


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
				from transformers.pipelines.conversational import Conversation


lowerCAmelCase__       :	Any     				=logging.get_logger(__name__)

lowerCAmelCase__       :	List[str]     				={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase__       :	Optional[int]     				={
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

lowerCAmelCase__       :	Optional[int]     				={
    'microsoft/deberta-base': 5_12,
    'microsoft/deberta-large': 5_12,
    'microsoft/deberta-xlarge': 5_12,
    'microsoft/deberta-base-mnli': 5_12,
    'microsoft/deberta-large-mnli': 5_12,
    'microsoft/deberta-xlarge-mnli': 5_12,
}

lowerCAmelCase__       :	int     				={
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							_UpperCAmelCase     =		VOCAB_FILES_NAMES
							_UpperCAmelCase     =		PRETRAINED_VOCAB_FILES_MAP
							_UpperCAmelCase     =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_UpperCAmelCase     =		["""input_ids""", """attention_mask""", """token_type_ids"""]
							_UpperCAmelCase     =		DebertaTokenizer






							def __init__(	self       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__="replace"       ,		lowerCAmelCase__="[CLS]"       ,		lowerCAmelCase__="[SEP]"       ,		lowerCAmelCase__="[SEP]"       ,		lowerCAmelCase__="[CLS]"       ,		lowerCAmelCase__="[UNK]"       ,		lowerCAmelCase__="[PAD]"       ,		lowerCAmelCase__="[MASK]"       ,		lowerCAmelCase__=False       ,		**lowerCAmelCase__       ,		):





										"""simple docstring"""




										super().__init__(
										    lowerCAmelCase__       ,		lowerCAmelCase__       ,		tokenizer_file=lowerCAmelCase__       ,		errors=lowerCAmelCase__       ,		bos_token=lowerCAmelCase__       ,		eos_token=lowerCAmelCase__       ,		unk_token=lowerCAmelCase__       ,		sep_token=lowerCAmelCase__       ,		cls_token=lowerCAmelCase__       ,		pad_token=lowerCAmelCase__       ,		mask_token=lowerCAmelCase__       ,		add_prefix_space=lowerCAmelCase__       ,		**lowerCAmelCase__       ,		)
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= kwargs.pop('add_bos_token'       ,		lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						:			str       						= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
										if pre_tok_state.get('add_prefix_space'       ,		lowerCAmelCase__    ) != add_prefix_space:
													SCREAMING_SNAKE_CASE_						:			int       						= getattr(lowerCAmelCase__       ,		pre_tok_state.pop('type'    )    )
													SCREAMING_SNAKE_CASE_						:			Tuple       						= add_prefix_space
													SCREAMING_SNAKE_CASE_						:			List[str]       						= pre_tok_class(**lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= add_prefix_space






							@property
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										if self._mask_token is None:
													if self.verbose:
																logger.error('Using mask_token, but it is not set yet.'    )
													return None
										return str(self._mask_token    )






							@mask_token.setter
							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= AddedToken(lowerCAmelCase__       ,		lstrip=lowerCAmelCase__       ,		rstrip=lowerCAmelCase__    ) if isinstance(lowerCAmelCase__       ,		lowerCAmelCase__    ) else value
										SCREAMING_SNAKE_CASE_						:			str       						= value






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__ = None    ):





										"""simple docstring"""




										if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= [self.cls_token_id]
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= [self.sep_token_id]
										return cls + token_ids_a + sep + token_ids_a + sep






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__ = None    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Tuple       						= [self.sep_token_id]
										SCREAMING_SNAKE_CASE_						:			Tuple       						= [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  UpperCamelCase__			(	self       ,		*lowerCAmelCase__       ,		**lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			str       						= kwargs.get('is_split_into_words'       ,		lowerCAmelCase__    )
										assert self.add_prefix_space or not is_split_into_words, (
										    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
										    "to use it with pretokenized inputs."
										)

										return super()._batch_encode_plus(*lowerCAmelCase__       ,		**lowerCAmelCase__    )






							def  UpperCamelCase__			(	self       ,		*lowerCAmelCase__       ,		**lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Tuple       						= kwargs.get('is_split_into_words'       ,		lowerCAmelCase__    )

										assert self.add_prefix_space or not is_split_into_words, (
										    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
										    "to use it with pretokenized inputs."
										)

										return super()._encode_plus(*lowerCAmelCase__       ,		**lowerCAmelCase__    )






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__ = None    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Tuple       						= self._tokenizer.model.save(lowerCAmelCase__       ,		name=lowerCAmelCase__    )
										return tuple(lowerCAmelCase__    )






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Dict       						= []
										for is_user, text in conversation.iter_texts():
													input_ids.extend(self.encode(lowerCAmelCase__       ,		add_special_tokens=lowerCAmelCase__    ) + [self.eos_token_id]    )

										if len(lowerCAmelCase__    ) > self.model_max_length:
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= input_ids[-self.model_max_length :]
										return input_ids











import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


UpperCamelCase__              =						1_0_0_0_0_0_0_0_0_0_0_0
UpperCamelCase__              =						1_0_0_0_0

UpperCamelCase__       , UpperCamelCase__              =						os.path.split(__file__)
UpperCamelCase__              =						os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))






def 					snake_case					(							A						,   A			)							->							datasets.Dataset:




	'''simple docstring'''


	lowerCamelCase_							:		int							   =							pa.Table.from_pydict({'col': [0] * chunk_size}			)
	lowerCamelCase_							:		List[str]							   =							pa.concat_tables([table] * (num_examples // chunk_size)			)
	return datasets.Dataset(A						,   fingerprint='table_100B'			)


@dataclass
class 				__SCREAMING_SNAKE_CASE							:
					_lowerCamelCase		  = 42
					_lowerCamelCase		  = 42
					_lowerCamelCase		  = 42
					_lowerCamelCase		  = 42

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							   =							np.random.default_rng(self.seed					)
						lowerCamelCase_							:		Any							   =							rng.integers(low=self.low ,       high=self.high ,       size=self.size					).tolist()

					def __iter__(				self					):







						'''simple docstring'''




						return iter(self._sampled_values					)





					def __len__(				self					):







						'''simple docstring'''




						return self.size






@get_duration
def 					snake_case					(							A			)							->							Optional[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Union[str, Any]							   =							dataset[0]






@get_duration
def 					snake_case					(							A			)							->							Any:




	'''simple docstring'''


	lowerCamelCase_							:		List[Any]							   =							dataset[-1]






@get_duration
def 					snake_case					(							A			)							->							Optional[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							   =							dataset[range(len(A			) // 2						,   len(A			) // 2 + 1024			)]






@get_duration
def 					snake_case					(							A			)							->							str:




	'''simple docstring'''


	lowerCamelCase_							:		List[str]							   =							dataset[RandIter(0						,   len(A			)						,   1024						,   seed=42			)]






def 					snake_case					(							)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							   =							{'num examples': SPEED_TEST_N_EXAMPLES}
	lowerCamelCase_							:		Optional[int]							   =							(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
	print('generating dataset'			)
	lowerCamelCase_							:		List[Any]							   =							generate_100B_dataset(num_examples=A						,   chunk_size=A			)
	print('Functions'			)
	for func in functions:
		print(func.__name__			)
		lowerCamelCase_							:		str							   =							func(A			)

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


if __name__ == "__main__":  # useful to run the profiler
							benchmark_table_100B()
