


import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

__UpperCAmelCase : str 			=				{
    "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
    "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}



class    UpperCAmelCase_	(							_a):
							'''simple docstring'''
							__UpperCamelCase      :     Any							=					"xlnet"
							__UpperCamelCase      :     Optional[Any]							=					["mems"]
							__UpperCamelCase      :     Dict							=					{
							    "n_token": "vocab_size",  # Backward compatibility
							    "hidden_size": "d_model",
							    "num_attention_heads": "n_head",
							    "num_hidden_layers": "n_layer",
							}






							def __init__(     self ,					__SCREAMING_SNAKE_CASE=32_000 ,					__SCREAMING_SNAKE_CASE=1_024 ,					__SCREAMING_SNAKE_CASE=24 ,					__SCREAMING_SNAKE_CASE=16 ,					__SCREAMING_SNAKE_CASE=4_096 ,					__SCREAMING_SNAKE_CASE="gelu" ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE="bi" ,					__SCREAMING_SNAKE_CASE=0.02 ,					__SCREAMING_SNAKE_CASE=1e-12 ,					__SCREAMING_SNAKE_CASE=0.1 ,					__SCREAMING_SNAKE_CASE=512 ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE=-1 ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE="last" ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE="tanh" ,					__SCREAMING_SNAKE_CASE=0.1 ,					__SCREAMING_SNAKE_CASE=5 ,					__SCREAMING_SNAKE_CASE=5 ,					__SCREAMING_SNAKE_CASE=5 ,					__SCREAMING_SNAKE_CASE=1 ,					__SCREAMING_SNAKE_CASE=2 ,					**__SCREAMING_SNAKE_CASE ,					):




														"""simple docstring"""




														UpperCamelCase    :	Union[str, Any]   	=		vocab_size
														UpperCamelCase    :	Dict   	=		d_model
														UpperCamelCase    :	str   	=		n_layer
														UpperCamelCase    :	List[Any]   	=		n_head
														if d_model % n_head != 0:
																					raise ValueError(f"""'d_model % n_head' ({d_model % n_head}) should be equal to 0"""						)
														if "d_head" in kwargs:
																					if kwargs["d_head"] != d_model // n_head:
																												raise ValueError(
																												    f"""`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})"""						)
														UpperCamelCase    :	List[str]   	=		d_model // n_head
														UpperCamelCase    :	Optional[Any]   	=		ff_activation
														UpperCamelCase    :	Any   	=		d_inner
														UpperCamelCase    :	Optional[int]   	=		untie_r
														UpperCamelCase    :	str   	=		attn_type

														UpperCamelCase    :	List[str]   	=		initializer_range
														UpperCamelCase    :	int   	=		layer_norm_eps

														UpperCamelCase    :	List[Any]   	=		dropout
														UpperCamelCase    :	Dict   	=		mem_len
														UpperCamelCase    :	Optional[Any]   	=		reuse_len
														UpperCamelCase    :	List[Any]   	=		bi_data
														UpperCamelCase    :	Union[str, Any]   	=		clamp_len
														UpperCamelCase    :	str   	=		same_length

														UpperCamelCase    :	Union[str, Any]   	=		summary_type
														UpperCamelCase    :	List[str]   	=		summary_use_proj
														UpperCamelCase    :	Dict   	=		summary_activation
														UpperCamelCase    :	Union[str, Any]   	=		summary_last_dropout
														UpperCamelCase    :	Tuple   	=		start_n_top
														UpperCamelCase    :	int   	=		end_n_top

														UpperCamelCase    :	Optional[int]   	=		bos_token_id
														UpperCamelCase    :	Union[str, Any]   	=		pad_token_id
														UpperCamelCase    :	str   	=		eos_token_id

														if "use_cache" in kwargs:
																					warnings.warn(
																					    '''The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'''
																					    ''' instead.''' ,					__SCREAMING_SNAKE_CASE ,					)
																					UpperCamelCase    :	Any   	=		kwargs['''use_cache''']

														UpperCamelCase    :	Dict   	=		use_mems_eval
														UpperCamelCase    :	Dict   	=		use_mems_train
														super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE ,					bos_token_id=__SCREAMING_SNAKE_CASE ,					eos_token_id=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						)






							@property
							def  _lowercase   (     self						):




														"""simple docstring"""




														logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit."""						)
														return -1






							@max_position_embeddings.setter
							def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




														"""simple docstring"""




														raise NotImplementedError(
														    f"""The model {self.model_type} is one of the few models that has no sequence length limit."""						)





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__UpperCAmelCase : Optional[int] 			=				500000

__UpperCAmelCase			,    __UpperCAmelCase : Any 			=				os.path.split(__file__)
__UpperCAmelCase : int 			=				os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))


@get_duration
def      a	(							SCREAMING_SNAKE_CASE_      :		datasets.Dataset							,			**SCREAMING_SNAKE_CASE_      :		Union[str, Any]       ):






       """simple docstring"""





       UpperCamelCase    :	Tuple   	=		dataset.map(**SCREAMING_SNAKE_CASE_       )


@get_duration
def      a	(							SCREAMING_SNAKE_CASE_      :		datasets.Dataset							,			**SCREAMING_SNAKE_CASE_      :		Any       ):






       """simple docstring"""





       UpperCamelCase    :	int   	=		dataset.filter(**SCREAMING_SNAKE_CASE_       )


def      a	(							):






       """simple docstring"""





       UpperCamelCase    :	Optional[int]   	=		{'''num examples''': SPEED_TEST_N_EXAMPLES}
       with tempfile.TemporaryDirectory() as tmp_dir:
              UpperCamelCase    :	Dict   	=		datasets.Features({'''text''': datasets.Value('''string'''       ), '''numbers''': datasets.Value('''float32'''       )}       )
              UpperCamelCase    :	List[str]   	=		generate_example_dataset(
                  os.path.join(SCREAMING_SNAKE_CASE_							,			'''dataset.arrow'''       )							,			SCREAMING_SNAKE_CASE_							,			num_examples=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	Union[str, Any]   	=		transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''							,			use_fast=SCREAMING_SNAKE_CASE_       )

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

              UpperCamelCase    :	List[Any]   	=		map(SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[str]   	=		map(SCREAMING_SNAKE_CASE_							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[Any]   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''numpy'''       ):
                     UpperCamelCase    :	Tuple   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''pandas'''       ):
                     UpperCamelCase    :	int   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''torch'''							,			columns='''numbers'''       ):
                     UpperCamelCase    :	Dict   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''tensorflow'''							,			columns='''numbers'''       ):
                     UpperCamelCase    :	Tuple   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[str]   	=		map(SCREAMING_SNAKE_CASE_							,			function=SCREAMING_SNAKE_CASE_							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	Dict   	=		filter(SCREAMING_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(SCREAMING_SNAKE_CASE_							,			'''wb'''       ) as f:
              f.write(json.dumps(SCREAMING_SNAKE_CASE_       ).encode('''utf-8'''       )       )


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

