import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


_lowercase:	str       			=		5_0_0_0_0_0

_lowercase			,							_lowercase:	str       			=		os.path.split(__file__)
_lowercase:	Any       			=		os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))






@get_duration
def _lowerCamelCase  (		snake_case     , **snake_case  ):
							_lowerCAmelCase							=							dataset.map(**snake_case  )






@get_duration
def _lowerCamelCase  (		snake_case     , **snake_case  ):
							_lowerCAmelCase							=							dataset.filter(**snake_case  )






def _lowerCamelCase  (		):
							_lowerCAmelCase							=							{'num examples': SPEED_TEST_N_EXAMPLES}
							with tempfile.TemporaryDirectory() as tmp_dir:
														_lowerCAmelCase							=							datasets.Features({'text': datasets.Value('string'  ), 'numbers': datasets.Value('float32'  )}  )
														_lowerCAmelCase							=							generate_example_dataset(
														    os.path.join(snake_case     , 'dataset.arrow'  )     , snake_case     , num_examples=snake_case  )

														_lowerCAmelCase							=							transformers.AutoTokenizer.from_pretrained('bert-base-cased'     , use_fast=snake_case  )

														def tokenize(snake_case  ):
																					return tokenizer(examples['text']  )

														_lowerCAmelCase							=							map(snake_case  )

														_lowerCAmelCase							=							map(snake_case     , batched=snake_case  )

														_lowerCAmelCase							=							map(snake_case     , function=lambda snake_case  : None     , batched=snake_case  )

														with dataset.formatted_as(type='numpy'  ):
																					_lowerCAmelCase							=							map(snake_case     , function=lambda snake_case  : None     , batched=snake_case  )

														with dataset.formatted_as(type='pandas'  ):
																					_lowerCAmelCase							=							map(snake_case     , function=lambda snake_case  : None     , batched=snake_case  )

														with dataset.formatted_as(type='torch'     , columns='numbers'  ):
																					_lowerCAmelCase							=							map(snake_case     , function=lambda snake_case  : None     , batched=snake_case  )

														with dataset.formatted_as(type='tensorflow'     , columns='numbers'  ):
																					_lowerCAmelCase							=							map(snake_case     , function=lambda snake_case  : None     , batched=snake_case  )

														_lowerCAmelCase							=							map(snake_case     , function=snake_case     , batched=snake_case  )

														_lowerCAmelCase							=							filter(snake_case  )

														# Activate later when tokenizer support batched inputs
														# with dataset.formatted_as(type='numpy'):
														#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

							with open(snake_case     , 'wb'  ) as f:
														f.write(json.dumps(snake_case  ).encode('utf-8'  )  )


if __name__ == "__main__":  # useful to run the profiler
				benchmark_map_filter()







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase:	Optional[Any]       			=		logging.get_logger(__name__)

_lowercase:	Tuple       			=		{
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}



class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							="roberta-prelayernorm"





			def __init__(       self	:	int  ,  lowercase__	:	int=5_02_65  ,  lowercase__	:	str=7_68  ,  lowercase__	:	List[Any]=12  ,  lowercase__	:	Any=12  ,  lowercase__	:	Optional[Any]=30_72  ,  lowercase__	:	List[Any]="gelu"  ,  lowercase__	:	str=0.1  ,  lowercase__	:	List[Any]=0.1  ,  lowercase__	:	Tuple=5_12  ,  lowercase__	:	Optional[int]=2  ,  lowercase__	:	Dict=0.0_2  ,  lowercase__	:	Tuple=1e-12  ,  lowercase__	:	Optional[int]=1  ,  lowercase__	:	Optional[int]=0  ,  lowercase__	:	Union[str, Any]=2  ,  lowercase__	:	Any="absolute"  ,  lowercase__	:	Any=True  ,  lowercase__	:	Any=None  ,  **lowercase__	:	Optional[int]  ,  ):
										super().__init__(pad_token_id=lowercase__  ,  bos_token_id=lowercase__  ,  eos_token_id=lowercase__  ,  **lowercase__						)

										_lowerCAmelCase							=							vocab_size
										_lowerCAmelCase							=							hidden_size
										_lowerCAmelCase							=							num_hidden_layers
										_lowerCAmelCase							=							num_attention_heads
										_lowerCAmelCase							=							hidden_act
										_lowerCAmelCase							=							intermediate_size
										_lowerCAmelCase							=							hidden_dropout_prob
										_lowerCAmelCase							=							attention_probs_dropout_prob
										_lowerCAmelCase							=							max_position_embeddings
										_lowerCAmelCase							=							type_vocab_size
										_lowerCAmelCase							=							initializer_range
										_lowerCAmelCase							=							layer_norm_eps
										_lowerCAmelCase							=							position_embedding_type
										_lowerCAmelCase							=							use_cache
										_lowerCAmelCase							=							classifier_dropout







class        lowerCamelCase__				(   UpperCAmelCase				):





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										if self.task == "multiple-choice":
																	_lowerCAmelCase							=							{0: 'batch', 1: 'choice', 2: 'sequence'}
										else:
																	_lowerCAmelCase							=							{0: 'batch', 1: 'sequence'}
										return OrderedDict(
										    [
										        ('input_ids', dynamic_axis),
										        ('attention_mask', dynamic_axis),
										    ]						)






