

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


snake_case_							=			logging.get_logger(__name__)

snake_case_							=			{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_							=			{
    '''vocab_file''': {
        '''google/realm-cc-news-pretrained-embedder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt'''
        ),
        '''google/realm-cc-news-pretrained-encoder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt'''
        ),
        '''google/realm-cc-news-pretrained-scorer''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt'''
        ),
        '''google/realm-cc-news-pretrained-openqa''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt'''
        ),
        '''google/realm-orqa-nq-openqa''': '''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt''',
        '''google/realm-orqa-nq-reader''': '''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt''',
        '''google/realm-orqa-wq-openqa''': '''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt''',
        '''google/realm-orqa-wq-reader''': '''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''google/realm-cc-news-pretrained-embedder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont'''
        ),
        '''google/realm-cc-news-pretrained-encoder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json'''
        ),
        '''google/realm-cc-news-pretrained-scorer''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json'''
        ),
        '''google/realm-cc-news-pretrained-openqa''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-nq-openqa''': (
            '''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-nq-reader''': (
            '''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-wq-openqa''': (
            '''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-wq-reader''': (
            '''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json'''
        ),
    },
}

snake_case_							=			{
    '''google/realm-cc-news-pretrained-embedder''': 5_1_2,
    '''google/realm-cc-news-pretrained-encoder''': 5_1_2,
    '''google/realm-cc-news-pretrained-scorer''': 5_1_2,
    '''google/realm-cc-news-pretrained-openqa''': 5_1_2,
    '''google/realm-orqa-nq-openqa''': 5_1_2,
    '''google/realm-orqa-nq-reader''': 5_1_2,
    '''google/realm-orqa-wq-openqa''': 5_1_2,
    '''google/realm-orqa-wq-reader''': 5_1_2,
}

snake_case_							=			{
    '''google/realm-cc-news-pretrained-embedder''': {'''do_lower_case''': True},
    '''google/realm-cc-news-pretrained-encoder''': {'''do_lower_case''': True},
    '''google/realm-cc-news-pretrained-scorer''': {'''do_lower_case''': True},
    '''google/realm-cc-news-pretrained-openqa''': {'''do_lower_case''': True},
    '''google/realm-orqa-nq-openqa''': {'''do_lower_case''': True},
    '''google/realm-orqa-nq-reader''': {'''do_lower_case''': True},
    '''google/realm-orqa-wq-openqa''': {'''do_lower_case''': True},
    '''google/realm-orqa-wq-reader''': {'''do_lower_case''': True},
}
class        snake_case_		(			_A):
		lowerCamelCase    :List[str]						=     VOCAB_FILES_NAMES
		lowerCamelCase    :Dict						=     PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase    :Optional[Any]						=     PRETRAINED_INIT_CONFIGURATION
		lowerCamelCase    :Tuple						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase    :Dict						=     RealmTokenizer







		def __init__(		self			,     __lowercase=None			,     __lowercase=None			,     __lowercase=True			,     __lowercase="[UNK]"			,     __lowercase="[SEP]"			,     __lowercase="[PAD]"			,     __lowercase="[CLS]"			,     __lowercase="[MASK]"			,     __lowercase=True			,     __lowercase=None			,     **__lowercase			,     )				->   Dict:
				super().__init__(
				    __lowercase			,     tokenizer_file=__lowercase			,     do_lower_case=__lowercase			,     unk_token=__lowercase			,     sep_token=__lowercase			,     pad_token=__lowercase			,     cls_token=__lowercase			,     mask_token=__lowercase			,     tokenize_chinese_chars=__lowercase			,     strip_accents=__lowercase			,     **__lowercase			,     )

