake_case__		,     **snake_case__						):






         """simple docstring"""





         super().__init__(*snake_case__		,     **snake_case__						)

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

         lowerCAmelCase     :     Any								=							data_args
         lowerCAmelCase     :     Tuple								=							self.config.tgt_vocab_size if isinstance(self.config		,     snake_case__						) 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:
           lowerCAmelCase     :     int								=							torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id						)
         else:
           # dynamically import label_smoothed_nll_loss
           from utils import label_smoothed_nll_loss

           lowerCAmelCase     :     Tuple								=							label_smoothed_nll_loss

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         if self.optimizer is None:
           lowerCAmelCase     :     Optional[int]								=							["bias", "LayerNorm.weight"]
           lowerCAmelCase     :     str								=							[
               {
                   "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,
               },
           ]
           lowerCAmelCase     :     Union[str, Any]								=							Adafactor if self.args.adafactor else AdamW
           if self.args.adafactor:
             lowerCAmelCase     :     Dict								=							Adafactor
             lowerCAmelCase     :     Optional[int]								=							{"scale_parameter": False, "relative_step": False}
           else:
             lowerCAmelCase     :     int								=							AdamW
             lowerCAmelCase     :     int								=							{
                 "betas": (self.args.adam_betaa, self.args.adam_betaa),
                 "eps": self.args.adam_epsilon,
             }
           lowerCAmelCase     :     Any								=							self.args.learning_rate
           if self.sharded_ddp:
             lowerCAmelCase     :     int								=							OSS(
                 params=snake_case__		,     optim=snake_case__		,     **snake_case__		,     )
           else:
             lowerCAmelCase     :     Any								=							optimizer_cls(snake_case__		,     **snake_case__						)

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

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							arg_to_scheduler[self.args.lr_scheduler]
         if self.args.lr_scheduler == "constant":
           lowerCAmelCase     :     Tuple								=							schedule_func(self.optimizer						)
         elif self.args.lr_scheduler == "constant_w_warmup":
           lowerCAmelCase     :     Any								=							schedule_func(self.optimizer		,     num_warmup_steps=self.args.warmup_steps						)
         else:
           lowerCAmelCase     :     str								=							schedule_func(
               self.optimizer		,     num_warmup_steps=self.args.warmup_steps		,     num_training_steps=snake_case__						)
         return scheduler

       def        lowercase__							(						self						):






         """simple docstring"""





         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        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         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
             lowerCAmelCase     :     Dict								=							model(**snake_case__		,     use_cache=snake_case__						)[0]
             lowerCAmelCase     :     List[Any]								=							self.loss_fn(logits.view(-1		,     logits.shape[-1]						)		,     labels.view(-1						)						)
           else:
             # compute usual loss via models
             lowerCAmelCase						,  lowerCAmelCase     :     str								=							model(**snake_case__		,     labels=snake_case__		,     use_cache=snake_case__						)[:2]
         else:
           # compute label smoothed loss
           lowerCAmelCase     :     int								=							model(**snake_case__		,     use_cache=snake_case__						)[0]
           lowerCAmelCase     :     List[Any]								=							torch.nn.functional.log_softmax(snake_case__		,     dim=-1						)
           lowerCAmelCase						,  lowerCAmelCase     :     str								=							self.loss_fn(snake_case__		,     snake_case__		,     self.args.label_smoothing		,     ignore_index=self.config.pad_token_id						)
         return loss, logits

       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							inputs.pop("labels"						)
         lowerCAmelCase						,  lowerCAmelCase     :     str								=							self._compute_loss(snake_case__		,     snake_case__		,     snake_case__						)
         return loss

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__ = None		,     ):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							self._prepare_inputs(snake_case__						)

         lowerCAmelCase     :     Union[str, Any]								=							{
             "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:
           lowerCAmelCase     :     Dict								=							self.model.generate(
               inputs["input_ids"]		,     attention_mask=inputs["attention_mask"]		,     **snake_case__		,     )
           # in case the batch is shorter than max length, the output should be padded
           if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
             lowerCAmelCase     :     Dict								=							self._pad_tensors_to_max_len(snake_case__		,     gen_kwargs["max_length"]						)

         lowerCAmelCase     :     Optional[Any]								=							inputs.pop("labels"						)
         with torch.no_grad():
           # compute loss on predict data
           lowerCAmelCase						,  lowerCAmelCase     :     Dict								=							self._compute_loss(snake_case__		,     snake_case__		,     snake_case__						)

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

         lowerCAmelCase     :     int								=							generated_tokens if self.args.predict_with_generate else logits

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

         return (loss, logits, labels)







       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							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}"""						)

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





