



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[str]				  =				logging.getLogger(__name__)
def 	lowercase_							():
       snake_case__							:     int            =     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=1_0		, help='batch size'				)
       parser.add_argument('--tgt_len'		, type=A		, default=1_2_8		, 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=1_6_0_0		, help='length of the retained previous heads'				)
       parser.add_argument('--clamp_len'		, type=A		, default=1_0_0_0		, 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.'				)
       snake_case__							:     Dict            =     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()

       snake_case__							:     Optional[int]            =     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 )
       snake_case__							:     Dict            =     TransfoXLCorpus.from_pretrained(args.model_name				)

       snake_case__							:     Dict            =     corpus.get_iterator('valid'		, args.batch_size		, args.tgt_len		, device=A		, ext_len=args.ext_len				)
       snake_case__							:     List[Any]            =     corpus.get_iterator('test'		, args.batch_size		, args.tgt_len		, device=A		, ext_len=args.ext_len				)

       # Load a pre-trained model
       snake_case__							:     str            =     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:
              snake_case__							:     int            =     args.clamp_len
       if args.same_length:
              snake_case__							:     Union[str, Any]            =     True

       ###############################################################################
       # Evaluation code
       ###############################################################################
       def evaluate(A			:			List[Any]				):
              # Turn on evaluation mode which disables dropout.
              model.eval()
              snake_case__     ,    snake_case__							:     Tuple            =     0, 0.0
              snake_case__							:     List[Any]            =     time.time()
              with torch.no_grad():
                     snake_case__							:     Optional[Any]            =     None
                     for idx, (data, target, seq_len) in enumerate(A				):
                            snake_case__							:     str            =     model(A		, lm_labels=A		, mems=A				)
                            snake_case__     ,    snake_case__     ,    snake_case__							:     str            =     ret
                            snake_case__							:     List[Any]            =     loss.mean()
                            total_loss += seq_len * loss.item()
                            total_len += seq_len
                     snake_case__							:     Dict            =     time.time() - start_time
              logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(A		, 1_0_0_0 * total_time / (idx + 1)				)				)
              return total_loss / total_len

       # Run on test data.
       if args.split == "all":
              snake_case__							:     int            =     evaluate(A				)
              snake_case__							:     int            =     evaluate(A				)
       elif args.split == "valid":
              snake_case__							:     Union[str, Any]            =     evaluate(A				)
              snake_case__							:     Optional[Any]            =     None
       elif args.split == "test":
              snake_case__							:     Tuple            =     evaluate(A				)
              snake_case__							:     List[str]            =     None

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

       snake_case__							:     str            =     ''
       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_0_0				)
       logger.info(A				)
       logger.info('=' * 1_0_0				)


if __name__ == "__main__":
 main()











import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list




class  snake_case__						(	unittest.TestCase					):






     """simple docstring"""


     def        lowercase_       (	self  :  Optional[int] )       ->Optional[Any]:
            snake_case__							:     Tuple            =     '<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'.split(' ' )
            snake_case__							:     Any            =     dict(zip(_snake_case,			range(len(_snake_case ) ) ) )

            snake_case__							:     List[str]            =     {
                'pad_token': '<pad>',
                'unk_token': '<unk>',
                'bos_token': '<s>',
                'eos_token': '</s>',
            }
            snake_case__							:     Optional[int]            =     {
                'feature_size': 1,
                'padding_value': 0.0,
                'sampling_rate': 1_6_0_0_0,
                'return_attention_mask': False,
                'do_normalize': True,
            }

            snake_case__							:     Union[str, Any]            =     tempfile.mkdtemp()
            snake_case__							:     Optional[int]            =     os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES['vocab_file'] )
            snake_case__							:     int            =     os.path.join(self.tmpdirname,			_snake_case )
            with open(self.vocab_file,			'w',			encoding='utf-8' ) as fp:
                   fp.write(json.dumps(_snake_case ) + '\n' )

            with open(self.feature_extraction_file,			'w',			encoding='utf-8' ) as fp:
                   fp.write(json.dumps(_snake_case ) + '\n' )






     def        lowercase_       (	self  :  Tuple,			**_snake_case  :  Dict )       ->Union[str, Any]:
            snake_case__							:     Tuple            =     self.add_kwargs_tokens_map.copy()
            kwargs.update(_snake_case )
            return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname,			**_snake_case )






     def        lowercase_       (	self  :  Optional[Any],			**_snake_case  :  Dict )       ->Union[str, Any]:
            return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname,			**_snake_case )






     def        lowercase_       (	self  :  List[Any] )       ->str:
            shutil.rmtree(self.tmpdirname )