				lowerCamelCase   : List[str]								=json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
				if (
				    normalizer_state.get('''lowercase'''			,     __lowercase   ) != do_lower_case
				    or normalizer_state.get('''strip_accents'''			,     __lowercase   ) != strip_accents
				    or normalizer_state.get('''handle_chinese_chars'''			,     __lowercase   ) != tokenize_chinese_chars
				):
						lowerCamelCase   : Optional[int]								=getattr(__lowercase			,     normalizer_state.pop('''type'''   )   )
						lowerCamelCase   : Any								=do_lower_case
						lowerCamelCase   : Optional[Any]								=strip_accents
						lowerCamelCase   : int								=tokenize_chinese_chars
						lowerCamelCase   : int								=normalizer_class(**__lowercase   )

				lowerCamelCase   : Optional[int]								=do_lower_case







		def 			__lowercase   (		self			,     __lowercase			,     **__lowercase   )				->   Optional[int]:
				lowerCamelCase   : List[str]								=PaddingStrategy.MAX_LENGTH

				lowerCamelCase   : Optional[int]								=text
				lowerCamelCase   : Optional[int]								=kwargs.pop('''text_pair'''			,     __lowercase   )
				lowerCamelCase   : Tuple								=kwargs.pop('''return_tensors'''			,     __lowercase   )

				lowerCamelCase   : Any								={
				    '''input_ids''': [],
				    '''attention_mask''': [],
				    '''token_type_ids''': [],
				}

				for idx, candidate_text in enumerate(__lowercase   ):
						if batch_text_pair is not None:
								lowerCamelCase   : List[str]								=batch_text_pair[idx]
						else:
								lowerCamelCase   : Any								=None

						lowerCamelCase   : List[str]								=super().__call__(__lowercase			,     __lowercase			,     return_tensors=__lowercase			,     **__lowercase   )

						lowerCamelCase   : str								=encoded_candidates.get('''input_ids'''   )
						lowerCamelCase   : Any								=encoded_candidates.get('''attention_mask'''   )
						lowerCamelCase   : List[Any]								=encoded_candidates.get('''token_type_ids'''   )

						if encoded_input_ids is not None:
								output_data["input_ids"].append(__lowercase   )
						if encoded_attention_mask is not None:
								output_data["attention_mask"].append(__lowercase   )
						if encoded_token_type_ids is not None:
								output_data["token_type_ids"].append(__lowercase   )

				lowerCamelCase   : Any								={key: item for key, item in output_data.items() if len(__lowercase   ) != 0}

				return BatchEncoding(__lowercase			,     tensor_type=__lowercase   )







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase=None   )				->   Optional[Any]:
				lowerCamelCase   : str								=[self.cls_token_id] + token_ids_a + [self.sep_token_id]

				if token_ids_a:
						output += token_ids_a + [self.sep_token_id]

				return output







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase = None   )				->   List[int]:
				lowerCamelCase   : List[Any]								=[self.sep_token_id]
				lowerCamelCase   : 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 			__lowercase   (		self			,     __lowercase			,     __lowercase = None   )				->   Tuple[str]:
				lowerCamelCase   : Dict								=self._tokenizer.model.save(__lowercase			,     name=__lowercase   )
				return tuple(__lowercase   )








from collections import defaultdict
from math import ceil, sqrt


def 					A__	(       SCREAMING_SNAKE_CASE_ = 1_0_0_0_0_0_0							,       SCREAMING_SNAKE_CASE_ = 1_0		)   ->     int:
		lowerCamelCase   : defaultdict								=defaultdict(SCREAMING_SNAKE_CASE_		)

		for outer_width in range(3							,       (t_limit // 4) + 2		):
				if outer_width * outer_width > t_limit:
						lowerCamelCase   : Any								=max(
						    ceil(sqrt(outer_width * outer_width - t_limit		)		)							,       1		)
				else:
						lowerCamelCase   : str								=1

				hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

				for hole_width in range(SCREAMING_SNAKE_CASE_							,       outer_width - 1							,       2		):
						count[outer_width * outer_width - hole_width * hole_width] += 1

		return sum(1 for n in count.values() if 1 <= n <= 1_0		)


if __name__ == "__main__":
				print(F"""{solution() = }""")

