




"""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,
    get_resize_output_image_size,
    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, logging


_A													=      logging.get_logger(__name__)


class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Optional[int]     				=     ["pixel_values"]






   def __init__(		self       :		Union[str, Any]   ,     UpperCamelCase       :		bool = True   ,     UpperCamelCase       :		Optional[Dict[str, int]] = None   ,     UpperCamelCase       :		PILImageResampling = PILImageResampling.BILINEAR   ,     UpperCamelCase       :		bool = True   ,     UpperCamelCase       :		Dict[str, int] = None   ,     UpperCamelCase       :		bool = True   ,     UpperCamelCase       :		Union[int, float] = 1 / 2_55   ,     UpperCamelCase       :		bool = True   ,     UpperCamelCase       :		Optional[Union[float, List[float]]] = None   ,     UpperCamelCase       :		Optional[Union[float, List[float]]] = None   ,     **UpperCamelCase       :		Any   ,     )			->						None:

         """simple docstring"""
         super().__init__(**UpperCamelCase   )
         lowerCAmelCase__      :			str   =	size if size is not None else {"""shortest_edge""": 2_56}
         lowerCAmelCase__      :			Dict   =	get_size_dict(UpperCamelCase   ,     default_to_square=UpperCamelCase   )
         lowerCAmelCase__      :			Optional[int]   =	crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
         lowerCAmelCase__      :			List[str]   =	get_size_dict(UpperCamelCase   )
         lowerCAmelCase__      :			Union[str, Any]   =	do_resize
         lowerCAmelCase__      :			Dict   =	size
         lowerCAmelCase__      :			List[str]   =	resample
         lowerCAmelCase__      :			List[str]   =	do_center_crop
         lowerCAmelCase__      :			List[str]   =	crop_size
         lowerCAmelCase__      :			Optional[Any]   =	do_rescale
         lowerCAmelCase__      :			Optional[Any]   =	rescale_factor
         lowerCAmelCase__      :			List[str]   =	do_normalize
         lowerCAmelCase__      :			List[Any]   =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         lowerCAmelCase__      :			List[str]   =	image_std if image_std is not None else IMAGENET_STANDARD_STD






   def 	_lowerCAmelCase   (		self       :		Dict   ,     UpperCamelCase       :		np.ndarray   ,     UpperCamelCase       :		Dict[str, int]   ,     UpperCamelCase       :		PILImageResampling = PILImageResampling.BICUBIC   ,     UpperCamelCase       :		Optional[Union[str, ChannelDimension]] = None   ,     **UpperCamelCase       :		Tuple   ,     )			->						np.ndarray:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	get_size_dict(UpperCamelCase   ,     default_to_square=UpperCamelCase   )
         if "shortest_edge" not in size:
               raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""   )
         lowerCAmelCase__      :			Any   =	get_resize_output_image_size(UpperCamelCase   ,     size=size["""shortest_edge"""]   ,     default_to_square=UpperCamelCase   )
         return resize(UpperCamelCase   ,     size=UpperCamelCase   ,     resample=UpperCamelCase   ,     data_format=UpperCamelCase   ,     **UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   ,     UpperCamelCase       :		np.ndarray   ,     UpperCamelCase       :		Dict[str, int]   ,     UpperCamelCase       :		Optional[Union[str, ChannelDimension]] = None   ,     **UpperCamelCase       :		int   ,     )			->						np.ndarray:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[Any]   =	get_size_dict(UpperCamelCase   )
         return center_crop(UpperCamelCase   ,     size=(size["""height"""], size["""width"""])   ,     data_format=UpperCamelCase   ,     **UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Any   ,     UpperCamelCase       :		np.ndarray   ,     UpperCamelCase       :		float   ,     UpperCamelCase       :		Optional[Union[str, ChannelDimension]] = None   ,     **UpperCamelCase       :		Any   )			->						np.ndarray:

