


"""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
)
__snake_case												=							logging.getLogger(__name__)
def    A_ ( ):






     """simple docstring"""

     _a     						=					argparse.ArgumentParser(description='''PyTorch Transformer Language Model'''							)
     parser.add_argument('''--model_name''',  type=_lowerCAmelCase,  default='''transfo-xl-wt103''',  help='''pretrained model name'''							)
     parser.add_argument(
         '''--split''',  type=_lowerCAmelCase,  default='''test''',  choices=['''all''', '''valid''', '''test'''],  help='''which split to evaluate'''							)
     parser.add_argument('''--batch_size''',  type=_lowerCAmelCase,  default=10,  help='''batch size'''							)
     parser.add_argument('''--tgt_len''',  type=_lowerCAmelCase,  default=1_28,  help='''number of tokens to predict'''							)
     parser.add_argument('''--ext_len''',  type=_lowerCAmelCase,  default=0,  help='''length of the extended context'''							)
     parser.add_argument('''--mem_len''',  type=_lowerCAmelCase,  default=16_00,  help='''length of the retained previous heads'''							)
     parser.add_argument('''--clamp_len''',  type=_lowerCAmelCase,  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=_lowerCAmelCase,  required=_lowerCAmelCase,  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=_lowerCAmelCase,  default='''''',  help='''Can be used for distant debugging.'''							)
     parser.add_argument('''--server_port''',  type=_lowerCAmelCase,  default='''''',  help='''Can be used for distant debugging.'''							)
     _a     						=					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=_lowerCAmelCase							)
          ptvsd.wait_for_attach()

     _a     						=					torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''							)
     logger.info('''device: {}'''.format(_lowerCAmelCase							)							)

     # 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 )
     _a     						=					TransfoXLCorpus.from_pretrained(args.model_name							)

     _a     						=					corpus.get_iterator('''valid''',  args.batch_size,  args.tgt_len,  device=_lowerCAmelCase,  ext_len=args.ext_len							)
     _a     						=					corpus.get_iterator('''test''',  args.batch_size,  args.tgt_len,  device=_lowerCAmelCase,  ext_len=args.ext_len							)

     # Load a pre-trained model
     _a     						=					TransfoXLLMHeadModel.from_pretrained(args.model_name							)
     model.to(_lowerCAmelCase							)

     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:
          _a     						=					args.clamp_len
     if args.same_length:
          _a     						=					True

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

     # Run on test data.
     if args.split == "all":
          _a     						=					evaluate(_lowerCAmelCase							)
          _a     						=					evaluate(_lowerCAmelCase							)
     elif args.split == "valid":
          _a     						=					evaluate(_lowerCAmelCase							)
          _a     						=					None
     elif args.split == "test":
          _a     						=					evaluate(_lowerCAmelCase							)
          _a     						=					None

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

     _a     						=					''''''
     if valid_loss is not None:
          log_str += format_log(_lowerCAmelCase,  '''valid'''							)
     if test_loss is not None:
          log_str += format_log(_lowerCAmelCase,  '''test'''							)

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


if __name__ == "__main__":
   main()
"""simple docstring"""


import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand







def        __a (		A   ):






     '''simple docstring'''



     return EnvironmentCommand()






class  a__     ( _a						):


   @staticmethod
   def 						snake_case__						(  _UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				parser.add_parser("env",					help="Print relevant system environment info."	)
        download_parser.set_defaults(func=_UpperCAmelCase	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				{
            "`datasets` version": version,
            "Platform": platform.platform(),
            "Python version": platform.python_version(),
            "Huggingface_hub version": huggingface_hub.__version__,
            "PyArrow version": pyarrow.__version__,
            "Pandas version": pandas.__version__,
        }

        print("\nCopy-and-paste the text below in your GitHub issue.\n"	)
        print(self.format_dict(_UpperCAmelCase	)	)

        return info


   @staticmethod
   def 						snake_case__						(  _UpperCAmelCase	):
        '''simple docstring'''



        return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]	) + "\n"




