






"""simple docstring"""







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 ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ = False ,		)			->			Optional[int]:



         """simple docstring"""





         super().__init__()

         UpperCAmelCase	    =			nn.Embedding(snake_case__ ,		snake_case__				)

         UpperCAmelCase	    =			nn.Embedding(snake_case__ ,		snake_case__				)
         UpperCAmelCase	    =			False

         UpperCAmelCase	    =			nn.Dropout(p=snake_case__				)

         UpperCAmelCase	    =			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__ ,		)

         UpperCAmelCase	    =			nn.ModuleList()
         for lyr_num in range(snake_case__				):
                UpperCAmelCase	    =			TaBlock(snake_case__				)
                self.encoders.append(snake_case__				)

         UpperCAmelCase	    =			TaLayerNorm(snake_case__				)
         UpperCAmelCase	    =			nn.Dropout(p=snake_case__				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__				)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			self.token_embedder(snake_case__				)

         UpperCAmelCase	    =			encoder_input_tokens.shape[1]
         UpperCAmelCase	    =			torch.arange(snake_case__ ,		device=encoder_input_tokens.device				)
         x += self.position_encoding(snake_case__				)

         UpperCAmelCase	    =			self.dropout_pre(snake_case__				)

         # inverted the attention mask
         UpperCAmelCase	    =			encoder_input_tokens.size()
         UpperCAmelCase	    =			self.get_extended_attention_mask(snake_case__ ,		snake_case__				)

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

         return self.dropout_post(snake_case__				), encoder_inputs_mask








"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_	:    Tuple            =      logging.get_logger(__name__)

lowerCAmelCase_	:    List[str]            =      {
    '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json''',
    '''google/bigbird-roberta-large''': '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json''',
    '''google/bigbird-base-trivia-itc''': '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json''',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}





class   UpperCamelCase_ (       a_  ):
  _A       :					Any				=	'big_bird'



  def __init__(      self ,		snake_case__=5_03_58 ,		snake_case__=7_68 ,		snake_case__=12 ,		snake_case__=12 ,		snake_case__=30_72 ,		snake_case__="gelu_new" ,		snake_case__=0.1 ,		snake_case__=0.1 ,		snake_case__=40_96 ,		snake_case__=2 ,		snake_case__=0.02 ,		snake_case__=1e-12 ,		snake_case__=True ,		snake_case__=0 ,		snake_case__=1 ,		snake_case__=2 ,		snake_case__=66 ,		snake_case__="block_sparse" ,		snake_case__=True ,		snake_case__=False ,		snake_case__=64 ,		snake_case__=3 ,		snake_case__=None ,		**snake_case__ ,		)			->			List[str]:



         """simple docstring"""





         super().__init__(
             pad_token_id=snake_case__ ,		bos_token_id=snake_case__ ,		eos_token_id=snake_case__ ,		sep_token_id=snake_case__ ,		**snake_case__ ,		)

         UpperCAmelCase	    =			vocab_size
         UpperCAmelCase	    =			max_position_embeddings
         UpperCAmelCase	    =			hidden_size
         UpperCAmelCase	    =			num_hidden_layers
         UpperCAmelCase	    =			num_attention_heads
         UpperCAmelCase	    =			intermediate_size
         UpperCAmelCase	    =			hidden_act
         UpperCAmelCase	    =			hidden_dropout_prob
         UpperCAmelCase	    =			attention_probs_dropout_prob
         UpperCAmelCase	    =			initializer_range
         UpperCAmelCase	    =			type_vocab_size
         UpperCAmelCase	    =			layer_norm_eps
         UpperCAmelCase	    =			use_cache

         UpperCAmelCase	    =			rescale_embeddings
         UpperCAmelCase	    =			attention_type
         UpperCAmelCase	    =			use_bias
         UpperCAmelCase	    =			block_size
         UpperCAmelCase	    =			num_random_blocks
         UpperCAmelCase	    =			classifier_dropout





class   UpperCamelCase_ (       a_  ):



  @property
  def 		UpperCamelCase_				(      self				)			->			Mapping[str, Mapping[int, str]]:



         """simple docstring"""





         if self.task == "multiple-choice":
                UpperCAmelCase	    =			{0: """batch""", 1: """choice""", 2: """sequence"""}
         else:
                UpperCAmelCase	    =			{0: """batch""", 1: """sequence"""}
         return OrderedDict(
             [
                 ("""input_ids""", dynamic_axis),
                 ("""attention_mask""", dynamic_axis),
             ]				)
