
"""simple docstring"""




from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_A			:       Optional[Any]   		=       logging.get_logger(__name__)

class 		a__						(      a_						):
   __lowerCAmelCase            =				["""pixel_values"""]
   def __init__(			self	,    _a = True	,    _a = None	,    _a = PILImageResampling.BICUBIC	,    _a = True	,    _a = None	,    _a = True	,    _a = 1 / 255	,    _a = True	,    _a = IMAGENET_DEFAULT_MEAN	,    _a = IMAGENET_DEFAULT_STD	,    **_a	,    ):
          super().__init__(**_a				)
          lowercase    :				Dict       		=							size if size is not None else {"shortest_edge": 224}
          lowercase    :				Dict       		=							get_size_dict(_a	,    default_to_square=_a				)
          lowercase    :				int       		=							crop_size if crop_size is not None else {"height": 224, "width": 224}
          lowercase    :				int       		=							get_size_dict(_a	,    param_name="crop_size"				)

          lowercase    :				int       		=							do_resize
          lowercase    :				str       		=							size
          lowercase    :				Union[str, Any]       		=							resample
          lowercase    :				Dict       		=							do_center_crop
          lowercase    :				Dict       		=							crop_size
          lowercase    :				Optional[int]       		=							do_rescale
          lowercase    :				Any       		=							rescale_factor
          lowercase    :				str       		=							do_normalize
          lowercase    :				str       		=							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
          lowercase    :				Any       		=							image_std if image_std is not None else IMAGENET_DEFAULT_STD
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a = PILImageResampling.BICUBIC	,    _a = None	,    **_a	,    ):
          lowercase    :				Dict       		=							get_size_dict(_a	,    default_to_square=_a				)
          # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
          if "shortest_edge" in size:
                 lowercase    :				str       		=							int((256 / 224) * size["shortest_edge"]				)
                 lowercase    :				int       		=							get_resize_output_image_size(_a	,    size=_a	,    default_to_square=_a				)
                 lowercase    :				str       		=							{"height": output_size[0], "width": output_size[1]}
          if "height" not in size_dict or "width" not in size_dict:
                 raise ValueError(
                     f"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""				)
          return resize(
              _a	,    size=(size_dict["height"], size_dict["width"])	,    resample=_a	,    data_format=_a	,    **_a				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a = None	,    **_a	,    ):
          lowercase    :				List[Any]       		=							get_size_dict(_a				)
          if "height" not in size or "width" not in size:
                 raise ValueError(f"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""				)
          return center_crop(_a	,    size=(size["height"], size["width"])	,    data_format=_a	,    **_a				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a = None	,    **_a	,    ):
          return rescale(_a	,    scale=_a	,    data_format=_a	,    **_a				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    _a = None	,    **_a	,    ):
          return normalize(_a	,    mean=_a	,    std=_a	,    data_format=_a	,    **_a				)

   def 			__magic_name__			(			self	,    _a	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = ChannelDimension.FIRST	,    **_a	,    ):
          lowercase    :				Dict       		=							do_resize if do_resize is not None else self.do_resize
          lowercase    :				Union[str, Any]       		=							resample if resample is not None else self.resample
          lowercase    :				List[Any]       		=							do_center_crop if do_center_crop is not None else self.do_center_crop
          lowercase    :				List[Any]       		=							do_rescale if do_rescale is not None else self.do_rescale
          lowercase    :				List[Any]       		=							rescale_factor if rescale_factor is not None else self.rescale_factor
          lowercase    :				List[Any]       		=							do_normalize if do_normalize is not None else self.do_normalize
          lowercase    :				List[Any]       		=							image_mean if image_mean is not None else self.image_mean
          lowercase    :				Optional[int]       		=							image_std if image_std is not None else self.image_std

          lowercase    :				str       		=							size if size is not None else self.size
          lowercase    :				Tuple       		=							get_size_dict(_a	,    default_to_square=_a				)
          lowercase    :				str       		=							crop_size if crop_size is not None else self.crop_size
          lowercase    :				Union[str, Any]       		=							get_size_dict(_a	,    param_name="crop_size"				)

