


from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


snake_case__			 =						logging.get_logger(__name__)

snake_case__			 =						{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case__			 =						{
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

snake_case__			 =						{'''allegro/herbert-base-cased''': 514}
snake_case__			 =						{}


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     VOCAB_FILES_NAMES
						lowerCamelCase_         =     PRETRAINED_VOCAB_FILES_MAP
						lowerCamelCase_         =     PRETRAINED_INIT_CONFIGURATION
						lowerCamelCase_         =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCamelCase_         =     HerbertTokenizer




						def __init__(	self						:   str   ,     __A						:   Dict=None   ,     __A						:   Dict=None   ,     __A						:   Dict=None   ,     __A						:   Union[str, Any]="<s>"   ,     __A						:   Any="<unk>"   ,     __A						:   int="<pad>"   ,     __A						:   Union[str, Any]="<mask>"   ,     __A						:   Union[str, Any]="</s>"   ,     **__A						:   Tuple   ,     )      ->Tuple:






									"""simple docstring"""






									super().__init__(
									    __A   ,     __A   ,     tokenizer_file=__A   ,     cls_token=__A   ,     unk_token=__A   ,     pad_token=__A   ,     mask_token=__A   ,     sep_token=__A   ,     **__A   ,     )




						def  _snake_case					(	self						:   List[Any]   ,     __A						:   List[int]   ,     __A						:   Optional[List[int]] = None						)      ->List[int]:






									"""simple docstring"""






									a__ :str						  =    [self.cls_token_id]
									a__ :str						  =    [self.sep_token_id]
									if token_ids_a is None:
												return cls + token_ids_a + sep

									return cls + token_ids_a + sep + token_ids_a + sep




						def  _snake_case					(	self						:   List[str]   ,     __A						:   List[int]   ,     __A						:   Optional[List[int]] = None   ,     __A						:   bool = False						)      ->List[int]:






									"""simple docstring"""






									if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=__A   ,     token_ids_a=__A   ,     already_has_special_tokens=__A						)

									if token_ids_a is None:
												return [1] + ([0] * len(__A						)) + [1]
									return [1] + ([0] * len(__A						)) + [1] + ([0] * len(__A						)) + [1]




						def  _snake_case					(	self						:   List[str]   ,     __A						:   List[int]   ,     __A						:   Optional[List[int]] = None						)      ->List[int]:






									"""simple docstring"""






									a__ :int						  =    [self.sep_token_id]
									a__ :List[str]						  =    [self.cls_token_id]

									if token_ids_a is None:
												return len(cls + token_ids_a + sep						) * [0]
									return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]






						def  _snake_case					(	self						:   List[str]   ,     __A						:   str   ,     __A						:   Optional[str] = None						)      ->Tuple[str]:






									"""simple docstring"""






									a__ :int						  =    self._tokenizer.model.save(__A   ,     name=__A						)
									return tuple(__A						)






import os
import time

import numpy as np
import onnxruntime as ort


snake_case__			 =						'''1'''
snake_case__			 =						'''0'''
snake_case__			 =						'''1'''

snake_case__			 =						ort.SessionOptions()
snake_case__			 =						ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('''Create inference session...''')
snake_case__			 =						['''TensorrtExecutionProvider''', '''CUDAExecutionProvider''']
snake_case__			 =						ort.InferenceSession('''model.onnx''', sess_options=sess_opt, providers=execution_provider)
snake_case__			 =						ort.RunOptions()

snake_case__			 =						128
snake_case__			 =						1
snake_case__			 =						np.ones((batch, sequence), dtype=np.intaa)
snake_case__			 =						np.ones((batch, sequence), dtype=np.intaa)
snake_case__			 =						np.ones((batch, sequence), dtype=np.intaa)

print('''Warm up phase...''')
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print('''Start inference...''')
snake_case__			 =						time.time()
snake_case__			 =						2000
snake_case__			 =						{}
for iter in range(max_iters):
		snake_case__			 =						sess.run(
		    None,
		    {
		        sess.get_inputs()[0].name: input_ids,
		        sess.get_inputs()[1].name: attention_mask,
		        sess.get_inputs()[2].name: token_type_ids,
		    },
		    run_options=run_opt,
		)
print('''Average Inference Time = {:.3f} ms'''.format((time.time() - start_time) * 1000 / max_iters))


