





"""simple docstring"""
import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


lowerCamelCase_          =	logging.get_logger(__name__)

lowerCamelCase_          =	{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

lowerCamelCase_          =	{
    "vocab_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/vocab.json"},
    "merges_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/merges.txt"},
}

lowerCamelCase_          =	{
    "openai-gpt": 5_1_2,
}



def   __lowerCamelCase     (							a_  :	Optional[Any]							)    ->   Any:
      __SCREAMING_SNAKE_CASE     :Tuple	       =						text.strip()
      if not text:
            return []
      __SCREAMING_SNAKE_CASE     :int	       =						text.split()
      return tokens






class       _SCREAMING_SNAKE_CASE(     A						):




       def __init__(		self	,SCREAMING_SNAKE_CASE__=True	,SCREAMING_SNAKE_CASE__=None	,SCREAMING_SNAKE_CASE__=True	,SCREAMING_SNAKE_CASE__=None	,SCREAMING_SNAKE_CASE__=True	,)    ->   int:





             """simple docstring"""






             if never_split is None:
                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						[]
             __SCREAMING_SNAKE_CASE     :str	       =						do_lower_case
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						set(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Dict	       =						tokenize_chinese_chars
             __SCREAMING_SNAKE_CASE     :Tuple	       =						strip_accents
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						do_split_on_punc




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=None							)    ->   List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						self.never_split.union(set(SCREAMING_SNAKE_CASE__							)							) if never_split else self.never_split
             __SCREAMING_SNAKE_CASE     :int	       =						self._clean_text(SCREAMING_SNAKE_CASE__							)

             # This was added on November 1st, 2018 for the multilingual and Chinese
             # models. This is also applied to the English models now, but it doesn't
             # matter since the English models were not trained on any Chinese data
             # and generally don't have any Chinese data in them (there are Chinese
             # characters in the vocabulary because Wikipedia does have some Chinese
             # words in the English Wikipedia.).
             if self.tokenize_chinese_chars:
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						self._tokenize_chinese_chars(SCREAMING_SNAKE_CASE__							)
             # prevents treating the same character with different unicode codepoints as different characters
             __SCREAMING_SNAKE_CASE     :Tuple	       =						unicodedata.normalize('''NFC'''	,SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						whitespace_tokenize(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Dict	       =						[]
             for token in orig_tokens:
                   if token not in never_split:
                         if self.do_lower_case:
                               __SCREAMING_SNAKE_CASE     :str	       =						token.lower()
                               if self.strip_accents is not False:
                                     __SCREAMING_SNAKE_CASE     :Dict	       =						self._run_strip_accents(SCREAMING_SNAKE_CASE__							)
                         elif self.strip_accents:
                               __SCREAMING_SNAKE_CASE     :Any	       =						self._run_strip_accents(SCREAMING_SNAKE_CASE__							)
                   split_tokens.extend(self._run_split_on_punc(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)							)

             __SCREAMING_SNAKE_CASE     :List[Any]	       =						whitespace_tokenize(''' '''.join(SCREAMING_SNAKE_CASE__							)							)
             return output_tokens




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :List[str]	       =						unicodedata.normalize('''NFD'''	,SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :List[str]	       =						[]
             for char in text:
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						unicodedata.category(SCREAMING_SNAKE_CASE__							)
                   if cat == "Mn":
                         continue
                   output.append(SCREAMING_SNAKE_CASE__							)
             return "".join(SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=None							)    ->   Optional[Any]:





             """simple docstring"""






             if not self.do_split_on_punc or (never_split is not None and text in never_split):
                   return [text]
             __SCREAMING_SNAKE_CASE     :Tuple	       =						list(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						0
             __SCREAMING_SNAKE_CASE     :str	       =						True
             __SCREAMING_SNAKE_CASE     :Any	       =						[]
             while i < len(SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE     :Dict	       =						chars[i]
                   if _is_punctuation(SCREAMING_SNAKE_CASE__							):
                         output.append([char]							)
                         __SCREAMING_SNAKE_CASE     :str	       =						True
                   else:
                         if start_new_word:
                               output.append([]							)
                         __SCREAMING_SNAKE_CASE     :str	       =						False
                         output[-1].append(SCREAMING_SNAKE_CASE__							)
                   i += 1

             return ["".join(SCREAMING_SNAKE_CASE__							) for x in output]




