



"""simple docstring"""
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


A:     Optional[int]										=	logging.get_logger(__name__)

def    _snake_case		(		UpperCamelCase		:	Tuple=None    ,	UpperCamelCase		:	int=None		):
 return field(default_factory=lambda: default    ,	metadata=UpperCamelCase		)



@dataclass
class    SCREAMING_SNAKE_CASE__						:
   __lowerCAmelCase						:     List[str]		=		list_field(
       default=[]   ,    metadata={
           'help': (
               'Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'
               ' of all available models'
           )
       }   ,    )

   __lowerCAmelCase						:     List[int]		=		list_field(
       default=[8]   ,    metadata={'help': 'List of batch sizes for which memory and time performance will be evaluated'}				)

   __lowerCAmelCase						:     List[int]		=		list_field(
       default=[8, 32, 128, 512]   ,    metadata={'help': 'List of sequence lengths for which memory and time performance will be evaluated'}   ,    )

   __lowerCAmelCase						:     bool		=		field(
       default=UpperCAmelCase__   ,    metadata={'help': 'Whether to benchmark inference of model. Inference can be disabled via --no-inference.'}   ,    )
   __lowerCAmelCase						:     bool		=		field(
       default=UpperCAmelCase__   ,    metadata={'help': 'Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'}   ,    )
   __lowerCAmelCase						:     bool		=		field(
       default=UpperCAmelCase__   ,    metadata={'help': 'Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'}				)
   __lowerCAmelCase						:     bool		=		field(default=UpperCAmelCase__   ,    metadata={'help': 'Use FP16 to accelerate inference.'}				)
   __lowerCAmelCase						:     bool		=		field(default=UpperCAmelCase__   ,    metadata={'help': 'Benchmark training of model'}				)
   __lowerCAmelCase						:     bool		=		field(default=UpperCAmelCase__   ,    metadata={'help': 'Verbose memory tracing'}				)
   __lowerCAmelCase						:     bool		=		field(
       default=UpperCAmelCase__   ,    metadata={'help': 'Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'}   ,    )
   __lowerCAmelCase						:     bool		=		field(
       default=UpperCAmelCase__   ,    metadata={
           'help': 'Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'
       }   ,    )
   __lowerCAmelCase						:     bool		=		field(default=UpperCAmelCase__   ,    metadata={'help': 'Trace memory line by line'}				)
   __lowerCAmelCase						:     bool		=		field(default=UpperCAmelCase__   ,    metadata={'help': 'Save result to a CSV file'}				)
   __lowerCAmelCase						:     bool		=		field(default=UpperCAmelCase__   ,    metadata={'help': 'Save all print statements in a log file'}				)
   __lowerCAmelCase						:     bool		=		field(default=UpperCAmelCase__   ,    metadata={'help': 'Whether to print environment information'}				)
   __lowerCAmelCase						:     bool		=		field(
       default=UpperCAmelCase__   ,    metadata={
           'help': (
               'Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'
               ' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'
               ' for debugging / testing and on TPU.'
           )
       }   ,    )
   __lowerCAmelCase						:     str		=		field(
       default=F"inference_time_{round(time()				)}.csv"   ,    metadata={'help': 'CSV filename used if saving time results to csv.'}   ,    )
   __lowerCAmelCase						:     str		=		field(
       default=F"inference_memory_{round(time()				)}.csv"   ,    metadata={'help': 'CSV filename used if saving memory results to csv.'}   ,    )
   __lowerCAmelCase						:     str		=		field(
       default=F"train_time_{round(time()				)}.csv"   ,    metadata={'help': 'CSV filename used if saving time results to csv for training.'}   ,    )
   __lowerCAmelCase						:     str		=		field(
       default=F"train_memory_{round(time()				)}.csv"   ,    metadata={'help': 'CSV filename used if saving memory results to csv for training.'}   ,    )
   __lowerCAmelCase						:     str		=		field(
       default=F"env_info_{round(time()				)}.csv"   ,    metadata={'help': 'CSV filename used if saving environment information.'}   ,    )
   __lowerCAmelCase						:     str		=		field(
       default=F"log_{round(time()				)}.csv"   ,    metadata={'help': 'Log filename used if print statements are saved in log.'}   ,    )
   __lowerCAmelCase						:     int		=		field(default=3   ,    metadata={'help': 'Times an experiment will be run.'}				)
   __lowerCAmelCase						:     bool		=		field(
       default=UpperCAmelCase__   ,    metadata={
           'help': (
               'Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'
               ' model weights.'
           )
       }   ,    )

