

from collections import OrderedDict
from typing import Mapping

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


_lowerCAmelCase :	Tuple     				=    logging.get_logger(__name__)

_lowerCAmelCase :	List[str]     				=    {
    'uclanlp/plbart-base': 'https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}


class     lowerCAmelCase				(    __UpperCamelCase				):


       '''simple docstring'''







       snake_case    							=	'plbart'
       snake_case    							=	['past_key_values']
       snake_case    							=	{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

       def __init__(	self					:  Optional[Any]						,	__snake_case					:  List[str]=50005						,	__snake_case					:  Tuple=1024						,	__snake_case					:  List[Any]=6						,	__snake_case					:  int=3072						,	__snake_case					:  Dict=12						,	__snake_case					:  Dict=6						,	__snake_case					:  Tuple=3072						,	__snake_case					:  Optional[Any]=12						,	__snake_case					:  Tuple=0.0						,	__snake_case					:  Tuple=0.0						,	__snake_case					:  str=True						,	__snake_case					:  str=True						,	__snake_case					:  Tuple="gelu"						,	__snake_case					:  int=768						,	__snake_case					:  Union[str, Any]=0.1						,	__snake_case					:  Any=0.1						,	__snake_case					:  Any=0.0						,	__snake_case					:  List[Any]=0.02						,	__snake_case					:  str=0.0						,	__snake_case					:  Tuple=True						,	__snake_case					:  Any=1						,	__snake_case					:  Optional[int]=0						,	__snake_case					:  Dict=2						,	__snake_case					:  int=2						,	**__snake_case					:  List[str]						,	)   -> Union[str, Any]:







        '''simple docstring'''

        lowerCamelCase      						=    vocab_size
        lowerCamelCase      						=    max_position_embeddings
        lowerCamelCase      						=    d_model
        lowerCamelCase      						=    encoder_ffn_dim
        lowerCamelCase      						=    encoder_layers
        lowerCamelCase      						=    encoder_attention_heads
        lowerCamelCase      						=    decoder_ffn_dim
        lowerCamelCase      						=    decoder_layers
        lowerCamelCase      						=    decoder_attention_heads
        lowerCamelCase      						=    dropout
        lowerCamelCase      						=    attention_dropout
        lowerCamelCase      						=    activation_dropout
        lowerCamelCase      						=    activation_function
        lowerCamelCase      						=    init_std
        lowerCamelCase      						=    encoder_layerdrop
        lowerCamelCase      						=    decoder_layerdrop
        lowerCamelCase      						=    classifier_dropout
        lowerCamelCase      						=    use_cache
        lowerCamelCase      						=    encoder_layers
        lowerCamelCase      						=    scale_embedding  # scale factor will be sqrt(d_model) if True
        super().__init__(
            pad_token_id=__snake_case						,	bos_token_id=__snake_case						,	eos_token_id=__snake_case						,	is_encoder_decoder=__snake_case						,	forced_eos_token_id=__snake_case						,	**__snake_case						,	)


class     lowerCAmelCase				(    __UpperCamelCase				):


       '''simple docstring'''







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







        '''simple docstring'''

        return OrderedDict(
            [
                ('input_ids', {0: 'batch', 1: 'sequence'}),
                ('attention_mask', {0: 'batch', 1: 'sequence'}),
            ]     )





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







        '''simple docstring'''

        if self.use_past:
         return OrderedDict(
             [
                 ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
                 ('past_keys', {0: 'batch', 2: 'sequence'}),
                 ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
             ]     )
        else:
         return OrderedDict(
             [
                 ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
                 ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
             ]     )







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				(    __UpperCamelCase   ,				__UpperCamelCase   ,				__UpperCamelCase				):


       '''simple docstring'''







       @register_to_config
       def __init__(	self					:  Any						,	__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						,	)   -> int:







        '''simple docstring'''

        super().__init__()

        lowerCamelCase      						=    nn.Linear(__snake_case						,	__snake_case						,	bias=__snake_case     )

        lowerCamelCase      						=    nn.Embedding(__snake_case						,	__snake_case     )
        lowerCamelCase      						=    False

        lowerCamelCase      						=    nn.Dropout(p=__snake_case     )

        lowerCamelCase      						=    TaConfig(
            d_model=__snake_case						,	num_heads=__snake_case						,	d_kv=__snake_case						,	d_ff=__snake_case						,	feed_forward_proj=__snake_case						,	dropout_rate=__snake_case						,	is_decoder=__snake_case						,	is_encoder_decoder=__snake_case						,	)
        lowerCamelCase      						=    nn.ModuleList()
        for lyr_num in range(__snake_case     ):
         lowerCamelCase      						=    TaBlock(__snake_case     )
         self.encoders.append(__snake_case     )

        lowerCamelCase      						=    TaLayerNorm(__snake_case     )
        lowerCamelCase      						=    nn.Dropout(p=__snake_case     )





       def   lowerCamelCase__						(	self					:  str						,	__snake_case					:  List[Any]						,	__snake_case					:  List[Any]     )   -> Dict:







        '''simple docstring'''

        lowerCamelCase      						=    self.input_proj(__snake_case     )

        # terminal relative positional encodings
        lowerCamelCase      						=    encoder_inputs.shape[1]
        lowerCamelCase      						=    torch.arange(__snake_case						,	device=encoder_inputs.device     )

        lowerCamelCase      						=    encoder_inputs_mask.sum(-1     )
        lowerCamelCase      						=    torch.roll(input_positions.unsqueeze(0     )						,	tuple(seq_lens.tolist()     )						,	dims=0     )
        x += self.position_encoding(__snake_case     )

        lowerCamelCase      						=    self.dropout_pre(__snake_case     )

        # inverted the attention mask
        lowerCamelCase      						=    encoder_inputs.size()
        lowerCamelCase      						=    self.get_extended_attention_mask(__snake_case						,	__snake_case     )

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

        return self.dropout_post(__snake_case     ), encoder_inputs_mask




