

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 				lowercase       ( lowercase_			,						lowercase_			,						lowercase_    ):




    @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   ,     ):
           super().__init__()

           snake_case_           =					nn.Embedding(snake_case   ,     snake_case )

           snake_case_           =					nn.Embedding(snake_case   ,     snake_case )
           snake_case_           =					False

           snake_case_           =					nn.Dropout(p=snake_case )

           snake_case_           =					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   ,     )

           snake_case_           =					nn.ModuleList()
           for lyr_num in range(snake_case ):
                  snake_case_           =					TaBlock(snake_case )
                  self.encoders.append(snake_case )

           snake_case_           =					TaLayerNorm(snake_case )
           snake_case_           =					nn.Dropout(p=snake_case )




    def 					a							(      self   ,     snake_case   ,     snake_case ):
           snake_case_           =					self.token_embedder(snake_case )

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

           snake_case_           =					self.dropout_pre(snake_case )

           # inverted the attention mask
           snake_case_           =					encoder_input_tokens.size()
           snake_case_           =					self.get_extended_attention_mask(snake_case   ,     snake_case )

           for lyr in self.encoders:
                  snake_case_           =					lyr(snake_case   ,     snake_case )[0]
           snake_case_           =					self.layer_norm(snake_case )

           return self.dropout_post(snake_case ), encoder_inputs_mask







import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


_UpperCAmelCase	:     Any		       = logging.get_logger(__name__)







class 				lowercase       ( lowercase_    ):




				def __init__(      self   ,     *snake_case   ,     **snake_case ):
											warnings.warn(
											    'The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
											    ' Please use DeformableDetrImageProcessor instead.'   ,     snake_case   ,     )
											super().__init__(*snake_case   ,     **snake_case )




