






from typing import Callable, Optional, Union

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


A__			:    Dict         =				logging.get_logger(__name__)

A__			:    Tuple         =				{
    'microsoft/xprophetnet-large-wiki100-cased': (
        'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'
    ),
}
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= """xlm-prophetnet"""
				lowercase__       							= ["""past_key_values"""]
				lowercase__       							= {
				    """num_attention_heads""": """num_encoder_attention_heads""",
				}




				def __init__(  self		:					List[Any],						lowerCamelCase		:					Optional[float] = 0.1,						lowerCamelCase		:					Optional[Union[str, Callable]] = "gelu",						lowerCamelCase		:					Optional[int] = 30_522,						lowerCamelCase		:					Optional[int] = 1_024,						lowerCamelCase		:					Optional[int] = 4_096,						lowerCamelCase		:					Optional[int] = 12,						lowerCamelCase		:					Optional[int] = 16,						lowerCamelCase		:					Optional[int] = 4_096,						lowerCamelCase		:					Optional[int] = 12,						lowerCamelCase		:					Optional[int] = 16,						lowerCamelCase		:					Optional[float] = 0.1,						lowerCamelCase		:					Optional[float] = 0.1,						lowerCamelCase		:					Optional[int] = 512,						lowerCamelCase		:					Optional[float] = 0.02,						lowerCamelCase		:					Optional[bool] = True,						lowerCamelCase		:					Optional[bool] = True,						lowerCamelCase		:					Optional[int] = 0,						lowerCamelCase		:					Optional[int] = 2,						lowerCamelCase		:					Optional[int] = 32,						lowerCamelCase		:					Optional[int] = 128,						lowerCamelCase		:					Optional[bool] = False,						lowerCamelCase		:					Optional[float] = 0.0,						lowerCamelCase		:					Optional[bool] = True,						lowerCamelCase		:					Optional[int] = 0,						lowerCamelCase		:					Optional[int] = 1,						lowerCamelCase		:					Optional[int] = 2,						**lowerCamelCase		:					Union[str, Any],						):

								'''simple docstring'''
								lowercase__     				=    vocab_size
								lowercase__     				=    hidden_size
								lowercase__     				=    encoder_ffn_dim
								lowercase__     				=    num_encoder_layers
								lowercase__     				=    num_encoder_attention_heads
								lowercase__     				=    decoder_ffn_dim
								lowercase__     				=    num_decoder_layers
								lowercase__     				=    num_decoder_attention_heads
								lowercase__     				=    max_position_embeddings
								lowercase__     				=    init_std  # Normal(0, this parameter)
								lowercase__     				=    activation_function

								# parameters for xlmprophetnet
								lowercase__     				=    ngram
								lowercase__     				=    num_buckets
								lowercase__     				=    relative_max_distance
								lowercase__     				=    disable_ngram_loss
								lowercase__     				=    eps

								# 3 Types of Dropout
								lowercase__     				=    attention_dropout
								lowercase__     				=    activation_dropout
								lowercase__     				=    dropout

								lowercase__     				=    use_cache

								super().__init__(
								    pad_token_id=lowerCamelCase,						bos_token_id=lowerCamelCase,						eos_token_id=lowerCamelCase,						is_encoder_decoder=lowerCamelCase,						add_cross_attention=lowerCamelCase,						decoder_start_token_id=lowerCamelCase,						**lowerCamelCase,						)




				@property
				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								return self.num_encoder_layers + self.num_decoder_layers




				@num_hidden_layers.setter
				def        lowercase__     (  self		:					Tuple,						lowerCamelCase		:					Optional[Any]						):

								'''simple docstring'''
								raise NotImplementedError(
								    '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
								    ''' `num_decoder_layers`.'''						)












import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


A__			:    Dict         =				50_00_00

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






@get_duration
def 						a    (		lowerCamelCase_	,     **lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    dataset.map(**lowerCamelCase_       )






@get_duration
def 						a    (		lowerCamelCase_	,     **lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    dataset.filter(**lowerCamelCase_       )






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    {'''num examples''': SPEED_TEST_N_EXAMPLES}
				with tempfile.TemporaryDirectory() as tmp_dir:
								lowercase__     				=    datasets.Features({'''text''': datasets.Value('''string'''       ), '''numbers''': datasets.Value('''float32'''       )}       )
								lowercase__     				=    generate_example_dataset(
								    os.path.join(lowerCamelCase_	,     '''dataset.arrow'''       )	,     lowerCamelCase_	,     num_examples=lowerCamelCase_       )

								lowercase__     				=    transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''	,     use_fast=lowerCamelCase_       )

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

								lowercase__     				=    map(lowerCamelCase_       )

								lowercase__     				=    map(lowerCamelCase_	,     batched=lowerCamelCase_       )

								lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

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

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

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

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

								lowercase__     				=    map(lowerCamelCase_	,     function=lowerCamelCase_	,     batched=lowerCamelCase_       )

								lowercase__     				=    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()