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :str	       =						[]
             for char in text:
                   __SCREAMING_SNAKE_CASE     :int	       =						ord(SCREAMING_SNAKE_CASE__							)
                   if self._is_chinese_char(SCREAMING_SNAKE_CASE__							):
                         output.append(''' '''							)
                         output.append(SCREAMING_SNAKE_CASE__							)
                         output.append(''' '''							)
                   else:
                         output.append(SCREAMING_SNAKE_CASE__							)
             return "".join(SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Tuple:





             """simple docstring"""






             if (
                 (cp >= 0x4_e00 and cp <= 0x9_fff)
                 or (cp >= 0x3_400 and cp <= 0x4_dbf)  #
                 or (cp >= 0x20_000 and cp <= 0x2a_6df)  #
                 or (cp >= 0x2a_700 and cp <= 0x2b_73f)  #
                 or (cp >= 0x2b_740 and cp <= 0x2b_81f)  #
                 or (cp >= 0x2b_820 and cp <= 0x2c_eaf)  #
                 or (cp >= 0xf_900 and cp <= 0xf_aff)
                 or (cp >= 0x2f_800 and cp <= 0x2f_a1f)  #
             ):  #
                   return True

             return False




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Dict	       =						[]
             for char in text:
                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						ord(SCREAMING_SNAKE_CASE__							)
                   if cp == 0 or cp == 0xf_ffd or _is_control(SCREAMING_SNAKE_CASE__							):
                         continue
                   if _is_whitespace(SCREAMING_SNAKE_CASE__							):
                         output.append(''' '''							)
                   else:
                         output.append(SCREAMING_SNAKE_CASE__							)
             return "".join(SCREAMING_SNAKE_CASE__							)




def   __lowerCamelCase     (							a_  :	Union[str, Any]							)    ->   Optional[int]:
      __SCREAMING_SNAKE_CASE     :int	       =						set()
      __SCREAMING_SNAKE_CASE     :Dict	       =						word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)							)
            __SCREAMING_SNAKE_CASE     :Optional[int]	       =						char
      return pairs



def   __lowerCamelCase     (							a_  :	Union[str, Any]							)    ->   int:
      __SCREAMING_SNAKE_CASE     :Tuple	       =						text.replace('''—'''				,     '''-'''							)
      __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						text.replace('''–'''				,     '''-'''							)
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						text.replace('''―'''				,     '''-'''							)
      __SCREAMING_SNAKE_CASE     :str	       =						text.replace('''…'''				,     '''...'''							)
      __SCREAMING_SNAKE_CASE     :Tuple	       =						text.replace('''´'''				,     '''\''''							)
      __SCREAMING_SNAKE_CASE     :List[Any]	       =						re.sub(r'''(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)'''				,     r''' \1 '''				,     a_							)
      __SCREAMING_SNAKE_CASE     :List[str]	       =						re.sub(r'''\s*\n\s*'''				,     ''' \n '''				,     a_							)
      __SCREAMING_SNAKE_CASE     :Dict	       =						re.sub(r'''[^\S\n]+'''				,     ''' '''				,     a_							)
      return text.strip()







class       _SCREAMING_SNAKE_CASE(     A						):
       SCREAMING_SNAKE_CASE_     :   Union[str, Any]					=						VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_     :   str					=						PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_     :   Dict					=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE_     :   Tuple					=						['''input_ids''', '''attention_mask''']




       def __init__(		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__="<unk>"	,**SCREAMING_SNAKE_CASE__							)    ->   List[Any]:





             """simple docstring"""






             super().__init__(unk_token=SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__							)

             try:
                   import ftfy
                   from spacy.lang.en import English

                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						English()
                   __SCREAMING_SNAKE_CASE     :Dict	       =						_nlp.tokenizer
                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						ftfy.fix_text
             except ImportError:
                   logger.warning('''ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.'''							)
                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						None

             with open(SCREAMING_SNAKE_CASE__	,encoding='''utf-8'''							) as vocab_handle:
                   __SCREAMING_SNAKE_CASE     :Any	       =						json.load(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						{v: k for k, v in self.encoder.items()}
             with open(SCREAMING_SNAKE_CASE__	,encoding='''utf-8'''							) as merges_handle:
                   __SCREAMING_SNAKE_CASE     :int	       =						merges_handle.read().split('''\n'''							)[1:-1]
             __SCREAMING_SNAKE_CASE     :List[str]	       =						[tuple(merge.split()							) for merge in merges]
             __SCREAMING_SNAKE_CASE     :Any	       =						dict(zip(SCREAMING_SNAKE_CASE__	,range(len(SCREAMING_SNAKE_CASE__							)							)							)							)
             __SCREAMING_SNAKE_CASE     :Tuple	       =						{}




