import warnings
from typing import List, Optional, Union

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


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
		"""simple docstring"""







		lowerCamelCase__     =						['''image_processor''', '''tokenizer''']
		lowerCamelCase__     =						'''LayoutLMv3ImageProcessor'''
		lowerCamelCase__     =						('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')







		def __init__(							self :       Tuple  ,     __magic_name__ :       Any=None  ,     __magic_name__ :       Optional[Any]=None  ,     **__magic_name__ :       str   )  ->	List[Any]:
								SCREAMING_SNAKE_CASE_          =  None
								if "feature_extractor" in kwargs:
														warnings.warn(
														    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
														    " instead."  ,     _lowerCAmelCase  ,     )
														SCREAMING_SNAKE_CASE_          =  kwargs.pop("feature_extractor"   )

								SCREAMING_SNAKE_CASE_          =  image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
														raise ValueError("You need to specify an `image_processor`."   )
								if tokenizer is None:
														raise ValueError("You need to specify a `tokenizer`."   )

								super().__init__(_lowerCAmelCase  ,     _lowerCAmelCase   )







		def __call__(							self :       Tuple  ,     __magic_name__ :       Any  ,     __magic_name__ :       Optional[Any] = None  ,     __magic_name__ :       Tuple = None  ,     __magic_name__ :       Optional[Any] = None  ,     __magic_name__ :       Dict = None  ,     __magic_name__ :       Dict = True  ,     __magic_name__ :       Optional[Any] = False  ,     __magic_name__ :       str = None  ,     __magic_name__ :       Optional[int] = None  ,     __magic_name__ :       Tuple = 0  ,     __magic_name__ :       Tuple = None  ,     __magic_name__ :       Union[str, Any] = None  ,     __magic_name__ :       Dict = None  ,     __magic_name__ :       Any = False  ,     __magic_name__ :       Union[str, Any] = False  ,     __magic_name__ :       Dict = False  ,     __magic_name__ :       Tuple = False  ,     __magic_name__ :       Any = True  ,     __magic_name__ :       Optional[int] = None  ,     **__magic_name__ :       Union[str, Any]  ,     )  ->	List[Any]:
								# verify input
								if self.image_processor.apply_ocr and (boxes is not None):
														raise ValueError(
														    "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."   )

								if self.image_processor.apply_ocr and (word_labels is not None):
														raise ValueError(
														    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."   )

								# first, apply the image processor
								SCREAMING_SNAKE_CASE_          =  self.image_processor(images=_lowerCAmelCase  ,     return_tensors=_lowerCAmelCase   )

								# second, apply the tokenizer
								if text is not None and self.image_processor.apply_ocr and text_pair is None:
														if isinstance(_lowerCAmelCase  ,     _lowerCAmelCase   ):
																				SCREAMING_SNAKE_CASE_          =  [text]  # add batch dimension (as the image processor always adds a batch dimension)
														SCREAMING_SNAKE_CASE_          =  features["words"]

								SCREAMING_SNAKE_CASE_          =  self.tokenizer(
								    text=text if text is not None else features["words"]  ,     text_pair=text_pair if text_pair is not None else None  ,     boxes=boxes if boxes is not None else features["boxes"]  ,     word_labels=_lowerCAmelCase  ,     add_special_tokens=_lowerCAmelCase  ,     padding=_lowerCAmelCase  ,     truncation=_lowerCAmelCase  ,     max_length=_lowerCAmelCase  ,     stride=_lowerCAmelCase  ,     pad_to_multiple_of=_lowerCAmelCase  ,     return_token_type_ids=_lowerCAmelCase  ,     return_attention_mask=_lowerCAmelCase  ,     return_overflowing_tokens=_lowerCAmelCase  ,     return_special_tokens_mask=_lowerCAmelCase  ,     return_offsets_mapping=_lowerCAmelCase  ,     return_length=_lowerCAmelCase  ,     verbose=_lowerCAmelCase  ,     return_tensors=_lowerCAmelCase  ,     **_lowerCAmelCase  ,     )

