


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 							a						(_lowerCAmelCase	,							_lowerCAmelCase	,							_lowerCAmelCase ):




							"""simple docstring"""


							@register_to_config
							def __init__(				self			:    int       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    float       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    str       ,      lowerCamelCase			:    bool = False       ,      )      ->       Any:
														super().__init__()

														__snake_case						:     str									=  nn.Embedding(lowerCamelCase       ,      lowerCamelCase							)

														__snake_case						:     Tuple									=  nn.Embedding(lowerCamelCase       ,      lowerCamelCase							)
														__snake_case						:     Optional[Any]									=  False

														__snake_case						:     int									=  nn.Dropout(p=lowerCamelCase							)

														__snake_case						:     int									=  TaConfig(
														    vocab_size=lowerCamelCase       ,      d_model=lowerCamelCase       ,      num_heads=lowerCamelCase       ,      d_kv=lowerCamelCase       ,      d_ff=lowerCamelCase       ,      dropout_rate=lowerCamelCase       ,      feed_forward_proj=lowerCamelCase       ,      is_decoder=lowerCamelCase       ,      is_encoder_decoder=lowerCamelCase       ,      )

														__snake_case						:     Tuple									=  nn.ModuleList()
														for lyr_num in range(lowerCamelCase							):
																					__snake_case						:     int									=  TaBlock(lowerCamelCase							)
																					self.encoders.append(lowerCamelCase							)

														__snake_case						:     Union[str, Any]									=  TaLayerNorm(lowerCamelCase							)
														__snake_case						:     List[str]									=  nn.Dropout(p=lowerCamelCase							)






							def        __snake_case   (				self			:    Optional[Any]       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    List[str]							)      ->       List[Any]:
														__snake_case						:     Dict									=  self.token_embedder(lowerCamelCase							)

														__snake_case						:     Optional[int]									=  encoder_input_tokens.shape[1]
														__snake_case						:     Optional[int]									=  torch.arange(lowerCamelCase       ,      device=encoder_input_tokens.device							)
														x += self.position_encoding(lowerCamelCase							)

														__snake_case						:     Any									=  self.dropout_pre(lowerCamelCase							)

														# inverted the attention mask
														__snake_case						:     int									=  encoder_input_tokens.size()
														__snake_case						:     int									=  self.get_extended_attention_mask(lowerCamelCase       ,      lowerCamelCase							)

														for lyr in self.encoders:
																					__snake_case						:     List[Any]									=  lyr(lowerCamelCase       ,      lowerCamelCase							)[0]
														__snake_case						:     Union[str, Any]									=  self.layer_norm(lowerCamelCase							)

														return self.dropout_post(lowerCamelCase							), encoder_inputs_mask



import copy

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


__a         =   logging.get_logger(__name__)
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :List[str]       	=      'encoder-decoder'
	a     :Any       	=      True
	def __init__(			self    :     List[Any]			,				**SCREAMING_SNAKE_CASE_    :     List[Any]			)			->	Optional[int]:
					super().__init__(**SCREAMING_SNAKE_CASE_			)
					assert (
					    "encoder" in kwargs and "decoder" in kwargs
					), "Config has to be initialized with encoder and decoder config"
					lowercase_	    =   kwargs.pop('''encoder'''			)
					lowercase_	    =   encoder_config.pop('''model_type'''			)
					lowercase_	    =   kwargs.pop('''decoder'''			)
					lowercase_	    =   decoder_config.pop('''model_type'''			)

					from ..auto.configuration_auto import AutoConfig

					lowercase_	    =   AutoConfig.for_model(SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   AutoConfig.for_model(SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   True
	@classmethod
	def 		_lowercase    (			cls    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     PretrainedConfig			,				SCREAMING_SNAKE_CASE_    :     PretrainedConfig			,				**SCREAMING_SNAKE_CASE_    :     Optional[int]			)			->	PretrainedConfig:
					logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''			)
					lowercase_	    =   True
					lowercase_	    =   True

					return cls(encoder=encoder_config.to_dict()			,				decoder=decoder_config.to_dict()			,				**SCREAMING_SNAKE_CASE_			)




	def 		_lowercase    (			self    :     Tuple			)			->	List[Any]:
					lowercase_	    =   copy.deepcopy(self.__dict__			)
					lowercase_	    =   self.encoder.to_dict()
					lowercase_	    =   self.decoder.to_dict()
					lowercase_	    =   self.__class__.model_type
					return output