       @property
       def 					_UpperCamelCase  (		self							)    ->   Dict:





             """simple docstring"""






             return True




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





             """simple docstring"""






             return len(self.encoder							)




       def 					_UpperCamelCase  (		self							)    ->   List[Any]:





             """simple docstring"""






             return dict(self.encoder	,**self.added_tokens_encoder							)




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						tuple(token[:-1]							) + (token[-1] + '''</w>''',)
             if token in self.cache:
                   return self.cache[token]
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						get_pairs(SCREAMING_SNAKE_CASE__							)

             if not pairs:
                   return token + "</w>"

             while True:
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						min(SCREAMING_SNAKE_CASE__	,key=lambda SCREAMING_SNAKE_CASE__							: self.bpe_ranks.get(SCREAMING_SNAKE_CASE__	,float('''inf'''							)							)							)
                   if bigram not in self.bpe_ranks:
                         break
                   __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Dict	       =						bigram
                   __SCREAMING_SNAKE_CASE     :Dict	       =						[]
                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						0
                   while i < len(SCREAMING_SNAKE_CASE__							):
                         try:
                               __SCREAMING_SNAKE_CASE     :Tuple	       =						word.index(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)
                         except ValueError:
                               new_word.extend(word[i:]							)
                               break
                         else:
                               new_word.extend(word[i:j]							)
                               __SCREAMING_SNAKE_CASE     :List[str]	       =						j

                         if word[i] == first and i < len(SCREAMING_SNAKE_CASE__							) - 1 and word[i + 1] == second:
                               new_word.append(first + second							)
                               i += 2
                         else:
                               new_word.append(word[i]							)
                               i += 1
                   __SCREAMING_SNAKE_CASE     :str	       =						tuple(SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE     :Any	       =						new_word
                   if len(SCREAMING_SNAKE_CASE__							) == 1:
                         break
                   else:
                         __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						get_pairs(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Dict	       =						''' '''.join(SCREAMING_SNAKE_CASE__							)
             if word == "\n  </w>":
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						'''\n</w>'''
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						word
             return word




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :int	       =						[]
             if self.fix_text is None:
                   # Using BERT's BasicTokenizer
                   __SCREAMING_SNAKE_CASE     :str	       =						self.nlp.tokenize(SCREAMING_SNAKE_CASE__							)
                   for token in text:
                         split_tokens.extend(list(self.bpe(SCREAMING_SNAKE_CASE__							).split(''' '''							)							)							)
             else:
                   # Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						self.nlp(text_standardize(self.fix_text(SCREAMING_SNAKE_CASE__							)							)							)
                   for token in text:
                         split_tokens.extend(list(self.bpe(token.text.lower()							).split(''' '''							)							)							)
             return split_tokens




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Any:





             """simple docstring"""






             return self.encoder.get(SCREAMING_SNAKE_CASE__	,self.encoder.get(self.unk_token							)							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Optional[Any]:





             """simple docstring"""






             return self.decoder.get(SCREAMING_SNAKE_CASE__	,self.unk_token							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						''''''.join(SCREAMING_SNAKE_CASE__							).replace('''</w>'''	,''' '''							).strip()
             return out_string




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None							)    ->   Tuple[str]:





             """simple docstring"""






             if not os.path.isdir(SCREAMING_SNAKE_CASE__							):
                   logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
                   return
             __SCREAMING_SNAKE_CASE     :Dict	       =						os.path.join(
                 SCREAMING_SNAKE_CASE__	,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)
             __SCREAMING_SNAKE_CASE     :str	       =						os.path.join(
                 SCREAMING_SNAKE_CASE__	,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']							)

             with open(SCREAMING_SNAKE_CASE__	,'''w'''	,encoding='''utf-8'''							) as f:
                   f.write(json.dumps(self.encoder	,indent=2	,sort_keys=SCREAMING_SNAKE_CASE__	,ensure_ascii=SCREAMING_SNAKE_CASE__							) + '''\n'''							)

