
import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A     :	int    = logging.get_logger(__name__)







class 			__A( __UpperCAmelCase			):
				snake_case_						   =    ['''input_ids''', '''attention_mask''']




				def __init__(     self		,     _snake_case="</s>"		,     _snake_case="<unk>"		,     _snake_case="<pad>"		,     _snake_case=125		,     _snake_case=None		,     **_snake_case		,     )    ->      Optional[int]:


								'''simple docstring'''




								if extra_ids > 0 and additional_special_tokens is None:
												__a										=    [F"""<extra_id_{i}>""" for i in range(__SCREAMING_SNAKE_CASE				)]
								elif extra_ids > 0 and additional_special_tokens is not None:
												# Check that we have the right number of extra_id special tokens
												__a										=    len(set(filter(lambda _snake_case				: bool('''extra_id''' in str(__SCREAMING_SNAKE_CASE				)				)		,     __SCREAMING_SNAKE_CASE				)				)				)
												if extra_tokens != extra_ids:
																raise ValueError(
																    F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
																    ''' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'''
																    ''' extra_ids tokens'''				)

								__a										=    AddedToken(__SCREAMING_SNAKE_CASE		,     lstrip=__SCREAMING_SNAKE_CASE		,     rstrip=__SCREAMING_SNAKE_CASE				) if isinstance(__SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE				) else pad_token
								__a										=    AddedToken(__SCREAMING_SNAKE_CASE		,     lstrip=__SCREAMING_SNAKE_CASE		,     rstrip=__SCREAMING_SNAKE_CASE				) if isinstance(__SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE				) else eos_token
								__a										=    AddedToken(__SCREAMING_SNAKE_CASE		,     lstrip=__SCREAMING_SNAKE_CASE		,     rstrip=__SCREAMING_SNAKE_CASE				) if isinstance(__SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE				) else unk_token

								super().__init__(
								    eos_token=__SCREAMING_SNAKE_CASE		,     unk_token=__SCREAMING_SNAKE_CASE		,     pad_token=__SCREAMING_SNAKE_CASE		,     extra_ids=__SCREAMING_SNAKE_CASE		,     additional_special_tokens=__SCREAMING_SNAKE_CASE		,     **__SCREAMING_SNAKE_CASE		,     )

								__a										=    extra_ids

								__a										=    2**8  # utf is 8 bits

								# define special tokens dict
								__a										=    {
								    self.pad_token: 0,
								    self.eos_token: 1,
								    self.unk_token: 2,
								}
								__a										=    len(self.special_tokens_encoder				)
								__a										=    len(__SCREAMING_SNAKE_CASE				)
								for i, token in enumerate(__SCREAMING_SNAKE_CASE				):
												__a										=    self.vocab_size + i - n
								__a										=    {v: k for k, v in self.special_tokens_encoder.items()}




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[int]:


								'''simple docstring'''




								return self._utf_vocab_size + self._num_special_tokens + self._extra_ids




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None		,     _snake_case = False				)    ->      Dict:


								'''simple docstring'''




								if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=__SCREAMING_SNAKE_CASE		,     token_ids_a=__SCREAMING_SNAKE_CASE		,     already_has_special_tokens=__SCREAMING_SNAKE_CASE				)

								# normal case: some special tokens
								if token_ids_a is None:
												return ([0] * len(__SCREAMING_SNAKE_CASE				)) + [1]
								return ([0] * len(__SCREAMING_SNAKE_CASE				)) + [1] + ([0] * len(__SCREAMING_SNAKE_CASE				)) + [1]




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Any:


								'''simple docstring'''




								if len(__SCREAMING_SNAKE_CASE				) > 0 and token_ids[-1] == self.eos_token_id:
												warnings.warn(
												    F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
												    ''' eos tokens being added.'''				)
												return token_ids
								else:
												return token_ids + [self.eos_token_id]




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    [self.eos_token_id]

								if token_ids_a is None:
												return len(token_ids_a + eos				) * [0]
								return len(token_ids_a + eos + token_ids_a + eos				) * [0]




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None				)    ->      int:


								'''simple docstring'''




								__a										=    self._add_eos_if_not_present(__SCREAMING_SNAKE_CASE				)
								if token_ids_a is None:
												return token_ids_a
								else:
												__a										=    self._add_eos_if_not_present(__SCREAMING_SNAKE_CASE				)
												return token_ids_a + token_ids_a




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Dict:


								'''simple docstring'''




