
'''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""": 512,
    """google/realm-cc-news-pretrained-encoder""": 512,
    """google/realm-cc-news-pretrained-scorer""": 512,
    """google/realm-cc-news-pretrained-openqa""": 512,
    """google/realm-orqa-nq-openqa""": 512,
    """google/realm-orqa-nq-reader""": 512,
    """google/realm-orqa-wq-openqa""": 512,
    """google/realm-orqa-wq-reader""": 512,
}

_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   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				Dict									=       VOCAB_FILES_NAMES
						_lowercase		:				int									=       PRETRAINED_VOCAB_FILES_MAP
						_lowercase		:				int									=       PRETRAINED_INIT_CONFIGURATION
						_lowercase		:				List[Any]									=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowercase		:				str									=       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   ,       ):

												"""simple docstring"""



												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  						=			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  						=			getattr(_lowercase   ,       normalizer_state.pop("""type"""				)				)
																		_lowerCAmelCase  						=			do_lower_case
																		_lowerCAmelCase  						=			strip_accents
																		_lowerCAmelCase  						=			tokenize_chinese_chars
																		_lowerCAmelCase  						=			normalizer_class(**_lowercase				)

												_lowerCAmelCase  						=			do_lower_case




						def  _lowercase     ( self   ,       _lowercase   ,       **_lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			PaddingStrategy.MAX_LENGTH

												_lowerCAmelCase  						=			text
												_lowerCAmelCase  						=			kwargs.pop("""text_pair"""   ,       _lowercase				)
												_lowerCAmelCase  						=			kwargs.pop("""return_tensors"""   ,       _lowercase				)

												_lowerCAmelCase  						=			{
												    """input_ids""": [],
												    """attention_mask""": [],
												    """token_type_ids""": [],
												}

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

																		_lowerCAmelCase  						=			super().__call__(_lowercase   ,       _lowercase   ,       return_tensors=_lowercase   ,       **_lowercase				)

																		_lowerCAmelCase  						=			encoded_candidates.get("""input_ids"""				)
																		_lowerCAmelCase  						=			encoded_candidates.get("""attention_mask"""				)
																		_lowerCAmelCase  						=			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  						=			{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				):

												"""simple docstring"""



												_lowerCAmelCase  						=			[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				):

												"""simple docstring"""



												_lowerCAmelCase  						=			[self.sep_token_id]
												_lowerCAmelCase  						=			[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				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self._tokenizer.model.save(_lowercase   ,       name=_lowercase				)
												return tuple(_lowercase				)


'''simple docstring'''



from __future__ import annotations




class   UpperCAmelCase_					:






						'''simple docstring'''






						def __init__( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			order

												# a_{0} ... a_{k}
												_lowerCAmelCase  						=			[1.0] + [0.0] * order
												# b_{0} ... b_{k}
												_lowerCAmelCase  						=			[1.0] + [0.0] * order

												# x[n-1] ... x[n-k]
												_lowerCAmelCase  						=			[0.0] * self.order
												# y[n-1] ... y[n-k]
												_lowerCAmelCase  						=			[0.0] * self.order




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												if len(_lowercase				) < self.order:
																		_lowerCAmelCase  						=			[1.0, *a_coeffs]

												if len(_lowercase				) != self.order + 1:
																		_lowerCAmelCase  						=			(
																		    F'Expected a_coeffs to have {self.order + 1} elements '
																		    F'for {self.order}-order filter, got {len(_lowercase				)}'
																		)
																		raise ValueError(_lowercase				)

												if len(_lowercase				) != self.order + 1:
																		_lowerCAmelCase  						=			(
																		    F'Expected b_coeffs to have {self.order + 1} elements '
																		    F'for {self.order}-order filter, got {len(_lowercase				)}'
																		)
																		raise ValueError(_lowercase				)

												_lowerCAmelCase  						=			a_coeffs
												_lowerCAmelCase  						=			b_coeffs




						def  _lowercase     ( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			0.0

												# Start at index 1 and do index 0 at the end.
												for i in range(1   ,       self.order + 1				):
																		result += (
																		    self.b_coeffs[i] * self.input_history[i - 1]
																		    - self.a_coeffs[i] * self.output_history[i - 1]
																		)

												_lowerCAmelCase  						=			(result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

												_lowerCAmelCase  						=			self.input_history[:-1]
												_lowerCAmelCase  						=			self.output_history[:-1]

												_lowerCAmelCase  						=			sample
												_lowerCAmelCase  						=			result

												return result
