




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool





class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	Any    		=   'philschmid/bart-large-cnn-samsum'
      UpperCAmelCase__      :	str    		=   (
          'This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '
          'and returns a summary of the text.'
      )
      UpperCAmelCase__      :	List[str]    		=   'summarizer'
      UpperCAmelCase__      :	Tuple    		=   AutoTokenizer
      UpperCAmelCase__      :	Tuple    		=   AutoModelForSeqaSeqLM

      UpperCAmelCase__      :	Any    		=   ['text']
      UpperCAmelCase__      :	List[str]    		=   ['text']
      def 			lowerCAmelCase__						(				self: Union[str, Any]    ,       UpperCamelCase_: Optional[int]   ):
             return self.pre_processor(UpperCamelCase_    ,       return_tensors="""pt"""    ,       truncation=UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: Union[str, Any]   ):
             return self.model.generate(**UpperCamelCase_   )[0]




      def 			lowerCAmelCase__						(				self: Tuple    ,       UpperCamelCase_: List[Any]   ):
             return self.pre_processor.decode(UpperCamelCase_    ,       skip_special_tokens=UpperCamelCase_    ,       clean_up_tokenization_spaces=UpperCamelCase_   )



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 			UpperCamelCase					(    __lowerCamelCase     : Dict[str, torch.Tensor]    ):
       snake_case       :   List[str]							=      []
       snake_case       :   Optional[int]							=      []
       snake_case       :   Any							=      []

       for rt in rc.restypes:
              snake_case       :   List[Any]							=      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]    )
              snake_case       :   str							=      {name: i for i, name in enumerate(__lowerCamelCase    )}
              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    )

       snake_case       :   Optional[Any]							=      torch.tensor(
           __lowerCamelCase				,					dtype=torch.intaa				,					device=protein["aatype"].device				,					)
       snake_case       :   List[Any]							=      torch.tensor(
           __lowerCamelCase				,					dtype=torch.intaa				,					device=protein["aatype"].device				,					)
       snake_case       :   int							=      torch.tensor(
           __lowerCamelCase				,					dtype=torch.floataa				,					device=protein["aatype"].device				,					)
       snake_case       :   int							=      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
       snake_case       :   List[Any]							=      restype_atomaa_to_atomaa[protein_aatype]
       snake_case       :   str							=      restype_atomaa_mask[protein_aatype]

       snake_case       :   str							=      residx_atomaa_mask
       snake_case       :   Any							=      residx_atomaa_to_atomaa.long()

       # create the gather indices for mapping back
       snake_case       :   List[str]							=      restype_atomaa_to_atomaa[protein_aatype]
       snake_case       :   List[Any]							=      residx_atomaa_to_atomaa.long()

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

       snake_case       :   List[Any]							=      restype_atomaa_mask[protein_aatype]
       snake_case       :   int							=      residx_atomaa_mask

       return protein




def 			UpperCamelCase					(    __lowerCamelCase     : Dict[str, torch.Tensor]    ):
       snake_case       :   Dict							=      tree_map(lambda __lowerCamelCase    : torch.tensor(__lowerCamelCase				,					device=batch["aatype"].device    )				,					__lowerCamelCase				,					np.ndarray    )
       snake_case       :   List[str]							=      tensor_tree_map(lambda __lowerCamelCase    : np.array(__lowerCamelCase    )				,					make_atomaa_masks(__lowerCamelCase    )    )
       return out





