


'''simple docstring'''







import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


A__:   Union[str, Any]    =	logging.get_logger(__name__)







@dataclass
class A__	:
      __UpperCamelCase	:  str
      __UpperCamelCase	:  str
      __UpperCamelCase	:  Optional[str]     	=							None
      __UpperCamelCase	:  Optional[str]     	=							None






      def __UpperCAmelCase	(			self      :Any     )	-> Any:




             '''simple docstring'''
             return json.dumps(dataclasses.asdict(self     )					,						indent=2     ) + "\n"







@dataclass(frozen=UpperCAmelCase__				)
class A__	:
      __UpperCamelCase	:  List[int]
      __UpperCamelCase	:  Optional[List[int]]     	=							None
      __UpperCamelCase	:  Optional[List[int]]     	=							None
      __UpperCamelCase	:  Optional[Union[int, float]]     	=							None






      def __UpperCAmelCase	(			self      :Optional[int]     )	-> Union[str, Any]:




             '''simple docstring'''
             return json.dumps(dataclasses.asdict(self     )     ) + "\n"







class A__	:






      def __UpperCAmelCase	(			self      :str					,						SCREAMING_SNAKE_CASE      :Optional[Any]     )	-> Dict:




             '''simple docstring'''
             raise NotImplementedError()






      def __UpperCAmelCase	(			self      :int					,						SCREAMING_SNAKE_CASE      :Dict     )	-> List[Any]:




             '''simple docstring'''
             raise NotImplementedError()






      def __UpperCAmelCase	(			self      :Union[str, Any]					,						SCREAMING_SNAKE_CASE      :int     )	-> List[Any]:




             '''simple docstring'''
             raise NotImplementedError()






      def __UpperCAmelCase	(			self      :Union[str, Any]					,						SCREAMING_SNAKE_CASE      :Optional[int]     )	-> Dict:




             '''simple docstring'''
             raise NotImplementedError()






      def __UpperCAmelCase	(			self      :Dict     )	-> Tuple:




             '''simple docstring'''
             raise NotImplementedError()






      def __UpperCAmelCase	(			self      :Dict					,						SCREAMING_SNAKE_CASE      :str     )	-> List[str]:




             '''simple docstring'''
             if len(self.get_labels()     ) > 1:
                    _a   :			Tuple					 =self.get_labels()[int(example.label     )]
             return example






      @classmethod
      def __UpperCAmelCase	(			cls      :str					,						SCREAMING_SNAKE_CASE      :Union[str, Any]					,						SCREAMING_SNAKE_CASE      :Optional[int]=None     )	-> Optional[Any]:




             '''simple docstring'''
             with open(SCREAMING_SNAKE_CASE					,						"""r"""					,						encoding="""utf-8-sig"""     ) as f:
                    return list(csv.reader(SCREAMING_SNAKE_CASE					,						delimiter="""\t"""					,						quotechar=SCREAMING_SNAKE_CASE     )     )







class A__	(						UpperCAmelCase__				):






      def __init__(			self      :Dict					,						SCREAMING_SNAKE_CASE      :Any=None					,						SCREAMING_SNAKE_CASE      :Dict=None					,						SCREAMING_SNAKE_CASE      :Optional[Any]="classification"					,						SCREAMING_SNAKE_CASE      :Any=False     )	-> List[Any]:




             '''simple docstring'''
             _a   :			int					 =[] if labels is None else labels
             _a   :			str					 =[] if examples is None else examples
             _a   :			str					 =mode
             _a   :			str					 =verbose






      def __len__(			self      :Optional[Any]     )	-> List[Any]:




             '''simple docstring'''
             return len(self.examples     )






      def __getitem__(			self      :List[Any]					,						SCREAMING_SNAKE_CASE      :Any     )	-> Optional[Any]:




             '''simple docstring'''
             if isinstance(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     ):
                    return SingleSentenceClassificationProcessor(labels=self.labels					,						examples=self.examples[idx]     )
             return self.examples[idx]






      @classmethod
      def __UpperCAmelCase	(			cls      :int					,						SCREAMING_SNAKE_CASE      :Any					,						SCREAMING_SNAKE_CASE      :Union[str, Any]=""					,						SCREAMING_SNAKE_CASE      :Tuple=0					,						SCREAMING_SNAKE_CASE      :Union[str, Any]=1					,						SCREAMING_SNAKE_CASE      :Optional[int]=None					,						SCREAMING_SNAKE_CASE      :Union[str, Any]=False					,						**SCREAMING_SNAKE_CASE      :Dict     )	-> Any:




