






"""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		(lowercase__						,						lowercase__						,						lowercase__     ):






						@register_to_config
						def __init__(		self  :Any			, __magic_name__  :int			, __magic_name__  :Dict			, __magic_name__  :Optional[Any]			, __magic_name__  :List[str]			, __magic_name__  :Optional[Any]			, __magic_name__  :Optional[Any]			, __magic_name__  :int			, __magic_name__  :Union[str, Any]			, __magic_name__  :Any			, __magic_name__  :Optional[int] = False			, )      ->str:
										super().__init__()

										lowercase    :      Any       = nn.Embedding(lowercase_			, lowercase_				)

										lowercase    :      Optional[Any]       = nn.Embedding(lowercase_			, lowercase_				)
										lowercase    :      List[Any]       = False

										lowercase    :      Union[str, Any]       = nn.Dropout(p=lowercase_				)

										lowercase    :      List[str]       = TaConfig(
										    vocab_size=lowercase_			, d_model=lowercase_			, num_heads=lowercase_			, d_kv=lowercase_			, d_ff=lowercase_			, dropout_rate=lowercase_			, feed_forward_proj=lowercase_			, is_decoder=lowercase_			, is_encoder_decoder=lowercase_			, )

										lowercase    :      Union[str, Any]       = nn.ModuleList()
										for lyr_num in range(lowercase_				):
														lowercase    :      Union[str, Any]       = TaBlock(lowercase_				)
														self.encoders.append(lowercase_				)

										lowercase    :      Union[str, Any]       = TaLayerNorm(lowercase_				)
										lowercase    :      int       = nn.Dropout(p=lowercase_				)






						def      __snake_case      (		self  :List[str]			, __magic_name__  :Optional[Any]			, __magic_name__  :int				)      ->Tuple:
										lowercase    :      List[Any]       = self.token_embedder(lowercase_				)

										lowercase    :      Dict       = encoder_input_tokens.shape[1]
										lowercase    :      str       = torch.arange(lowercase_			, device=encoder_input_tokens.device				)
										x += self.position_encoding(lowercase_				)

										lowercase    :      Any       = self.dropout_pre(lowercase_				)

										# inverted the attention mask
										lowercase    :      str       = encoder_input_tokens.size()
										lowercase    :      Optional[Any]       = self.get_extended_attention_mask(lowercase_			, lowercase_				)

										for lyr in self.encoders:
														lowercase    :      Tuple       = lyr(lowercase_			, lowercase_				)[0]
										lowercase    :      Union[str, Any]       = self.layer_norm(lowercase_				)

										return self.dropout_post(lowercase_				), encoder_inputs_mask




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowerCAmelCase:						Optional[Any]   				=       {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase:						List[str]   				=       [
	    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SwiftFormerForImageClassification',
	    'SwiftFormerModel',
	    'SwiftFormerPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_swiftformer import (
	    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    SwiftFormerConfig,
	    SwiftFormerOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swiftformer import (
		    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwiftFormerForImageClassification,
		    SwiftFormerModel,
		    SwiftFormerPreTrainedModel,
		)

else:
	import sys

	_lowerCAmelCase:						List[Any]   				=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

