

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


lowerCAmelCase__		      =       logging.get_logger(__name__)

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

lowerCAmelCase__		      =       {
    """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"""
        ),
    },
}

lowerCAmelCase__		      =       {
    """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,
}

lowerCAmelCase__		      =       {
    """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     a__  (    snake_case	):
			"""simple docstring"""







			__lowerCamelCase       				=				VOCAB_FILES_NAMES
			__lowerCamelCase       				=				PRETRAINED_VOCAB_FILES_MAP
			__lowerCamelCase       				=				PRETRAINED_INIT_CONFIGURATION
			__lowerCamelCase       				=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__lowerCamelCase       				=				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					,					)     ->  List[str]:
							'''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					,					)

							A__						=	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
							):
											A__						=	getattr(lowercase					,					normalizer_state.pop("type"				)				)
											A__						=	do_lower_case
											A__						=	strip_accents
											A__						=	tokenize_chinese_chars
											A__						=	normalizer_class(**lowercase				)

							A__						=	do_lower_case


			def    UpperCamelCase						(			self					,					lowercase					,					**lowercase				)     ->  Optional[int]:
							'''simple docstring'''






							A__						=	PaddingStrategy.MAX_LENGTH

							A__						=	text
							A__						=	kwargs.pop("text_pair"					,					lowercase				)
							A__						=	kwargs.pop("return_tensors"					,					lowercase				)

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

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

											A__						=	super().__call__(lowercase					,					lowercase					,					return_tensors=lowercase					,					**lowercase				)

											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(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				)

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

							return BatchEncoding(lowercase					,					tensor_type=lowercase				)


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase=None				)     ->  str:
							'''simple docstring'''






							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					,					lowercase					,					lowercase = None				)     ->  List[int]:
							'''simple docstring'''






							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					,					lowercase					,					lowercase = None				)     ->  Tuple[str]:
							'''simple docstring'''






							A__						=	self._tokenizer.model.save(lowercase					,					name=lowercase				)
							return tuple(lowercase				)






'''simple docstring'''





import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__a          =		logging.getLogger(__name__)







@dataclass
@add_start_docstrings(TrainingArguments.__doc__	)
class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		field(default=_a		,		metadata={"help": "Whether to use SortishSampler or not."}	)
    lowercase					=		field(
        default=_a		,		metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}	)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": (
                "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                "to the `max_length` value of the model configuration."
            )
        }		,		)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": (
                "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                "to the `num_beams` value of the model configuration."
            )
        }		,		)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
        }		,		)




    def lowerCamelCase       (    self					:     List[str]						):
          snake_case__  :    int    						=      super().to_dict()
          for k, v in d.items():
                if isinstance(snake_case_ ,     snake_case_						):
                      snake_case__  :    Optional[int]    						=      v.to_dict()
          return d
