
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__       :   int   						=					logging.getLogger(__name__)



def      _a     (   )     ->   Optional[int]:
			lowerCAmelCase__ :    Dict				      =			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=128	,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=1600	,help='''length of the retained previous heads'''    )
			parser.add_argument('''--clamp_len'''	,type=__UpperCamelCase	,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=__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__ :    List[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=__UpperCamelCase    )
						ptvsd.wait_for_attach()

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

			lowerCAmelCase__ :    Any				      =			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__ :    Optional[Any]				      =			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__ :    List[Any]				      =			args.clamp_len
			if args.same_length:
						lowerCAmelCase__ :    Dict				      =			True

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

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

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

			lowerCAmelCase__ :    Optional[int]				      =			''''''
			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('''=''' * 100    )
			logger.info(__UpperCamelCase    )
			logger.info('''=''' * 100    )


if __name__ == "__main__":
							main()





import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils





class 						lowercase       (  unittest.TestCase					):
					@patch('''diffusers.utils.hub_utils.get_full_repo_name'''    )
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    str				      =			'''full_repo_name'''
								with TemporaryDirectory() as tmpdir:
											# Dummy args values
											lowerCAmelCase__ :    Optional[int]				      =			Mock()
											lowerCAmelCase__ :    Optional[int]				      =			tmpdir
											lowerCAmelCase__ :    Optional[Any]				      =			0
											lowerCAmelCase__ :    Optional[int]				      =			'''hub_token'''
											lowerCAmelCase__ :    Tuple				      =			'''dataset_name'''
											lowerCAmelCase__ :    int				      =			0.01
											lowerCAmelCase__ :    Optional[int]				      =			100000
											lowerCAmelCase__ :    Any				      =			10000
											lowerCAmelCase__ :    Union[str, Any]				      =			0.01
											lowerCAmelCase__ :    int				      =			0.02
											lowerCAmelCase__ :    Union[str, Any]				      =			0.03
											lowerCAmelCase__ :    str				      =			0.0_005
											lowerCAmelCase__ :    Dict				      =			0.000_001
											lowerCAmelCase__ :    Tuple				      =			1
											lowerCAmelCase__ :    Optional[int]				      =			10
											lowerCAmelCase__ :    int				      =			0.001
											lowerCAmelCase__ :    Union[str, Any]				      =			0.1
											lowerCAmelCase__ :    Tuple				      =			0.2
											lowerCAmelCase__ :    Optional[int]				      =			True

											# Model card mush be rendered and saved
											diffusers.utils.hub_utils.create_model_card(SCREAMING_SNAKE_CASE__ ,	model_name='''model_name'''    )
											self.assertTrue((Path(SCREAMING_SNAKE_CASE__    ) / '''README.md''').is_file()    )



