





"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


__A											=		logging.get_logger(__name__)






class 					_lowerCAmelCase   (     a       ):
       """simple docstring"""




       def __init__(    self    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , **__UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Union[str, Any]											=							feature_size
         lowerCAmelCase__			:int											=							sampling_rate
         lowerCAmelCase__			:List[Any]											=							padding_value

         lowerCAmelCase__			:Union[str, Any]											=							kwargs.pop('padding_side'    , 'right'      )
         lowerCAmelCase__			:List[str]											=							kwargs.pop('return_attention_mask'    , __UpperCAmelCase      )

         super().__init__(**__UpperCAmelCase      )






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase = True    , __UpperCAmelCase = None    , __UpperCAmelCase = False    , __UpperCAmelCase = None    , __UpperCAmelCase = None    , __UpperCAmelCase = None    , ):



         '''simple docstring'''

         if isinstance(__UpperCAmelCase    , (list, tuple)      ) and isinstance(processed_features[0]    , (dict, BatchFeature)      ):
           lowerCAmelCase__			:Any											=							{
               key: [example[key] for example in processed_features] for key in processed_features[0].keys()
           }

         # The model's main input name, usually `input_values`, has be passed for padding
         if self.model_input_names[0] not in processed_features:
           raise ValueError(
               'You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'
               F" to this method that includes {self.model_input_names[0]}, but you provided"
               F" {list(processed_features.keys()      )}"      )

         lowerCAmelCase__			:List[Any]											=							processed_features[self.model_input_names[0]]
         lowerCAmelCase__			:Union[str, Any]											=							(
             return_attention_mask if return_attention_mask is not None else self.return_attention_mask
         )

         if len(__UpperCAmelCase      ) == 0:
           if return_attention_mask:
             lowerCAmelCase__			:Optional[Any]											=							[]
           return processed_features

         # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
         # and rebuild them afterwards if no return_tensors is specified
         # Note that we lose the specific device the tensor may be on for PyTorch

         lowerCAmelCase__			:Tuple											=							required_input[0]
         if isinstance(__UpperCAmelCase    , (list, tuple)      ):
           # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
           lowerCAmelCase__			:int											=							0
           while len(required_input[index]      ) == 0:
             index += 1
           if index < len(__UpperCAmelCase      ):
             lowerCAmelCase__			:int											=							required_input[index][0]

         if return_tensors is None:
           if is_tf_tensor(__UpperCAmelCase      ):
             lowerCAmelCase__			:str											=							'tf'
           elif is_torch_tensor(__UpperCAmelCase      ):
             lowerCAmelCase__			:Optional[Any]											=							'pt'
           elif isinstance(__UpperCAmelCase    , (int, float, list, tuple, np.ndarray)      ):
             lowerCAmelCase__			:Union[str, Any]											=							'np'
           else:
             raise ValueError(
                 F"type of {first_element} unknown: {type(__UpperCAmelCase      )}. "
                 'Should be one of a python, numpy, pytorch or tensorflow object.'      )

         for key, value in processed_features.items():
           if isinstance(value[0]    , (int, float)      ):
             lowerCAmelCase__			:Optional[Any]											=							to_numpy(__UpperCAmelCase      )
           else:
             lowerCAmelCase__			:List[Any]											=							[to_numpy(__UpperCAmelCase      ) for v in value]

        # Convert padding_strategy in PaddingStrategy
         lowerCAmelCase__			:List[Any]											=							self._get_padding_strategies(padding=__UpperCAmelCase    , max_length=__UpperCAmelCase      )

         lowerCAmelCase__			:Dict											=							processed_features[self.model_input_names[0]]

         lowerCAmelCase__			:List[str]											=							len(__UpperCAmelCase      )
         if not all(len(__UpperCAmelCase      ) == batch_size for v in processed_features.values()      ):
           raise ValueError('Some items in the output dictionary have a different batch size than others.'      )

         lowerCAmelCase__			:Any											=							[]
         for i in range(__UpperCAmelCase      ):
           lowerCAmelCase__			:Dict											=							{k: v[i] for k, v in processed_features.items()}
           # truncation
           lowerCAmelCase__			:str											=							self._truncate(
               __UpperCAmelCase    , max_length=__UpperCAmelCase    , pad_to_multiple_of=__UpperCAmelCase    , truncation=__UpperCAmelCase    , )
           truncated_inputs.append(__UpperCAmelCase      )

         if padding_strategy == PaddingStrategy.LONGEST:
           # make sure that `max_length` cannot be longer than the longest truncated length
           lowerCAmelCase__			:Dict											=							max(len(input_slice[self.model_input_names[0]]      ) for input_slice in truncated_inputs      )
           lowerCAmelCase__			:str											=							PaddingStrategy.MAX_LENGTH

         lowerCAmelCase__			:Tuple											=							{}
         for i in range(__UpperCAmelCase      ):
           # padding
           lowerCAmelCase__			:List[str]											=							self._pad(
               truncated_inputs[i]    , max_length=__UpperCAmelCase    , padding_strategy=__UpperCAmelCase    , pad_to_multiple_of=__UpperCAmelCase    , return_attention_mask=__UpperCAmelCase    , )

           for key, value in outputs.items():
             if key not in batch_outputs:
               lowerCAmelCase__			:Tuple											=							[]
             if value.dtype is np.dtype(np.floataa      ):
               lowerCAmelCase__			:Optional[Any]											=							value.astype(np.floataa      )
             batch_outputs[key].append(__UpperCAmelCase      )

         return BatchFeature(__UpperCAmelCase    , tensor_type=__UpperCAmelCase      )






