






"""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
)
__a							:						List[Any]   							=			logging.getLogger(__name__)





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

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

					# 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=lowercase_			,  ext_len=args.ext_len)
					a__ 							=	corpus.get_iterator('''test'''			,  args.batch_size			,  args.tgt_len			,  device=lowercase_			,  ext_len=args.ext_len)

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

					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_):
										# Turn on evaluation mode which disables dropout.
										model.eval()
										a__ 							=	0, 0.0
										a__ 							=	time.time()
										with torch.no_grad():
															a__ 							=	None
															for idx, (data, target, seq_len) in enumerate(lowercase_):
																				a__ 							=	model(lowercase_			,  lm_labels=lowercase_			,  mems=lowercase_)
																				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(lowercase_			,  1000 * total_time / (idx + 1)))
										return total_loss / total_len

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

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

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

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


if __name__ == "__main__":
	main()



from .number_theory.prime_numbers import next_prime




class  __lowerCAmelCase	:



 '''simple docstring'''

 def __init__(      self							:		List[Any]							,      lowerCamelCase							:		int							,      lowerCamelCase							:		int | None = None							,      lowerCamelCase							:		float | None = None							,      )			->				None:
        _UpperCamelCase  :							Tuple	    =	size_table
        _UpperCamelCase  :							Tuple	    =	[None] * self.size_table
        _UpperCamelCase  :							int	    =	0.7_5 if lim_charge is None else lim_charge
        _UpperCamelCase  :							Tuple	    =	1 if charge_factor is None else charge_factor
        _UpperCamelCase  :							list	    =	[]
        _UpperCamelCase  :							dict	    =	{}

 def        __snake_case       (      self							:		List[Any]	)			->				Union[str, Any]:
        return self._keys

 def        __snake_case       (      self							:		List[Any]	)			->				Optional[int]:
        return sum(1 for slot in self.values if slot is not None	) / (
            self.size_table * self.charge_factor
        )

 def        __snake_case       (      self							:		Union[str, Any]							,      lowerCamelCase							:		int	)			->				Any:
        return key % self.size_table

 def        __snake_case       (      self							:		Any							,      lowerCamelCase							:		Any	)			->				Optional[Any]:
        print(f"step {step_ord}"	)
        print(list(range(len(self.values	)	)	)	)
        print(self.values	)

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		Optional[int]	)			->				Any:
        _UpperCamelCase  :							int	    =	1
        _UpperCamelCase  :							Union[str, Any]	    =	values
        for value in values:
               self.insert_data(lowerCamelCase	)
               self._step_by_step(lowerCamelCase	)
               i += 1

 def        __snake_case       (      self							:		Optional[Any]							,      lowerCamelCase							:		Any							,      lowerCamelCase							:		Optional[int]	)			->				Any:
        _UpperCamelCase  :							int	    =	data
        _UpperCamelCase  :							Optional[int]	    =	data

 def        __snake_case       (      self							:		Optional[int]							,      lowerCamelCase							:		Optional[int]							,      lowerCamelCase							:		str=None	)			->				List[Any]:
        _UpperCamelCase  :							Union[str, Any]	    =	self.hash_function(key + 1	)

        while self.values[new_key] is not None and self.values[new_key] != key:
               if self.values.count(lowerCamelCase	) > 0:
                      _UpperCamelCase  :							Optional[int]	    =	self.hash_function(new_key + 1	)
               else:
                      _UpperCamelCase  :							Tuple	    =	None
                      break

        return new_key

 def        __snake_case       (      self							:		Optional[Any]	)			->				Optional[Any]:
        _UpperCamelCase  :							Union[str, Any]	    =	[value for value in self.values if value is not None]
        _UpperCamelCase  :							Union[str, Any]	    =	next_prime(self.size_table							,      factor=2	)
        self._keys.clear()
        _UpperCamelCase  :							Tuple	    =	[None] * self.size_table  # hell's pointers D: don't DRY ;/
        for value in survivor_values:
               self.insert_data(lowerCamelCase	)

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		str	)			->				Union[str, Any]:
        _UpperCamelCase  :							Dict	    =	self.hash_function(lowerCamelCase	)

        if self.values[key] is None:
               self._set_value(lowerCamelCase							,      lowerCamelCase	)

        elif self.values[key] == data:
               pass

        else:
               _UpperCamelCase  :							Tuple	    =	self._collision_resolution(lowerCamelCase							,      lowerCamelCase	)
               if collision_resolution is not None:
                      self._set_value(lowerCamelCase							,      lowerCamelCase	)
               else:
                      self.rehashing()
                      self.insert_data(lowerCamelCase	)



