




"""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     lowercase (      __UpperCAmelCase       ,		__UpperCAmelCase       ,		__UpperCAmelCase):







				@register_to_config
				def __init__(							self				:   Tuple ,       _lowerCamelCase				:   int ,       _lowerCamelCase				:   int ,       _lowerCamelCase				:   int ,       _lowerCamelCase				:   float ,       _lowerCamelCase				:   int ,       _lowerCamelCase				:   int ,       _lowerCamelCase				:   int ,       _lowerCamelCase				:   int ,       _lowerCamelCase				:   str ,       _lowerCamelCase				:   bool = False ,       ):



					"""simple docstring"""

					super().__init__()

					A_					:			List[Any]						=						nn.Linear(_lowerCamelCase ,       _lowerCamelCase ,       bias=_lowerCamelCase    )

					A_					:			Optional[Any]						=						nn.Embedding(_lowerCamelCase ,       _lowerCamelCase    )
					A_					:			List[Any]						=						False

					A_					:			str						=						nn.Dropout(p=_lowerCamelCase    )

					A_					:			Union[str, Any]						=						TaConfig(
					    d_model=_lowerCamelCase ,       num_heads=_lowerCamelCase ,       d_kv=_lowerCamelCase ,       d_ff=_lowerCamelCase ,       feed_forward_proj=_lowerCamelCase ,       dropout_rate=_lowerCamelCase ,       is_decoder=_lowerCamelCase ,       is_encoder_decoder=_lowerCamelCase ,       )
					A_					:			int						=						nn.ModuleList()
					for lyr_num in range(_lowerCamelCase    ):
						A_					:			Optional[int]						=						TaBlock(_lowerCamelCase    )
						self.encoders.append(_lowerCamelCase    )

					A_					:			List[str]						=						TaLayerNorm(_lowerCamelCase    )
					A_					:			Any						=						nn.Dropout(p=_lowerCamelCase    )







				def    a_					(							self				:   Union[str, Any] ,       _lowerCamelCase				:   Optional[Any] ,       _lowerCamelCase				:   Union[str, Any]    ):



					"""simple docstring"""

					A_					:			Dict						=						self.input_proj(_lowerCamelCase    )

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

					A_					:			int						=						encoder_inputs_mask.sum(-1    )
					A_					:			Union[str, Any]						=						torch.roll(input_positions.unsqueeze(0    ) ,       tuple(seq_lens.tolist()    ) ,       dims=0    )
					x += self.position_encoding(_lowerCamelCase    )

					A_					:			Dict						=						self.dropout_pre(_lowerCamelCase    )

					# inverted the attention mask
					A_					:			Dict						=						encoder_inputs.size()
					A_					:			str						=						self.get_extended_attention_mask(_lowerCamelCase ,       _lowerCamelCase    )

					for lyr in self.encoders:
						A_					:			Optional[int]						=						lyr(_lowerCamelCase ,       _lowerCamelCase    )[0]
					A_					:			Any						=						self.layer_norm(_lowerCamelCase    )

					return self.dropout_post(_lowerCamelCase    ), encoder_inputs_mask











"""simple docstring"""

from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims





