



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        lowerCAmelCase_	(						a__ ,					a__ ,					a__							):
							@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,    )  ->	Union[str, Any]:
										super().__init__()

										UpperCamelCase					:						Optional[Any]					=			nn.Linear(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    bias=SCREAMING_SNAKE_CASE_ )

										UpperCamelCase					:						Optional[Any]					=			nn.Embedding(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )
										UpperCamelCase					:						Any					=			False

										UpperCamelCase					:						str					=			nn.Dropout(p=SCREAMING_SNAKE_CASE_ )

										UpperCamelCase					:						Optional[Any]					=			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_,    )
										UpperCamelCase					:						List[Any]					=			nn.ModuleList()
										for lyr_num in range(SCREAMING_SNAKE_CASE_ ):
													UpperCamelCase					:						Tuple					=			TaBlock(SCREAMING_SNAKE_CASE_ )
													self.encoders.append(SCREAMING_SNAKE_CASE_ )

										UpperCamelCase					:						int					=			TaLayerNorm(SCREAMING_SNAKE_CASE_ )
										UpperCamelCase					:						Dict					=			nn.Dropout(p=SCREAMING_SNAKE_CASE_ )






							def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )  ->	Optional[int]:
										UpperCamelCase					:						str					=			self.input_proj(SCREAMING_SNAKE_CASE_ )

										# terminal relative positional encodings
										UpperCamelCase					:						int					=			encoder_inputs.shape[1]
										UpperCamelCase					:						Optional[Any]					=			torch.arange(SCREAMING_SNAKE_CASE_,    device=encoder_inputs.device )

										UpperCamelCase					:						Tuple					=			encoder_inputs_mask.sum(-1 )
										UpperCamelCase					:						Any					=			torch.roll(input_positions.unsqueeze(0 ),    tuple(seq_lens.tolist() ),    dims=0 )
										x += self.position_encoding(SCREAMING_SNAKE_CASE_ )

										UpperCamelCase					:						Any					=			self.dropout_pre(SCREAMING_SNAKE_CASE_ )

										# inverted the attention mask
										UpperCamelCase					:						Optional[int]					=			encoder_inputs.size()
										UpperCamelCase					:						List[Any]					=			self.get_extended_attention_mask(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )

										for lyr in self.encoders:
													UpperCamelCase					:						List[Any]					=			lyr(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )[0]
										UpperCamelCase					:						Any					=			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_torch_available, is_vision_available


__UpperCAmelCase      = {
    '''configuration_deta''': ['''DETA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DetaConfig'''],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCAmelCase      = ['''DetaImageProcessor''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCAmelCase      = [
        '''DETA_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''DetaForObjectDetection''',
        '''DetaModel''',
        '''DetaPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .image_processing_deta import DetaImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deta import (
            DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
            DetaForObjectDetection,
            DetaModel,
            DetaPreTrainedModel,
        )

else:
    import sys

    __UpperCAmelCase      = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



