


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					:   Dict,		__lowerCamelCase					:   int,		__lowerCamelCase					:   int,		__lowerCamelCase					:   int,		__lowerCamelCase					:   float,		__lowerCamelCase					:   int,		__lowerCamelCase					:   int,		__lowerCamelCase					:   int,		__lowerCamelCase					:   int,		__lowerCamelCase					:   str,		__lowerCamelCase					:   bool = False,		)  -> Union[str, Any]:
           super().__init__()

           UpperCamelCase__							:  Any								=     nn.Linear(__lowerCamelCase,		__lowerCamelCase,		bias=__lowerCamelCase   )

           UpperCamelCase__							:  List[Any]								=     nn.Embedding(__lowerCamelCase,		__lowerCamelCase   )
           UpperCamelCase__							:  Any								=     False

           UpperCamelCase__							:  Tuple								=     nn.Dropout(p=__lowerCamelCase   )

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

           UpperCamelCase__							:  List[Any]								=     TaLayerNorm(__lowerCamelCase   )
           UpperCamelCase__							:  Optional[Any]								=     nn.Dropout(p=__lowerCamelCase   )






      def 	__lowercase(     self					:   int,		__lowerCamelCase					:   Optional[int],		__lowerCamelCase					:   List[Any]   )  -> List[Any]:
           UpperCamelCase__							:  Optional[int]								=     self.input_proj(__lowerCamelCase   )

           # terminal relative positional encodings
           UpperCamelCase__							:  Optional[int]								=     encoder_inputs.shape[1]
           UpperCamelCase__							:  Optional[int]								=     torch.arange(__lowerCamelCase,		device=encoder_inputs.device   )

           UpperCamelCase__							:  Any								=     encoder_inputs_mask.sum(-1   )
           UpperCamelCase__							:  int								=     torch.roll(input_positions.unsqueeze(0   ),		tuple(seq_lens.tolist()   ),		dims=0   )
           x += self.position_encoding(__lowerCamelCase   )

           UpperCamelCase__							:  Optional[int]								=     self.dropout_pre(__lowerCamelCase   )

           # inverted the attention mask
           UpperCamelCase__							:  Optional[int]								=     encoder_inputs.size()
           UpperCamelCase__							:  List[Any]								=     self.get_extended_attention_mask(__lowerCamelCase,		__lowerCamelCase   )

           for lyr in self.encoders:
                UpperCamelCase__							:  Optional[int]								=     lyr(__lowerCamelCase,		__lowerCamelCase   )[0]
           UpperCamelCase__							:  List[str]								=     self.layer_norm(__lowerCamelCase   )

           return self.dropout_post(__lowerCamelCase   ), encoder_inputs_mask








import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


_SCREAMING_SNAKE_CASE :					List[str]			=	logging.get_logger(__name__)

class  UpperCamelCase__ ( __lowerCamelCase			):






      def __init__(     self					:   List[str],		*__lowerCamelCase					:   Optional[int],		**__lowerCamelCase					:   Dict   )  -> None:
           warnings.warn(
               '''The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
               ''' Please use MobileNetV1ImageProcessor instead.''',		__lowerCamelCase,		)
           super().__init__(*__lowerCamelCase,		**__lowerCamelCase   )