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase = None    , __UpperCAmelCase = PaddingStrategy.DO_NOT_PAD    , __UpperCAmelCase = None    , __UpperCAmelCase = None    , ):



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							processed_features[self.model_input_names[0]]

         if padding_strategy == PaddingStrategy.LONGEST:
           lowerCAmelCase__			:Optional[int]											=							len(__UpperCAmelCase      )

         if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
           lowerCAmelCase__			:List[Any]											=							((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

         lowerCAmelCase__			:Union[str, Any]											=							padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__UpperCAmelCase      ) < max_length

         if return_attention_mask and "attention_mask" not in processed_features:
           lowerCAmelCase__			:str											=							np.ones(len(__UpperCAmelCase      )    , dtype=np.intaa      )

         if needs_to_be_padded:
           lowerCAmelCase__			:Dict											=							max_length - len(__UpperCAmelCase      )
           if self.padding_side == "right":
             if return_attention_mask:
               lowerCAmelCase__			:List[str]											=							np.pad(
                   processed_features['attention_mask']    , (0, difference)      )
             lowerCAmelCase__			:Union[str, Any]											=							((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
             lowerCAmelCase__			:Optional[int]											=							np.pad(
                 __UpperCAmelCase    , __UpperCAmelCase    , 'constant'    , constant_values=self.padding_value      )
           elif self.padding_side == "left":
             if return_attention_mask:
               lowerCAmelCase__			:List[str]											=							np.pad(
                   processed_features['attention_mask']    , (difference, 0)      )
             lowerCAmelCase__			:List[str]											=							((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
             lowerCAmelCase__			:List[Any]											=							np.pad(
                 __UpperCAmelCase    , __UpperCAmelCase    , 'constant'    , constant_values=self.padding_value      )
           else:
             raise ValueError('Invalid padding strategy:' + str(self.padding_side      )      )

         return processed_features






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase = None    , __UpperCAmelCase = None    , __UpperCAmelCase = None    , ):



         '''simple docstring'''

         if not truncation:
           return processed_features
         elif truncation and max_length is None:
           raise ValueError('When setting ``truncation=True``, make sure that ``max_length`` is defined.'      )

         lowerCAmelCase__			:Tuple											=							processed_features[self.model_input_names[0]]

         # find `max_length` that fits `pad_to_multiple_of`
         if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
           lowerCAmelCase__			:Dict											=							((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

         lowerCAmelCase__			:Optional[Any]											=							len(__UpperCAmelCase      ) > max_length

         if needs_to_be_truncated:
           lowerCAmelCase__			:Dict											=							processed_features[self.model_input_names[0]][:max_length]
           if "attention_mask" in processed_features:
             lowerCAmelCase__			:Optional[int]											=							processed_features['attention_mask'][:max_length]

         return processed_features






       def      snake_case						(    self    , __UpperCAmelCase=False    , __UpperCAmelCase=None      ):



         '''simple docstring'''

         if padding is not False:
           if padding is True:
             lowerCAmelCase__			:Union[str, Any]											=							PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
           elif not isinstance(__UpperCAmelCase    , __UpperCAmelCase      ):
             lowerCAmelCase__			:Tuple											=							PaddingStrategy(__UpperCAmelCase      )
           elif isinstance(__UpperCAmelCase    , __UpperCAmelCase      ):
             lowerCAmelCase__			:Optional[int]											=							padding
         else:
           lowerCAmelCase__			:List[str]											=							PaddingStrategy.DO_NOT_PAD

         # Set max length if needed
         if max_length is None:
           if padding_strategy == PaddingStrategy.MAX_LENGTH:
             raise ValueError(
                 F"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"      )

        # Test if we have a padding value
         if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
           raise ValueError(
               'Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'
               ' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'      )

         return padding_strategy







"""simple docstring"""





import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


__A											=		logging.getLogger(__name__)






class 					_lowerCAmelCase   (     a       ):
       """simple docstring"""




       def __init__(    self    , __UpperCAmelCase=-1      ):



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							label_idx






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase      ):



