
"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A						=			logging.get_logger(__name__)

_A						=			{
    'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}






class 	_lowercase       (    __lowerCAmelCase		):
	lowercase_				=     '''switch_transformers'''
	lowercase_				=     ['''past_key_values''']
	lowercase_				=     {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}




	def __init__(			self		,			UpperCAmelCase_=32128		,			UpperCAmelCase_=768		,			UpperCAmelCase_=64		,			UpperCAmelCase_=2048		,			UpperCAmelCase_=64		,			UpperCAmelCase_=12		,			UpperCAmelCase_=3		,			UpperCAmelCase_=12		,			UpperCAmelCase_=3		,			UpperCAmelCase_=12		,			UpperCAmelCase_=8		,			UpperCAmelCase_=False		,			UpperCAmelCase_=0.01		,			UpperCAmelCase_="float32"		,			UpperCAmelCase_=False		,			UpperCAmelCase_=32		,			UpperCAmelCase_=128		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=1E-6		,			UpperCAmelCase_=0.001		,			UpperCAmelCase_=0.001		,			UpperCAmelCase_=1.0		,			UpperCAmelCase_="relu"		,			UpperCAmelCase_=True		,			UpperCAmelCase_=False		,			UpperCAmelCase_=True		,			UpperCAmelCase_=0		,			UpperCAmelCase_=1		,			**UpperCAmelCase_		,			)		->							int:
			lowerCamelCase					:      Union[str, Any]          =   vocab_size
			lowerCamelCase					:      Any          =   d_model
			lowerCamelCase					:      str          =   d_kv
			lowerCamelCase					:      List[Any]          =   d_ff

			lowerCamelCase					:      str          =   num_sparse_encoder_layers

			lowerCamelCase					:      Optional[Any]          =   num_layers
			lowerCamelCase					:      Any          =   (
			    num_decoder_layers if num_decoder_layers is not None else self.num_layers
			)  # default = symmetry
			lowerCamelCase					:      Tuple          =   num_sparse_decoder_layers

			# This tells us, each how many encoder layer we'll have to set a sparse layer.
			if self.num_sparse_encoder_layers > 0:
					lowerCamelCase					:      Optional[int]          =   self.num_layers // self.num_sparse_encoder_layers
			else:
					lowerCamelCase					:      Any          =   self.num_layers  # HACK: this will create 0 sparse layers

			# This tells us, each how many encoder layer we'll have to set a sparse layer.
			if self.num_sparse_decoder_layers > 0:
					lowerCamelCase					:      Tuple          =   self.num_decoder_layers // self.num_sparse_decoder_layers
			else:
					lowerCamelCase					:      Dict          =   self.num_decoder_layers  # HACK: this will create 0 sparse layers

			lowerCamelCase					:      Tuple          =   num_heads
			lowerCamelCase					:      List[str]          =   num_experts
			lowerCamelCase					:      Optional[Any]          =   expert_capacity
			lowerCamelCase					:      List[Any]          =   router_bias
			lowerCamelCase					:      int          =   router_jitter_noise
			if router_dtype not in ["float32", "float16", "bfloat16"]:
					raise ValueError(F"""`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}""" )
			lowerCamelCase					:      Optional[int]          =   router_dtype

			lowerCamelCase					:      Union[str, Any]          =   router_ignore_padding_tokens
			lowerCamelCase					:      int          =   relative_attention_num_buckets
			lowerCamelCase					:      Any          =   relative_attention_max_distance

			lowerCamelCase					:      List[Any]          =   dropout_rate
			lowerCamelCase					:      Optional[Any]          =   layer_norm_epsilon
			lowerCamelCase					:      Optional[Any]          =   initializer_factor
			lowerCamelCase					:      Optional[Any]          =   feed_forward_proj
			lowerCamelCase					:      Union[str, Any]          =   use_cache
			lowerCamelCase					:      Optional[Any]          =   add_router_probs

			lowerCamelCase					:      int          =   router_z_loss_coef
			lowerCamelCase					:      Dict          =   router_aux_loss_coef

			lowerCamelCase					:      Optional[int]          =   self.feed_forward_proj.split('-' )
			lowerCamelCase					:      Any          =   act_info[-1]
			lowerCamelCase					:      int          =   act_info[0] == 'gated'

			if len(lowerCAmelCase_ ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase_ ) > 2:
					raise ValueError(
					    F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
					    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
					    '\'gated-gelu\' or \'relu\'' )

