






import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm
       import torch_xla.debug.metrics as met







class  _lowercase   (   _UpperCAmelCase						):
      def __init__(     self				,			*a				,			a=None				,			a=None				,			**a   ):
       super().__init__(*lowercase__				,			**lowercase__   )
       snake_case__  :    List[str]							       =eval_examples
       snake_case__  :    int							       =post_process_function
      def 						lowercase__					(     self				,			a = None				,			a=None				,			a = None				,			a = "eval"				,			**a				,			):
       snake_case__  :    Any							       =gen_kwargs.copy()
       snake_case__  :    List[str]							       =(
           gen_kwargs["max_length"] if gen_kwargs.get("""max_length"""   ) is not None else self.args.generation_max_length
       )
       snake_case__  :    Dict							       =(
           gen_kwargs["num_beams"] if gen_kwargs.get("""num_beams"""   ) is not None else self.args.generation_num_beams
       )
       snake_case__  :    Optional[int]							       =gen_kwargs

       snake_case__  :    Union[str, Any]							       =self.eval_dataset if eval_dataset is None else eval_dataset
       snake_case__  :    List[str]							       =self.get_eval_dataloader(lowercase__   )
       snake_case__  :    Optional[Any]							       =self.eval_examples if eval_examples is None else eval_examples

       # Temporarily disable metric computation, we will do it in the loop here.
       snake_case__  :    int							       =self.compute_metrics
       snake_case__  :    Optional[Any]							       =None
       snake_case__  :    List[str]							       =time.time()
       snake_case__  :    str							       =self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       try:
        snake_case__  :    str							       =eval_loop(
            lowercase__				,			description="""Evaluation"""				,			prediction_loss_only=True if compute_metrics is None else None				,			ignore_keys=lowercase__				,			metric_key_prefix=lowercase__				,			)
       finally:
        snake_case__  :    List[str]							       =compute_metrics
       snake_case__  :    Any							       =self.args.eval_batch_size * self.args.world_size
       if F"{metric_key_prefix}_jit_compilation_time" in output.metrics:
        start_time += output.metrics[F"{metric_key_prefix}_jit_compilation_time"]
       output.metrics.update(
           speed_metrics(
               lowercase__				,			lowercase__				,			num_samples=output.num_samples				,			num_steps=math.ceil(output.num_samples / total_batch_size   )				,			)   )

       if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
        # Only the main node write the results by default
        snake_case__  :    Any							       =self.post_process_function(lowercase__				,			lowercase__				,			lowercase__   )
        snake_case__  :    str							       =self.compute_metrics(lowercase__   )

        # Prefix all keys with metric_key_prefix + '_'
        for key in list(metrics.keys()   ):
         if not key.startswith(F"{metric_key_prefix}_"   ):
          snake_case__  :    Tuple							       =metrics.pop(lowercase__   )

        metrics.update(output.metrics   )
       else:
        snake_case__  :    Tuple							       =output.metrics

       if self.args.should_log:
        # Only the main node log the results by default
        self.log(lowercase__   )

       if self.args.tpu_metrics_debug or self.args.debug:
        # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
        xm.master_print(met.metrics_report()   )

       snake_case__  :    Optional[int]							       =self.callback_handler.on_evaluate(self.args				,			self.state				,			self.control				,			lowercase__   )
       return metrics



      def 						lowercase__					(     self				,			a				,			a				,			a=None				,			a = "test"				,			**a   ):
       snake_case__  :    Dict							       =gen_kwargs.copy()

       snake_case__  :    List[str]							       =self.get_test_dataloader(lowercase__   )

       # Temporarily disable metric computation, we will do it in the loop here.
       snake_case__  :    List[str]							       =self.compute_metrics
       snake_case__  :    Optional[Any]							       =None
       snake_case__  :    Dict							       =time.time()
       snake_case__  :    Optional[int]							       =self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       try:
        snake_case__  :    Union[str, Any]							       =eval_loop(
            lowercase__				,			description="""Prediction"""				,			prediction_loss_only=True if compute_metrics is None else None				,			ignore_keys=lowercase__				,			metric_key_prefix=lowercase__				,			)
       finally:
        snake_case__  :    str							       =compute_metrics

       snake_case__  :    Tuple							       =self.args.eval_batch_size * self.args.world_size
       if F"{metric_key_prefix}_jit_compilation_time" in output.metrics:
        start_time += output.metrics[F"{metric_key_prefix}_jit_compilation_time"]
       output.metrics.update(
           speed_metrics(
               lowercase__				,			lowercase__				,			num_samples=output.num_samples				,			num_steps=math.ceil(output.num_samples / total_batch_size   )				,			)   )
       if self.post_process_function is None or self.compute_metrics is None:
        return output

       snake_case__  :    List[Any]							       =self.post_process_function(lowercase__				,			lowercase__				,			lowercase__				,			"""predict"""   )
       snake_case__  :    Dict							       =self.compute_metrics(lowercase__   )

       # Prefix all keys with metric_key_prefix + '_'
       for key in list(metrics.keys()   ):
        if not key.startswith(F"{metric_key_prefix}_"   ):
         snake_case__  :    int							       =metrics.pop(lowercase__   )
       metrics.update(output.metrics   )
       return PredictionOutput(predictions=predictions.predictions				,			label_ids=predictions.label_ids				,			metrics=lowercase__   )













