


'''simple docstring'''
from typing import Dict, Optional

import numpy as np

import datasets


UpperCAmelCase_ :     int								=      '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n'

UpperCAmelCase_ :     Optional[int]								=      '\nArgs:\n    predictions (`List[ndarray]`):\n        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    references (`List[ndarray]`):\n        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    num_labels (`int`):\n        Number of classes (categories).\n    ignore_index (`int`):\n        Index that will be ignored during evaluation.\n    nan_to_num (`int`, *optional*):\n        If specified, NaN values will be replaced by the number defined by the user.\n    label_map (`dict`, *optional*):\n        If specified, dictionary mapping old label indices to new label indices.\n    reduce_labels (`bool`, *optional*, defaults to `False`):\n        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n    `Dict[str, float | ndarray]` comprising various elements:\n    - *mean_iou* (`float`):\n        Mean Intersection-over-Union (IoU averaged over all categories).\n    - *mean_accuracy* (`float`):\n        Mean accuracy (averaged over all categories).\n    - *overall_accuracy* (`float`):\n        Overall accuracy on all images.\n    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n        Per category accuracy.\n    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n        Per category IoU.\n\nExamples:\n\n    >>> import numpy as np\n\n    >>> mean_iou = datasets.load_metric("mean_iou")\n\n    >>> # suppose one has 3 different segmentation maps predicted\n    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n    >>> predicted = [predicted_1, predicted_2, predicted_3]\n    >>> ground_truth = [actual_1, actual_2, actual_3]\n\n    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), \'per_category_accuracy\': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}\n'

UpperCAmelCase_ :     Dict								=      '\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}'


def 			_lowercase       (				UpperCamelCase__       :					Any,				UpperCamelCase__       :					int,				UpperCamelCase__       :					List[str],				UpperCamelCase__       :					bool,				UpperCamelCase__       :					Optional[Dict[int, int]] = None,				UpperCamelCase__       :					bool = False,				):
 if label_map is not None:
  for old_id, new_id in label_map.items():
   __A  :		int       =			new_id

    # turn into Numpy arrays
 __A  :		Union[str, Any]       =			np.array(UpperCamelCase__			)
 __A  :		Optional[Any]       =			np.array(UpperCamelCase__			)

 if reduce_labels:
  __A  :		List[Any]       =			255
  __A  :		Any       =			label - 1
  __A  :		Optional[int]       =			255

 __A  :		List[Any]       =			label != ignore_index
 __A  :		List[str]       =			np.not_equal(UpperCamelCase__,				UpperCamelCase__			)
 __A  :		Tuple       =			pred_label[mask]
 __A  :		str       =			np.array(UpperCamelCase__			)[mask]

 __A  :		Optional[int]       =			pred_label[pred_label == label]

 __A  :		Tuple       =			np.histogram(UpperCamelCase__,				bins=UpperCamelCase__,				range=(0, num_labels - 1)			)[0]
 __A  :		Any       =			np.histogram(UpperCamelCase__,				bins=UpperCamelCase__,				range=(0, num_labels - 1)			)[0]
 __A  :		List[str]       =			np.histogram(UpperCamelCase__,				bins=UpperCamelCase__,				range=(0, num_labels - 1)			)[0]

 __A  :		Optional[int]       =			area_pred_label + area_label - area_intersect

 return area_intersect, area_union, area_pred_label, area_label


def 			_lowercase       (				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					Optional[Any],				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					bool,				UpperCamelCase__       :					Optional[Dict[int, int]] = None,				UpperCamelCase__       :					bool = False,				):
 __A  :		Optional[Any]       =			np.zeros((num_labels,),				dtype=np.floataa			)
 __A  :		List[str]       =			np.zeros((num_labels,),				dtype=np.floataa			)
 __A  :		Any       =			np.zeros((num_labels,),				dtype=np.floataa			)
 __A  :		str       =			np.zeros((num_labels,),				dtype=np.floataa			)
 for result, gt_seg_map in zip(UpperCamelCase__,				UpperCamelCase__			):
  __A     ,__A     ,__A     ,__A  :		Tuple       =			intersect_and_union(
      UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
  total_area_intersect += area_intersect
  total_area_union += area_union
  total_area_pred_label += area_pred_label
  total_area_label += area_label
 return total_area_intersect, total_area_union, total_area_pred_label, total_area_label


def 			_lowercase       (				UpperCamelCase__       :					Union[str, Any],				UpperCamelCase__       :					str,				UpperCamelCase__       :					Any,				UpperCamelCase__       :					bool,				UpperCamelCase__       :					Optional[int] = None,				UpperCamelCase__       :					Optional[Dict[int, int]] = None,				UpperCamelCase__       :					bool = False,				):
 __A     ,__A     ,__A     ,__A  :		Dict       =			total_intersect_and_union(
     UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)