			# for backwards compatibility
			if feed_forward_proj == "gated-gelu":
					lowerCamelCase					:      List[Any]          =   'gelu_new'

			super().__init__(
			    pad_token_id=lowerCAmelCase_		,			eos_token_id=lowerCAmelCase_		,			is_encoder_decoder=lowerCAmelCase_		,			**lowerCAmelCase_		,			)








"""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_bart import BartTokenizer


_A						=			logging.get_logger(__name__)


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

# See all BART models at https://huggingface.co/models?filter=bart
_A						=			{
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

_A						=			{
    'facebook/bart-base': 1_0_2_4,
    'facebook/bart-large': 1_0_2_4,
    'facebook/bart-large-mnli': 1_0_2_4,
    'facebook/bart-large-cnn': 1_0_2_4,
    'facebook/bart-large-xsum': 1_0_2_4,
    'yjernite/bart_eli5': 1_0_2_4,
}






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     VOCAB_FILES_NAMES
	lowercase_				=     PRETRAINED_VOCAB_FILES_MAP
	lowercase_				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase_				=     ['input_ids', 'attention_mask']
	lowercase_				=     BartTokenizer




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

			lowerCamelCase					:      List[Any]          =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
			if pre_tok_state.get('add_prefix_space'		,			UpperCAmelCase_ ) != add_prefix_space:
					lowerCamelCase					:      Tuple          =   getattr(UpperCAmelCase_		,			pre_tok_state.pop('type' ) )
					lowerCamelCase					:      Optional[Any]          =   add_prefix_space
					lowerCamelCase					:      str          =   pre_tok_class(**UpperCAmelCase_ )

			lowerCamelCase					:      Optional[Any]          =   add_prefix_space

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

					lowerCamelCase					:      Optional[Any]          =   False

					if state.get('add_prefix_space'		,			UpperCAmelCase_ ) != add_prefix_space:
							lowerCamelCase					:      Dict          =   add_prefix_space
							lowerCamelCase					:      Tuple          =   True

					if state.get('trim_offsets'		,			UpperCAmelCase_ ) != trim_offsets:
							lowerCamelCase					:      Tuple          =   trim_offsets
							lowerCamelCase					:      Dict          =   True

					if changes_to_apply:
							lowerCamelCase					:      Optional[int]          =   getattr(UpperCAmelCase_		,			state.pop('type' ) )
							lowerCamelCase					:      Any          =   component_class(**UpperCAmelCase_ )
							setattr(self.backend_tokenizer		,			UpperCAmelCase_		,			UpperCAmelCase_ )




	@property
	def       _UpperCamelCase   (			self )		->							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       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							List[Any]:
			lowerCamelCase					:      Optional[int]          =   AddedToken(UpperCAmelCase_		,			lstrip=UpperCAmelCase_		,			rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_		,			UpperCAmelCase_ ) else value
			lowerCamelCase					:      int          =   value




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							BatchEncoding:
			lowerCamelCase					:      str          =   kwargs.get('is_split_into_words'		,			UpperCAmelCase_ )

			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(*UpperCAmelCase_		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							BatchEncoding:
			lowerCamelCase					:      Optional[Any]          =   kwargs.get('is_split_into_words'		,			UpperCAmelCase_ )

			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(*UpperCAmelCase_		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = None )		->							Tuple[str]:
			lowerCamelCase					:      Any          =   self._tokenizer.model.save(UpperCAmelCase_		,			name=UpperCAmelCase_ )
			return tuple(UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=None )		->							List[Any]:
			lowerCamelCase					:      Union[str, Any]          =   [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		,			UpperCAmelCase_		,			UpperCAmelCase_ = None )		->							List[int]:
			lowerCamelCase					:      List[Any]          =   [self.sep_token_id]
			lowerCamelCase					:      str          =   [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]






