
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__(    UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase							):



	"""simple docstring"""




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

					lowercase_					  = nn.Linear(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							bias=SCREAMING_SNAKE_CASE_	)

					lowercase_					  = nn.Embedding(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	)
					lowercase_					  = False

					lowercase_					  = nn.Dropout(p=SCREAMING_SNAKE_CASE_	)

					lowercase_					  = 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_	,							)
					lowercase_					  = nn.ModuleList()
					for lyr_num in range(SCREAMING_SNAKE_CASE_	):
									lowercase_					  = TaBlock(SCREAMING_SNAKE_CASE_	)
									self.encoders.append(SCREAMING_SNAKE_CASE_	)

					lowercase_					  = TaLayerNorm(SCREAMING_SNAKE_CASE_	)
					lowercase_					  = nn.Dropout(p=SCREAMING_SNAKE_CASE_	)




	def 						_lowercase  (	self  :      Union[str, Any]	,							SCREAMING_SNAKE_CASE_  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      List[Any]	)	->					Optional[Any]:
					lowercase_					  = self.input_proj(SCREAMING_SNAKE_CASE_	)

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

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

					lowercase_					  = self.dropout_pre(SCREAMING_SNAKE_CASE_	)

					# inverted the attention mask
					lowercase_					  = encoder_inputs.size()
					lowercase_					  = self.get_extended_attention_mask(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	)

					for lyr in self.encoders:
									lowercase_					  = lyr(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	)[0]
					lowercase_					  = 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_tokenizers_available, is_torch_available


__a        = {
    'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a        = [
						    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'NystromformerForMaskedLM',
						    'NystromformerForMultipleChoice',
						    'NystromformerForQuestionAnswering',
						    'NystromformerForSequenceClassification',
						    'NystromformerForTokenClassification',
						    'NystromformerLayer',
						    'NystromformerModel',
						    'NystromformerPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nystromformer import (
												    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NystromformerForMaskedLM,
												    NystromformerForMultipleChoice,
												    NystromformerForQuestionAnswering,
												    NystromformerForSequenceClassification,
												    NystromformerForTokenClassification,
												    NystromformerLayer,
												    NystromformerModel,
												    NystromformerPreTrainedModel,
												)


else:
						import sys

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