             '''simple docstring'''
             _a   :			Tuple					 =cls(**SCREAMING_SNAKE_CASE     )
             processor.add_examples_from_csv(
                 SCREAMING_SNAKE_CASE					,						split_name=SCREAMING_SNAKE_CASE					,						column_label=SCREAMING_SNAKE_CASE					,						column_text=SCREAMING_SNAKE_CASE					,						column_id=SCREAMING_SNAKE_CASE					,						skip_first_row=SCREAMING_SNAKE_CASE					,						overwrite_labels=SCREAMING_SNAKE_CASE					,						overwrite_examples=SCREAMING_SNAKE_CASE					,						)
             return processor






      @classmethod
      def __UpperCAmelCase	(			cls      :Optional[int]					,						SCREAMING_SNAKE_CASE      :Optional[Any]					,						SCREAMING_SNAKE_CASE      :Optional[int]=None					,						**SCREAMING_SNAKE_CASE      :Optional[int]     )	-> List[str]:




             '''simple docstring'''
             _a   :			List[str]					 =cls(**SCREAMING_SNAKE_CASE     )
             processor.add_examples(SCREAMING_SNAKE_CASE					,						labels=SCREAMING_SNAKE_CASE     )
             return processor






      def __UpperCAmelCase	(			self      :int					,						SCREAMING_SNAKE_CASE      :List[str]					,						SCREAMING_SNAKE_CASE      :int=""					,						SCREAMING_SNAKE_CASE      :str=0					,						SCREAMING_SNAKE_CASE      :List[str]=1					,						SCREAMING_SNAKE_CASE      :Union[str, Any]=None					,						SCREAMING_SNAKE_CASE      :Dict=False					,						SCREAMING_SNAKE_CASE      :Union[str, Any]=False					,						SCREAMING_SNAKE_CASE      :Any=False					,						)	-> Optional[Any]:




             '''simple docstring'''
             _a   :			str					 =self._read_tsv(SCREAMING_SNAKE_CASE     )
             if skip_first_row:
                    _a   :			Optional[int]					 =lines[1:]
             _a   :			Dict					 =[]
             _a   :			Optional[Any]					 =[]
             _a   :			int					 =[]
             for i, line in enumerate(SCREAMING_SNAKE_CASE     ):
                    texts.append(line[column_text]     )
                    labels.append(line[column_label]     )
                    if column_id is not None:
                           ids.append(line[column_id]     )
                    else:
                           _a   :			Tuple					 =f"{split_name}-{i}" if split_name else str(SCREAMING_SNAKE_CASE     )
                           ids.append(SCREAMING_SNAKE_CASE     )

             return self.add_examples(
                 SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						overwrite_labels=SCREAMING_SNAKE_CASE					,						overwrite_examples=SCREAMING_SNAKE_CASE     )






      def __UpperCAmelCase	(			self      :Union[str, Any]					,						SCREAMING_SNAKE_CASE      :List[Any]					,						SCREAMING_SNAKE_CASE      :Optional[int]=None					,						SCREAMING_SNAKE_CASE      :Optional[int]=None					,						SCREAMING_SNAKE_CASE      :Any=False					,						SCREAMING_SNAKE_CASE      :List[str]=False     )	-> List[str]:




             '''simple docstring'''
             if labels is not None and len(SCREAMING_SNAKE_CASE     ) != len(SCREAMING_SNAKE_CASE     ):
                    raise ValueError(
                        f"Text and labels have mismatched lengths {len(SCREAMING_SNAKE_CASE     )} and {len(SCREAMING_SNAKE_CASE     )}"     )
             if ids is not None and len(SCREAMING_SNAKE_CASE     ) != len(SCREAMING_SNAKE_CASE     ):
                    raise ValueError(f"Text and ids have mismatched lengths {len(SCREAMING_SNAKE_CASE     )} and {len(SCREAMING_SNAKE_CASE     )}"     )
             if ids is None:
                    _a   :			List[Any]					 =[None] * len(SCREAMING_SNAKE_CASE     )
             if labels is None:
                    _a   :			Tuple					 =[None] * len(SCREAMING_SNAKE_CASE     )
             _a   :			Optional[Any]					 =[]
             _a   :			str					 =set()
             for text_or_text_and_label, label, guid in zip(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     ):
                    if isinstance(SCREAMING_SNAKE_CASE					,						(tuple, list)     ) and label is None:
                           _a ,   _a   :			Optional[Any]					 =text_or_text_and_label
                    else:
                           _a   :			Optional[Any]					 =text_or_text_and_label
                    added_labels.add(SCREAMING_SNAKE_CASE     )
                    examples.append(InputExample(guid=SCREAMING_SNAKE_CASE					,						text_a=SCREAMING_SNAKE_CASE					,						text_b=SCREAMING_SNAKE_CASE					,						label=SCREAMING_SNAKE_CASE     )     )