          lowercase    :				str       		=							make_list_of_images(_a				)

          if not valid_images(_a				):
                 raise ValueError(
                     "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                     "torch.Tensor, tf.Tensor or jax.ndarray."				)

          if do_resize and size is None:
                 raise ValueError("Size must be specified if do_resize is True."				)

          if do_center_crop and crop_size is None:
                 raise ValueError("Crop size must be specified if do_center_crop is True."				)

          if do_rescale and rescale_factor is None:
                 raise ValueError("Rescale factor must be specified if do_rescale is True."				)

          if do_normalize and (image_mean is None or image_std is None):
                 raise ValueError("Image mean and std must be specified if do_normalize is True."				)

          # All transformations expect numpy arrays.
          lowercase    :				Dict       		=							[to_numpy_array(_a				) for image in images]

          if do_resize:
                 lowercase    :				Optional[Any]       		=							[self.resize(_a	,    _a	,    _a				) for image in images]

          if do_center_crop:
                 lowercase    :				Any       		=							[self.center_crop(_a	,    _a				) for image in images]

          if do_rescale:
                 lowercase    :				Union[str, Any]       		=							[self.rescale(_a	,    _a				) for image in images]

          if do_normalize:
                 lowercase    :				Optional[int]       		=							[self.normalize(_a	,    _a	,    _a				) for image in images]

          lowercase    :				int       		=							[to_channel_dimension_format(_a	,    _a				) for image in images]

          lowercase    :				Union[str, Any]       		=							{"pixel_values": images}
          return BatchFeature(data=_a	,    tensor_type=_a				)




"""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			:       Optional[int]   		=       logging.getLogger(__name__)

class 		a__						(      a_						):
   def __init__(			self	,    _a=-1				):
          # in NER datasets, the last column is usually reserved for NER label
          lowercase    :				List[str]       		=							label_idx
   def 			__magic_name__			(			self	,    _a	,    _a				):
          if isinstance(_a	,    _a				):
                 lowercase    :				Optional[Any]       		=							mode.value
          lowercase    :				List[str]       		=							os.path.join(_a	,    f"""{mode}.txt"""				)
          lowercase    :				str       		=							1
          lowercase    :				Optional[int]       		=							[]
          with open(_a	,    encoding="utf-8"				) as f:
                 lowercase    :				List[Any]       		=							[]
                 lowercase    :				Optional[int]       		=							[]
                 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
                                      lowercase    :				int       		=							[]
                                      lowercase    :				int       		=							[]
                        else:
                               lowercase    :				Optional[Any]       		=							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 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				List[str]       		=							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]:
                        lowercase    :				Any       		=							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 			__magic_name__			(			self	,    _a				):
          if path:
                 with open(_a	,    "r"				) as f:
                        lowercase    :				Optional[Any]       		=							f.read().splitlines()
                 if "O" not in labels:
                        lowercase    :				List[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 		a__						(      a_						):
   def __init__(			self				):
          # in CONLL2003 dataset chunk column is second-to-last
          super().__init__(label_idx=-2				)

   def 			__magic_name__			(			self	,    _a				):
          if path:
                 with open(_a	,    "r"				) as f:
                        lowercase    :				Tuple       		=							f.read().splitlines()
                 if "O" not in labels:
                        lowercase    :				Optional[int]       		=							["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 		a__						(      a_						):
   def 			__magic_name__			(			self	,    _a	,    _a				):
          if isinstance(_a	,    _a				):
                 lowercase    :				List[Any]       		=							mode.value
          lowercase    :				Optional[int]       		=							os.path.join(_a	,    f"""{mode}.txt"""				)
          lowercase    :				Tuple       		=							1
          lowercase    :				List[str]       		=							[]

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

   def 			__magic_name__			(			self	,    _a				):
          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",
                 ]