         """simple docstring"""
         return rescale(UpperCamelCase   ,     scale=UpperCamelCase   ,     data_format=UpperCamelCase   ,     **UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Tuple   ,     UpperCamelCase       :		np.ndarray   ,     UpperCamelCase       :		Union[float, List[float]]   ,     UpperCamelCase       :		Union[float, List[float]]   ,     UpperCamelCase       :		Optional[Union[str, ChannelDimension]] = None   ,     **UpperCamelCase       :		int   ,     )			->						np.ndarray:

         """simple docstring"""
         return normalize(UpperCamelCase   ,     mean=UpperCamelCase   ,     std=UpperCamelCase   ,     data_format=UpperCamelCase   ,     **UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Tuple   ,     UpperCamelCase       :		ImageInput   ,     UpperCamelCase       :		Optional[bool] = None   ,     UpperCamelCase       :		Dict[str, int] = None   ,     UpperCamelCase       :		PILImageResampling = None   ,     UpperCamelCase       :		bool = None   ,     UpperCamelCase       :		Dict[str, int] = None   ,     UpperCamelCase       :		Optional[bool] = None   ,     UpperCamelCase       :		Optional[float] = None   ,     UpperCamelCase       :		Optional[bool] = None   ,     UpperCamelCase       :		Optional[Union[float, List[float]]] = None   ,     UpperCamelCase       :		Optional[Union[float, List[float]]] = None   ,     UpperCamelCase       :		Optional[Union[str, TensorType]] = None   ,     UpperCamelCase       :		Union[str, ChannelDimension] = ChannelDimension.FIRST   ,     **UpperCamelCase       :		Optional[int]   ,     )			->						Optional[int]:

         """simple docstring"""
         lowerCAmelCase__      :			Tuple   =	do_resize if do_resize is not None else self.do_resize
         lowerCAmelCase__      :			Optional[int]   =	size if size is not None else self.size
         lowerCAmelCase__      :			Any   =	get_size_dict(UpperCamelCase   ,     default_to_square=UpperCamelCase   )
         lowerCAmelCase__      :			List[str]   =	resample if resample is not None else self.resample
         lowerCAmelCase__      :			Optional[int]   =	do_center_crop if do_center_crop is not None else self.do_center_crop
         lowerCAmelCase__      :			Optional[Any]   =	crop_size if crop_size is not None else self.crop_size
         lowerCAmelCase__      :			List[str]   =	get_size_dict(UpperCamelCase   )
         lowerCAmelCase__      :			Union[str, Any]   =	do_rescale if do_rescale is not None else self.do_rescale
         lowerCAmelCase__      :			Optional[int]   =	rescale_factor if rescale_factor is not None else self.rescale_factor
         lowerCAmelCase__      :			Optional[Any]   =	do_normalize if do_normalize is not None else self.do_normalize
         lowerCAmelCase__      :			Dict   =	image_mean if image_mean is not None else self.image_mean
         lowerCAmelCase__      :			int   =	image_std if image_std is not None else self.image_std

         lowerCAmelCase__      :			List[str]   =	make_list_of_images(UpperCamelCase   )

         if not valid_images(UpperCamelCase   ):
               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.
         lowerCAmelCase__      :			int   =	[to_numpy_array(UpperCamelCase   ) for image in images]

         if do_resize:
               lowerCAmelCase__      :			List[Any]   =	[self.resize(image=UpperCamelCase   ,     size=UpperCamelCase   ,     resample=UpperCamelCase   ) for image in images]

         if do_center_crop:
               lowerCAmelCase__      :			Optional[int]   =	[self.center_crop(image=UpperCamelCase   ,     size=UpperCamelCase   ) for image in images]

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

         if do_normalize:
               lowerCAmelCase__      :			Dict   =	[self.normalize(image=UpperCamelCase   ,     mean=UpperCamelCase   ,     std=UpperCamelCase   ) for image in images]

         lowerCAmelCase__      :			int   =	[to_channel_dimension_format(UpperCamelCase   ,     UpperCamelCase   ) for image in images]

         lowerCAmelCase__      :			List[str]   =	{"""pixel_values""": images}
         return BatchFeature(data=UpperCamelCase   ,     tensor_type=UpperCamelCase   )