             # Update examples
             if overwrite_examples:
                    _a   :			Optional[int]					 =examples
             else:
                    self.examples.extend(SCREAMING_SNAKE_CASE     )

             # Update labels
             if overwrite_labels:
                    _a   :			Any					 =list(SCREAMING_SNAKE_CASE     )
             else:
                    _a   :			Union[str, Any]					 =list(set(self.labels     ).union(SCREAMING_SNAKE_CASE     )     )

             return self.examples






      def __UpperCAmelCase	(			self      :Dict					,						SCREAMING_SNAKE_CASE      :Optional[int]					,						SCREAMING_SNAKE_CASE      :Union[str, Any]=None					,						SCREAMING_SNAKE_CASE      :Optional[Any]=False					,						SCREAMING_SNAKE_CASE      :Any=0					,						SCREAMING_SNAKE_CASE      :Optional[Any]=True					,						SCREAMING_SNAKE_CASE      :Any=None					,						)	-> List[str]:




             '''simple docstring'''
             if max_length is None:
                    _a   :			List[str]					 =tokenizer.max_len

             _a   :			List[str]					 ={label: i for i, label in enumerate(self.labels     )}

             _a   :			List[str]					 =[]
             for ex_index, example in enumerate(self.examples     ):
                    if ex_index % 1_0_0_0_0 == 0:
                           logger.info(f"Tokenizing example {ex_index}"     )

                    _a   :			Dict					 =tokenizer.encode(
                        example.text_a					,						add_special_tokens=SCREAMING_SNAKE_CASE					,						max_length=min(SCREAMING_SNAKE_CASE					,						tokenizer.max_len     )					,						)
                    all_input_ids.append(SCREAMING_SNAKE_CASE     )

             _a   :			Any					 =max(len(SCREAMING_SNAKE_CASE     ) for input_ids in all_input_ids     )

             _a   :			List[str]					 =[]
             for ex_index, (input_ids, example) in enumerate(zip(SCREAMING_SNAKE_CASE					,						self.examples     )     ):
                    if ex_index % 1_0_0_0_0 == 0:
                           logger.info(f"Writing example {ex_index}/{len(self.examples     )}"     )
                    # The mask has 1 for real tokens and 0 for padding tokens. Only real
                    # tokens are attended to.
                    _a   :			List[Any]					 =[1 if mask_padding_with_zero else 0] * len(SCREAMING_SNAKE_CASE     )

                    # Zero-pad up to the sequence length.
                    _a   :			Any					 =batch_length - len(SCREAMING_SNAKE_CASE     )
                    if pad_on_left:
                           _a   :			Any					 =([pad_token] * padding_length) + input_ids
                           _a   :			Optional[int]					 =([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
                    else:
                           _a   :			Union[str, Any]					 =input_ids + ([pad_token] * padding_length)
                           _a   :			str					 =attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

                    if len(SCREAMING_SNAKE_CASE     ) != batch_length:
                           raise ValueError(f"Error with input length {len(SCREAMING_SNAKE_CASE     )} vs {batch_length}"     )
                    if len(SCREAMING_SNAKE_CASE     ) != batch_length:
                           raise ValueError(f"Error with input length {len(SCREAMING_SNAKE_CASE     )} vs {batch_length}"     )

                    if self.mode == "classification":
                           _a   :			Union[str, Any]					 =label_map[example.label]
                    elif self.mode == "regression":
                           _a   :			str					 =float(example.label     )
                    else:
                           raise ValueError(self.mode     )

                    if ex_index < 5 and self.verbose:
                           logger.info("""*** Example ***"""     )
                           logger.info(f"guid: {example.guid}"     )
                           logger.info(f"input_ids: {' '.join([str(SCREAMING_SNAKE_CASE     ) for x in input_ids]     )}"     )
                           logger.info(f"attention_mask: {' '.join([str(SCREAMING_SNAKE_CASE     ) for x in attention_mask]     )}"     )
                           logger.info(f"label: {example.label} (id = {label})"     )

                    features.append(InputFeatures(input_ids=SCREAMING_SNAKE_CASE					,						attention_mask=SCREAMING_SNAKE_CASE					,						label=SCREAMING_SNAKE_CASE     )     )

             if return_tensors is None:
                    return features
             elif return_tensors == "tf":
                    if not is_tf_available():
                           raise RuntimeError("""return_tensors set to 'tf' but TensorFlow 2.0 can't be imported"""     )
                    import tensorflow as tf

                    def gen():
                           for ex in features:
                                  yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

