


"""simple docstring"""




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


lowercase_			     =  logging.get_logger(__name__)

lowercase_			     =  {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

lowercase_			     =  {
    "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"
        ),
    },
}

lowercase_			     =  {
    "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,
}

lowercase_			     =  {
    "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 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			List[str]										=				VOCAB_FILES_NAMES
		__UpperCAmelCase		:			Optional[int]										=				PRETRAINED_VOCAB_FILES_MAP
		__UpperCAmelCase		:			Optional[int]										=				PRETRAINED_INIT_CONFIGURATION
		__UpperCAmelCase		:			int										=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCAmelCase		:			Tuple										=				RealmTokenizer

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

				__a           =			json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
				if (
				    normalizer_state.get('''lowercase'''							, _a      ) != do_lower_case
				    or normalizer_state.get('''strip_accents'''							, _a      ) != strip_accents
				    or normalizer_state.get('''handle_chinese_chars'''							, _a      ) != tokenize_chinese_chars
				):
						__a           =			getattr(_a							, normalizer_state.pop('''type'''      )      )
						__a           =			do_lower_case
						__a           =			strip_accents
						__a           =			tokenize_chinese_chars
						__a           =			normalizer_class(**_a      )

				__a           =			do_lower_case

		def 			__UpperCAmelCase       (  self							, _a							, **_a      ):
				__a           =			PaddingStrategy.MAX_LENGTH

				__a           =			text
				__a           =			kwargs.pop('''text_pair'''							, _a      )
				__a           =			kwargs.pop('''return_tensors'''							, _a      )

				__a           =			{
				    '''input_ids''': [],
				    '''attention_mask''': [],
				    '''token_type_ids''': [],
				}

				for idx, candidate_text in enumerate(_a      ):
						if batch_text_pair is not None:
								__a           =			batch_text_pair[idx]
						else:
								__a           =			None

						__a           =			super().__call__(_a							, _a							, return_tensors=_a							, **_a      )

						__a           =			encoded_candidates.get('''input_ids'''      )
						__a           =			encoded_candidates.get('''attention_mask'''      )
						__a           =			encoded_candidates.get('''token_type_ids'''      )

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

				__a           =			{key: item for key, item in output_data.items() if len(_a      ) != 0}

				return BatchEncoding(_a							, tensor_type=_a      )

		def 			__UpperCAmelCase       (  self							, _a							, _a=None      ):
				__a           =			[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 			__UpperCAmelCase       (  self							, _a							, _a = None      ):
				__a           =			[self.sep_token_id]
				__a           =			[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							, _a							, _a = None      ):
				__a           =			self._tokenizer.model.save(_a							, name=_a      )
				return tuple(_a      )














from __future__ import annotations

import collections
import pprint
from pathlib import Path


def __A    (      __lowerCAmelCase	)->  str:





						"""simple docstring"""



						return "".join(sorted(__lowerCAmelCase	)	)


def __A    (      __lowerCAmelCase	)->  list[str]:





						"""simple docstring"""



						return word_by_signature[signature(__lowerCAmelCase	)]


_a      	=  Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
_a      	=  sorted({word.strip().lower() for word in data.splitlines()})

_a      	=  collections.defaultdict(list)
for word in word_list:
		word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
		_a      	=  {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

		with open('''anagrams.txt''', '''w''') as file:
				file.write('''all_anagrams = \n ''')
				file.write(pprint.pformat(all_anagrams))