         '''simple docstring'''

         if isinstance(__UpperCAmelCase    , __UpperCAmelCase      ):
           lowerCAmelCase__			:Optional[Any]											=							mode.value
         lowerCAmelCase__			:List[str]											=							os.path.join(__UpperCAmelCase    , F"{mode}.txt"      )
         lowerCAmelCase__			:List[str]											=							1
         lowerCAmelCase__			:Union[str, Any]											=							[]
         with open(__UpperCAmelCase    , encoding='utf-8'      ) as f:
           lowerCAmelCase__			:str											=							[]
           lowerCAmelCase__			:Dict											=							[]
           for line in f:
             if line.startswith('-DOCSTART-'      ) or line == "" or line == "\n":
               if words:
                 examples.append(InputExample(guid=F"{mode}-{guid_index}"    , words=__UpperCAmelCase    , labels=__UpperCAmelCase      )      )
                 guid_index += 1
                 lowerCAmelCase__			:Tuple											=							[]
                 lowerCAmelCase__			:List[str]											=							[]
             else:
               lowerCAmelCase__			:List[str]											=							line.split(' '      )
               words.append(splits[0]      )
               if len(__UpperCAmelCase      ) > 1:
                 labels.append(splits[self.label_idx].replace('\n'    , ''      )      )
               else:
                 # Examples could have no label for mode = "test"
                 labels.append('O'      )
           if words:
             examples.append(InputExample(guid=F"{mode}-{guid_index}"    , words=__UpperCAmelCase    , labels=__UpperCAmelCase      )      )
         return examples






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[int]											=							0
         for line in test_input_reader:
           if line.startswith('-DOCSTART-'      ) or line == "" or line == "\n":
             writer.write(__UpperCAmelCase      )
             if not preds_list[example_id]:
               example_id += 1
           elif preds_list[example_id]:
             lowerCAmelCase__			:Optional[Any]											=							line.split()[0] + ' ' + preds_list[example_id].pop(0      ) + '\n'
             writer.write(__UpperCAmelCase      )
           else:
             logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.'    , line.split()[0]      )






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         if path:
           with open(__UpperCAmelCase    , 'r'      ) as f:
             lowerCAmelCase__			:Any											=							f.read().splitlines()
           if "O" not in labels:
             lowerCAmelCase__			:Union[str, Any]											=							['O'] + labels
           return labels
         else:
           return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]






class 					_lowerCAmelCase   (     a       ):
       """simple docstring"""




       def __init__(    self      ):



         '''simple docstring'''

         super().__init__(label_idx=-2      )






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         if path:
           with open(__UpperCAmelCase    , 'r'      ) as f:
             lowerCAmelCase__			:str											=							f.read().splitlines()
           if "O" not in labels:
             lowerCAmelCase__			:Optional[Any]											=							['O'] + labels
           return labels
         else:
           return [
               "O",
               "B-ADVP",
               "B-INTJ",
               "B-LST",
               "B-PRT",
               "B-NP",
               "B-SBAR",
               "B-VP",
               "B-ADJP",
               "B-CONJP",
               "B-PP",
               "I-ADVP",
               "I-INTJ",
               "I-LST",
               "I-PRT",
               "I-NP",
               "I-SBAR",
               "I-VP",
               "I-ADJP",
               "I-CONJP",
               "I-PP",
           ]






class 					_lowerCAmelCase   (     a       ):
       """simple docstring"""




       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase      ):



         '''simple docstring'''

         if isinstance(__UpperCAmelCase    , __UpperCAmelCase      ):
           lowerCAmelCase__			:Union[str, Any]											=							mode.value
         lowerCAmelCase__			:Union[str, Any]											=							os.path.join(__UpperCAmelCase    , F"{mode}.txt"      )
         lowerCAmelCase__			:Any											=							1
         lowerCAmelCase__			:Optional[Any]											=							[]

         with open(__UpperCAmelCase    , encoding='utf-8'      ) as f:
           for sentence in parse_incr(__UpperCAmelCase      ):
             lowerCAmelCase__			:Dict											=							[]
             lowerCAmelCase__			:Dict											=							[]
             for token in sentence:
               words.append(token['form']      )
               labels.append(token['upos']      )
             assert len(__UpperCAmelCase      ) == len(__UpperCAmelCase      )
             if words:
               examples.append(InputExample(guid=F"{mode}-{guid_index}"    , words=__UpperCAmelCase    , labels=__UpperCAmelCase      )      )
               guid_index += 1
         return examples






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Any											=							0
         for sentence in parse_incr(__UpperCAmelCase      ):
           lowerCAmelCase__			:Optional[int]											=							preds_list[example_id]
           lowerCAmelCase__			:Tuple											=							''
           for token in sentence:
             out += F"{token['form']} ({token['upos']}|{s_p.pop(0      )}) "
           out += "\n"
           writer.write(__UpperCAmelCase      )
           example_id += 1






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         if path:
           with open(__UpperCAmelCase    , 'r'      ) as f:
             return f.read().splitlines()
         else:
           return [
               "ADJ",
               "ADP",
               "ADV",
               "AUX",
               "CCONJ",
               "DET",
               "INTJ",
               "NOUN",
               "NUM",
               "PART",
               "PRON",
               "PROPN",
               "PUNCT",
               "SCONJ",
               "SYM",
               "VERB",
               "X",
           ]