								__a										=    [chr(__SCREAMING_SNAKE_CASE				) for i in text.encode('''utf-8'''				)]
								return tokens




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								if token in self.special_tokens_encoder:
												__a										=    self.special_tokens_encoder[token]
								elif token in self.added_tokens_encoder:
												__a										=    self.added_tokens_encoder[token]
								elif len(__SCREAMING_SNAKE_CASE				) != 1:
												__a										=    self.unk_token_id
								else:
												__a										=    ord(__SCREAMING_SNAKE_CASE				) + self._num_special_tokens
								return token_id




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Tuple:


								'''simple docstring'''




								if index in self.special_tokens_decoder:
												__a										=    self.special_tokens_decoder[index]
								else:
												__a										=    chr(index - self._num_special_tokens				)
								return token




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Dict:


								'''simple docstring'''




								__a										=    B''''''
								for token in tokens:
												if token in self.special_tokens_decoder:
																__a										=    self.special_tokens_decoder[token].encode('''utf-8'''				)
												elif token in self.added_tokens_decoder:
																__a										=    self.special_tokens_decoder[token].encode('''utf-8'''				)
												elif token in self.special_tokens_encoder:
																__a										=    token.encode('''utf-8'''				)
												elif token in self.added_tokens_encoder:
																__a										=    token.encode('''utf-8'''				)
												else:
																__a										=    bytes([ord(__SCREAMING_SNAKE_CASE				)]				)
												bstring += tok_string
								__a										=    bstring.decode('''utf-8'''		,     errors='''ignore'''				)
								return string




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None				)    ->      Optional[int]:


								'''simple docstring'''




								return ()




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


__snake_case						:Union[str, Any]      		=     logging.get_logger(__name__)

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

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Optional[int]											=						'''switch_transformers'''
       UpperCamelCase__  :    Optional[Any]											=						['''past_key_values''']
       UpperCamelCase__  :    Optional[Any]											=						{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}



       def __init__(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     str=32_128     ,		__SCREAMING_SNAKE_CASE							:     int=768     ,		__SCREAMING_SNAKE_CASE							:     Any=64     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=2_048     ,		__SCREAMING_SNAKE_CASE							:     List[str]=64     ,		__SCREAMING_SNAKE_CASE							:     int=12     ,		__SCREAMING_SNAKE_CASE							:     Any=3     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=12     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=3     ,		__SCREAMING_SNAKE_CASE							:     Any=12     ,		__SCREAMING_SNAKE_CASE							:     Tuple=8     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=False     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=0.01     ,		__SCREAMING_SNAKE_CASE							:     Dict="float32"     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=False     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=32     ,		__SCREAMING_SNAKE_CASE							:     int=128     ,		__SCREAMING_SNAKE_CASE							:     Any=0.1     ,		__SCREAMING_SNAKE_CASE							:     int=1E-6     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.0_01     ,		__SCREAMING_SNAKE_CASE							:     List[str]=0.0_01     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=1.0     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]="relu"     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=True     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=False     ,		__SCREAMING_SNAKE_CASE							:     str=True     ,		__SCREAMING_SNAKE_CASE							:     int=0     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=1     ,		**__SCREAMING_SNAKE_CASE							:     Dict     ,		):


           '''simple docstring'''

           __a						       =				vocab_size
           __a						       =				d_model
           __a						       =				d_kv
           __a						       =				d_ff

           __a						       =				num_sparse_encoder_layers

           __a						       =				num_layers
           __a						       =				(
               num_decoder_layers if num_decoder_layers is not None else self.num_layers
           )  # default = symmetry
           __a						       =				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:
               __a						       =				self.num_layers // self.num_sparse_encoder_layers
           else:
               __a						       =				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:
               __a						       =				self.num_decoder_layers // self.num_sparse_decoder_layers
           else:
               __a						       =				self.num_decoder_layers  # HACK: this will create 0 sparse layers

           __a						       =				num_heads
           __a						       =				num_experts
           __a						       =				expert_capacity
           __a						       =				router_bias
           __a						       =				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}')
           __a						       =				router_dtype

           __a						       =				router_ignore_padding_tokens
           __a						       =				relative_attention_num_buckets
           __a						       =				relative_attention_max_distance

           __a						       =				dropout_rate
           __a						       =				layer_norm_epsilon
           __a						       =				initializer_factor
           __a						       =				feed_forward_proj
           __a						       =				use_cache
           __a						       =				add_router_probs

           __a						       =				router_z_loss_coef
           __a						       =				router_aux_loss_coef

           __a						       =				self.feed_forward_proj.split('''-''')
           __a						       =				act_info[-1]
           __a						       =				act_info[0] == '''gated'''

           if len(__SCREAMING_SNAKE_CASE) > 1 and act_info[0] != "gated" or len(__SCREAMING_SNAKE_CASE) > 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":
               __a						       =				'''gelu_new'''

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





