


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

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


SCREAMING_SNAKE_CASE       :		str         =				logging.getLogger(__name__)






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   def __init__(     self   ,							UpperCamelCase_=-1		):
        # in NER datasets, the last column is usually reserved for NER label
        lowercase_       :Dict     =       label_idx
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        if isinstance(UpperCamelCase_   ,							UpperCamelCase_		):
             lowercase_       :Dict     =       mode.value
        lowercase_       :int     =       os.path.join(UpperCamelCase_   ,							f"{mode}.txt"		)
        lowercase_       :Any     =       1
        lowercase_       :str     =       []
        with open(UpperCamelCase_   ,							encoding='''utf-8'''		) as f:
             lowercase_       :List[str]     =       []
             lowercase_       :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
                            lowercase_       :Optional[int]     =       []
                            lowercase_       :Dict     =       []
                  else:
                       lowercase_       :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 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Dict     =       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]:
                  lowercase_       :Union[str, 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 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        if path:
             with open(UpperCamelCase_   ,							'''r'''		) as f:
                  lowercase_       :Optional[Any]     =       f.read().splitlines()
             if "O" not in labels:
                  lowercase_       :Optional[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      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







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




   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        if path:
             with open(UpperCamelCase_   ,							'''r'''		) as f:
                  lowercase_       :str     =       f.read().splitlines()
             if "O" not in labels:
                  lowercase_       :Tuple     =       ['''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      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        if isinstance(UpperCamelCase_   ,							UpperCamelCase_		):
             lowercase_       :Any     =       mode.value
        lowercase_       :List[Any]     =       os.path.join(UpperCamelCase_   ,							f"{mode}.txt"		)
        lowercase_       :List[str]     =       1
        lowercase_       :List[Any]     =       []

        with open(UpperCamelCase_   ,							encoding='''utf-8'''		) as f:
             for sentence in parse_incr(UpperCamelCase_		):
                  lowercase_       :Tuple     =       []
                  lowercase_       :str     =       []
                  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 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :Any     =       0
        for sentence in parse_incr(UpperCamelCase_		):
             lowercase_       :Tuple     =       preds_list[example_id]
             lowercase_       :List[Any]     =       ''''''
             for token in sentence:
                  out += f"{token['form']} ({token['upos']}|{s_p.pop(0		)}) "
             out += "\n"
             writer.write(UpperCamelCase_		)
             example_id += 1




   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        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",
             ]






import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel

from ...utils import logging


SCREAMING_SNAKE_CASE       :		int         =				logging.get_logger(__name__)






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   lowercase					:	List[str]									=CLIPConfig

   lowercase					:	Tuple									=["""CLIPEncoderLayer"""]
   def __init__(     self   ,							UpperCamelCase_		):
        super().__init__(UpperCamelCase_		)

        lowercase_       :Union[str, Any]     =       CLIPVisionModelWithProjection(config.vision_config		)

        lowercase_       :str     =       nn.Linear(config.vision_config.projection_dim   ,							1		)
        lowercase_       :Optional[int]     =       nn.Linear(config.vision_config.projection_dim   ,							1		)




   @torch.no_grad()
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_=0.5   ,							UpperCamelCase_=0.5		):
        lowercase_       :Optional[int]     =       self.vision_model(UpperCamelCase_		)[0]

        lowercase_       :Optional[int]     =       self.p_head(UpperCamelCase_		)
        lowercase_       :List[Any]     =       nsfw_detected.flatten()
        lowercase_       :List[str]     =       nsfw_detected > p_threshold
        lowercase_       :Optional[int]     =       nsfw_detected.tolist()

        if any(UpperCamelCase_		):
             logger.warning(
                 '''Potential NSFW content was detected in one or more images. A black image will be returned instead.'''
                 ''' Try again with a different prompt and/or seed.'''		)

        for idx, nsfw_detected_ in enumerate(UpperCamelCase_		):
             if nsfw_detected_:
                  lowercase_       :str     =       np.zeros(images[idx].shape		)

        lowercase_       :int     =       self.w_head(UpperCamelCase_		)
        lowercase_       :Union[str, Any]     =       watermark_detected.flatten()
        lowercase_       :int     =       watermark_detected > w_threshold
        lowercase_       :int     =       watermark_detected.tolist()

        if any(UpperCamelCase_		):
             logger.warning(
                 '''Potential watermarked content was detected in one or more images. A black image will be returned instead.'''
                 ''' Try again with a different prompt and/or seed.'''		)

        for idx, watermark_detected_ in enumerate(UpperCamelCase_		):
             if watermark_detected_:
                  lowercase_       :Dict     =       np.zeros(images[idx].shape		)

        return images, nsfw_detected, watermark_detected