             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						0
             with open(SCREAMING_SNAKE_CASE__	,'''w'''	,encoding='''utf-8'''							) as writer:
                   writer.write('''#version: 0.2\n'''							)
                   for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	,key=lambda SCREAMING_SNAKE_CASE__							: kv[1]							):
                         if index != token_index:
                               logger.warning(
                                   f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                                   ''' Please check that the tokenizer is not corrupted!'''							)
                               __SCREAMING_SNAKE_CASE     :Dict	       =						token_index
                         writer.write(''' '''.join(SCREAMING_SNAKE_CASE__							) + '''\n'''							)
                         index += 1

             return vocab_file, merge_file






"""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
)
lowerCamelCase_          =	logging.getLogger(__name__)



def   __lowerCamelCase     (							)    ->   Optional[Any]:
      __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						argparse.ArgumentParser(description='''PyTorch Transformer Language Model'''							)
      parser.add_argument('''--model_name'''				,     type=a_				,     default='''transfo-xl-wt103'''				,     help='''pretrained model name'''							)
      parser.add_argument(
          '''--split'''				,     type=a_				,     default='''test'''				,     choices=['''all''', '''valid''', '''test''']				,     help='''which split to evaluate'''							)
      parser.add_argument('''--batch_size'''				,     type=a_				,     default=10				,     help='''batch size'''							)
      parser.add_argument('''--tgt_len'''				,     type=a_				,     default=1_28				,     help='''number of tokens to predict'''							)
      parser.add_argument('''--ext_len'''				,     type=a_				,     default=0				,     help='''length of the extended context'''							)
      parser.add_argument('''--mem_len'''				,     type=a_				,     default=16_00				,     help='''length of the retained previous heads'''							)
      parser.add_argument('''--clamp_len'''				,     type=a_				,     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=a_				,     required=a_				,     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=a_				,     default=''''''				,     help='''Can be used for distant debugging.'''							)
      parser.add_argument('''--server_port'''				,     type=a_				,     default=''''''				,     help='''Can be used for distant debugging.'''							)
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						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=a_							)
            ptvsd.wait_for_attach()

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

      # 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 )
      __SCREAMING_SNAKE_CASE     :Optional[int]	       =						TransfoXLCorpus.from_pretrained(args.model_name							)

      __SCREAMING_SNAKE_CASE     :Tuple	       =						corpus.get_iterator('''valid'''				,     args.batch_size				,     args.tgt_len				,     device=a_				,     ext_len=args.ext_len							)
      __SCREAMING_SNAKE_CASE     :Tuple	       =						corpus.get_iterator('''test'''				,     args.batch_size				,     args.tgt_len				,     device=a_				,     ext_len=args.ext_len							)

      # Load a pre-trained model
      __SCREAMING_SNAKE_CASE     :int	       =						TransfoXLLMHeadModel.from_pretrained(args.model_name							)
      model.to(a_							)

      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:
            __SCREAMING_SNAKE_CASE     :Dict	       =						args.clamp_len
      if args.same_length:
            __SCREAMING_SNAKE_CASE     :int	       =						True

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

      # Run on test data.
      if args.split == "all":
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						evaluate(a_							)
            __SCREAMING_SNAKE_CASE     :Any	       =						evaluate(a_							)
      elif args.split == "valid":
            __SCREAMING_SNAKE_CASE     :Tuple	       =						evaluate(a_							)
            __SCREAMING_SNAKE_CASE     :Optional[int]	       =						None
      elif args.split == "test":
            __SCREAMING_SNAKE_CASE     :Optional[int]	       =						evaluate(a_							)
            __SCREAMING_SNAKE_CASE     :List[Any]	       =						None

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

      __SCREAMING_SNAKE_CASE     :Tuple	       =						''''''
      if valid_loss is not None:
            log_str += format_log(a_				,     '''valid'''							)
      if test_loss is not None:
            log_str += format_log(a_				,     '''test'''							)

      logger.info('''=''' * 1_00							)
      logger.info(a_							)
      logger.info('''=''' * 1_00							)


if __name__ == "__main__":
  main()