




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
)
UpperCamelCase__						=		logging.getLogger(__name__)






def    snake_case   (				)				->				Dict:




	'''simple docstring'''


	lowerCamelCase_				:						Dict				  =				argparse.ArgumentParser(description='PyTorch Transformer Language Model'     )
	parser.add_argument('--model_name'		,  type=A		,  default='transfo-xl-wt103'		,  help='pretrained model name'     )
	parser.add_argument(
	    '--split'		,  type=A		,  default='test'		,  choices=['all', 'valid', 'test']		,  help='which split to evaluate'     )
	parser.add_argument('--batch_size'		,  type=A		,  default=10		,  help='batch size'     )
	parser.add_argument('--tgt_len'		,  type=A		,  default=128		,  help='number of tokens to predict'     )
	parser.add_argument('--ext_len'		,  type=A		,  default=0		,  help='length of the extended context'     )
	parser.add_argument('--mem_len'		,  type=A		,  default=1600		,  help='length of the retained previous heads'     )
	parser.add_argument('--clamp_len'		,  type=A		,  default=1000		,  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=A		,  required=A		,  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=A		,  default=''		,  help='Can be used for distant debugging.'     )
	parser.add_argument('--server_port'		,  type=A		,  default=''		,  help='Can be used for distant debugging.'     )
	lowerCamelCase_				:						str				  =				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=A     )
		ptvsd.wait_for_attach()

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

	# 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_				:						List[Any]				  =				TransfoXLCorpus.from_pretrained(args.model_name     )

	lowerCamelCase_				:						List[Any]				  =				corpus.get_iterator('valid'		,  args.batch_size		,  args.tgt_len		,  device=A		,  ext_len=args.ext_len     )
	lowerCamelCase_				:						Tuple				  =				corpus.get_iterator('test'		,  args.batch_size		,  args.tgt_len		,  device=A		,  ext_len=args.ext_len     )

	# Load a pre-trained model
	lowerCamelCase_				:						List[str]				  =				TransfoXLLMHeadModel.from_pretrained(args.model_name     )
	model.to(A     )

	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_				:						int				  =				args.clamp_len
	if args.same_length:
		lowerCamelCase_				:						List[str]				  =				True

	###############################################################################
	# Evaluation code
	###############################################################################
	def evaluate(A     ):
		# Turn on evaluation mode which disables dropout.
		model.eval()
		lowerCamelCase_  ,     lowerCamelCase_				:						Optional[int]				  =				0, 0.0
		lowerCamelCase_				:						str				  =				time.time()
		with torch.no_grad():
			lowerCamelCase_				:						int				  =				None
			for idx, (data, target, seq_len) in enumerate(A     ):
				lowerCamelCase_				:						int				  =				model(A		,  lm_labels=A		,  mems=A     )
				lowerCamelCase_  ,     lowerCamelCase_  ,     lowerCamelCase_				:						Optional[Any]				  =				ret
				lowerCamelCase_				:						Optional[int]				  =				loss.mean()
				total_loss += seq_len * loss.item()
				total_len += seq_len
			lowerCamelCase_				:						Tuple				  =				time.time() - start_time
		logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(A		,  1000 * total_time / (idx + 1)     )     )
		return total_loss / total_len

	# Run on test data.
	if args.split == "all":
		lowerCamelCase_				:						List[Any]				  =				evaluate(A     )
		lowerCamelCase_				:						Dict				  =				evaluate(A     )
	elif args.split == "valid":
		lowerCamelCase_				:						str				  =				evaluate(A     )
		lowerCamelCase_				:						Optional[Any]				  =				None
	elif args.split == "test":
		lowerCamelCase_				:						Optional[Any]				  =				evaluate(A     )
		lowerCamelCase_				:						Union[str, Any]				  =				None

	def format_log(A		,  A     ):
		lowerCamelCase_				:						Tuple				  =				'| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(A		,  A		,  math.exp(A     )     )
		return log_str

	lowerCamelCase_				:						Any				  =				''
	if valid_loss is not None:
		log_str += format_log(A		,  'valid'     )
	if test_loss is not None:
		log_str += format_log(A		,  'test'     )

	logger.info('=' * 100     )
	logger.info(A     )
	logger.info('=' * 100     )


if __name__ == "__main__":
							main()








import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader



class  __UpperCAmelCase						(_UpperCAmelCase       ):







			def __init__(      self:    Optional[int] ,      UpperCAmelCase_:    NestedDataStructureLike[PathLike] ,      UpperCAmelCase_:    Optional[NamedSplit] = None ,      UpperCAmelCase_:    Optional[Features] = None ,      UpperCAmelCase_:    str = None ,      UpperCAmelCase_:    bool = False ,      UpperCAmelCase_:    bool = False ,      UpperCAmelCase_:    Optional[int] = None ,      **UpperCAmelCase_:    Union[str, Any] ,      ):







