




import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


UpperCAmelCase_		:					List[str]          =  500000

UpperCAmelCase_    ,  UpperCAmelCase_		:					List[Any]          =  os.path.split(__file__)
UpperCAmelCase_		:					Any          =  os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))




@get_duration
def 		lowerCAmelCase_ (						lowerCamelCase						,  **lowerCamelCase ):
				__magic_name__						:						Tuple										=dataset.map(**lowerCamelCase )




@get_duration
def 		lowerCAmelCase_ (						lowerCamelCase						,  **lowerCamelCase ):
				__magic_name__						:						List[str]										=dataset.filter(**lowerCamelCase )




def 		lowerCAmelCase_ (						):
				__magic_name__						:						Optional[int]										={"""num examples""": SPEED_TEST_N_EXAMPLES}
				with tempfile.TemporaryDirectory() as tmp_dir:
								__magic_name__						:						str										=datasets.Features({"""text""": datasets.Value("""string""" ), """numbers""": datasets.Value("""float32""" )} )
								__magic_name__						:						str										=generate_example_dataset(
								    os.path.join(lowerCamelCase						,  """dataset.arrow""" )						,  lowerCamelCase						,  num_examples=lowerCamelCase )

								__magic_name__						:						Dict										=transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""						,  use_fast=lowerCamelCase )

								def tokenize(lowerCamelCase ):
												return tokenizer(examples["""text"""] )

								__magic_name__						:						Union[str, Any]										=map(lowerCamelCase )

								__magic_name__						:						List[Any]										=map(lowerCamelCase						,  batched=lowerCamelCase )

								__magic_name__						:						Optional[int]										=map(lowerCamelCase						,  function=lambda lowerCamelCase : None						,  batched=lowerCamelCase )

								with dataset.formatted_as(type="""numpy""" ):
												__magic_name__						:						Optional[Any]										=map(lowerCamelCase						,  function=lambda lowerCamelCase : None						,  batched=lowerCamelCase )

								with dataset.formatted_as(type="""pandas""" ):
												__magic_name__						:						int										=map(lowerCamelCase						,  function=lambda lowerCamelCase : None						,  batched=lowerCamelCase )

								with dataset.formatted_as(type="""torch"""						,  columns="""numbers""" ):
												__magic_name__						:						List[Any]										=map(lowerCamelCase						,  function=lambda lowerCamelCase : None						,  batched=lowerCamelCase )

								with dataset.formatted_as(type="""tensorflow"""						,  columns="""numbers""" ):
												__magic_name__						:						Optional[Any]										=map(lowerCamelCase						,  function=lambda lowerCamelCase : None						,  batched=lowerCamelCase )

								__magic_name__						:						Optional[Any]										=map(lowerCamelCase						,  function=lowerCamelCase						,  batched=lowerCamelCase )

								__magic_name__						:						Optional[int]										=filter(lowerCamelCase )

								# 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(lowerCamelCase						,  """wb""" ) as f:
								f.write(json.dumps(lowerCamelCase ).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


UpperCAmelCase_		:					Tuple          =  logging.get_logger(__name__)

UpperCAmelCase_		:					List[str]          =  {
    "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
    "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """xlm-roberta-xl"""

						def __init__(		self :Dict     ,			__snake_case :Optional[Any]=25_08_80     ,			__snake_case :List[Any]=25_60     ,			__snake_case :Optional[Any]=36     ,			__snake_case :Any=32     ,			__snake_case :int=1_02_40     ,			__snake_case :List[Any]="gelu"     ,			__snake_case :Union[str, Any]=0.1     ,			__snake_case :Optional[Any]=0.1     ,			__snake_case :str=5_14     ,			__snake_case :Union[str, Any]=1     ,			__snake_case :Optional[int]=0.02     ,			__snake_case :str=1E-05     ,			__snake_case :str=1     ,			__snake_case :int=0     ,			__snake_case :Tuple=2     ,			__snake_case :Optional[int]="absolute"     ,			__snake_case :str=True     ,			__snake_case :Any=None     ,			**__snake_case :Dict     ,			):
										'''simple docstring'''

										super().__init__(pad_token_id=__snake_case     ,			bos_token_id=__snake_case     ,			eos_token_id=__snake_case     ,			**__snake_case			)
										__magic_name__						:						List[str]										=vocab_size
										__magic_name__						:						List[str]										=hidden_size
										__magic_name__						:						Union[str, Any]										=num_hidden_layers
										__magic_name__						:						Any										=num_attention_heads
										__magic_name__						:						Any										=hidden_act
										__magic_name__						:						List[str]										=intermediate_size
										__magic_name__						:						Any										=hidden_dropout_prob
										__magic_name__						:						Union[str, Any]										=attention_probs_dropout_prob
										__magic_name__						:						Any										=max_position_embeddings
										__magic_name__						:						Any										=type_vocab_size
										__magic_name__						:						List[str]										=initializer_range
										__magic_name__						:						Optional[int]										=layer_norm_eps
										__magic_name__						:						Dict										=position_embedding_type
										__magic_name__						:						Any										=use_cache
										__magic_name__						:						Dict										=classifier_dropout




class 		__A   (      UpperCamelCase__      ):

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										if self.task == "multiple-choice":
														__magic_name__						:						str										={0: """batch""", 1: """choice""", 2: """sequence"""}
										else:
														__magic_name__						:						Optional[Any]										={0: """batch""", 1: """sequence"""}
										return OrderedDict(
										    [
										        ("""input_ids""", dynamic_axis),
										        ("""attention_mask""", dynamic_axis),
										    ]			)




