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_  (  __lowerCAmelCase				,					__lowerCAmelCase				,					__lowerCAmelCase						):



	"""simple docstring"""





	@register_to_config
	def __init__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE = False		,      )						->Optional[Any]:
								super().__init__()

								lowerCAmelCase     		=		nn.Linear(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      bias=__SCREAMING_SNAKE_CASE    )

								lowerCAmelCase     		=		nn.Embedding(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		False

								lowerCAmelCase     		=		nn.Dropout(p=__SCREAMING_SNAKE_CASE    )

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

								lowerCAmelCase     		=		TaLayerNorm(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		nn.Dropout(p=__SCREAMING_SNAKE_CASE    )




	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->Optional[int]:
								lowerCAmelCase     		=		self.input_proj(__SCREAMING_SNAKE_CASE    )

								# terminal relative positional encodings
								lowerCAmelCase     		=		encoder_inputs.shape[1]
								lowerCAmelCase     		=		torch.arange(__SCREAMING_SNAKE_CASE		,      device=encoder_inputs.device    )

								lowerCAmelCase     		=		encoder_inputs_mask.sum(-1    )
								lowerCAmelCase     		=		torch.roll(input_positions.unsqueeze(0    )		,      tuple(seq_lens.tolist()    )		,      dims=0    )
								x += self.position_encoding(__SCREAMING_SNAKE_CASE    )

								lowerCAmelCase     		=		self.dropout_pre(__SCREAMING_SNAKE_CASE    )

								# inverted the attention mask
								lowerCAmelCase     		=		encoder_inputs.size()
								lowerCAmelCase     		=		self.get_extended_attention_mask(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )

								for lyr in self.encoders:
															lowerCAmelCase     		=		lyr(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )[0]
								lowerCAmelCase     		=		self.layer_norm(__SCREAMING_SNAKE_CASE    )

								return self.dropout_post(__SCREAMING_SNAKE_CASE    ), encoder_inputs_mask




from typing import TYPE_CHECKING

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


lowercase__      :    Dict          =     {
    '''configuration_sam''': [
        '''SAM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SamConfig''',
        '''SamMaskDecoderConfig''',
        '''SamPromptEncoderConfig''',
        '''SamVisionConfig''',
    ],
    '''processing_sam''': ['''SamProcessor'''],
}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase__      :    Dict          =     [
	    '''SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''SamModel''',
	    '''SamPreTrainedModel''',
	]
try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase__      :    str          =     [
	    '''TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFSamModel''',
	    '''TFSamPreTrainedModel''',
	]
try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase__      :    Dict          =     ['''SamImageProcessor''']


if TYPE_CHECKING:
	from .configuration_sam import (
	    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    SamConfig,
	    SamMaskDecoderConfig,
	    SamPromptEncoderConfig,
	    SamVisionConfig,
	)
	from .processing_sam import SamProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_sam import SamImageProcessor

else:
	import sys

	lowercase__      :    Dict          =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



