
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        = logging.getLogger(__name__)




def     a						(							):


				'''simple docstring'''
				lowercase_					  = 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=128						,       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=1_600						,       help='''length of the retained previous heads''' )
				parser.add_argument('''--clamp_len'''						,       type=_lowerCamelCase						,       default=1_000						,       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.''' )
				lowercase_					  = 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()

				lowercase_					  = 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 )
				lowercase_					  = TransfoXLCorpus.from_pretrained(args.model_name )

				lowercase_					  = corpus.get_iterator('''valid'''						,       args.batch_size						,       args.tgt_len						,       device=_lowerCamelCase						,       ext_len=args.ext_len )
				lowercase_					  = corpus.get_iterator('''test'''						,       args.batch_size						,       args.tgt_len						,       device=_lowerCamelCase						,       ext_len=args.ext_len )

				# Load a pre-trained model
				lowercase_					  = 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:
								lowercase_					  = args.clamp_len
				if args.same_length:
								lowercase_					  = True

				###############################################################################
				# Evaluation code
				###############################################################################
				def evaluate(snake_case__: int ):
								# Turn on evaluation mode which disables dropout.
								model.eval()
								lowercase_					  = 0, 0.0
								lowercase_					  = time.time()
								with torch.no_grad():
												lowercase_					  = None
												for idx, (data, target, seq_len) in enumerate(_lowerCamelCase ):
																lowercase_					  = model(_lowerCamelCase						,       lm_labels=_lowerCamelCase						,       mems=_lowerCamelCase )
																lowercase_					  = ret
																lowercase_					  = loss.mean()
																total_loss += seq_len * loss.item()
																total_len += seq_len
												lowercase_					  = time.time() - start_time
								logger.info('''Time : {:.2f}s, {:.2f}ms/segment'''.format(_lowerCamelCase						,       1_000 * total_time / (idx + 1) ) )
								return total_loss / total_len

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

				def format_log(snake_case__: List[Any]						,       snake_case__: Tuple ):
								lowercase_					  = """| {0} loss {1:5.2f} | {0} ppl {2:9.3f} """.format(_lowerCamelCase						,       _lowerCamelCase						,       math.exp(_lowerCamelCase ) )
								return log_str

				lowercase_					  = """"""
				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('''=''' * 100 )
				logger.info(_lowerCamelCase )
				logger.info('''=''' * 100 )


if __name__ == "__main__":
							main()






from typing import TYPE_CHECKING

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


__a        = {
    'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a        = [
						    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'NystromformerForMaskedLM',
						    'NystromformerForMultipleChoice',
						    'NystromformerForQuestionAnswering',
						    'NystromformerForSequenceClassification',
						    'NystromformerForTokenClassification',
						    'NystromformerLayer',
						    'NystromformerModel',
						    'NystromformerPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nystromformer import (
												    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NystromformerForMaskedLM,
												    NystromformerForMultipleChoice,
												    NystromformerForQuestionAnswering,
												    NystromformerForSequenceClassification,
												    NystromformerForTokenClassification,
												    NystromformerLayer,
												    NystromformerModel,
												    NystromformerPreTrainedModel,
												)


else:
						import sys

						__a        = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




