



import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


SCREAMING_SNAKE_CASE_:List[Any]             =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Any             =						{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE_:int             =						{
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/tokenizer.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/tokenizer.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json""",
        """roberta-base-openai-detector""": (
            """https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"""
        ),
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE_:Any             =						{
    """roberta-base""": 512,
    """roberta-large""": 512,
    """roberta-large-mnli""": 512,
    """distilroberta-base""": 512,
    """roberta-base-openai-detector""": 512,
    """roberta-large-openai-detector""": 512,
}






class SCREAMING_SNAKE_CASE__     (		A_    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      VOCAB_FILES_NAMES
    __lowerCamelCase			: Optional[int]					=      PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase			: Optional[Any]					=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase			: Dict					=      ['''input_ids''', '''attention_mask''']
    __lowerCamelCase			: Optional[int]					=      RobertaTokenizer






    def __init__(       self,		lowerCamelCase__=None,		lowerCamelCase__=None,		lowerCamelCase__=None,		lowerCamelCase__="replace",		lowerCamelCase__="<s>",		lowerCamelCase__="</s>",		lowerCamelCase__="</s>",		lowerCamelCase__="<s>",		lowerCamelCase__="<unk>",		lowerCamelCase__="<pad>",		lowerCamelCase__="<mask>",		lowerCamelCase__=False,		lowerCamelCase__=True,		**lowerCamelCase__,		):
        super().__init__(
            lowerCamelCase__,		lowerCamelCase__,		tokenizer_file=lowerCamelCase__,		errors=lowerCamelCase__,		bos_token=lowerCamelCase__,		eos_token=lowerCamelCase__,		sep_token=lowerCamelCase__,		cls_token=lowerCamelCase__,		unk_token=lowerCamelCase__,		pad_token=lowerCamelCase__,		mask_token=lowerCamelCase__,		add_prefix_space=lowerCamelCase__,		trim_offsets=lowerCamelCase__,		**lowerCamelCase__,		)

        A :       Union[str, Any]          =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
        if pre_tok_state.get("""add_prefix_space""",		lowerCamelCase__		) != add_prefix_space:
            A :       Optional[Any]          =   getattr(lowerCamelCase__,		pre_tok_state.pop("""type"""		)		)
            A :       List[str]          =   add_prefix_space
            A :       List[Any]          =   pre_tok_class(**lowerCamelCase__		)

        A :       str          =   add_prefix_space

        A :       Union[str, Any]          =   """post_processor"""
        A :       Tuple          =   getattr(self.backend_tokenizer,		lowerCamelCase__,		lowerCamelCase__		)
        if tokenizer_component_instance:
            A :       Tuple          =   json.loads(tokenizer_component_instance.__getstate__()		)

            # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
            if "sep" in state:
                A :       List[str]          =   tuple(state["""sep"""]		)
            if "cls" in state:
                A :       Tuple          =   tuple(state["""cls"""]		)

            A :       Tuple          =   False

            if state.get("""add_prefix_space""",		lowerCamelCase__		) != add_prefix_space:
                A :       Optional[int]          =   add_prefix_space
                A :       int          =   True

            if state.get("""trim_offsets""",		lowerCamelCase__		) != trim_offsets:
                A :       Dict          =   trim_offsets
                A :       Optional[Any]          =   True

            if changes_to_apply:
                A :       Optional[Any]          =   getattr(lowerCamelCase__,		state.pop("""type"""		)		)
                A :       List[Any]          =   component_class(**lowerCamelCase__		)
                setattr(self.backend_tokenizer,		lowerCamelCase__,		lowerCamelCase__		)






    @property
    def 			_lowerCAmelCase					(       self		):
        if self._mask_token is None:
            if self.verbose:
                logger.error("""Using mask_token, but it is not set yet."""		)
            return None
        return str(self._mask_token		)






    @mask_token.setter
    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       List[str]          =   AddedToken(lowerCamelCase__,		lstrip=lowerCamelCase__,		rstrip=lowerCamelCase__		) if isinstance(lowerCamelCase__,		lowerCamelCase__		) else value
        A :       List[Any]          =   value






    def 			_lowerCAmelCase					(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        A :       Any          =   kwargs.get("""is_split_into_words""",		lowerCamelCase__		)
        assert self.add_prefix_space or not is_split_into_words, (
            f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
            "to use it with pretokenized inputs."
        )

        return super()._batch_encode_plus(*lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		*lowerCamelCase__,		**lowerCamelCase__		):
        A :       List[Any]          =   kwargs.get("""is_split_into_words""",		lowerCamelCase__		)

        assert self.add_prefix_space or not is_split_into_words, (
            f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
            "to use it with pretokenized inputs."
        )

        return super()._encode_plus(*lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = None		):
        A :       int          =   self._tokenizer.model.save(lowerCamelCase__,		name=lowerCamelCase__		)
        return tuple(lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__=None		):
        A :       Tuple          =   [self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
            return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = None		):
        A :       Any          =   [self.sep_token_id]
        A :       int          =   [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 + sep + token_ids_a + sep		) * [0]






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowerCAmelCase           =		{'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXConfig''']}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase           =		['''GPTNeoXTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase           =		[
	    '''GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''GPTNeoXForCausalLM''',
	    '''GPTNeoXForQuestionAnswering''',
	    '''GPTNeoXForSequenceClassification''',
	    '''GPTNeoXForTokenClassification''',
	    '''GPTNeoXLayer''',
	    '''GPTNeoXModel''',
	    '''GPTNeoXPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	lowerCAmelCase           =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






