

import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


UpperCAmelCase_							:					Any		 =   logging.get_logger(__name__)


UpperCAmelCase_							:					Tuple		 =   {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase_							:					int		 =   {
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

UpperCAmelCase_							:					Dict		 =   {
    '''allenai/led-base-16384''': 16_384,
}






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   str     					=				VOCAB_FILES_NAMES
	_lowercase		:   Union[str, Any]     					=				PRETRAINED_VOCAB_FILES_MAP
	_lowercase		:   Optional[int]     					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowercase		:   Dict     					=				LEDTokenizer
	_lowercase		:   List[Any]     					=				['''input_ids''', '''attention_mask''']




	def __init__(     self						:     Optional[Any]					,      __A						:     Dict=None					,      __A						:     Tuple=None					,      __A						:     Optional[int]=None					,      __A						:     Any="replace"					,      __A						:     Any="<s>"					,      __A						:     Any="</s>"					,      __A						:     List[str]="</s>"					,      __A						:     List[Any]="<s>"					,      __A						:     Dict="<unk>"					,      __A						:     Optional[int]="<pad>"					,      __A						:     List[Any]="<mask>"					,      __A						:     List[Any]=False					,      __A						:     Tuple=True					,      **__A						:     Optional[Any]					,      ):
		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 :			List[Any]		    =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
		if pre_tok_state.get("""add_prefix_space"""					,      __A							) != add_prefix_space:
			__A :			int		    =			getattr(__A					,      pre_tok_state.pop("""type"""							)							)
			__A :			Optional[Any]		    =			add_prefix_space
			__A :			Optional[int]		    =			pre_tok_class(**__A							)

		__A :			Optional[Any]		    =			add_prefix_space

		# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
		__A :			Optional[int]		    =			"""post_processor"""
		__A :			Tuple		    =			getattr(self.backend_tokenizer					,      __A					,      __A							)
		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 :			Dict		    =			tuple(state["""sep"""]							)
			if "cls" in state:
				__A :			Optional[Any]		    =			tuple(state["""cls"""]							)

			__A :			Optional[Any]		    =			False

			if state.get("""add_prefix_space"""					,      __A							) != add_prefix_space:
				__A :			Tuple		    =			add_prefix_space
				__A :			Union[str, Any]		    =			True

			if state.get("""trim_offsets"""					,      __A							) != trim_offsets:
				__A :			int		    =			trim_offsets
				__A :			Optional[Any]		    =			True

			if changes_to_apply:
				__A :			Union[str, Any]		    =			getattr(__A					,      state.pop("""type"""							)							)
				__A :			Union[str, Any]		    =			component_class(**__A							)
				setattr(self.backend_tokenizer					,      __A					,      __A							)




	@property
	# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
	def        lowerCAmelCase_       (     self						:     List[str]							):
		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						:     Optional[Any]					,      __A						:     Tuple							):
		__A :			Optional[Any]		    =			AddedToken(__A					,      lstrip=__A					,      rstrip=__A							) if isinstance(__A					,      __A							) else value
		__A :			List[Any]		    =			value




	def        lowerCAmelCase_       (     self						:     int					,      *__A						:     Optional[int]					,      **__A						:     Optional[int]							):
		__A :			Any		    =			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        lowerCAmelCase_       (     self						:     List[str]					,      *__A						:     Tuple					,      **__A						:     Optional[Any]							):
		__A :			Tuple		    =			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        lowerCAmelCase_       (     self						:     Dict					,      __A						:     str					,      __A						:     Optional[str] = None							):
		__A :			List[Any]		    =			self._tokenizer.model.save(__A					,      name=__A							)
		return tuple(__A							)




	def        lowerCAmelCase_       (     self						:     int					,      __A						:     int					,      __A						:     Tuple=None							):
		__A :			Optional[int]		    =			[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						:     str					,      __A						:     List[int]					,      __A						:     Optional[List[int]] = None							):
		__A :			Dict		    =			[self.sep_token_id]
		__A :			List[Any]		    =			[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]




	def        lowerCAmelCase_       (     self						:     Tuple					,      __A						:     Union[Dict[str, EncodedInput], BatchEncoding]					,      __A						:     Optional[int] = None					,      __A						:     PaddingStrategy = PaddingStrategy.DO_NOT_PAD					,      __A						:     Optional[int] = None					,      __A						:     Optional[bool] = None					,      ):
		__A :			Optional[Any]		    =			super()._pad(
		    encoded_inputs=__A					,      max_length=__A					,      padding_strategy=__A					,      pad_to_multiple_of=__A					,      return_attention_mask=__A					,      )

		# Load from model defaults
		if return_attention_mask is None:
			__A :			Dict		    =			"""attention_mask""" in self.model_input_names

		if return_attention_mask and "global_attention_mask" in encoded_inputs:
			__A :			Tuple		    =			encoded_inputs[self.model_input_names[0]]
			# `global_attention_mask` need to have the same length as other (sequential) inputs.
			__A :			Dict		    =			len(encoded_inputs["""global_attention_mask"""]							) != len(__A							)

			if needs_to_be_padded:
				__A :			Any		    =			len(__A							) - len(encoded_inputs["""global_attention_mask"""]							)

				if self.padding_side == "right":
					# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
					__A :			int		    =			(
					    encoded_inputs["""global_attention_mask"""] + [-1] * difference
					)
				elif self.padding_side == "left":
					__A :			List[Any]		    =			[-1] * difference + encoded_inputs[
					    """global_attention_mask"""
					]
				else:
					raise ValueError("""Invalid padding strategy:""" + str(self.padding_side							)							)

		return encoded_inputs








"""simple docstring"""
# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_A							=  {
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A							=  [
        """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """CpmAntForCausalLM""",
        """CpmAntModel""",
        """CpmAntPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
    from .tokenization_cpmant import CpmAntTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_cpmant import (
            CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
            CpmAntForCausalLM,
            CpmAntModel,
            CpmAntPreTrainedModel,
        )


else:
    import sys

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

