


'''simple docstring'''



import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
A__		: Optional[int]												=  logging.getLogger(__name__)



def       UpperCAmelCase__ (     )					->		Optional[Any]:
     __lowerCamelCase       :     Any        =		argparse.ArgumentParser(description='PyTorch Transformer Language Model'							)
     parser.add_argument('--model_name'				,   type=UpperCAmelCase_				,   default='transfo-xl-wt103'				,   help='pretrained model name'							)
     parser.add_argument(
         '--split'				,   type=UpperCAmelCase_				,   default='test'				,   choices=['all', 'valid', 'test']				,   help='which split to evaluate'							)
     parser.add_argument('--batch_size'				,   type=UpperCAmelCase_				,   default=10				,   help='batch size'							)
     parser.add_argument('--tgt_len'				,   type=UpperCAmelCase_				,   default=1_28				,   help='number of tokens to predict'							)
     parser.add_argument('--ext_len'				,   type=UpperCAmelCase_				,   default=0				,   help='length of the extended context'							)
     parser.add_argument('--mem_len'				,   type=UpperCAmelCase_				,   default=16_00				,   help='length of the retained previous heads'							)
     parser.add_argument('--clamp_len'				,   type=UpperCAmelCase_				,   default=10_00				,   help='max positional embedding index'							)
     parser.add_argument('--no_cuda'				,   action='store_true'				,   help='Do not use CUDA even though CUA is available'							)
     parser.add_argument('--work_dir'				,   type=UpperCAmelCase_				,   required=UpperCAmelCase_				,   help='path to the work_dir'							)
     parser.add_argument('--no_log'				,   action='store_true'				,   help='do not log the eval result'							)
     parser.add_argument('--same_length'				,   action='store_true'				,   help='set same length attention with masking'							)
     parser.add_argument('--server_ip'				,   type=UpperCAmelCase_				,   default=''				,   help='Can be used for distant debugging.'							)
     parser.add_argument('--server_port'				,   type=UpperCAmelCase_				,   default=''				,   help='Can be used for distant debugging.'							)
     __lowerCamelCase       :     Union[str, Any]        =		parser.parse_args()
     assert args.ext_len >= 0, "extended context length must be non-negative"

     if args.server_ip and args.server_port:
          # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
          import ptvsd

          print('Waiting for debugger attach'							)
          ptvsd.enable_attach(address=(args.server_ip, args.server_port)				,   redirect_output=UpperCAmelCase_							)
          ptvsd.wait_for_attach()

     __lowerCamelCase       :     List[Any]        =		torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'							)
     logger.info('device: {}'.format(UpperCAmelCase_							)							)

     # Load a pre-processed dataset
     # You can also build the corpus yourself using TransfoXLCorpus methods
     # The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
     # and tokenizing the dataset
     # The pre-processed corpus is a convertion (using the conversion script )
     __lowerCamelCase       :     str        =		TransfoXLCorpus.from_pretrained(args.model_name							)

     __lowerCamelCase       :     Optional[int]        =		corpus.get_iterator('valid'				,   args.batch_size				,   args.tgt_len				,   device=UpperCAmelCase_				,   ext_len=args.ext_len							)
     __lowerCamelCase       :     Optional[int]        =		corpus.get_iterator('test'				,   args.batch_size				,   args.tgt_len				,   device=UpperCAmelCase_				,   ext_len=args.ext_len							)

     # Load a pre-trained model
     __lowerCamelCase       :     int        =		TransfoXLLMHeadModel.from_pretrained(args.model_name							)
     model.to(UpperCAmelCase_							)

     logger.info(
         'Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'.format(
             args.batch_size				,   args.tgt_len				,   args.ext_len				,   args.mem_len				,   args.clamp_len							)							)

     model.reset_memory_length(args.mem_len							)
     if args.clamp_len > 0:
          __lowerCamelCase       :     Tuple        =		args.clamp_len
     if args.same_length:
          __lowerCamelCase       :     Optional[Any]        =		True

     ###############################################################################
     # Evaluation code
     ###############################################################################
     def evaluate(UpperCAmelCase_   :    Any							):
          # Turn on evaluation mode which disables dropout.
          model.eval()
          __lowerCamelCase    ,	__lowerCamelCase       :     Optional[int]        =		0, 0.0
          __lowerCamelCase       :     Optional[int]        =		time.time()
          with torch.no_grad():
               __lowerCamelCase       :     Union[str, Any]        =		None
               for idx, (data, target, seq_len) in enumerate(UpperCAmelCase_							):
                    __lowerCamelCase       :     Union[str, Any]        =		model(UpperCAmelCase_				,   lm_labels=UpperCAmelCase_				,   mems=UpperCAmelCase_							)
                    __lowerCamelCase    ,	__lowerCamelCase    ,	__lowerCamelCase       :     Dict        =		ret
                    __lowerCamelCase       :     Any        =		loss.mean()
                    total_loss += seq_len * loss.item()
                    total_len += seq_len
               __lowerCamelCase       :     Union[str, Any]        =		time.time() - start_time
          logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(UpperCAmelCase_				,   10_00 * total_time / (idx + 1)							)							)
          return total_loss / total_len

     # Run on test data.
     if args.split == "all":
          __lowerCamelCase       :     Any        =		evaluate(UpperCAmelCase_							)
          __lowerCamelCase       :     str        =		evaluate(UpperCAmelCase_							)
     elif args.split == "valid":
          __lowerCamelCase       :     Any        =		evaluate(UpperCAmelCase_							)
          __lowerCamelCase       :     str        =		None
     elif args.split == "test":
          __lowerCamelCase       :     Tuple        =		evaluate(UpperCAmelCase_							)
          __lowerCamelCase       :     Tuple        =		None

     def format_log(UpperCAmelCase_   :    List[str]				,   UpperCAmelCase_   :    Union[str, Any]							):
          __lowerCamelCase       :     Optional[Any]        =		'| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(UpperCAmelCase_				,   UpperCAmelCase_				,   math.exp(UpperCAmelCase_							)							)
          return log_str