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[int]:





    '''simple docstring'''




    warnings.warn(
        F"The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"
        """ are deprecated in general and it is advised to use external Benchmarking libraries """
        """ to benchmark Transformer models."""   ,  _SCREAMING_SNAKE_CASE   ,  )

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    return json.dumps(dataclasses.asdict(self		)   ,  indent=2		)

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





    '''simple docstring'''




    if len(self.models		) <= 0:
     raise ValueError(
         """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
         """ bert-base-cased` or `args.models = ['bert-base-cased']."""		)
    return self.models




   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    if not self.multi_process:
     return False
    elif self.is_tpu:
     logger.info("""Multiprocessing is currently not possible on TPU."""		)
     return False
    else:
     return True





"""simple docstring"""
import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__   ,    unittest.TestCase				):
   __lowerCAmelCase						:     List[Any]		=		TransfoXLTokenizer
   __lowerCAmelCase						:     Optional[int]		=		False
   __lowerCAmelCase						:     Optional[Any]		=		False

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





    '''simple docstring'''




    super().setUp()

    UpperCAmelCase			:      Optional[Any]			=			[
        """<unk>""",
        """[CLS]""",
        """[SEP]""",
        """want""",
        """unwanted""",
        """wa""",
        """un""",
        """running""",
        """,""",
        """low""",
        """l""",
    ]
    UpperCAmelCase			:      int			=			os.path.join(self.tmpdirname   ,  VOCAB_FILES_NAMES["""vocab_file"""]		)
    with open(self.vocab_file   ,  """w"""   ,  encoding="""utf-8"""		) as vocab_writer:
     vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]		)		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  **_SCREAMING_SNAKE_CASE		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			True
    return TransfoXLTokenizer.from_pretrained(self.tmpdirname   ,  **_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			"""<unk> UNwanted , running"""
    UpperCAmelCase			:      Dict			=			"""<unk> unwanted, running"""
    return input_text, output_text

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			TransfoXLTokenizer(vocab_file=self.vocab_file   ,  lower_case=_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Dict			=			tokenizer.tokenize("""<unk> UNwanted , running"""		)
    self.assertListEqual(_SCREAMING_SNAKE_CASE   ,  ["""<unk>""", """unwanted""", """,""", """running"""]		)

    self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		)   ,  [0, 4, 8, 7]		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			TransfoXLTokenizer(lower_case=_SCREAMING_SNAKE_CASE		)

    self.assertListEqual(
        tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """		)   ,  ["""hello""", """!""", """how""", """are""", """you""", """?"""]		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			TransfoXLTokenizer(lower_case=_SCREAMING_SNAKE_CASE		)

    self.assertListEqual(
        tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """		)   ,  ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			TransfoXLTokenizer(lower_case=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			"""Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"""
    UpperCAmelCase			:      Optional[Any]			=			[
        """Hello""",
        """(""",
        """bracket""",
        """)""",
        """and""",
        """side""",
        """@-@""",
        """scrolled""",
        """[""",
        """and""",
        """]""",
        """Henry""",
        """'s""",
        """$""",
        """5""",
        """@,@""",
        """000""",
        """with""",
        """3""",
        """@.@""",
        """34""",
        """m""",
        """.""",
        """What""",
        """'s""",
        """up""",
        """!""",
        """?""",
    ]

    self.assertListEqual(tokenizer.tokenize(_SCREAMING_SNAKE_CASE		)   ,  _SCREAMING_SNAKE_CASE		)

    self.assertEqual(tokenizer.convert_tokens_to_string(_SCREAMING_SNAKE_CASE		)   ,  _SCREAMING_SNAKE_CASE		)




   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			self.get_tokenizer()
    UpperCAmelCase			:      Tuple			=			len(_SCREAMING_SNAKE_CASE		)

    tokenizer.add_tokens(["""new1""", """new2"""]		)
    tokenizer.move_added_token("""new1"""   ,  1		)

    # Check that moved token is not copied (duplicate)
    self.assertEqual(len(_SCREAMING_SNAKE_CASE		)   ,  original_len + 2		)
    # Check that token is moved to specified id
    self.assertEqual(tokenizer.encode("""new1"""		)   ,  [1]		)
    self.assertEqual(tokenizer.decode([1]		)   ,  """new1"""		)
