





"""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		:					int	      =     logging.getLogger(__name__)


class 			__UpperCamelCase							(		a__		):






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






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






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






							def        __a  (  self ,   lowerCAmelCase__ )      ->					List[str]:
								if path:
									with open(lowerCAmelCase__ ,   "r" ) as f:
										a :						Any      =							f.read().splitlines()
									if "O" not in labels:
										a :						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							(		a__		):






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






							def        __a  (  self ,   lowerCAmelCase__ )      ->					List[str]:
								if path:
									with open(lowerCAmelCase__ ,   "r" ) as f:
										a :						List[Any]      =							f.read().splitlines()
									if "O" not in labels:
										a :						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 			__UpperCamelCase							(		a__		):






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					List[InputExample]:
								if isinstance(lowerCAmelCase__ ,   lowerCAmelCase__ ):
									a :						Optional[Any]      =							mode.value
								a :						Any      =							os.path.join(lowerCAmelCase__ ,   f"""{mode}.txt""" )
								a :						Optional[int]      =							1
								a :						str      =							[]

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






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






							def        __a  (  self ,   lowerCAmelCase__ )      ->					List[str]:
								if path:
									with open(lowerCAmelCase__ ,   "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 Dict, List, Optional, Union

import numpy as np

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


if is_vision_available():
		import PIL


a		:					int	      =     logging.get_logger(__name__)


class 			__UpperCamelCase							(		a__		):
							lowerCamelCase  :   Optional[int]    =["""pixel_values"""]






							def __init__(  self ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = PIL.Image.BICUBIC ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = 1 / 255 ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					None:
								super().__init__(**lowerCAmelCase__ )
								a :						Optional[int]      =							size if size is not None else {"height": 256, "width": 256}
								a :						Tuple      =							get_size_dict(lowerCAmelCase__ )
								a :						Any      =							crop_size if crop_size is not None else {"height": 224, "width": 224}
								a :						Dict      =							get_size_dict(lowerCAmelCase__ ,   param_name="crop_size" )

								a :						Tuple      =							do_resize
								a :						int      =							size
								a :						List[str]      =							resample
								a :						List[Any]      =							do_center_crop
								a :						List[Any]      =							crop_size
								a :						Union[str, Any]      =							do_rescale
								a :						List[Any]      =							rescale_factor
								a :						Union[str, Any]      =							do_normalize
								a :						Union[str, Any]      =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								a :						List[str]      =							image_std if image_std is not None else IMAGENET_STANDARD_STD






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = PIL.Image.BICUBIC ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					np.ndarray:
								a :						Optional[int]      =							get_size_dict(lowerCAmelCase__ )
								if "height" not in size or "width" not in size:
									raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""" )
								return resize(
								    lowerCAmelCase__ ,   size=(size["height"], size["width"]) ,   resample=lowerCAmelCase__ ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					np.ndarray:
								a :						List[str]      =							get_size_dict(lowerCAmelCase__ )
								if "height" not in size or "width" not in size:
									raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""" )
								return center_crop(lowerCAmelCase__ ,   size=(size["height"], size["width"]) ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					Union[str, Any]:
								return rescale(lowerCAmelCase__ ,   scale=lowerCAmelCase__ ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					np.ndarray:
								return normalize(lowerCAmelCase__ ,   mean=lowerCAmelCase__ ,   std=lowerCAmelCase__ ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__=None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = ChannelDimension.FIRST ,   **lowerCAmelCase__ ,   )      ->					PIL.Image.Image:
								a :						List[Any]      =							do_resize if do_resize is not None else self.do_resize
								a :						str      =							resample if resample is not None else self.resample
								a :						Dict      =							do_center_crop if do_center_crop is not None else self.do_center_crop
								a :						Union[str, Any]      =							do_rescale if do_rescale is not None else self.do_rescale
								a :						str      =							rescale_factor if rescale_factor is not None else self.rescale_factor
								a :						Dict      =							do_normalize if do_normalize is not None else self.do_normalize
								a :						List[str]      =							image_mean if image_mean is not None else self.image_mean
								a :						Union[str, Any]      =							image_std if image_std is not None else self.image_std

								a :						str      =							size if size is not None else self.size
								a :						Any      =							get_size_dict(lowerCAmelCase__ )
								a :						Dict      =							crop_size if crop_size is not None else self.crop_size
								a :						List[str]      =							get_size_dict(lowerCAmelCase__ ,   param_name="crop_size" )

								a :						str      =							make_list_of_images(lowerCAmelCase__ )

								if not valid_images(lowerCAmelCase__ ):
									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 or resample is None:
									raise ValueError("Size and resample 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.
								a :						Optional[Any]      =							[to_numpy_array(lowerCAmelCase__ ) for image in images]

								if do_resize:
									a :						Optional[int]      =							[self.resize(image=lowerCAmelCase__ ,   size=lowerCAmelCase__ ,   resample=lowerCAmelCase__ ) for image in images]

								if do_center_crop:
									a :						Dict      =							[self.center_crop(image=lowerCAmelCase__ ,   size=lowerCAmelCase__ ) for image in images]

								if do_rescale:
									a :						Any      =							[self.rescale(image=lowerCAmelCase__ ,   scale=lowerCAmelCase__ ) for image in images]

								if do_normalize:
									a :						Optional[Any]      =							[self.normalize(image=lowerCAmelCase__ ,   mean=lowerCAmelCase__ ,   std=lowerCAmelCase__ ) for image in images]

								a :						Optional[Any]      =							[to_channel_dimension_format(lowerCAmelCase__ ,   lowerCAmelCase__ ) for image in images]

								a :						Tuple      =							{"pixel_values": images}
								return BatchFeature(data=lowerCAmelCase__ ,   tensor_type=lowerCAmelCase__ )