     __lowerCamelCase       :     Tuple        =		''
     if valid_loss is not None:
          log_str += format_log(UpperCAmelCase_				,   'valid'							)
     if test_loss is not None:
          log_str += format_log(UpperCAmelCase_				,   'test'							)

     logger.info('=' * 1_00							)
     logger.info(UpperCAmelCase_							)
     logger.info('=' * 1_00							)


if __name__ == "__main__":
      main()










'''simple docstring'''



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


A__		: int												=  logging.get_logger(__name__)

A__		: Union[str, Any]												=  {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

A__		: int												=  {
    """vocab_file""": {
        """microsoft/mpnet-base""": """https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """microsoft/mpnet-base""": """https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json""",
    },
}

A__		: int												=  {
    """microsoft/mpnet-base""": 512,
}

A__		: Optional[Any]												=  {
    """microsoft/mpnet-base""": {"""do_lower_case""": True},
}





class        UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  : int												=      VOCAB_FILES_NAMES
    lowerCamelCase  : int												=      PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase  : List[Any]												=      PRETRAINED_INIT_CONFIGURATION
    lowerCamelCase  : int												=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase  : Tuple												=      MPNetTokenizer
    lowerCamelCase  : List[Any]												=      ['input_ids', 'attention_mask']







    def __init__(							self      ,					SCREAMING_SNAKE_CASE_=None      ,					SCREAMING_SNAKE_CASE_=None      ,					SCREAMING_SNAKE_CASE_=True      ,					SCREAMING_SNAKE_CASE_="<s>"      ,					SCREAMING_SNAKE_CASE_="</s>"      ,					SCREAMING_SNAKE_CASE_="</s>"      ,					SCREAMING_SNAKE_CASE_="<s>"      ,					SCREAMING_SNAKE_CASE_="[UNK]"      ,					SCREAMING_SNAKE_CASE_="<pad>"      ,					SCREAMING_SNAKE_CASE_="<mask>"      ,					SCREAMING_SNAKE_CASE_=True      ,					SCREAMING_SNAKE_CASE_=None      ,					**SCREAMING_SNAKE_CASE_      ,					)    ->					Any:
         super().__init__(
             SCREAMING_SNAKE_CASE_      ,					tokenizer_file=SCREAMING_SNAKE_CASE_      ,					do_lower_case=SCREAMING_SNAKE_CASE_      ,					bos_token=SCREAMING_SNAKE_CASE_      ,					eos_token=SCREAMING_SNAKE_CASE_      ,					sep_token=SCREAMING_SNAKE_CASE_      ,					cls_token=SCREAMING_SNAKE_CASE_      ,					unk_token=SCREAMING_SNAKE_CASE_      ,					pad_token=SCREAMING_SNAKE_CASE_      ,					mask_token=SCREAMING_SNAKE_CASE_      ,					tokenize_chinese_chars=SCREAMING_SNAKE_CASE_      ,					strip_accents=SCREAMING_SNAKE_CASE_      ,					**SCREAMING_SNAKE_CASE_      ,					)

         __lowerCamelCase       :     Union[str, Any]        =		json.loads(self.backend_tokenizer.normalizer.__getstate__()     )
         if (
             pre_tok_state.get('lowercase'      ,					SCREAMING_SNAKE_CASE_     ) != do_lower_case
             or pre_tok_state.get('strip_accents'      ,					SCREAMING_SNAKE_CASE_     ) != strip_accents
         ):
              __lowerCamelCase       :     List[Any]        =		getattr(SCREAMING_SNAKE_CASE_      ,					pre_tok_state.pop('type'     )     )
              __lowerCamelCase       :     List[str]        =		do_lower_case
              __lowerCamelCase       :     str        =		strip_accents
              __lowerCamelCase       :     Union[str, Any]        =		pre_tok_class(**SCREAMING_SNAKE_CASE_     )

         __lowerCamelCase       :     Tuple        =		do_lower_case







    @property
    def 				lowercase_    (							self     )    ->					str:
         if self._mask_token is None:
              if self.verbose:
                   logger.error('Using mask_token, but it is not set yet.'     )
              return None
         return str(self._mask_token     )







    @mask_token.setter
    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_     )    ->					Union[str, Any]:
         __lowerCamelCase       :     Any        =		AddedToken(SCREAMING_SNAKE_CASE_      ,					lstrip=SCREAMING_SNAKE_CASE_      ,					rstrip=SCREAMING_SNAKE_CASE_     ) if isinstance(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     ) else value
         __lowerCamelCase       :     int        =		value







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_=None     )    ->					Union[str, Any]:
         __lowerCamelCase       :     Dict        =		[self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
              return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ = None     )    ->					List[int]:
         __lowerCamelCase       :     List[Any]        =		[self.sep_token_id]
         __lowerCamelCase       :     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 + sep + token_ids_a + sep     ) * [0]







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ = None     )    ->					Tuple[str]:
         __lowerCamelCase       :     Optional[Any]        =		self._tokenizer.model.save(SCREAMING_SNAKE_CASE_      ,					name=SCREAMING_SNAKE_CASE_     )
         return tuple(SCREAMING_SNAKE_CASE_     )






