






'''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
)
_UpperCamelCase		:		Union[str, Any]								=			logging.getLogger(__name__)
def __UpperCAmelCase (			)  -> Optional[int]:
			UpperCAmelCase_     :   Tuple       =   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=1_0	,   help='''batch size'''						)
			parser.add_argument('''--tgt_len'''	,   type=A	,   default=1_2_8	,   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=1_6_0_0	,   help='''length of the retained previous heads'''						)
			parser.add_argument('''--clamp_len'''	,   type=A	,   default=1_0_0_0	,   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.'''						)
			UpperCAmelCase_     :   Tuple       =   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()

			UpperCAmelCase_     :   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 )
			UpperCAmelCase_     :   List[str]       =   TransfoXLCorpus.from_pretrained(args.model_name						)

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

			# Load a pre-trained model
			UpperCAmelCase_     :   Any       =   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:
						UpperCAmelCase_     :   Optional[Any]       =   args.clamp_len
			if args.same_length:
						UpperCAmelCase_     :   Dict       =   True

			###############################################################################
			# Evaluation code
			###############################################################################
			def evaluate(A			:	List[str]						):
						# Turn on evaluation mode which disables dropout.
						model.eval()
						UpperCAmelCase_  ,     UpperCAmelCase_     :   Union[str, Any]       =   0, 0.0
						UpperCAmelCase_     :   str       =   time.time()
						with torch.no_grad():
									UpperCAmelCase_     :   Dict       =   None
									for idx, (data, target, seq_len) in enumerate(A						):
												UpperCAmelCase_     :   int       =   model(A	,   lm_labels=A	,   mems=A						)
												UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_     :   int       =   ret
												UpperCAmelCase_     :   Dict       =   loss.mean()
												total_loss += seq_len * loss.item()
												total_len += seq_len
									UpperCAmelCase_     :   str       =   time.time() - start_time
						logger.info('''Time : {:.2f}s, {:.2f}ms/segment'''.format(A	,   1_0_0_0 * total_time / (idx + 1)						)						)
						return total_loss / total_len

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

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

			UpperCAmelCase_     :   str       =   ''''''
			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('''=''' * 1_0_0						)
			logger.info(A						)
			logger.info('''=''' * 1_0_0						)


if __name__ == "__main__":
						main()










'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


_UpperCamelCase		:		str								=			{}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase		:		List[Any]								=			['MBart50Tokenizer']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase		:		List[str]								=			['MBart50TokenizerFast']


if TYPE_CHECKING:
						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mbartaa import MBartaaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
						import sys

						_UpperCamelCase		:		Optional[int]								=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


