
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class   UpperCAmelCase		(						A_       ,A_       ,A_ ):



   @register_to_config
   def __init__(self		:					str    ,						snake_case__		:					int    ,						snake_case__		:					int    ,						snake_case__		:					int    ,						snake_case__		:					float    ,						snake_case__		:					int    ,						snake_case__		:					int    ,						snake_case__		:					int    ,						snake_case__		:					int    ,						snake_case__		:					str    ,						snake_case__		:					bool = False    ,						)		->	Optional[int]:


          '''simple docstring'''


          super().__init__()

          snake_case       :   List[Any]							=      nn.Embedding(snake_case__    ,						snake_case__ )

          snake_case       :   Tuple							=      nn.Embedding(snake_case__    ,						snake_case__ )
          snake_case       :   Dict							=      False

          snake_case       :   Optional[Any]							=      nn.Dropout(p=snake_case__ )

          snake_case       :   Dict							=      TaConfig(
              vocab_size=snake_case__    ,						d_model=snake_case__    ,						num_heads=snake_case__    ,						d_kv=snake_case__    ,						d_ff=snake_case__    ,						dropout_rate=snake_case__    ,						feed_forward_proj=snake_case__    ,						is_decoder=snake_case__    ,						is_encoder_decoder=snake_case__    ,						)

          snake_case       :   List[Any]							=      nn.ModuleList()
          for lyr_num in range(snake_case__ ):
                 snake_case       :   List[str]							=      TaBlock(snake_case__ )
                 self.encoders.append(snake_case__ )

          snake_case       :   Dict							=      TaLayerNorm(snake_case__ )
          snake_case       :   str							=      nn.Dropout(p=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					Optional[int] )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   Any							=      self.token_embedder(snake_case__ )

          snake_case       :   Union[str, Any]							=      encoder_input_tokens.shape[1]
          snake_case       :   Optional[int]							=      torch.arange(snake_case__    ,						device=encoder_input_tokens.device )
          x += self.position_encoding(snake_case__ )

          snake_case       :   str							=      self.dropout_pre(snake_case__ )

          # inverted the attention mask
          snake_case       :   Optional[int]							=      encoder_input_tokens.size()
          snake_case       :   Optional[int]							=      self.get_extended_attention_mask(snake_case__    ,						snake_case__ )

          for lyr in self.encoders:
                 snake_case       :   int							=      lyr(snake_case__    ,						snake_case__ )[0]
          snake_case       :   Optional[int]							=      self.layer_norm(snake_case__ )

          return self.dropout_post(snake_case__ ), encoder_inputs_mask












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


UpperCAmelCase	:	Optional[Any]		     =       logging.get_logger(__name__)

UpperCAmelCase	:	Optional[int]		     =       {
    """google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					List[Any]						=			"""canine"""







      def __init__(    self		,      lowerCAmelCase__=7_6_8		,      lowerCAmelCase__=1_2		,      lowerCAmelCase__=1_2		,      lowerCAmelCase__=3_0_7_2		,      lowerCAmelCase__="gelu"		,      lowerCAmelCase__=0.1		,      lowerCAmelCase__=0.1		,      lowerCAmelCase__=1_6_3_8_4		,      lowerCAmelCase__=1_6		,      lowerCAmelCase__=0.02		,      lowerCAmelCase__=1E-12		,      lowerCAmelCase__=0		,      lowerCAmelCase__=0XE0_00		,      lowerCAmelCase__=0XE0_01		,      lowerCAmelCase__=4		,      lowerCAmelCase__=4		,      lowerCAmelCase__=8		,      lowerCAmelCase__=1_6_3_8_4		,      lowerCAmelCase__=1_2_8		,      **lowerCAmelCase__		,      )   ->						Dict:



        '''simple docstring'''




        super().__init__(pad_token_id=lowerCAmelCase__		,      bos_token_id=lowerCAmelCase__		,      eos_token_id=lowerCAmelCase__		,      **lowerCAmelCase__							)

        a__	:					Optional[int]										=max_position_embeddings
        a__	:					str										=hidden_size
        a__	:					Optional[Any]										=num_hidden_layers
        a__	:					Tuple										=num_attention_heads
        a__	:					Optional[Any]										=intermediate_size
        a__	:					Optional[int]										=hidden_act
        a__	:					List[Any]										=hidden_dropout_prob
        a__	:					Union[str, Any]										=attention_probs_dropout_prob
        a__	:					Optional[Any]										=initializer_range
        a__	:					Union[str, Any]										=type_vocab_size
        a__	:					Optional[int]										=layer_norm_eps

        # Character config:
        a__	:					int										=downsampling_rate
        a__	:					Optional[Any]										=upsampling_kernel_size
        a__	:					Union[str, Any]										=num_hash_functions
        a__	:					Any										=num_hash_buckets
        a__	:					int										=local_transformer_stride






