





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)

class 		UpperCamelCase__ (      lowerCAmelCase_							,      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	Any			=			'''maskformer-swin'''

     __snake_case    :	Optional[Any]			=			{
         '''num_attention_heads''': '''num_heads''',
         '''num_hidden_layers''': '''num_layers''',
     }






     def __init__(							self						:    List[str]						,lowerCamelCase__						:    Optional[Any]=224						,lowerCamelCase__						:    Dict=4						,lowerCamelCase__						:    Optional[int]=3						,lowerCamelCase__						:    Any=96						,lowerCamelCase__						:    Union[str, Any]=[2, 2, 6, 2]						,lowerCamelCase__						:    Tuple=[3, 6, 12, 24]						,lowerCamelCase__						:    str=7						,lowerCamelCase__						:    Union[str, Any]=4.0						,lowerCamelCase__						:    List[Any]=True						,lowerCamelCase__						:    Dict=0.0						,lowerCamelCase__						:    List[str]=0.0						,lowerCamelCase__						:    Any=0.1						,lowerCamelCase__						:    Any="gelu"						,lowerCamelCase__						:    Tuple=False						,lowerCamelCase__						:    Optional[int]=0.02						,lowerCamelCase__						:    Union[str, Any]=1e-5						,lowerCamelCase__						:    str=None						,lowerCamelCase__						:    Union[str, Any]=None						,**lowerCamelCase__						:    str						,)     ->		Any:




           '''simple docstring'''


           super().__init__(**A__       )

           SCREAMING_SNAKE_CASE		=						image_size
           SCREAMING_SNAKE_CASE		=						patch_size
           SCREAMING_SNAKE_CASE		=						num_channels
           SCREAMING_SNAKE_CASE		=						embed_dim
           SCREAMING_SNAKE_CASE		=						depths
           SCREAMING_SNAKE_CASE		=						len(A__       )
           SCREAMING_SNAKE_CASE		=						num_heads
           SCREAMING_SNAKE_CASE		=						window_size
           SCREAMING_SNAKE_CASE		=						mlp_ratio
           SCREAMING_SNAKE_CASE		=						qkv_bias
           SCREAMING_SNAKE_CASE		=						hidden_dropout_prob
           SCREAMING_SNAKE_CASE		=						attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE		=						drop_path_rate
           SCREAMING_SNAKE_CASE		=						hidden_act
           SCREAMING_SNAKE_CASE		=						use_absolute_embeddings
           SCREAMING_SNAKE_CASE		=						layer_norm_eps
           SCREAMING_SNAKE_CASE		=						initializer_range
           # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
           # this indicates the channel dimension after the last stage of the model
           SCREAMING_SNAKE_CASE		=						int(embed_dim * 2 ** (len(A__       ) - 1)       )
           SCREAMING_SNAKE_CASE		=						["""stem"""] + [F"""stage{idx}""" for idx in range(1						,len(A__       ) + 1       )]
           SCREAMING_SNAKE_CASE		=						get_aligned_output_features_output_indices(
               out_features=A__						,out_indices=A__						,stage_names=self.stage_names       )





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						(__magic_name__		,   __magic_name__		,   __magic_name__				):


					'''simple docstring'''






					@register_to_config
					def __init__( self    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__ = False    ,       ):
						super().__init__()

						A__							:    List[str]        =		nn.Embedding(A__    ,       A__			)

						A__							:    Any        =		nn.Embedding(A__    ,       A__			)
						A__							:    Optional[int]        =		False

						A__							:    Optional[Any]        =		nn.Dropout(p=A__			)

						A__							:    int        =		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__    ,       )

						A__							:    Dict        =		nn.ModuleList()
						for lyr_num in range(A__			):
							A__							:    str        =		TaBlock(A__			)
							self.encoders.append(A__			)

						A__							:    Tuple        =		TaLayerNorm(A__			)
						A__							:    str        =		nn.Dropout(p=A__			)






					def   __A			( self    ,       A__    ,       A__			):
						A__							:    List[str]        =		self.token_embedder(A__			)

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

						A__							:    Optional[Any]        =		self.dropout_pre(A__			)

						# inverted the attention mask
						A__							:    Tuple        =		encoder_input_tokens.size()
						A__							:    Union[str, Any]        =		self.get_extended_attention_mask(A__    ,       A__			)

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

						return self.dropout_post(A__			), encoder_inputs_mask



