"""simple docstring"""







from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
       from ..models.auto import AutoTokenizer







class  UpperCamelCase	:
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   str:


        '''simple docstring'''


        raise NotImplementedError()







   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        raise NotImplementedError()







class  UpperCamelCase	(    lowercase_						):
   def __init__(    self  ,__UpperCamelCase  ,__UpperCamelCase = False  ,**__UpperCamelCase    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       Any									=			tokenizer
        lowercase_			:       Optional[Any]									=			skip_prompt
        lowercase_			:       Optional[int]									=			decode_kwargs

        # variables used in the streaming process
        lowercase_			:       Tuple									=			[]
        lowercase_			:       Optional[int]									=			0
        lowercase_			:       Union[str, Any]									=			True
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   Tuple:


        '''simple docstring'''


        if len(value.shape    ) > 1 and value.shape[0] > 1:
             raise ValueError('TextStreamer only supports batch size 1'    )
        elif len(value.shape    ) > 1:
             lowercase_			:       Union[str, Any]									=			value[0]

        if self.skip_prompt and self.next_tokens_are_prompt:
             lowercase_			:       Any									=			False
             return

        # Add the new token to the cache and decodes the entire thing.
        self.token_cache.extend(value.tolist()    )
        lowercase_			:       Optional[Any]									=			self.tokenizer.decode(self.token_cache  ,**self.decode_kwargs    )

        # After the symbol for a new line, we flush the cache.
        if text.endswith('\n'    ):
             lowercase_			:       Optional[Any]									=			text[self.print_len :]
             lowercase_			:       Dict									=			[]
             lowercase_			:       Tuple									=			0
        # If the last token is a CJK character, we print the characters.
        elif len(__UpperCamelCase    ) > 0 and self._is_chinese_char(ord(text[-1]    )    ):
             lowercase_			:       Union[str, Any]									=			text[self.print_len :]
             self.print_len += len(__UpperCamelCase    )
        # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
        # which may change with the subsequent token -- there are probably smarter ways to do this!)
        else:
             lowercase_			:       Optional[int]									=			text[self.print_len : text.rfind(' '    ) + 1]
             self.print_len += len(__UpperCamelCase    )

        self.on_finalized_text(__UpperCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


        if len(self.token_cache    ) > 0:
             lowercase_			:       Optional[int]									=			self.tokenizer.decode(self.token_cache  ,**self.decode_kwargs    )
             lowercase_			:       List[str]									=			text[self.print_len :]
             lowercase_			:       Optional[int]									=			[]
             lowercase_			:       Any									=			0
        else:
             lowercase_			:       List[str]									=			''

        lowercase_			:       Dict									=			True
        self.on_finalized_text(__UpperCamelCase  ,stream_end=__UpperCamelCase    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = False    )			->   Optional[Any]:


        '''simple docstring'''


        print(__UpperCamelCase  ,flush=__UpperCamelCase  ,end='' if not stream_end else None    )







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   List[str]:


        '''simple docstring'''


        if (
            (cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
            or (cp >= 0X3_4_0_0 and cp <= 0X4_D_B_F)  #
            or (cp >= 0X2_0_0_0_0 and cp <= 0X2_A_6_D_F)  #
            or (cp >= 0X2_A_7_0_0 and cp <= 0X2_B_7_3_F)  #
            or (cp >= 0X2_B_7_4_0 and cp <= 0X2_B_8_1_F)  #
            or (cp >= 0X2_B_8_2_0 and cp <= 0X2_C_E_A_F)  #
            or (cp >= 0XF_9_0_0 and cp <= 0XF_A_F_F)
            or (cp >= 0X2_F_8_0_0 and cp <= 0X2_F_A_1_F)  #
        ):  #
             return True

        return False







class  UpperCamelCase	(    lowercase_						):
   def __init__(    self  ,__UpperCamelCase  ,__UpperCamelCase = False  ,__UpperCamelCase = None  ,**__UpperCamelCase    )			->   Union[str, Any]:


        '''simple docstring'''


        super().__init__(__UpperCamelCase  ,__UpperCamelCase  ,**__UpperCamelCase    )
        lowercase_			:       Any									=			Queue()
        lowercase_			:       List[Any]									=			None
        lowercase_			:       List[str]									=			timeout
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = False    )			->   List[Any]:


        '''simple docstring'''


        self.text_queue.put(__UpperCamelCase  ,timeout=self.timeout    )
        if stream_end:
             self.text_queue.put(self.stop_signal  ,timeout=self.timeout    )
   def __iter__(    self    )			->   Any:


        '''simple docstring'''


        return self







   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       Optional[Any]									=			self.text_queue.get(timeout=self.timeout    )
        if value == self.stop_signal:
             raise StopIteration()
        else:
             return value







"""simple docstring"""







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


__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)

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







class  UpperCamelCase	(    lowercase_						):
   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=3_2128  ,__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  ,)			->   str:


        '''simple docstring'''


        lowercase_			:       List[str]									=			vocab_size
        lowercase_			:       Optional[Any]									=			d_model
        lowercase_			:       Dict									=			d_kv
        lowercase_			:       Dict									=			d_ff

        lowercase_			:       str									=			num_sparse_encoder_layers

        lowercase_			:       List[str]									=			num_layers
        lowercase_			:       int									=			(
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        lowercase_			:       Optional[Any]									=			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:
             lowercase_			:       Tuple									=			self.num_layers // self.num_sparse_encoder_layers
        else:
             lowercase_			:       Union[str, 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:
             lowercase_			:       Union[str, Any]									=			self.num_decoder_layers // self.num_sparse_decoder_layers
        else:
             lowercase_			:       List[Any]									=			self.num_decoder_layers  # HACK: this will create 0 sparse layers

        lowercase_			:       List[Any]									=			num_heads
        lowercase_			:       Dict									=			num_experts
        lowercase_			:       List[str]									=			expert_capacity
        lowercase_			:       Optional[Any]									=			router_bias
        lowercase_			:       Optional[Any]									=			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}'''    )
        lowercase_			:       Optional[Any]									=			router_dtype

        lowercase_			:       Union[str, Any]									=			router_ignore_padding_tokens
        lowercase_			:       Any									=			relative_attention_num_buckets
        lowercase_			:       List[Any]									=			relative_attention_max_distance

        lowercase_			:       str									=			dropout_rate
        lowercase_			:       Any									=			layer_norm_epsilon
        lowercase_			:       Tuple									=			initializer_factor
        lowercase_			:       str									=			feed_forward_proj
        lowercase_			:       List[Any]									=			use_cache
        lowercase_			:       str									=			add_router_probs

        lowercase_			:       Tuple									=			router_z_loss_coef
        lowercase_			:       int									=			router_aux_loss_coef

        lowercase_			:       List[str]									=			self.feed_forward_proj.split('-'    )
        lowercase_			:       List[str]									=			act_info[-1]
        lowercase_			:       Optional[Any]									=			act_info[0] == 'gated'

        if len(__UpperCamelCase    ) > 1 and act_info[0] != "gated" or len(__UpperCamelCase    ) > 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":
             lowercase_			:       Union[str, Any]									=			'gelu_new'

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

