


"""simple docstring"""


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_mvp import MvpTokenizer


a_       							=    logging.get_logger(__name__)


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

# See all MVP models at https://huggingface.co/models?filter=mvp
a_       							=    {
    "vocab_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json",
    },
    "added_tokens.json": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json",
    },
    "merges_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json",
    },
}

a_       							=    {
    "RUCAIBox/mvp": 10_24,
}
class snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			VOCAB_FILES_NAMES
		__UpperCamelCase           =			PRETRAINED_VOCAB_FILES_MAP
		__UpperCamelCase           =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCamelCase           =			['input_ids', 'attention_mask']
		__UpperCamelCase           =			MvpTokenizer







		def __init__(					self      :       List[Any]    ,   a__      :       Any=None    ,   a__      :       Optional[int]=None    ,   a__      :       int=None    ,   a__      :       Dict="replace"    ,   a__      :       List[Any]="<s>"    ,   a__      :       Dict="</s>"    ,   a__      :       int="</s>"    ,   a__      :       int="<s>"    ,   a__      :       Optional[int]="<unk>"    ,   a__      :       Tuple="<pad>"    ,   a__      :       List[Any]="<mask>"    ,   a__      :       Optional[Any]=False    ,   a__      :       Union[str, Any]=True    ,   **a__      :       Union[str, Any]    ,   )       ->						Optional[int]:





						'''simple docstring'''







						super().__init__(
						    a__    ,   a__    ,   tokenizer_file=a__    ,   errors=a__    ,   bos_token=a__    ,   eos_token=a__    ,   sep_token=a__    ,   cls_token=a__    ,   unk_token=a__    ,   pad_token=a__    ,   mask_token=a__    ,   add_prefix_space=a__    ,   trim_offsets=a__    ,   **a__    ,   )

						_A					  =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
						if pre_tok_state.get("add_prefix_space"    ,   a__		) != add_prefix_space:
										_A					  =			getattr(a__    ,   pre_tok_state.pop("type"		)		)
										_A					  =			add_prefix_space
										_A					  =			pre_tok_class(**a__		)

						_A					  =			add_prefix_space

						# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
						_A					  =			"post_processor"
						_A					  =			getattr(self.backend_tokenizer    ,   a__    ,   a__		)
						if tokenizer_component_instance:
										_A					  =			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					  =			tuple(state["sep"]		)
										if "cls" in state:
														_A					  =			tuple(state["cls"]		)

										_A					  =			False

										if state.get("add_prefix_space"    ,   a__		) != add_prefix_space:
														_A					  =			add_prefix_space
														_A					  =			True

										if state.get("trim_offsets"    ,   a__		) != trim_offsets:
														_A					  =			trim_offsets
														_A					  =			True

										if changes_to_apply:
														_A					  =			getattr(a__    ,   state.pop("type"		)		)
														_A					  =			component_class(**a__		)
														setattr(self.backend_tokenizer    ,   a__    ,   a__		)







		@property
		def  a_ (					self      :       Union[str, Any]		)       ->						str:





						'''simple docstring'''







						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  a_ (					self      :       List[Any]    ,   a__      :       List[str]		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			AddedToken(a__    ,   lstrip=a__    ,   rstrip=a__		) if isinstance(a__    ,   a__		) else value
						_A					  =			value







		def  a_ (					self      :       Optional[int]    ,   *a__      :       Any    ,   **a__      :       Dict		)       ->						BatchEncoding:





						'''simple docstring'''







						_A					  =			kwargs.get("is_split_into_words"    ,   a__		)

						if is_split_into_words and not self.add_prefix_space:
										raise ValueError(
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."		)

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







		def  a_ (					self      :       Optional[Any]    ,   *a__      :       Tuple    ,   **a__      :       List[Any]		)       ->						BatchEncoding:





						'''simple docstring'''







						_A					  =			kwargs.get("is_split_into_words"    ,   a__		)

						if is_split_into_words and not self.add_prefix_space:
										raise ValueError(
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."		)

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







		def  a_ (					self      :       Tuple    ,   a__      :       str    ,   a__      :       Optional[str] = None		)       ->						Tuple[str]:





						'''simple docstring'''







						_A					  =			self._tokenizer.model.save(a__    ,   name=a__		)
						return tuple(a__		)







		def  a_ (					self      :       int    ,   a__      :       List[Any]    ,   a__      :       int=None		)       ->						Any:





						'''simple docstring'''







						_A					  =			[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  a_ (					self      :       Any    ,   a__      :       List[int]    ,   a__      :       Optional[List[int]] = 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 + sep + token_ids_a + sep		) * [0]



"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_       							=    {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    ["RoFormerTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "RoFormerForCausalLM",
		    "RoFormerForMaskedLM",
		    "RoFormerForMultipleChoice",
		    "RoFormerForQuestionAnswering",
		    "RoFormerForSequenceClassification",
		    "RoFormerForTokenClassification",
		    "RoFormerLayer",
		    "RoFormerModel",
		    "RoFormerPreTrainedModel",
		    "load_tf_weights_in_roformer",
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFRoFormerForCausalLM",
		    "TFRoFormerForMaskedLM",
		    "TFRoFormerForMultipleChoice",
		    "TFRoFormerForQuestionAnswering",
		    "TFRoFormerForSequenceClassification",
		    "TFRoFormerForTokenClassification",
		    "TFRoFormerLayer",
		    "TFRoFormerModel",
		    "TFRoFormerPreTrainedModel",
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "FlaxRoFormerForMaskedLM",
		    "FlaxRoFormerForMultipleChoice",
		    "FlaxRoFormerForQuestionAnswering",
		    "FlaxRoFormerForSequenceClassification",
		    "FlaxRoFormerForTokenClassification",
		    "FlaxRoFormerModel",
		    "FlaxRoFormerPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
		from .tokenization_roformer import RoFormerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_roformer_fast import RoFormerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roformer import (
				    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoFormerForCausalLM,
				    RoFormerForMaskedLM,
				    RoFormerForMultipleChoice,
				    RoFormerForQuestionAnswering,
				    RoFormerForSequenceClassification,
				    RoFormerForTokenClassification,
				    RoFormerLayer,
				    RoFormerModel,
				    RoFormerPreTrainedModel,
				    load_tf_weights_in_roformer,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_roformer import (
				    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRoFormerForCausalLM,
				    TFRoFormerForMaskedLM,
				    TFRoFormerForMultipleChoice,
				    TFRoFormerForQuestionAnswering,
				    TFRoFormerForSequenceClassification,
				    TFRoFormerForTokenClassification,
				    TFRoFormerLayer,
				    TFRoFormerModel,
				    TFRoFormerPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_roformer import (
				    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FlaxRoFormerForMaskedLM,
				    FlaxRoFormerForMultipleChoice,
				    FlaxRoFormerForQuestionAnswering,
				    FlaxRoFormerForSequenceClassification,
				    FlaxRoFormerForTokenClassification,
				    FlaxRoFormerModel,
				    FlaxRoFormerPreTrainedModel,
				)


else:
		import sys

		a_       							=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)