									'''simple docstring'''




									super().__init__(
									    UpperCAmelCase_ ,      split=UpperCAmelCase_ ,      features=UpperCAmelCase_ ,      cache_dir=UpperCAmelCase_ ,      keep_in_memory=UpperCAmelCase_ ,      streaming=UpperCAmelCase_ ,      num_proc=UpperCAmelCase_ ,      **UpperCAmelCase_ ,      )
									_SCREAMING_SNAKE_CASE       =    path_or_paths if isinstance(UpperCAmelCase_ ,      UpperCAmelCase_	) else {self.split: path_or_paths}
									_SCREAMING_SNAKE_CASE       =    Csv(
									    cache_dir=UpperCAmelCase_ ,      data_files=UpperCAmelCase_ ,      features=UpperCAmelCase_ ,      **UpperCAmelCase_ ,      )







			def 				UpperCamelCase (      self:    int	):







									'''simple docstring'''




									if self.streaming:
															_SCREAMING_SNAKE_CASE       =    self.builder.as_streaming_dataset(split=self.split	)
									# Build regular (map-style) dataset
									else:
															_SCREAMING_SNAKE_CASE       =    None
															_SCREAMING_SNAKE_CASE       =    None
															_SCREAMING_SNAKE_CASE       =    None
															_SCREAMING_SNAKE_CASE       =    None

															self.builder.download_and_prepare(
															    download_config=UpperCAmelCase_ ,      download_mode=UpperCAmelCase_ ,      verification_mode=UpperCAmelCase_ ,      base_path=UpperCAmelCase_ ,      num_proc=self.num_proc ,      )
															_SCREAMING_SNAKE_CASE       =    self.builder.as_dataset(
															    split=self.split ,      verification_mode=UpperCAmelCase_ ,      in_memory=self.keep_in_memory	)
									return dataset



class  __UpperCAmelCase						:







			def __init__(      self:    List[str] ,      UpperCAmelCase_:    Dataset ,      UpperCAmelCase_:    Union[PathLike, BinaryIO] ,      UpperCAmelCase_:    Optional[int] = None ,      UpperCAmelCase_:    Optional[int] = None ,      **UpperCAmelCase_:    Any ,      ):







									'''simple docstring'''




									if num_proc is not None and num_proc <= 0:
															raise ValueError(F'num_proc {num_proc} must be an integer > 0.'	)

									_SCREAMING_SNAKE_CASE       =    dataset
									_SCREAMING_SNAKE_CASE       =    path_or_buf
									_SCREAMING_SNAKE_CASE       =    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
									_SCREAMING_SNAKE_CASE       =    num_proc
									_SCREAMING_SNAKE_CASE       =    """utf-8"""
									_SCREAMING_SNAKE_CASE       =    to_csv_kwargs







			def 				UpperCamelCase (      self:    Optional[int]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.to_csv_kwargs.pop("""path_or_buf""" ,      UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    self.to_csv_kwargs.pop("""header""" ,      UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    self.to_csv_kwargs.pop("""index""" ,      UpperCAmelCase_	)

									if isinstance(self.path_or_buf ,      (str, bytes, os.PathLike)	):
															with open(self.path_or_buf ,      """wb+"""	) as buffer:
																					_SCREAMING_SNAKE_CASE       =    self._write(file_obj=UpperCAmelCase_ ,      header=UpperCAmelCase_ ,      index=UpperCAmelCase_ ,      **self.to_csv_kwargs	)
									else:
															_SCREAMING_SNAKE_CASE       =    self._write(file_obj=self.path_or_buf ,      header=UpperCAmelCase_ ,      index=UpperCAmelCase_ ,      **self.to_csv_kwargs	)
									return written







			def 				UpperCamelCase (      self:    str ,      UpperCAmelCase_:    Tuple	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    args

									_SCREAMING_SNAKE_CASE       =    query_table(
									    table=self.dataset.data ,      key=slice(UpperCAmelCase_ ,      offset + self.batch_size	) ,      indices=self.dataset._indices ,      )
									_SCREAMING_SNAKE_CASE       =    batch.to_pandas().to_csv(
									    path_or_buf=UpperCAmelCase_ ,      header=header if (offset == 0) else False ,      index=UpperCAmelCase_ ,      **UpperCAmelCase_	)
									return csv_str.encode(self.encoding	)







			def 				UpperCamelCase (      self:    Optional[Any] ,      UpperCAmelCase_:    BinaryIO ,      UpperCAmelCase_:    Tuple ,      UpperCAmelCase_:    Any ,      **UpperCAmelCase_:    Union[str, Any]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    0

									if self.num_proc is None or self.num_proc == 1:
															for offset in logging.tqdm(
															    range(0 ,      len(self.dataset	) ,      self.batch_size	) ,      unit="""ba""" ,      disable=not logging.is_progress_bar_enabled() ,      desc="""Creating CSV from Arrow format""" ,      ):
																					_SCREAMING_SNAKE_CASE       =    self._batch_csv((offset, header, index, to_csv_kwargs)	)
																					written += file_obj.write(UpperCAmelCase_	)

									else:
															_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    len(self.dataset	), self.batch_size
															with multiprocessing.Pool(self.num_proc	) as pool:
																					for csv_str in logging.tqdm(
																					    pool.imap(
																					        self._batch_csv ,      [(offset, header, index, to_csv_kwargs) for offset in range(0 ,      UpperCAmelCase_ ,      UpperCAmelCase_	)] ,      ) ,      total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size ,      unit="""ba""" ,      disable=not logging.is_progress_bar_enabled() ,      desc="""Creating CSV from Arrow format""" ,      ):
																											written += file_obj.write(UpperCAmelCase_	)

									return written

