




import os
from shutil import copyfile
from typing import TYPE_CHECKING, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
from ...utils.versions import require_version


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

require_version('''tokenizers>=0.13.3''')

if is_sentencepiece_available():
						from .tokenization_llama import LlamaTokenizer
else:
						__a  :		Tuple			=    None

__a  :		int			=    logging.get_logger(__name__)
__a  :		int			=    {'''vocab_file''': '''tokenizer.model''', '''tokenizer_file''': '''tokenizer.json'''}

__a  :		Dict			=    '''[INST]''', '''[/INST]'''
__a  :		List[str]			=    '''<<SYS>>\n''', '''\n<</SYS>>\n\n'''

# fmt: off
__a  :		Optional[int]			=    '''You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
correct. If you don\'t know the answer to a question, please don\'t share false information.'''







class       UpperCAmelCase(     A__     ):



	"""simple docstring"""






	a	:					Dict         =     VOCAB_FILES_NAMES
	a	:					Union[str, Any]         =     LlamaTokenizer
	a	:					Optional[Any]         =     'left'
	a	:					Union[str, Any]         =     ['input_ids', 'attention_mask']

	def __init__(     self       ,      lowerCamelCase=None       ,      lowerCamelCase=None       ,      lowerCamelCase=False       ,      lowerCamelCase="<unk>"       ,      lowerCamelCase="<s>"       ,      lowerCamelCase="</s>"       ,      lowerCamelCase=True       ,      lowerCamelCase=False       ,      **lowerCamelCase       ,      )					->			List[str]:





								"""simple docstring"""


								super().__init__(
								    vocab_file=lowerCamelCase__       ,      tokenizer_file=lowerCamelCase__       ,      clean_up_tokenization_spaces=lowerCamelCase__       ,      unk_token=lowerCamelCase__       ,      bos_token=lowerCamelCase__       ,      eos_token=lowerCamelCase__       ,      **lowerCamelCase__       ,      )
								lowercase__  :       Optional[int]   					=							add_bos_token
								lowercase__  :       Union[str, Any]   					=							add_eos_token
								self.update_post_processor()

								lowercase__  :       Tuple   					=							vocab_file
								lowercase__  :       Dict   					=							False if not self.vocab_file else True

	def     __a	(     self				)					->			List[str]:





								"""simple docstring"""


								lowercase__  :       Union[str, Any]   					=							self.bos_token
								lowercase__  :       Tuple   					=							self.bos_token_id

								lowercase__  :       Tuple   					=							self.eos_token
								lowercase__  :       Union[str, Any]   					=							self.eos_token_id

								lowercase__  :       Dict   					=							f"""{(bos+":0 ") * self.add_bos_token}$A:0{(" "+eos+":0") * self.add_eos_token}"""
								lowercase__  :       int   					=							f"""{single}{(" "+bos+":1") * self.add_bos_token} $B:1{(" "+eos+":1") * self.add_eos_token}"""

								lowercase__  :       Union[str, Any]   					=							[]
								if self.add_bos_token:
															special_tokens.append((bos, bos_token_id)				)
								if self.add_eos_token:
															special_tokens.append((eos, eos_token_id)				)
								lowercase__  :       List[Any]   					=							processors.TemplateProcessing(
								    single=lowerCamelCase__       ,      pair=lowerCamelCase__       ,      special_tokens=lowerCamelCase__				)

	@property
	def     __a	(     self				)					->			Optional[int]:





								"""simple docstring"""


								return self._add_eos_token

	@property
	def     __a	(     self				)					->			List[str]:





								"""simple docstring"""


								return self._add_bos_token

	@add_eos_token.setter
	def     __a	(     self       ,      lowerCamelCase				)					->			List[Any]:





								"""simple docstring"""


								lowercase__  :       Dict   					=							value
								self.update_post_processor()

	@add_bos_token.setter
	def     __a	(     self       ,      lowerCamelCase				)					->			Tuple:





								"""simple docstring"""


								lowercase__  :       Any   					=							value
								self.update_post_processor()

	def     __a	(     self       ,      lowerCamelCase       ,      lowerCamelCase = None				)					->			Dict:





								"""simple docstring"""


								if not self.can_save_slow_tokenizer:
															raise ValueError(
															    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
															    "tokenizer."				)

								if not os.path.isdir(lowerCamelCase__				):
															logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
															return
								lowercase__  :       Optional[Any]   					=							os.path.join(
								    lowerCamelCase__       ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

								if os.path.abspath(self.vocab_file				) != os.path.abspath(lowerCamelCase__				):
															copyfile(self.vocab_file       ,      lowerCamelCase__				)

								return (out_vocab_file,)







	def     __a	(     self       ,      lowerCamelCase				)					->			Any:





								"""simple docstring"""


								lowercase__  :       int   					=							list(conversation.iter_texts()				)
								if not all([is_user for is_user, msg in dialogue[::2]]				) or not all(
								    [not is_user for is_user, msg in dialogue[1::2]]				):
															raise ValueError(
															    "The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)"				)

								lowercase__  :       Tuple   					=							[]
								if len(conversation.past_user_inputs				) > 0:
															if not conversation.past_user_inputs[0].startswith(lowerCamelCase__				) or E_SYS not in conversation.past_user_inputs[0]:
																						lowercase__  :       List[Any]   					=							(
																						    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
																						)
								elif not dialogue[0][1].startswith(lowerCamelCase__				) or E_SYS not in dialogue[0][1]:
															lowercase__  :       Dict   					=							(dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

								dialog_tokens += sum(
								    [
								        [self.bos_token_id]
								        + self.encode(
								            f"""{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} """       ,      add_special_tokens=lowerCamelCase__				)
								        + [self.eos_token_id]
								        for prompt, answer in zip(dialogue[::2]       ,      dialogue[1::2]				)
								    ]       ,      []       ,      )
								if not (dialogue[-1][0]):
															raise ValueError(f"""Last message must be from user, got {dialogue[-1]["role"]}"""				)
								dialog_tokens += [self.bos_token_id] + self.encode(
								    f"""{B_INST} {(dialogue[-1][1]).strip()} {E_INST}"""       ,      add_special_tokens=lowerCamelCase__				)
								return dialog_tokens




"""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
)
__SCREAMING_SNAKE_CASE     :  Dict										=					logging.getLogger(__name__)



def 				lowerCAmelCase_(      )		->				Optional[int]:
				_lowerCamelCase					     = 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=1_28  ,      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=16_00  ,      help='''length of the retained previous heads'''							)
				parser.add_argument('''--clamp_len'''  ,      type=lowercase_  ,      default=10_00  ,      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.'''							)
				_lowerCamelCase					     = 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()

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

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

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

				###############################################################################
				# Evaluation code
				###############################################################################
				def evaluate(lowercase_   :							Any							):
								# Turn on evaluation mode which disables dropout.
								model.eval()
								_lowerCamelCase     ,   _lowerCamelCase					     = 0, 0.0
								_lowerCamelCase					     = time.time()
								with torch.no_grad():
												_lowerCamelCase					     = None
												for idx, (data, target, seq_len) in enumerate(lowercase_							):
																_lowerCamelCase					     = model(lowercase_  ,      lm_labels=lowercase_  ,      mems=lowercase_							)
																_lowerCamelCase     ,   _lowerCamelCase     ,   _lowerCamelCase					     = ret
																_lowerCamelCase					     = loss.mean()
																total_loss += seq_len * loss.item()
																total_len += seq_len
												_lowerCamelCase					     = time.time() - start_time
								logger.info('''Time : {:.2f}s, {:.2f}ms/segment'''.format(lowercase_  ,      10_00 * total_time / (idx + 1)							)							)
								return total_loss / total_len

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

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

				_lowerCamelCase					     = ''''''
				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('''=''' * 1_00							)
				logger.info(lowercase_							)
				logger.info('''=''' * 1_00							)


if __name__ == "__main__":
			main()
