


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

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


snake_case    :   int	    =					logging.getLogger(__name__)







class 		_snake_case  (				_snake_case							):




 def __init__(		self ,  _lowerCamelCase=-1   ):
       # in NER datasets, the last column is usually reserved for NER label
       a						:Any										=	label_idx




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase   ):
       if isinstance(_lowerCamelCase ,  _lowerCamelCase   ):
             a						:str										=	mode.value
       a						:List[str]										=	os.path.join(_lowerCamelCase ,  F'''{mode}.txt'''   )
       a						:Any										=	1
       a						:Union[str, Any]										=	[]
       with open(_lowerCamelCase ,  encoding='''utf-8'''   ) as f:
             a						:Tuple										=	[]
             a						:Tuple										=	[]
             for line in f:
                   if line.startswith('''-DOCSTART-'''   ) or line == "" or line == "\n":
                         if words:
                               examples.append(InputExample(guid=F'''{mode}-{guid_index}''' ,  words=_lowerCamelCase ,  labels=_lowerCamelCase   )   )
                               guid_index += 1
                               a						:Optional[int]										=	[]
                               a						:int										=	[]
                   else:
                         a						:int										=	line.split(''' '''   )
                         words.append(splits[0]   )
                         if len(_lowerCamelCase   ) > 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=_lowerCamelCase ,  labels=_lowerCamelCase   )   )
       return examples




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   ):
       a						:Union[str, Any]										=	0
       for line in test_input_reader:
             if line.startswith('''-DOCSTART-'''   ) or line == "" or line == "\n":
                   writer.write(_lowerCamelCase   )
                   if not preds_list[example_id]:
                         example_id += 1
             elif preds_list[example_id]:
                   a						:Dict										=	line.split()[0] + ''' ''' + preds_list[example_id].pop(0   ) + '''\n'''
                   writer.write(_lowerCamelCase   )
             else:
                   logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.''' ,  line.split()[0]   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       if path:
             with open(_lowerCamelCase ,  '''r'''   ) as f:
                   a						:Tuple										=	f.read().splitlines()
             if "O" not in labels:
                   a						:Dict										=	['''O'''] + labels
             return labels
       else:
             return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]







class 		_snake_case  (				_snake_case							):




 def __init__(		self   ):
       # in CONLL2003 dataset chunk column is second-to-last
       super().__init__(label_idx=-2   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       if path:
             with open(_lowerCamelCase ,  '''r'''   ) as f:
                   a						:Optional[int]										=	f.read().splitlines()
             if "O" not in labels:
                   a						:List[str]										=	['''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 		_snake_case  (				_snake_case							):




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase   ):
       if isinstance(_lowerCamelCase ,  _lowerCamelCase   ):
             a						:List[str]										=	mode.value
       a						:Optional[Any]										=	os.path.join(_lowerCamelCase ,  F'''{mode}.txt'''   )
       a						:Dict										=	1
       a						:Optional[Any]										=	[]

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




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   ):
       a						:int										=	0
       for sentence in parse_incr(_lowerCamelCase   ):
             a						:str										=	preds_list[example_id]
             a						:List[Any]										=	''''''
             for token in sentence:
                   out += F'''{token['form']} ({token['upos']}|{s_p.pop(0   )}) '''
             out += "\n"
             writer.write(_lowerCamelCase   )
             example_id += 1




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       if path:
             with open(_lowerCamelCase ,  '''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",
             ]









from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_tf_available():
     import tensorflow as tf

     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
     from ..tf_utils import stable_softmax

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

snake_case    :   str	    =					logging.get_logger(__name__)







@add_end_docstrings(_snake_case							)
class 		_snake_case  (				_snake_case							):




 def __init__(		self ,  *_lowerCamelCase ,  **_lowerCamelCase   ):
       super().__init__(*_lowerCamelCase ,  **_lowerCamelCase   )
       requires_backends(self ,  '''vision'''   )
       self.check_model_type(
           TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
           if self.framework == '''tf'''
           else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase=None   ):
       a						:Tuple										=	{}
       if top_k is not None:
             a						:int										=	top_k
       return {}, {}, postprocess_params




 def __call__(		self ,  _lowerCamelCase ,  **_lowerCamelCase   ):
       return super().__call__(_lowerCamelCase ,  **_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:str										=	load_image(_lowerCamelCase   )
       a						:Any										=	self.image_processor(images=_lowerCamelCase ,  return_tensors=self.framework   )
       return model_inputs




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:List[str]										=	self.model(**_lowerCamelCase   )
       return model_outputs




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=5   ):
       if top_k > self.model.config.num_labels:
             a						:List[Any]										=	self.model.config.num_labels

       if self.framework == "pt":
             a						:int										=	model_outputs.logits.softmax(-1   )[0]
             a							,    a						:Union[str, Any]										=	probs.topk(_lowerCamelCase   )
       elif self.framework == "tf":
             a						:Optional[Any]										=	stable_softmax(model_outputs.logits ,  axis=-1   )[0]
             a						:Union[str, Any]										=	tf.math.top_k(_lowerCamelCase ,  k=_lowerCamelCase   )
             a							,    a						:Optional[int]										=	topk.values.numpy(), topk.indices.numpy()
       else:
             raise ValueError(F'''Unsupported framework: {self.framework}'''   )

       a						:Optional[int]										=	scores.tolist()
       a						:str										=	ids.tolist()
       return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_lowerCamelCase ,  _lowerCamelCase   )]





