

'''simple docstring'''


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       (__lowerCamelCase			,   __lowerCamelCase			,   __lowerCamelCase			):







	@register_to_config
	def __init__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase = False ,				)    ->    Dict:
							'''simple docstring'''






							super().__init__()

							lowercase        =							nn.Embedding(a_ ,				a_						)

							lowercase        =							nn.Embedding(a_ ,				a_						)
							lowercase        =							False

							lowercase        =							nn.Dropout(p=a_						)

							lowercase        =							TaConfig(
							    vocab_size=a_ ,				d_model=a_ ,				num_heads=a_ ,				d_kv=a_ ,				d_ff=a_ ,				dropout_rate=a_ ,				feed_forward_proj=a_ ,				is_decoder=a_ ,				is_encoder_decoder=a_ ,				)

							lowercase        =							nn.ModuleList()
							for lyr_num in range(a_						):
													lowercase        =							TaBlock(a_						)
													self.encoders.append(a_						)

							lowercase        =							TaLayerNorm(a_						)
							lowercase        =							nn.Dropout(p=a_						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase						)    ->    int:
							'''simple docstring'''






							lowercase        =							self.token_embedder(a_						)

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

							lowercase        =							self.dropout_pre(a_						)

							# inverted the attention mask
							lowercase        =							encoder_input_tokens.size()
							lowercase        =							self.get_extended_attention_mask(a_ ,				a_						)

							for lyr in self.encoders:
													lowercase        =							lyr(a_ ,				a_						)[0]
							lowercase        =							self.layer_norm(a_						)

							return self.dropout_post(a_						), encoder_inputs_mask







'''simple docstring'''


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


lowercase_      :				Any   					=    logging.get_logger(__name__)

lowercase_      :				str   					=    {
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					'''vit_msn'''







	def __init__(    self ,				_lowerCAmelCase=768 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=3072 ,				_lowerCAmelCase="gelu" ,				_lowerCAmelCase=0.0 ,				_lowerCAmelCase=0.0 ,				_lowerCAmelCase=0.02 ,				_lowerCAmelCase=1E-06 ,				_lowerCAmelCase=224 ,				_lowerCAmelCase=16 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=True ,				**_lowerCAmelCase ,				)    ->    List[Any]:
							'''simple docstring'''






							super().__init__(**_lowerCAmelCase						)

							lowercase        =							hidden_size
							lowercase        =							num_hidden_layers
							lowercase        =							num_attention_heads
							lowercase        =							intermediate_size
							lowercase        =							hidden_act
							lowercase        =							hidden_dropout_prob
							lowercase        =							attention_probs_dropout_prob
							lowercase        =							initializer_range
							lowercase        =							layer_norm_eps
							lowercase        =							image_size
							lowercase        =							patch_size
							lowercase        =							num_channels
							lowercase        =							qkv_bias


