





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


_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)


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

_SCREAMING_SNAKE_CASE   =      {
    '''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''',
    },
}

_SCREAMING_SNAKE_CASE   =      {
    '''allenai/led-base-16384''': 1_6_3_8_4,
}






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Optional[int]     =    VOCAB_FILES_NAMES
	a      :			Optional[int]     =    PRETRAINED_VOCAB_FILES_MAP
	a      :			Dict     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a      :			Union[str, Any]     =    LEDTokenizer
	a      :			Union[str, Any]     =    ["input_ids", "attention_mask"]


	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       ,)							->							Union[str, Any]:







				'''simple docstring'''





				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       ,)

				__lowercase			  =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
				if pre_tok_state.get('''add_prefix_space'''       ,_lowerCamelCase	) != add_prefix_space:
							__lowercase			  =							getattr(_lowerCamelCase       ,pre_tok_state.pop('''type'''	)	)
							__lowercase			  =							add_prefix_space
							__lowercase			  =							pre_tok_class(**_lowerCamelCase	)

				__lowercase			  =							add_prefix_space

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

							__lowercase			  =							False

							if state.get('''add_prefix_space'''       ,_lowerCamelCase	) != add_prefix_space:
										__lowercase			  =							add_prefix_space
										__lowercase			  =							True

							if state.get('''trim_offsets'''       ,_lowerCamelCase	) != trim_offsets:
										__lowercase			  =							trim_offsets
										__lowercase			  =							True

							if changes_to_apply:
										__lowercase			  =							getattr(_lowerCamelCase       ,state.pop('''type'''	)	)
										__lowercase			  =							component_class(**_lowerCamelCase	)
										setattr(self.backend_tokenizer       ,_lowerCamelCase       ,_lowerCamelCase	)


	@property
	# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
	def   _UpperCAmelCase       (self	)							->							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   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase			  =							AddedToken(_lowerCamelCase       ,lstrip=_lowerCamelCase       ,rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase       ,_lowerCamelCase	) else value
				__lowercase			  =							value


	def   _UpperCAmelCase       (self       ,*_lowerCamelCase       ,**_lowerCamelCase	)							->							BatchEncoding:







				'''simple docstring'''





				__lowercase			  =							kwargs.get('''is_split_into_words'''       ,_lowerCamelCase	)

				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(*_lowerCamelCase       ,**_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,*_lowerCamelCase       ,**_lowerCamelCase	)							->							BatchEncoding:







				'''simple docstring'''





				__lowercase			  =							kwargs.get('''is_split_into_words'''       ,_lowerCamelCase	)

				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(*_lowerCamelCase       ,**_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase = None	)							->							Tuple[str]:







				'''simple docstring'''





				__lowercase			  =							self._tokenizer.model.save(_lowerCamelCase       ,name=_lowerCamelCase	)
				return tuple(_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase=None	)							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase			  =							[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   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase = None	)							->							List[int]:







				'''simple docstring'''





				__lowercase			  =							[self.sep_token_id]
				__lowercase			  =							[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   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase = None       ,_lowerCamelCase = PaddingStrategy.DO_NOT_PAD       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,)							->							dict:







				'''simple docstring'''





				__lowercase			  =							super()._pad(
				    encoded_inputs=_lowerCamelCase       ,max_length=_lowerCamelCase       ,padding_strategy=_lowerCamelCase       ,pad_to_multiple_of=_lowerCamelCase       ,return_attention_mask=_lowerCamelCase       ,)

				# Load from model defaults
				if return_attention_mask is None:
							__lowercase			  =							'''attention_mask''' in self.model_input_names

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

							if needs_to_be_padded:
										__lowercase			  =							len(_lowerCamelCase	) - 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`
													__lowercase			  =							(
													    encoded_inputs['''global_attention_mask'''] + [-1] * difference
													)
										elif self.padding_side == "left":
													__lowercase			  =							[-1] * difference + encoded_inputs[
													    '''global_attention_mask'''
													]
										else:
													raise ValueError('''Invalid padding strategy:''' + str(self.padding_side	)	)

				return encoded_inputs












'''simple docstring'''



from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE   =      {
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''BlenderbotForCausalLM''',
	    '''BlenderbotForConditionalGeneration''',
	    '''BlenderbotModel''',
	    '''BlenderbotPreTrainedModel''',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''TFBlenderbotForConditionalGeneration''',
	    '''TFBlenderbotModel''',
	    '''TFBlenderbotPreTrainedModel''',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''FlaxBlenderbotForConditionalGeneration''',
	    '''FlaxBlenderbotModel''',
	    '''FlaxBlenderbotPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_blenderbot import (
	    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BlenderbotConfig,
	    BlenderbotOnnxConfig,
	)
	from .tokenization_blenderbot import BlenderbotTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blenderbot import (
		    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlenderbotForCausalLM,
		    BlenderbotForConditionalGeneration,
		    BlenderbotModel,
		    BlenderbotPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blenderbot import (
		    TFBlenderbotForConditionalGeneration,
		    TFBlenderbotModel,
		    TFBlenderbotPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    FlaxBlenderbotPreTrainedModel,
		)

else:
	import sys

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





