





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 					__lowerCAmelCase       (			_a,     _a,     _a     ):






			@register_to_config
			def __init__(self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__ = False					,							)     ->   Optional[int]:


							'''simple docstring'''



							super().__init__()

							snake_case_						:    Tuple				=  nn.Embedding(__magic_name__					,							__magic_name__    )

							snake_case_						:    Union[str, Any]				=  nn.Embedding(__magic_name__					,							__magic_name__    )
							snake_case_						:    List[str]				=  False

							snake_case_						:    Union[str, Any]				=  nn.Dropout(p=__magic_name__    )

							snake_case_						:    Any				=  TaConfig(
							    vocab_size=__magic_name__					,							d_model=__magic_name__					,							num_heads=__magic_name__					,							d_kv=__magic_name__					,							d_ff=__magic_name__					,							dropout_rate=__magic_name__					,							feed_forward_proj=__magic_name__					,							is_decoder=__magic_name__					,							is_encoder_decoder=__magic_name__					,							)

							snake_case_						:    List[Any]				=  nn.ModuleList()
							for lyr_num in range(__magic_name__    ):
											snake_case_						:    List[Any]				=  TaBlock(__magic_name__    )
											self.encoders.append(__magic_name__    )

							snake_case_						:    List[str]				=  TaLayerNorm(__magic_name__    )
							snake_case_						:    int				=  nn.Dropout(p=__magic_name__    )






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__    )     ->   int:


							'''simple docstring'''



							snake_case_						:    Dict				=  self.token_embedder(__magic_name__    )

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

							snake_case_						:    Any				=  self.dropout_pre(__magic_name__    )

							# inverted the attention mask
							snake_case_						:    Dict				=  encoder_input_tokens.size()
							snake_case_						:    Dict				=  self.get_extended_attention_mask(__magic_name__					,							__magic_name__    )

							for lyr in self.encoders:
											snake_case_						:    Dict				=  lyr(__magic_name__					,							__magic_name__    )[0]
							snake_case_						:    Optional[int]				=  self.layer_norm(__magic_name__    )

							return self.dropout_post(__magic_name__    ), encoder_inputs_mask







def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						str:






				"""simple docstring"""


				return "\n".join(
				    f'''{number} * {i} = {number * i}''' for i in range(1 ,				number_of_terms + 1 ) )


if __name__ == "__main__":
	print(multiplication_table(number=5, number_of_terms=1_0))