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

import sentencepiece as spm

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


__lowerCamelCase	:		Dict										=   logging.get_logger(__name__)

__lowerCamelCase	:		Any										=   {"""vocab_file""": """spiece.model"""}

__lowerCamelCase	:		Optional[Any]										=   {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

__lowerCamelCase	:		List[str]										=   {
    """google/bigbird-roberta-base""": 40_96,
    """google/bigbird-roberta-large""": 40_96,
    """google/bigbird-base-trivia-itc""": 40_96,
}







class  _lowercase   (   _A						):
      _a							:						int										=							VOCAB_FILES_NAMES
      _a							:						List[str]										=							PRETRAINED_VOCAB_FILES_MAP
      _a							:						Any										=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _a							:						Tuple										=							['input_ids', 'attention_mask']
      _a							:						List[int]										=							[]
      def __init__(     self				,			a				,			a="<unk>"				,			a="<s>"				,			a="</s>"				,			a="<pad>"				,			a="[SEP]"				,			a="[MASK]"				,			a="[CLS]"				,			a = None				,			**a				,			):
       snake_case__  :    str							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else bos_token
       snake_case__  :    Optional[int]							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else eos_token
       snake_case__  :    Any							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else unk_token
       snake_case__  :    List[str]							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else pad_token
       snake_case__  :    int							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else cls_token
       snake_case__  :    Tuple							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else sep_token

       # Mask token behave like a normal word, i.e. include the space before it
       snake_case__  :    Union[str, Any]							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else mask_token

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

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

       snake_case__  :    Optional[int]							       =vocab_file

       snake_case__  :    List[str]							       =spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(a   )
      @property
      def 						lowercase__					(     self   ):
       return self.sp_model.get_piece_size()
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[Any]							       ={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   ):
       snake_case__  :    List[Any]							       =self.__dict__.copy()
       snake_case__  :    Optional[int]							       =None
       return state
      def __setstate__(     self				,			a   ):
       snake_case__  :    Optional[Any]							       =d

       # for backward compatibility
       if not hasattr(self				,			"""sp_model_kwargs"""   ):
        snake_case__  :    Optional[Any]							       ={}

       snake_case__  :    Tuple							       =spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(self.vocab_file   )
      def 						lowercase__					(     self				,			a   ):
       return self.sp_model.encode(a				,			out_type=a   )
      def 						lowercase__					(     self				,			a   ):
       return self.sp_model.piece_to_id(a   )
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    Dict							       =self.sp_model.IdToPiece(a   )
       return token
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    Optional[int]							       =[]
       snake_case__  :    Dict							       =""""""
       snake_case__  :    Dict							       =False
       for token in tokens:
        # make sure that special tokens are not decoded using sentencepiece model
        if token in self.all_special_tokens:
         if not prev_is_special:
          out_string += " "
         out_string += self.sp_model.decode(a   ) + token
         snake_case__  :    Optional[Any]							       =True
         snake_case__  :    Dict							       =[]
        else:
         current_sub_tokens.append(a   )
         snake_case__  :    List[Any]							       =False
       out_string += self.sp_model.decode(a   )
       return out_string.strip()
      def 						lowercase__					(     self				,			a				,			a = False				,			a = None				,			a = True				,			**a				,			):
       snake_case__  :    List[str]							       =kwargs.pop("""use_source_tokenizer"""				,			a   )

       snake_case__  :    int							       =self.convert_ids_to_tokens(a				,			skip_special_tokens=a   )

       # To avoid mixing byte-level and unicode for byte-level BPT
       # we need to build string separately for added tokens and byte-level tokens
       # cf. https://github.com/huggingface/transformers/issues/1133
       snake_case__  :    List[Any]							       =[]
       snake_case__  :    int							       =[]
       for token in filtered_tokens:
        if skip_special_tokens and token in self.all_special_ids:
         continue
        if token in self.added_tokens_encoder:
         if current_sub_text:
          sub_texts.append(self.convert_tokens_to_string(a   )   )
          snake_case__  :    List[Any]							       =[]
         sub_texts.append(a   )
        else:
         current_sub_text.append(a   )
       if current_sub_text:
        sub_texts.append(self.convert_tokens_to_string(a   )   )

       # Mimic the behavior of the Rust tokenizer:
       # No space before [MASK] and [SEP]
       if spaces_between_special_tokens:
        snake_case__  :    Dict							       =re.sub(R""" (\[(MASK|SEP)\])"""				,			R"""\1"""				,			""" """.join(a   )   )
       else:
        snake_case__  :    Dict							       ="""""".join(a   )

       snake_case__  :    str							       =(
           clean_up_tokenization_spaces
           if clean_up_tokenization_spaces is not None
           else self.clean_up_tokenization_spaces
       )
       if clean_up_tokenization_spaces:
        snake_case__  :    Any							       =self.clean_up_tokenization(a   )
        return clean_text
       else:
        return text
      def 						lowercase__					(     self				,			a				,			a = None   ):
       if not os.path.isdir(a   ):
        logger.error(F"Vocabulary path ({save_directory}) should be a directory"   )
        return
       snake_case__  :    Any							       =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   ) and os.path.isfile(self.vocab_file   ):
        copyfile(self.vocab_file				,			a   )
       elif not os.path.isfile(self.vocab_file   ):
        with open(a				,			"""wb"""   ) as fi:
         snake_case__  :    Any							       =self.sp_model.serialized_model_proto()
         fi.write(a   )

       return (out_vocab_file,)
      def 						lowercase__					(     self				,			a				,			a = None   ):
       if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       snake_case__  :    Union[str, Any]							       =[self.cls_token_id]
       snake_case__  :    Dict							       =[self.sep_token_id]
       return cls + token_ids_a + sep + token_ids_a + sep
      def 						lowercase__					(     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] + ([0] * len(a   )) + [1]



      def 						lowercase__					(     self				,			a				,			a = None   ):
       snake_case__  :    List[str]							       =[self.sep_token_id]
       snake_case__  :    List[Any]							       =[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   ) * [0] + len(token_ids_a + sep   ) * [1]





