



'''simple docstring'''

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

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


UpperCAmelCase     		=   logging.getLogger(__name__)

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						def __init__( self     ,	A_=-1				)     ->     Dict:
							# in NER datasets, the last column is usually reserved for NER label
							lowerCAmelCase   =     label_idx


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


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




						def     __snake_case   ( self     ,	A_				)     ->     List[str]:
							if path:
								with open(A_     ,	"""r"""				) as f:
									lowerCAmelCase   =     f.read().splitlines()
								if "O" not in labels:
									lowerCAmelCase   =     ["""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(				_lowerCAmelCase ):


						'''simple docstring'''



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




						def     __snake_case   ( self     ,	A_				)     ->     List[str]:
							if path:
								with open(A_     ,	"""r"""				) as f:
									lowerCAmelCase   =     f.read().splitlines()
								if "O" not in labels:
									lowerCAmelCase   =     ["""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(				_lowerCAmelCase ):


						'''simple docstring'''



						def     __snake_case   ( self     ,	A_     ,	A_				)     ->     List[InputExample]:
							if isinstance(A_     ,	A_				):
								lowerCAmelCase   =     mode.value
							lowerCAmelCase   =     os.path.join(A_     ,	f'{mode}.txt'				)
							lowerCAmelCase   =     1
							lowerCAmelCase   =     []

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


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_				)     ->     Dict:
							lowerCAmelCase   =     0
							for sentence in parse_incr(A_				):
								lowerCAmelCase   =     preds_list[example_id]
								lowerCAmelCase   =     """"""
								for token in sentence:
									out += f'{token["form"]} ({token["upos"]}|{s_p.pop(0				)}) '
								out += "\n"
								writer.write(A_				)
								example_id += 1




						def     __snake_case   ( self     ,	A_				)     ->     List[str]:
							if path:
								with open(A_     ,	"""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",
								]




'''simple docstring'''

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 __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Union[str, Any]									=		["image_processor", "tokenizer"]
						UpperCAmelCase							:   Tuple									=		"BlipImageProcessor"
						UpperCAmelCase							:   Union[str, Any]									=		("BertTokenizer", "BertTokenizerFast")


						def __init__( self     ,	A_     ,	A_				)     ->     Dict:
							lowerCAmelCase   =     False
							super().__init__(A_     ,	A_				)
							lowerCAmelCase   =     self.image_processor


						def __call__( self     ,	A_ = None     ,	A_ = None     ,	A_ = True     ,	A_ = False     ,	A_ = None     ,	A_ = None     ,	A_ = 0     ,	A_ = None     ,	A_ = None     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = True     ,	A_ = None     ,	**A_     ,	)     ->     BatchEncoding:
							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:
								lowerCAmelCase   =     self.tokenizer
								lowerCAmelCase   =     self.tokenizer(
								    text=A_     ,	add_special_tokens=A_     ,	padding=A_     ,	truncation=A_     ,	max_length=A_     ,	stride=A_     ,	pad_to_multiple_of=A_     ,	return_attention_mask=A_     ,	return_overflowing_tokens=A_     ,	return_special_tokens_mask=A_     ,	return_offsets_mapping=A_     ,	return_token_type_ids=A_     ,	return_length=A_     ,	verbose=A_     ,	return_tensors=A_     ,	**A_     ,	)
								return text_encoding

							# add pixel_values
							lowerCAmelCase   =     self.image_processor(A_     ,	return_tensors=A_				)

							if text is not None:
								lowerCAmelCase   =     self.tokenizer(
								    text=A_     ,	add_special_tokens=A_     ,	padding=A_     ,	truncation=A_     ,	max_length=A_     ,	stride=A_     ,	pad_to_multiple_of=A_     ,	return_attention_mask=A_     ,	return_overflowing_tokens=A_     ,	return_special_tokens_mask=A_     ,	return_offsets_mapping=A_     ,	return_token_type_ids=A_     ,	return_length=A_     ,	verbose=A_     ,	return_tensors=A_     ,	**A_     ,	)
							else:
								lowerCAmelCase   =     None

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

							return encoding_image_processor


						def     __snake_case   ( self     ,	*A_     ,	**A_				)     ->     Union[str, Any]:
							return self.tokenizer.batch_decode(*A_     ,	**A_				)


						def     __snake_case   ( self     ,	*A_     ,	**A_				)     ->     Tuple:
							return self.tokenizer.decode(*A_     ,	**A_				)




						@property
						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     self.tokenizer.model_input_names
							lowerCAmelCase   =     self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)