



import math
import time

from transformers import Trainer, 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     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     Optional[int]     ,		*__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     str=None     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=None     ,		**__SCREAMING_SNAKE_CASE							:     List[str]):


           '''simple docstring'''

           super().__init__(*__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)
           __a						       =				eval_examples
           __a						       =				post_process_function



       def 		_lowerCamelCase			(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=None     ,		__SCREAMING_SNAKE_CASE							:     Tuple=None     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=None     ,		__SCREAMING_SNAKE_CASE							:     str = "eval"):


           '''simple docstring'''

           __a						       =				self.eval_dataset if eval_dataset is None else eval_dataset
           __a						       =				self.get_eval_dataloader(__SCREAMING_SNAKE_CASE)
           __a						       =				self.eval_examples if eval_examples is None else eval_examples

           # Temporarily disable metric computation, we will do it in the loop here.
           __a						       =				self.compute_metrics
           __a						       =				None
           __a						       =				self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
           __a						       =				time.time()
           try:
               __a						       =				eval_loop(
                   __SCREAMING_SNAKE_CASE     ,		description='''Evaluation'''     ,		prediction_loss_only=True if compute_metrics is None else None     ,		ignore_keys=__SCREAMING_SNAKE_CASE     ,		metric_key_prefix=__SCREAMING_SNAKE_CASE     ,		)
           finally:
               __a						       =				compute_metrics
           __a						       =				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(
                   __SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		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
               __a						       =				self.post_process_function(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		output.predictions)
               __a						       =				self.compute_metrics(__SCREAMING_SNAKE_CASE)

               # Prefix all keys with metric_key_prefix + '_'
               for key in list(metrics.keys()):
                   if not key.startswith(F'{metric_key_prefix}_'):
                       __a						       =				metrics.pop(__SCREAMING_SNAKE_CASE)
               metrics.update(output.metrics)
           else:
               __a						       =				output.metrics

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

           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())

           __a						       =				self.callback_handler.on_evaluate(self.args     ,		self.state     ,		self.control     ,		__SCREAMING_SNAKE_CASE)
           return metrics





       def 		_lowerCamelCase			(		self							:     int     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     str=None     ,		__SCREAMING_SNAKE_CASE							:     str = "test"):


           '''simple docstring'''

           __a						       =				self.get_test_dataloader(__SCREAMING_SNAKE_CASE)

           # Temporarily disable metric computation, we will do it in the loop here.
           __a						       =				self.compute_metrics
           __a						       =				None
           __a						       =				self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
           __a						       =				time.time()
           try:
               __a						       =				eval_loop(
                   __SCREAMING_SNAKE_CASE     ,		description='''Prediction'''     ,		prediction_loss_only=True if compute_metrics is None else None     ,		ignore_keys=__SCREAMING_SNAKE_CASE     ,		metric_key_prefix=__SCREAMING_SNAKE_CASE     ,		)
           finally:
               __a						       =				compute_metrics
           __a						       =				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(
                   __SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		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

           __a						       =				self.post_process_function(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		output.predictions     ,		'''predict''')
           __a						       =				self.compute_metrics(__SCREAMING_SNAKE_CASE)

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










import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


__snake_case						:str      		=     logging.get_logger(__name__)

__snake_case						:int      		=     {'''vocab_file''': '''vocab.txt'''}

__snake_case						:List[Any]      		=     {
    '''vocab_file''': {
        '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt''',
        '''YituTech/conv-bert-medium-small''': (
            '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'''
        ),
        '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt''',
    }
}

__snake_case						:List[str]      		=     {
    '''YituTech/conv-bert-base''': 512,
    '''YituTech/conv-bert-medium-small''': 512,
    '''YituTech/conv-bert-small''': 512,
}


__snake_case						:Optional[int]      		=     {
    '''YituTech/conv-bert-base''': {'''do_lower_case''': True},
    '''YituTech/conv-bert-medium-small''': {'''do_lower_case''': True},
    '''YituTech/conv-bert-small''': {'''do_lower_case''': True},
}

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Optional[int]											=						VOCAB_FILES_NAMES
       UpperCamelCase__  :    str											=						PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase__  :    Optional[Any]											=						PRETRAINED_INIT_CONFIGURATION
       UpperCamelCase__  :    Optional[int]											=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase__  :    int											=						ConvBertTokenizer



       def __init__(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=None     ,		__SCREAMING_SNAKE_CASE							:     Dict=None     ,		__SCREAMING_SNAKE_CASE							:     Tuple=True     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]="[UNK]"     ,		__SCREAMING_SNAKE_CASE							:     int="[SEP]"     ,		__SCREAMING_SNAKE_CASE							:     List[Any]="[PAD]"     ,		__SCREAMING_SNAKE_CASE							:     int="[CLS]"     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]="[MASK]"     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=True     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=None     ,		**__SCREAMING_SNAKE_CASE							:     Dict     ,		):


           '''simple docstring'''

           super().__init__(
               __SCREAMING_SNAKE_CASE     ,		tokenizer_file=__SCREAMING_SNAKE_CASE     ,		do_lower_case=__SCREAMING_SNAKE_CASE     ,		unk_token=__SCREAMING_SNAKE_CASE     ,		sep_token=__SCREAMING_SNAKE_CASE     ,		pad_token=__SCREAMING_SNAKE_CASE     ,		cls_token=__SCREAMING_SNAKE_CASE     ,		mask_token=__SCREAMING_SNAKE_CASE     ,		tokenize_chinese_chars=__SCREAMING_SNAKE_CASE     ,		strip_accents=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE     ,		)

           __a						       =				json.loads(self.backend_tokenizer.normalizer.__getstate__())
           if (
               normalizer_state.get('''lowercase'''     ,		__SCREAMING_SNAKE_CASE) != do_lower_case
               or normalizer_state.get('''strip_accents'''     ,		__SCREAMING_SNAKE_CASE) != strip_accents
               or normalizer_state.get('''handle_chinese_chars'''     ,		__SCREAMING_SNAKE_CASE) != tokenize_chinese_chars
           ):
               __a						       =				getattr(__SCREAMING_SNAKE_CASE     ,		normalizer_state.pop('''type'''))
               __a						       =				do_lower_case
               __a						       =				strip_accents
               __a						       =				tokenize_chinese_chars
               __a						       =				normalizer_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				do_lower_case



       def 		_lowerCamelCase			(		self							:     int     ,		__SCREAMING_SNAKE_CASE							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Any=None):


           '''simple docstring'''

           __a						       =				[self.cls_token_id] + token_ids_a + [self.sep_token_id]

           if token_ids_a:
               output += token_ids_a + [self.sep_token_id]

           return output



       def 		_lowerCamelCase			(		self							:     str     ,		__SCREAMING_SNAKE_CASE							:     List[int]     ,		__SCREAMING_SNAKE_CASE							:     Optional[List[int]] = None):


           '''simple docstring'''

           __a						       =				[self.sep_token_id]
           __a						       =				[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]





       def 		_lowerCamelCase			(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     Optional[str] = None):


           '''simple docstring'''

           __a						       =				self._tokenizer.model.save(__SCREAMING_SNAKE_CASE     ,		name=__SCREAMING_SNAKE_CASE)
           return tuple(__SCREAMING_SNAKE_CASE)