     def        lowercase_       (	self  :  Optional[int] )       ->Any:
            snake_case__							:     Any            =     self.get_tokenizer()
            snake_case__							:     Any            =     self.get_feature_extractor()

            snake_case__							:     Tuple            =     WavaVecaProcessor(tokenizer=_snake_case,			feature_extractor=_snake_case )

            processor.save_pretrained(self.tmpdirname )
            snake_case__							:     List[Any]            =     WavaVecaProcessor.from_pretrained(self.tmpdirname )

            self.assertEqual(processor.tokenizer.get_vocab(),			tokenizer.get_vocab() )
            self.assertIsInstance(processor.tokenizer,			_snake_case )

            self.assertEqual(processor.feature_extractor.to_json_string(),			feature_extractor.to_json_string() )
            self.assertIsInstance(processor.feature_extractor,			_snake_case )






     def        lowercase_       (	self  :  Optional[Any] )       ->Any:
            snake_case__							:     str            =     WavaVecaProcessor(tokenizer=self.get_tokenizer(),			feature_extractor=self.get_feature_extractor() )
            processor.save_pretrained(self.tmpdirname )

            snake_case__							:     Optional[int]            =     self.get_tokenizer(bos_token='(BOS)',			eos_token='(EOS)' )
            snake_case__							:     List[str]            =     self.get_feature_extractor(do_normalize=_snake_case,			padding_value=1.0 )

            snake_case__							:     List[str]            =     WavaVecaProcessor.from_pretrained(
                self.tmpdirname,			bos_token='(BOS)',			eos_token='(EOS)',			do_normalize=_snake_case,			padding_value=1.0 )

            self.assertEqual(processor.tokenizer.get_vocab(),			tokenizer_add_kwargs.get_vocab() )
            self.assertIsInstance(processor.tokenizer,			_snake_case )

            self.assertEqual(processor.feature_extractor.to_json_string(),			feature_extractor_add_kwargs.to_json_string() )
            self.assertIsInstance(processor.feature_extractor,			_snake_case )






     def        lowercase_       (	self  :  str )       ->Optional[Any]:
            snake_case__							:     Optional[Any]            =     self.get_feature_extractor()
            snake_case__							:     Tuple            =     self.get_tokenizer()

            snake_case__							:     List[Any]            =     WavaVecaProcessor(tokenizer=_snake_case,			feature_extractor=_snake_case )

            snake_case__							:     Any            =     floats_list((3, 1_0_0_0) )

            snake_case__							:     int            =     feature_extractor(_snake_case,			return_tensors='np' )
            snake_case__							:     Union[str, Any]            =     processor(_snake_case,			return_tensors='np' )

            for key in input_feat_extract.keys():
                   self.assertAlmostEqual(input_feat_extract[key].sum(),			input_processor[key].sum(),			delta=1e-2 )






     def        lowercase_       (	self  :  Optional[int] )       ->str:
            snake_case__							:     int            =     self.get_feature_extractor()
            snake_case__							:     Any            =     self.get_tokenizer()

            snake_case__							:     List[str]            =     WavaVecaProcessor(tokenizer=_snake_case,			feature_extractor=_snake_case )

            snake_case__							:     Optional[Any]            =     'This is a test string'

            snake_case__							:     Optional[Any]            =     processor(text=_snake_case )

            snake_case__							:     Any            =     tokenizer(_snake_case )

            for key in encoded_tok.keys():
                   self.assertListEqual(encoded_tok[key],			encoded_processor[key] )






     def        lowercase_       (	self  :  Union[str, Any] )       ->List[str]:
            snake_case__							:     Union[str, Any]            =     self.get_feature_extractor()
            snake_case__							:     List[str]            =     self.get_tokenizer()

            snake_case__							:     Union[str, Any]            =     WavaVecaProcessor(tokenizer=_snake_case,			feature_extractor=_snake_case )

            snake_case__							:     Optional[Any]            =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            snake_case__							:     Dict            =     processor.batch_decode(_snake_case )
            snake_case__							:     Optional[int]            =     tokenizer.batch_decode(_snake_case )

            self.assertListEqual(_snake_case,			_snake_case )






     def        lowercase_       (	self  :  List[str] )       ->Any:
            snake_case__							:     List[Any]            =     self.get_feature_extractor()
            snake_case__							:     List[str]            =     self.get_tokenizer()

            snake_case__							:     Union[str, Any]            =     WavaVecaProcessor(tokenizer=_snake_case,			feature_extractor=_snake_case )

            self.assertListEqual(
                processor.model_input_names,			feature_extractor.model_input_names,			msg='`processor` and `feature_extractor` model input names do not match',			)






