





'''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_		:  List[str]		 =						logging.getLogger(__name__)
class 					__snake_case  (					__SCREAMING_SNAKE_CASE							):






   '''simple docstring'''





   def __init__(							self	,		__SCREAMING_SNAKE_CASE=-1       ):
    # in NER datasets, the last column is usually reserved for NER label
    snake_case__     :					List[str]    =  label_idx

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

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


   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE       ):
    if path:
     with open(__SCREAMING_SNAKE_CASE	,		"""r"""       ) as f:
      snake_case__     :					List[Any]    =  f.read().splitlines()
     if "O" not in labels:
      snake_case__     :					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 					__snake_case  (					__SCREAMING_SNAKE_CASE							):






   '''simple docstring'''





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


   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE       ):
    if path:
     with open(__SCREAMING_SNAKE_CASE	,		"""r"""       ) as f:
      snake_case__     :					Optional[int]    =  f.read().splitlines()
     if "O" not in labels:
      snake_case__     :					List[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 					__snake_case  (					__SCREAMING_SNAKE_CASE							):






   '''simple docstring'''





   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ):
    if isinstance(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ):
     snake_case__     :					Optional[Any]    =  mode.value
    snake_case__     :					int    =  os.path.join(__SCREAMING_SNAKE_CASE	,		f"{mode}.txt"       )
    snake_case__     :					Union[str, Any]    =  1
    snake_case__     :					Any    =  []

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

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ):
    snake_case__     :					Tuple    =  0
    for sentence in parse_incr(__SCREAMING_SNAKE_CASE       ):
     snake_case__     :					int    =  preds_list[example_id]
     snake_case__     :					Any    =  """"""
     for token in sentence:
      out += f"{token['form']} ({token['upos']}|{s_p.pop(0       )}) "
     out += "\n"
     writer.write(__SCREAMING_SNAKE_CASE       )
     example_id += 1


   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE       ):
    if path:
     with open(__SCREAMING_SNAKE_CASE	,		"""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, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 		_A						( UpperCAmelCase_ ):
  lowercase_						:			Tuple						=					['''image_processor''', '''tokenizer''']
  lowercase_						:			str						=					'''BlipImageProcessor'''
  lowercase_						:			str						=					('''BertTokenizer''', '''BertTokenizerFast''')

  def __init__(    self						:	List[str]   ,				lowerCamelCase__						:	str   ,				lowerCamelCase__						:	Union[str, Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Optional[int]					=					False
     super().__init__(lowerCamelCase__   ,				lowerCamelCase__			)
     __UpperCamelCase	:			Tuple					=					self.image_processor

  def __call__(    self						:	Optional[Any]   ,				lowerCamelCase__						:	ImageInput = None   ,				lowerCamelCase__						:	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,				lowerCamelCase__						:	bool = True   ,				lowerCamelCase__						:	Union[bool, str, PaddingStrategy] = False   ,				lowerCamelCase__						:	Union[bool, str, TruncationStrategy] = None   ,				lowerCamelCase__						:	Optional[int] = None   ,				lowerCamelCase__						:	int = 0   ,				lowerCamelCase__						:	Optional[int] = None   ,				lowerCamelCase__						:	Optional[bool] = None   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = True   ,				lowerCamelCase__						:	Optional[Union[str, TensorType]] = None   ,				**lowerCamelCase__						:	int   ,				):
     """simple docstring"""

     if images is None and text is None:
        raise ValueError("""You have to specify either images or text."""			)

     # Get only text
     if images is None:
        __UpperCamelCase	:			Optional[int]					=					self.tokenizer
        __UpperCamelCase	:			Dict					=					self.tokenizer(
            text=lowerCamelCase__   ,				add_special_tokens=lowerCamelCase__   ,				padding=lowerCamelCase__   ,				truncation=lowerCamelCase__   ,				max_length=lowerCamelCase__   ,				stride=lowerCamelCase__   ,				pad_to_multiple_of=lowerCamelCase__   ,				return_attention_mask=lowerCamelCase__   ,				return_overflowing_tokens=lowerCamelCase__   ,				return_special_tokens_mask=lowerCamelCase__   ,				return_offsets_mapping=lowerCamelCase__   ,				return_token_type_ids=lowerCamelCase__   ,				return_length=lowerCamelCase__   ,				verbose=lowerCamelCase__   ,				return_tensors=lowerCamelCase__   ,				**lowerCamelCase__   ,				)
        return text_encoding

     # add pixel_values
     __UpperCamelCase	:			str					=					self.image_processor(lowerCamelCase__   ,				return_tensors=lowerCamelCase__			)

     if text is not None:
        __UpperCamelCase	:			Optional[int]					=					self.tokenizer(
            text=lowerCamelCase__   ,				add_special_tokens=lowerCamelCase__   ,				padding=lowerCamelCase__   ,				truncation=lowerCamelCase__   ,				max_length=lowerCamelCase__   ,				stride=lowerCamelCase__   ,				pad_to_multiple_of=lowerCamelCase__   ,				return_attention_mask=lowerCamelCase__   ,				return_overflowing_tokens=lowerCamelCase__   ,				return_special_tokens_mask=lowerCamelCase__   ,				return_offsets_mapping=lowerCamelCase__   ,				return_token_type_ids=lowerCamelCase__   ,				return_length=lowerCamelCase__   ,				verbose=lowerCamelCase__   ,				return_tensors=lowerCamelCase__   ,				**lowerCamelCase__   ,				)
     else:
        __UpperCamelCase	:			Optional[Any]					=					None

     if text_encoding is not None:
        encoding_image_processor.update(lowerCamelCase__			)

     return encoding_image_processor

  def    a					(    self						:	List[str]   ,				*lowerCamelCase__						:	Any   ,				**lowerCamelCase__						:	List[Any]			):
     """simple docstring"""

     return self.tokenizer.batch_decode(*lowerCamelCase__   ,				**lowerCamelCase__			)

  def    a					(    self						:	List[str]   ,				*lowerCamelCase__						:	int   ,				**lowerCamelCase__						:	Tuple			):
     """simple docstring"""

     return self.tokenizer.decode(*lowerCamelCase__   ,				**lowerCamelCase__			)

  @property
  def    a					(    self						:	Any			):
     """simple docstring"""

     __UpperCamelCase	:			str					=					self.tokenizer.model_input_names
     __UpperCamelCase	:			str					=					self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)



