


"""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


UpperCamelCase						:				Tuple             =      logging.get_logger(__name__)







def  __snake_case       (   UpperCamelCase__=None ,			UpperCamelCase__=None	)      ->		Any:







		"""simple docstring"""





		return field(default_factory=lambda: default ,			metadata=UpperCamelCase__	)



@dataclass
class 			lowerCamelCase__						:
						lowerCAmelCase       =     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_field(
						    default=[8]							,    metadata={"""help""": """List of batch sizes for which memory and time performance will be evaluated"""}			)

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

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







						def __a						(							self   :    Dict		):
								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.'			,			_lowercase			,			)







						def __a						(							self   :    str		):
								return json.dumps(dataclasses.asdict(self		)			,			indent=2		)







						@property
						def __a						(							self   :    Optional[int]		):
								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 __a						(							self   :    Tuple		):
								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 			lowerCamelCase__						(   UpperCAmelCase_							,    unittest.TestCase			):
						lowerCAmelCase       =     TransfoXLTokenizer
						lowerCAmelCase       =     False
						lowerCAmelCase       =     False







						def __a						(							self   :    Union[str, Any]		):
								super().setUp()

								A					       = [
								    '<unk>',
								    '[CLS]',
								    '[SEP]',
								    'want',
								    'unwanted',
								    'wa',
								    'un',
								    'running',
								    ',',
								    'low',
								    'l',
								]
								A					       = 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 __a						(							self   :    int			,			**_lowercase   :    List[str]		):
								A					       = True
								return TransfoXLTokenizer.from_pretrained(self.tmpdirname			,			**_lowercase		)







						def __a						(							self   :    int			,			_lowercase   :    List[Any]		):
								A					       = '<unk> UNwanted , running'
								A					       = '<unk> unwanted, running'
								return input_text, output_text







						def __a						(							self   :    List[str]		):
								A					       = TransfoXLTokenizer(vocab_file=self.vocab_file			,			lower_case=_lowercase		)

								A					       = tokenizer.tokenize('<unk> UNwanted , running'		)
								self.assertListEqual(_lowercase			,			['<unk>', 'unwanted', ',', 'running']		)

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







						def __a						(							self   :    int		):
								A					       = TransfoXLTokenizer(lower_case=_lowercase		)

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







						def __a						(							self   :    Optional[Any]		):
								A					       = TransfoXLTokenizer(lower_case=_lowercase		)

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







						def __a						(							self   :    str		):
								A					       = TransfoXLTokenizer(lower_case=_lowercase		)
								A					       = 'Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
								A					       = [
								    'Hello',
								    '(',
								    'bracket',
								    ')',
								    'and',
								    'side',
								    '@-@',
								    'scrolled',
								    '[',
								    'and',
								    ']',
								    'Henry',
								    '\'s',
								    '$',
								    '5',
								    '@,@',
								    '000',
								    'with',
								    '3',
								    '@.@',
								    '34',
								    'm',
								    '.',
								    'What',
								    '\'s',
								    'up',
								    '!',
								    '?',
								]

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

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







						def __a						(							self   :    Dict		):
								A					       = self.get_tokenizer()
								A					       = len(_lowercase		)

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

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





