"""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	:		str   =   logging.getLogger(__name__)



def       A__    (	)  ->							Optional[Any]:
   __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=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.'						)
   __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_     :	Optional[int]						):
      # 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_      , 1000 * 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_     :	Tuple      , lowercase_     :	Dict						):
      __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('=' * 100						)
   logger.info(lowercase_						)
   logger.info('=' * 100						)


if __name__ == "__main__":
     main()









import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      str )				-> str:





    '''simple docstring'''






    A__        =  min(re.search('^ +'       ,     SCREAMING_SNAKE_CASE__ ).end() if t.startswith(' ' ) else 0 for t in string.splitlines() )
    return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(SCREAMING_SNAKE_CASE__ )] )


lowercase_									=    "\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

lowercase_									=    "\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


lowercase_									=    "\\n# Begin of markdown\n\nSome cool dataset card\n"


class 					A		(				unittest.TestCase					):







    """simple docstring"""





    def 		snake_case__	( self					:			List[Any]						)->       str:





        '''simple docstring'''







        with tempfile.TemporaryDirectory() as tmp_dir:
            A__        =  Path(lowercase_						) / 'README.md'
            with open(lowercase_,'w+'						) as readme_file:
                readme_file.write(lowercase_						)
            A__        =  DatasetMetadata.from_readme(lowercase_						)
            self.assertDictEqual(lowercase_,{'language': ['zh', 'en'], 'task_ids': ['sentiment-classification']}						)

            with open(lowercase_,'w+'						) as readme_file:
                readme_file.write(lowercase_						)
            A__        =  DatasetMetadata.from_readme(lowercase_						)
            self.assertDictEqual(lowercase_,{}						)

            with open(lowercase_,'w+'						) as readme_file:
                readme_file.write(lowercase_						)
            A__        =  DatasetMetadata.from_readme(lowercase_						)
            self.assertEqual(lowercase_,{}						)


    def 		snake_case__	( self					:			Tuple						)->       List[Any]:





        '''simple docstring'''







        A__        =  _dedent(
            '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            '						)
        assert DatasetMetadata.from_yaml_string(lowercase_						)

        A__        =  _dedent(
            '\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            '						)
        with self.assertRaises(lowercase_						):
            DatasetMetadata.from_yaml_string(lowercase_						)

        A__        =  _dedent(
            '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            '						)
        assert DatasetMetadata.from_yaml_string(lowercase_						)

