

from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map

def       __snake_case						( __UpperCamelCase	: Dict[str, torch.Tensor]    ):



      """simple docstring"""

      A_						  =				[]
      A_						  =				[]
      A_						  =				[]

      for rt in rc.restypes:
            A_						  =				rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
            restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]    )
            A_						  =				{name: i for i, name in enumerate(__UpperCamelCase    )}
            restype_atomaa_to_atomaa_list.append(
                [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]    )

            restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]    )

      # Add dummy mapping for restype 'UNK'
      restype_atomaa_to_atomaa_list.append([0] * 14    )
      restype_atomaa_to_atomaa_list.append([0] * 37    )
      restype_atomaa_mask_list.append([0.0] * 14    )

      A_						  =				torch.tensor(
          __UpperCamelCase				,dtype=torch.intaa				,device=protein["aatype"].device				,)
      A_						  =				torch.tensor(
          __UpperCamelCase				,dtype=torch.intaa				,device=protein["aatype"].device				,)
      A_						  =				torch.tensor(
          __UpperCamelCase				,dtype=torch.floataa				,device=protein["aatype"].device				,)
      A_						  =				protein["aatype"].to(torch.long    )

      # create the mapping for (residx, atom14) --> atom37, i.e. an array
      # with shape (num_res, 14) containing the atom37 indices for this protein
      A_						  =				restype_atomaa_to_atomaa[protein_aatype]
      A_						  =				restype_atomaa_mask[protein_aatype]

      A_						  =				residx_atomaa_mask
      A_						  =				residx_atomaa_to_atomaa.long()

      # create the gather indices for mapping back
      A_						  =				restype_atomaa_to_atomaa[protein_aatype]
      A_						  =				residx_atomaa_to_atomaa.long()

      # create the corresponding mask
      A_						  =				torch.zeros([21, 37]				,dtype=torch.floataa				,device=protein["aatype"].device    )
      for restype, restype_letter in enumerate(rc.restypes    ):
            A_						  =				rc.restype_atoa[restype_letter]
            A_						  =				rc.residue_atoms[restype_name]
            for atom_name in atom_names:
                  A_						  =				rc.atom_order[atom_name]
                  A_						  =				1

      A_						  =				restype_atomaa_mask[protein_aatype]
      A_						  =				residx_atomaa_mask

      return protein



def       __snake_case						( __UpperCamelCase	: Dict[str, torch.Tensor]    ):



      """simple docstring"""

      A_						  =				tree_map(lambda __UpperCamelCase    : torch.tensor(__UpperCamelCase				,device=batch["aatype"].device    )				,__UpperCamelCase				,np.ndarray    )
      A_						  =				tensor_tree_map(lambda __UpperCamelCase    : np.array(__UpperCamelCase    )				,make_atomaa_masks(__UpperCamelCase    )    )
      return out


"""simple docstring"""





from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__UpperCamelCase    : List[Any]				 =						logging.get_logger(__name__)

__UpperCamelCase    : Any				 =						{
    '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/config.json''',
    '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/config.json''',
    '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/config.json''',
    '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/config.json''',
    '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/config.json''',
}







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			"t5"
					lowercase__												=			["past_key_values"]
					lowercase__												=			{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}







					def __init__(			self     :							Tuple  ,lowercase_     :							Tuple=3_2_1_2_8  ,lowercase_     :							Union[str, Any]=5_1_2  ,lowercase_     :							Dict=6_4  ,lowercase_     :							Any=2_0_4_8  ,lowercase_     :							Dict=6  ,lowercase_     :							Dict=None  ,lowercase_     :							Dict=8  ,lowercase_     :							Optional[int]=3_2  ,lowercase_     :							List[Any]=1_2_8  ,lowercase_     :							Any=0.1  ,lowercase_     :							Optional[Any]=1E-6  ,lowercase_     :							Any=1.0  ,lowercase_     :							Dict="relu"  ,lowercase_     :							Tuple=True  ,lowercase_     :							List[Any]=True  ,lowercase_     :							Tuple=0  ,lowercase_     :							Optional[Any]=1  ,**lowercase_     :							Any  ,):
						lowerCAmelCase__      :		Tuple    							=      vocab_size
						lowerCAmelCase__      :		List[str]    							=      d_model
						lowerCAmelCase__      :		Union[str, Any]    							=      d_kv
						lowerCAmelCase__      :		List[Any]    							=      d_ff
						lowerCAmelCase__      :		int    							=      num_layers
						lowerCAmelCase__      :		Union[str, Any]    							=      (
						    num_decoder_layers if num_decoder_layers is not None else self.num_layers
						)  # default = symmetry
						lowerCAmelCase__      :		Any    							=      num_heads
						lowerCAmelCase__      :		Optional[int]    							=      relative_attention_num_buckets
						lowerCAmelCase__      :		Union[str, Any]    							=      relative_attention_max_distance
						lowerCAmelCase__      :		List[Any]    							=      dropout_rate
						lowerCAmelCase__      :		str    							=      layer_norm_epsilon
						lowerCAmelCase__      :		int    							=      initializer_factor
						lowerCAmelCase__      :		Dict    							=      feed_forward_proj
						lowerCAmelCase__      :		int    							=      use_cache

						lowerCAmelCase__      :		int    							=      self.feed_forward_proj.split('''-'''						)
						lowerCAmelCase__      :		str    							=      act_info[-1]
						lowerCAmelCase__      :		Tuple    							=      act_info[0] == '''gated'''

						if len(lowercase_						) > 1 and act_info[0] != "gated" or len(lowercase_						) > 2:
							raise ValueError(
							    F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
							    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
							    '''\'gated-gelu\' or \'relu\''''						)

						# for backwards compatibility
						if feed_forward_proj == "gated-gelu":
							lowerCAmelCase__      :		Tuple    							=      '''gelu_new'''

						super().__init__(
						    pad_token_id=lowercase_  ,eos_token_id=lowercase_  ,is_encoder_decoder=lowercase_  ,**lowercase_  ,)







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					@property
					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						lowerCAmelCase__      :		int    							=      {
						    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
						    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
						}
						if self.use_past:
							lowerCAmelCase__      :		Union[str, Any]    							=      '''past_encoder_sequence + sequence'''
							lowerCAmelCase__      :		List[Any]    							=      {0: '''batch'''}
							lowerCAmelCase__      :		Union[str, Any]    							=      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
						else:
							lowerCAmelCase__      :		Union[str, Any]    							=      {0: '''batch''', 1: '''decoder_sequence'''}
							lowerCAmelCase__      :		Any    							=      {0: '''batch''', 1: '''decoder_sequence'''}

						if self.use_past:
							self.fill_with_past_key_values_(lowercase_  ,direction='''inputs'''						)

						return common_inputs







					@property
					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						return 1_3




