

import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


A__						=							logging.get_logger(__name__)




def 					_lowercase     (				a_				:					Optional[Any]=None					,a_				:					Union[str, Any]=None				)				->	Any:




					'''simple docstring'''







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


@dataclass
class 				__UpperCamelCase				:
	_lowercase  :   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"
	        )
	    }  ,						)

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

	_lowercase  :   List[int]       				=  list_field(
	    default=[8, 3_2, 1_2_8, 5_1_2]  ,						metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}  ,						)

	_lowercase  :   bool       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}  ,						)
	_lowercase  :   bool       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}  ,						)
	_lowercase  :   bool       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}			)
	_lowercase  :   bool       				=  field(default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Use FP16 to accelerate inference."}			)
	_lowercase  :   bool       				=  field(default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Benchmark training of model"}			)
	_lowercase  :   bool       				=  field(default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Verbose memory tracing"}			)
	_lowercase  :   bool       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}  ,						)
	_lowercase  :   bool       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={
	        "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
	    }  ,						)
	_lowercase  :   bool       				=  field(default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Trace memory line by line"}			)
	_lowercase  :   bool       				=  field(default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Save result to a CSV file"}			)
	_lowercase  :   bool       				=  field(default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Save all print statements in a log file"}			)
	_lowercase  :   bool       				=  field(default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Whether to print environment information"}			)
	_lowercase  :   bool       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						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."
	        )
	    }  ,						)
	_lowercase  :   str       				=  field(
	    default=F'''inference_time_{round(time()			)}.csv'''  ,						metadata={"help": "CSV filename used if saving time results to csv."}  ,						)
	_lowercase  :   str       				=  field(
	    default=F'''inference_memory_{round(time()			)}.csv'''  ,						metadata={"help": "CSV filename used if saving memory results to csv."}  ,						)
	_lowercase  :   str       				=  field(
	    default=F'''train_time_{round(time()			)}.csv'''  ,						metadata={"help": "CSV filename used if saving time results to csv for training."}  ,						)
	_lowercase  :   str       				=  field(
	    default=F'''train_memory_{round(time()			)}.csv'''  ,						metadata={"help": "CSV filename used if saving memory results to csv for training."}  ,						)
	_lowercase  :   str       				=  field(
	    default=F'''env_info_{round(time()			)}.csv'''  ,						metadata={"help": "CSV filename used if saving environment information."}  ,						)
	_lowercase  :   str       				=  field(
	    default=F'''log_{round(time()			)}.csv'''  ,						metadata={"help": "Log filename used if print statements are saved in log."}  ,						)
	_lowercase  :   int       				=  field(default=3  ,						metadata={"help": "Times an experiment will be run."}			)
	_lowercase  :   bool       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						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: Union[str, Any]      ):

						'''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.'  , __UpperCamelCase  , )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





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







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''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: Dict      ):

						'''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







import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   Tuple       				=  LayoutLMTokenizer
	_lowercase  :   List[str]       				=  LayoutLMTokenizerFast
	_lowercase  :   List[Any]       				=  True
	_lowercase  :   Optional[int]       				=  True







	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						super().setUp()

						__magic_name__							=    [
						    '[UNK]',
						    '[CLS]',
						    '[SEP]',
						    'want',
						    '##want',
						    '##ed',
						    'wa',
						    'un',
						    'runn',
						    '##ing',
						    ',',
						    'low',
						    'lowest',
						]
						__magic_name__							=    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: Union[str, Any]  , **__UpperCamelCase: Union[str, Any]      ):

						'''simple docstring'''





						return LayoutLMTokenizer.from_pretrained(self.tmpdirname  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple  , __UpperCamelCase: List[str]      ):

						'''simple docstring'''





						__magic_name__							=    'UNwant\u00E9d,running'
						__magic_name__							=    'unwanted, running'
						return input_text, output_text







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.tokenizer_class(self.vocab_file      )

						__magic_name__							=    tokenizer.tokenize('UNwant\u00E9d,running'      )
						self.assertListEqual(__UpperCamelCase  , ['un', '##want', '##ed', ',', 'runn', '##ing']      )
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase      )  , [7, 4, 5, 10, 8, 9]      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						pass