                    _a   :			List[Any]					 =tf.data.Dataset.from_generator(
                        SCREAMING_SNAKE_CASE					,						({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)					,						({"""input_ids""": tf.TensorShape([None]     ), """attention_mask""": tf.TensorShape([None]     )}, tf.TensorShape([]     ))					,						)
                    return dataset
             elif return_tensors == "pt":
                    if not is_torch_available():
                           raise RuntimeError("""return_tensors set to 'pt' but PyTorch can't be imported"""     )
                    import torch
                    from torch.utils.data import TensorDataset

                    _a   :			Union[str, Any]					 =torch.tensor([f.input_ids for f in features]					,						dtype=torch.long     )
                    _a   :			int					 =torch.tensor([f.attention_mask for f in features]					,						dtype=torch.long     )
                    if self.mode == "classification":
                           _a   :			List[Any]					 =torch.tensor([f.label for f in features]					,						dtype=torch.long     )
                    elif self.mode == "regression":
                           _a   :			List[Any]					 =torch.tensor([f.label for f in features]					,						dtype=torch.float     )

                    _a   :			int					 =TensorDataset(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )
                    return dataset
             else:
                    raise ValueError("""return_tensors should be one of 'tf' or 'pt'"""     )






'''simple docstring'''







from typing import Dict, Optional, Tuple

import torch


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     torch.Tensor  )  ->      torch.Tensor:
       _a   :			Tuple					 =boundaries[1] - boundaries[0]
       _a   :			Dict					 =boundaries + step / 2
       _a   :			List[Any]					 =torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1  )]   ,dim=0  )
       return bin_centers


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     torch.Tensor   ,_UpperCAmelCase     :     torch.Tensor   ,)  ->      Tuple[torch.Tensor, torch.Tensor]:
       _a   :			Any					 =_calculate_bin_centers(_UpperCAmelCase  )
       return (
           torch.sum(aligned_distance_error_probs * bin_centers   ,dim=-1  ),
           bin_centers[-1],
       )


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     torch.Tensor   ,_UpperCAmelCase     :     int = 31   ,_UpperCAmelCase     :     int = 64   ,**_UpperCAmelCase     :     Dict   ,)  ->      Dict[str, torch.Tensor]:
       _a   :			Dict					 =torch.linspace(0   ,_UpperCAmelCase   ,steps=(no_bins - 1)   ,device=logits.device  )

       _a   :			int					 =torch.nn.functional.softmax(_UpperCAmelCase   ,dim=-1  )
       _a ,   _a   :			List[Any]					 =_calculate_expected_aligned_error(
           alignment_confidence_breaks=_UpperCAmelCase   ,aligned_distance_error_probs=_UpperCAmelCase   ,)

       return {
           "aligned_confidence_probs": aligned_confidence_probs,
           "predicted_aligned_error": predicted_aligned_error,
           "max_predicted_aligned_error": max_predicted_aligned_error,
       }


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     torch.Tensor   ,_UpperCAmelCase     :     Optional[torch.Tensor] = None   ,_UpperCAmelCase     :     int = 31   ,_UpperCAmelCase     :     int = 64   ,_UpperCAmelCase     :     float = 1e-8   ,**_UpperCAmelCase     :     Optional[Any]   ,)  ->      torch.Tensor:
       if residue_weights is None:
              _a   :			Dict					 =logits.new_ones(logits.shape[-2]  )

       _a   :			str					 =torch.linspace(0   ,_UpperCAmelCase   ,steps=(no_bins - 1)   ,device=logits.device  )

       _a   :			Optional[int]					 =_calculate_bin_centers(_UpperCAmelCase  )
       torch.sum(_UpperCAmelCase  )
       _a   :			List[str]					 =logits.shape[-2]
       _a   :			List[Any]					 =max(_UpperCAmelCase   ,19  )

       _a   :			Union[str, Any]					 =1.2_4 * (clipped_n - 15) ** (1.0 / 3) - 1.8

       _a   :			Tuple					 =torch.nn.functional.softmax(_UpperCAmelCase   ,dim=-1  )

       _a   :			Dict					 =1.0 / (1 + (bin_centers**2) / (da**2))
       _a   :			int					 =torch.sum(probs * tm_per_bin   ,dim=-1  )

       _a   :			List[Any]					 =residue_weights / (eps + residue_weights.sum())
       _a   :			List[str]					 =torch.sum(predicted_tm_term * normed_residue_mask   ,dim=-1  )

       _a   :			int					 =per_alignment * residue_weights

       _a   :			List[Any]					 =(weighted == torch.max(_UpperCAmelCase  )).nonzero()[0]
       return per_alignment[tuple(_UpperCAmelCase  )]


