



'''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 				__lowerCAmelCase(							lowerCAmelCase__		,			lowerCAmelCase__		,			lowerCAmelCase__      ):






			@register_to_config
			def __init__(   self  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							float      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							str      ,       SCREAMING_SNAKE_CASE  :							bool = False      ,       ):




					"""simple docstring"""







					super().__init__()

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

					SCREAMING_SNAKE_CASE_						:Any										=      nn.Embedding(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:int										=      False

					SCREAMING_SNAKE_CASE_						:Tuple										=      nn.Dropout(p=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:int										=      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      ,       )
					SCREAMING_SNAKE_CASE_						:Dict										=      nn.ModuleList()
					for lyr_num in range(SCREAMING_SNAKE_CASE	):
							SCREAMING_SNAKE_CASE_						:List[str]										=      TaBlock(SCREAMING_SNAKE_CASE	)
							self.encoders.append(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:List[Any]										=      TaLayerNorm(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:List[Any]										=      nn.Dropout(p=SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							int      ,       SCREAMING_SNAKE_CASE  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      self.input_proj(SCREAMING_SNAKE_CASE	)

					# terminal relative positional encodings
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      encoder_inputs.shape[1]
					SCREAMING_SNAKE_CASE_						:List[str]										=      torch.arange(SCREAMING_SNAKE_CASE      ,       device=encoder_inputs.device	)

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

					SCREAMING_SNAKE_CASE_						:int										=      self.dropout_pre(SCREAMING_SNAKE_CASE	)

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

					for lyr in self.encoders:
							SCREAMING_SNAKE_CASE_						:Any										=      lyr(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)[0]
					SCREAMING_SNAKE_CASE_						:str										=      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


A										=      {'configuration_detr': ['DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetrConfig', 'DetrOnnxConfig']}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A										=      ['DetrFeatureExtractor']
				A										=      ['DetrImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A										=      [
				    'DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'DetrForObjectDetection',
				    'DetrForSegmentation',
				    'DetrModel',
				    'DetrPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_detr import DetrFeatureExtractor
								from .image_processing_detr import DetrImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_detr import (
								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DetrForObjectDetection,
								    DetrForSegmentation,
								    DetrModel,
								    DetrPreTrainedModel,
								)

else:
				import sys

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