
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
)
lowercase__							:							Tuple													=     logging.getLogger(__name__)




def 		A_	(							) ->      Optional[Any]:



						'''simple docstring'''

						__UpperCamelCase       					=     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=1600	, help='''length of the retained previous heads'''	)
						parser.add_argument('''--clamp_len'''	, type=_lowerCamelCase	, 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=_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.'''	)
						__UpperCamelCase       					=     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()

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

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

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

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

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

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

						__UpperCamelCase       					=     """"""
						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()






import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available




def 		A_	(							snake_case							:				Union[str, Any]	, snake_case							:				str	, snake_case							:				Dict	, snake_case							:				Optional[Any]=None	) ->      Optional[Any]:



						'''simple docstring'''

						__UpperCamelCase       					=     None
						# Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
						if str(weight.dtype	) == "torch.bfloat16":
												# Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
												__UpperCamelCase       					=     weight.view(torch.intaa	)
												__UpperCamelCase       					=     '''bfloat16'''
						__UpperCamelCase       					=     weight.cpu().numpy()
						__UpperCamelCase       					=     os.path.join(snake_case	, f"{weight_name}.dat"	)
						if index is not None:
												if dtype is None:
																		__UpperCamelCase       					=     str(array.dtype	)
												__UpperCamelCase       					=     {'''dtype''': dtype, '''shape''': list(array.shape	)}
						if array.ndim == 0:
												__UpperCamelCase       					=     array[None]
						__UpperCamelCase       					=     np.memmap(snake_case	, dtype=array.dtype	, mode='''w+'''	, shape=array.shape	)
						__UpperCamelCase       					=     array[:]
						file_array.flush()
						return index




def 		A_	(							snake_case							:				Tuple	, snake_case							:				Union[str, Any]	) ->      Dict:



						'''simple docstring'''

						__UpperCamelCase       					=     tuple(weight_info['''shape''']	)
						if shape == ():
												# NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
												__UpperCamelCase       					=     (1,)

						__UpperCamelCase       					=     weight_info['''dtype''']
						if dtype == "bfloat16":
												# NumPy does not support bfloat16 so this was saved as a int16
												__UpperCamelCase       					=     '''int16'''

						__UpperCamelCase       					=     np.memmap(snake_case	, dtype=snake_case	, shape=snake_case	, mode='''r'''	)

						if len(weight_info['''shape''']	) == 0:
												__UpperCamelCase       					=     weight[0]
						__UpperCamelCase       					=     torch.tensor(snake_case	)
						if weight_info["dtype"] == "bfloat16":
												__UpperCamelCase       					=     weight.view(torch.bfloataa	)

						return weight




def 		A_	(							snake_case							:				Dict	, snake_case							:				int	) ->      str:



						'''simple docstring'''

						if index is None or len(snake_case	) == 0:
												# Nothing to save
												return

						__UpperCamelCase       					=     os.path.join(snake_case	, '''index.json'''	)
						if os.path.isfile(snake_case	):
												with open(snake_case	, '''r'''	, encoding='''utf-8'''	) as f:
																		__UpperCamelCase       					=     json.load(snake_case	)
						else:
												__UpperCamelCase       					=     {}
						current_index.update(snake_case	)

						with open(snake_case	, '''w'''	, encoding='''utf-8'''	) as f:
												json.dump(snake_case	, snake_case	, indent=2	)




def 		A_	(							snake_case							:				Union[str, os.PathLike]	, snake_case							:				Dict[str, torch.Tensor]	) ->      Dict:



						'''simple docstring'''

						os.makedirs(snake_case	, exist_ok=snake_case	)
						__UpperCamelCase       					=     {}
						for name, parameter in state_dict.items():
												__UpperCamelCase       					=     offload_weight(snake_case	, snake_case	, snake_case	, index=snake_case	)

						# Update index
						save_offload_index(snake_case	, snake_case	)






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							def __init__(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				str:





													'''simple docstring'''






													__UpperCamelCase       					=     dataset
													__UpperCamelCase       					=     prefix



							def __getitem__(					self       ,		SCREAMING_SNAKE_CASE_     )->				Union[str, Any]:





													'''simple docstring'''






													return self.dataset[F"{self.prefix}{key}"]



							def __iter__(					self     )->				Union[str, Any]:





													'''simple docstring'''






													return iter([key for key in self.dataset if key.startswith(self.prefix     )]     )



							def __len__(					self     )->				str:





													'''simple docstring'''






													return len(self.dataset     )






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							def __init__(					self       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_=None       ,		)->				Tuple:





													'''simple docstring'''






													if state_dict is None and save_folder is None:
																			raise ValueError('''Need either a `state_dict` or a `save_folder` containing offloaded weights.'''     )

													__UpperCamelCase       					=     {} if state_dict is None else state_dict
													__UpperCamelCase       					=     save_folder
													if index is None and save_folder is not None:
																			with open(os.path.join(SCREAMING_SNAKE_CASE_       ,		'''index.json'''     )     ) as f:
																									__UpperCamelCase       					=     json.load(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     {} if index is None else index
													__UpperCamelCase       					=     list(self.state_dict.keys()     )
													self.all_keys.extend([key for key in self.index if key not in self.all_keys]     )
													__UpperCamelCase       					=     device



							def __getitem__(					self       ,		SCREAMING_SNAKE_CASE_     )->				Tuple:





													'''simple docstring'''






													if key in self.state_dict:
																			return self.state_dict[key]
													__UpperCamelCase       					=     self.index[key]
													if weight_info.get('''safetensors_file'''     ) is not None:
																			if not is_safetensors_available():
																									raise ImportError('''These offloaded weights require the use of safetensors: `pip install safetensors`.'''     )

																			from safetensors import safe_open

																			__UpperCamelCase       					=     '''cpu''' if self.device is None else self.device
																			with safe_open(weight_info['''safetensors_file''']       ,		framework='''pt'''       ,		device=SCREAMING_SNAKE_CASE_     ) as f:
																									__UpperCamelCase       					=     f.get_tensor(weight_info.get('''weight_name'''       ,		SCREAMING_SNAKE_CASE_     )     )

																			if "dtype" in weight_info:
																									return tensor.to(getattr(SCREAMING_SNAKE_CASE_       ,		weight_info['''dtype''']     )     )
																			else:
																									return tensor

													__UpperCamelCase       					=     os.path.join(self.save_folder       ,		F"{key}.dat"     )
													return load_offloaded_weight(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )



							def __iter__(					self     )->				Dict:





													'''simple docstring'''






													return iter(self.all_keys     )



							def __len__(					self     )->				Dict:





													'''simple docstring'''






													return len(self.all_keys     )





def 		A_	(							snake_case							:				Dict[str, torch.Tensor]	, snake_case							:				List[str]	) ->      Tuple:



						'''simple docstring'''

						__UpperCamelCase       					=     {}
						for module_name in submodule_names:
												# We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
												# submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
												result.update(
												    {
												        key: param
												        for key, param in state_dict.items()
												        if key == module_name or key.startswith(module_name + '''.'''	)
												    }	)
						return result