								# add pixel values
								SCREAMING_SNAKE_CASE_          =  features.pop("pixel_values"   )
								if return_overflowing_tokens is True:
														SCREAMING_SNAKE_CASE_          =  self.get_overflowing_images(_lowerCAmelCase  ,     encoded_inputs["overflow_to_sample_mapping"]   )
								SCREAMING_SNAKE_CASE_          =  images

								return encoded_inputs







		def    __A     (							self :       Union[str, Any]  ,     __magic_name__ :       Dict  ,     __magic_name__ :       Dict   )  ->	Optional[Any]:
								# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
								SCREAMING_SNAKE_CASE_          =  []
								for sample_idx in overflow_to_sample_mapping:
														images_with_overflow.append(images[sample_idx]   )

								if len(_lowerCAmelCase   ) != len(_lowerCAmelCase   ):
														raise ValueError(
														    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
														    F''' {len(_lowerCAmelCase   )} and {len(_lowerCAmelCase   )}'''   )

								return images_with_overflow







		def    __A     (							self :       List[Any]  ,     *__magic_name__ :       Optional[int]  ,     **__magic_name__ :       int   )  ->	List[str]:
								return self.tokenizer.batch_decode(*_lowerCAmelCase  ,     **_lowerCAmelCase   )







		def    __A     (							self :       int  ,     *__magic_name__ :       str  ,     **__magic_name__ :       Any   )  ->	Union[str, Any]:
								return self.tokenizer.decode(*_lowerCAmelCase  ,     **_lowerCAmelCase   )







		@property
		def    __A     (							self :       Dict   )  ->	Union[str, Any]:
								return ["input_ids", "bbox", "attention_mask", "pixel_values"]







		@property
		def    __A     (							self :       Dict   )  ->	Optional[int]:
								warnings.warn(
								    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."  ,     _lowerCAmelCase  ,     )
								return self.image_processor_class







		@property
		def    __A     (							self :       Dict   )  ->	int:
								warnings.warn(
								    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."  ,     _lowerCAmelCase  ,     )
								return self.image_processor



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

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


A     :  Any					     =       logging.getLogger(__name__)


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def __init__(							self :       List[Any]  ,     __magic_name__ :       Optional[Any]=-1   )  ->	Optional[Any]:
        # in NER datasets, the last column is usually reserved for NER label
        SCREAMING_SNAKE_CASE_          =  label_idx







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







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







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       str   )  ->	List[str]:
        if path:
              with open(__magic_name__  ,     "r"   ) as f:
                    SCREAMING_SNAKE_CASE_          =  f.read().splitlines()
              if "O" not in labels:
                    SCREAMING_SNAKE_CASE_          =  ["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					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def __init__(							self :       Optional[int]   )  ->	str:
        # in CONLL2003 dataset chunk column is second-to-last
        super().__init__(label_idx=-2   )







  def    __A     (							self :       Any  ,     __magic_name__ :       str   )  ->	List[str]:
        if path:
              with open(__magic_name__  ,     "r"   ) as f:
                    SCREAMING_SNAKE_CASE_          =  f.read().splitlines()
              if "O" not in labels:
                    SCREAMING_SNAKE_CASE_          =  ["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					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def    __A     (							self :       str  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       Union[Split, str]   )  ->	List[InputExample]:
        if isinstance(__magic_name__  ,     __magic_name__   ):
              SCREAMING_SNAKE_CASE_          =  mode.value
        SCREAMING_SNAKE_CASE_          =  os.path.join(__magic_name__  ,     F'''{mode}.txt'''   )
        SCREAMING_SNAKE_CASE_          =  1
        SCREAMING_SNAKE_CASE_          =  []

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







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       TextIO  ,     __magic_name__ :       TextIO  ,     __magic_name__ :       List   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  0
        for sentence in parse_incr(__magic_name__   ):
              SCREAMING_SNAKE_CASE_          =  preds_list[example_id]
              SCREAMING_SNAKE_CASE_          =  ""
              for token in sentence:
                    out += F'''{token["form"]} ({token["upos"]}|{s_p.pop(0   )}) '''
              out += "\n"
              writer.write(__magic_name__   )
              example_id += 1







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       str   )  ->	List[str]:
        if path:
              with open(__magic_name__  ,     "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",
              ]


