
'''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					:							Optional[int]   =      logging.get_logger(__name__)

lowercase					:							List[str]   =      {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

lowercase					:							Any   =      {
    "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					:							List[Any]   =      {
    "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					:							Optional[Any]   =      {
    "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   (      _lowerCamelCase		):
							__lowercase   	= VOCAB_FILES_NAMES
							__lowercase   	= PRETRAINED_VOCAB_FILES_MAP
							__lowercase   	= PRETRAINED_INIT_CONFIGURATION
							__lowercase   	= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__lowercase   	= RealmTokenizer


							def __init__( self					,     lowerCAmelCase_=None					,     lowerCAmelCase_=None					,     lowerCAmelCase_=True					,     lowerCAmelCase_="[UNK]"					,     lowerCAmelCase_="[SEP]"					,     lowerCAmelCase_="[PAD]"					,     lowerCAmelCase_="[CLS]"					,     lowerCAmelCase_="[MASK]"					,     lowerCAmelCase_=True					,     lowerCAmelCase_=None					,     **lowerCAmelCase_					,     ):





								"""simple docstring"""






								super().__init__(
								    lowerCAmelCase_					,     tokenizer_file=lowerCAmelCase_					,     do_lower_case=lowerCAmelCase_					,     unk_token=lowerCAmelCase_					,     sep_token=lowerCAmelCase_					,     pad_token=lowerCAmelCase_					,     cls_token=lowerCAmelCase_					,     mask_token=lowerCAmelCase_					,     tokenize_chinese_chars=lowerCAmelCase_					,     strip_accents=lowerCAmelCase_					,     **lowerCAmelCase_					,     )

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

								_snake_case     		=	do_lower_case


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     **lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	PaddingStrategy.MAX_LENGTH

								_snake_case     		=	text
								_snake_case     		=	kwargs.pop('text_pair'					,     lowerCAmelCase_						)
								_snake_case     		=	kwargs.pop('return_tensors'					,     lowerCAmelCase_						)

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

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

									_snake_case     		=	super().__call__(lowerCAmelCase_					,     lowerCAmelCase_					,     return_tensors=lowerCAmelCase_					,     **lowerCAmelCase_						)

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

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

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

								return BatchEncoding(lowerCAmelCase_					,     tensor_type=lowerCAmelCase_						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_=None						):





								"""simple docstring"""






								_snake_case     		=	[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 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_ = None						):





								"""simple docstring"""






								_snake_case     		=	[self.sep_token_id]
								_snake_case     		=	[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 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_ = None						):





								"""simple docstring"""






								_snake_case     		=	self._tokenizer.model.save(lowerCAmelCase_					,     name=lowerCAmelCase_						)
								return tuple(lowerCAmelCase_						)






'''simple docstring'''







from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A = "x"    ,       __A = 10**-10    ,       __A = 1    ,       ) ->			complex:
	_snake_case     		=	symbols(__A			)
	_snake_case     		=	lambdify(__A    ,       __A			)
	_snake_case     		=	lambdify(__A    ,       diff(__A    ,       __A			)			)

	_snake_case     		=	starting_point

	while True:
		if diff_function(__A			) != 0:
			_snake_case     		=	prev_guess - multiplicity * func(__A			) / diff_function(
			    __A			)
		else:
			raise ZeroDivisionError('Could not find root'			) from None

		# Precision is checked by comparing the difference of consecutive guesses
		if abs(next_guess - prev_guess			) < precision:
			return next_guess

		_snake_case     		=	next_guess


# Let's Execute
if __name__ == "__main__":
					# Find root of trigonometric function
					# Find value of pi
					print(F'''The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}''')

					# Find root of polynomial
					# Find fourth Root of 5
					print(F'''The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5J)}''')

					# Find value of e
					print(
					    "The root of log(y) - 1 = 0 is ",
					    F'''{newton_raphson("log(y) - 1", 2, variable="y")}''',
					)

					# Exponential Roots
					print(
					    "The root of exp(x) - 1 = 0 is",
					    F'''{newton_raphson("exp(x) - 1", 10, precision=0.005)}''',
					)

					# Find root of cos(x)
					print(F'''The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}''')