"""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													=      logging.getLogger(__name__)


class _lowerCamelCase    (					a_						):






   def __init__(		self       :		List[Any]   ,     UpperCamelCase       :		Dict=-1   )			->						List[Any]:

         """simple docstring"""
         # in NER datasets, the last column is usually reserved for NER label
         lowerCAmelCase__      :			Optional[int]   =	label_idx






   def 	_lowerCAmelCase   (		self       :		Optional[int]   ,     UpperCamelCase       :		Optional[int]   ,     UpperCamelCase       :		Union[Split, str]   )			->						List[InputExample]:

         """simple docstring"""
         if isinstance(UpperCamelCase   ,     UpperCamelCase   ):
               lowerCAmelCase__      :			int   =	mode.value
         lowerCAmelCase__      :			Dict   =	os.path.join(UpperCamelCase   ,     f"""{mode}.txt"""   )
         lowerCAmelCase__      :			str   =	1
         lowerCAmelCase__      :			List[Any]   =	[]
         with open(UpperCamelCase   ,     encoding="""utf-8"""   ) as f:
               lowerCAmelCase__      :			str   =	[]
               lowerCAmelCase__      :			Any   =	[]
               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
                                 lowerCAmelCase__      :			Optional[Any]   =	[]
                                 lowerCAmelCase__      :			Tuple   =	[]
                     else:
                           lowerCAmelCase__      :			Optional[int]   =	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 	_lowerCAmelCase   (		self       :		Any   ,     UpperCamelCase       :		TextIO   ,     UpperCamelCase       :		TextIO   ,     UpperCamelCase       :		List   )			->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			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]:
                     lowerCAmelCase__      :			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 	_lowerCAmelCase   (		self       :		str   ,     UpperCamelCase       :		str   )			->						List[str]:

         """simple docstring"""
         if path:
               with open(UpperCamelCase   ,     """r"""   ) as f:
                     lowerCAmelCase__      :			Any   =	f.read().splitlines()
               if "O" not in labels:
                     lowerCAmelCase__      :			List[str]   =	["""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    (					a_						):






   def __init__(		self       :		Union[str, Any]   )			->						Any:

         """simple docstring"""
         # in CONLL2003 dataset chunk column is second-to-last
         super().__init__(label_idx=-2   )






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   ,     UpperCamelCase       :		str   )			->						List[str]:

         """simple docstring"""
         if path:
               with open(UpperCamelCase   ,     """r"""   ) as f:
                     lowerCAmelCase__      :			Any   =	f.read().splitlines()
               if "O" not in labels:
                     lowerCAmelCase__      :			str   =	["""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    (					a_						):






   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   ,     UpperCamelCase       :		Any   ,     UpperCamelCase       :		Union[Split, str]   )			->						List[InputExample]:

         """simple docstring"""
         if isinstance(UpperCamelCase   ,     UpperCamelCase   ):
               lowerCAmelCase__      :			Optional[Any]   =	mode.value
         lowerCAmelCase__      :			int   =	os.path.join(UpperCamelCase   ,     f"""{mode}.txt"""   )
         lowerCAmelCase__      :			Optional[int]   =	1
         lowerCAmelCase__      :			List[Any]   =	[]

         with open(UpperCamelCase   ,     encoding="""utf-8"""   ) as f:
               for sentence in parse_incr(UpperCamelCase   ):
                     lowerCAmelCase__      :			Union[str, Any]   =	[]
                     lowerCAmelCase__      :			List[Any]   =	[]
                     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 	_lowerCAmelCase   (		self       :		List[str]   ,     UpperCamelCase       :		TextIO   ,     UpperCamelCase       :		TextIO   ,     UpperCamelCase       :		List   )			->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	0
         for sentence in parse_incr(UpperCamelCase   ):
               lowerCAmelCase__      :			Union[str, Any]   =	preds_list[example_id]
               lowerCAmelCase__      :			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 	_lowerCAmelCase   (		self       :		Dict   ,     UpperCamelCase       :		str   )			->						List[str]:

         """simple docstring"""
         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",
               ]