 # compute metrics
 __A  :		Dict       =			{}

 __A  :		Any       =			total_area_intersect.sum() / total_area_label.sum()
 __A  :		Dict       =			total_area_intersect / total_area_union
 __A  :		Union[str, Any]       =			total_area_intersect / total_area_label

 __A  :		Optional[int]       =			np.nanmean(UpperCamelCase__			)
 __A  :		int       =			np.nanmean(UpperCamelCase__			)
 __A  :		Union[str, Any]       =			all_acc
 __A  :		int       =			iou
 __A  :		int       =			acc

 if nan_to_num is not None:
  __A  :		Dict       =			{metric: np.nan_to_num(UpperCamelCase__,				nan=UpperCamelCase__			) for metric, metric_value in metrics.items()}

 return metrics


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,		_KWARGS_DESCRIPTION				)
class 						_lowerCamelCase					(    datasets.Metric				):




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







  return datasets.MetricInfo(
      description=_DESCRIPTION      ,      citation=_CITATION      ,      inputs_description=_KWARGS_DESCRIPTION      ,      features=datasets.Features(
          # 1st Seq - height dim, 2nd - width dim
          {
              'predictions': datasets.Sequence(datasets.Sequence(datasets.Value('uint16'						)						)						),
              'references': datasets.Sequence(datasets.Sequence(datasets.Value('uint16'						)						)						),
          }						)      ,      reference_urls=[
          'https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'
      ]      ,      )







 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = False      ,      ):

  """simple docstring"""







  __A  :		Dict       =			mean_iou(
      results=__lowercase      ,      gt_seg_maps=__lowercase      ,      num_labels=__lowercase      ,      ignore_index=__lowercase      ,      nan_to_num=__lowercase      ,      label_map=__lowercase      ,      reduce_labels=__lowercase      ,      )
  return iou_result










'''simple docstring'''
import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()


def 			_lowercase       (				UpperCamelCase__       :					Optional[int],				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					Optional[int]			):
 # Initialise PyTorch model.
 # If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
 # TapasConfig to False.

 # initialize configuration from json file
 __A  :		Optional[int]       =			TapasConfig.from_json_file(UpperCamelCase__			)
 # set absolute/relative position embeddings parameter
 __A  :		List[str]       =			reset_position_index_per_cell

 # set remaining parameters of TapasConfig as well as the model based on the task
 if task == "SQA":
  __A  :		Optional[int]       =			TapasForQuestionAnswering(config=UpperCamelCase__			)
 elif task == "WTQ":
  # run_task_main.py hparams
  __A  :		List[str]       =			4
  __A  :		Any       =			True
  # hparam_utils.py hparams
  __A  :		Any       =			0.664694
  __A  :		Dict       =			0.207951
  __A  :		Optional[Any]       =			0.121194
  __A  :		Any       =			True
  __A  :		Any       =			True
  __A  :		int       =			False
  __A  :		Optional[int]       =			0.0352513

  __A  :		List[Any]       =			TapasForQuestionAnswering(config=UpperCamelCase__			)
 elif task == "WIKISQL_SUPERVISED":
  # run_task_main.py hparams
  __A  :		Tuple       =			4
  __A  :		Optional[int]       =			False
  # hparam_utils.py hparams
  __A  :		List[str]       =			36.4519
  __A  :		Union[str, Any]       =			0.903421
  __A  :		List[str]       =			222.088
  __A  :		Optional[Any]       =			True
  __A  :		Optional[int]       =			True
  __A  :		Union[str, Any]       =			True
  __A  :		str       =			0.763141

  __A  :		Union[str, Any]       =			TapasForQuestionAnswering(config=UpperCamelCase__			)
 elif task == "TABFACT":
  __A  :		Union[str, Any]       =			TapasForSequenceClassification(config=UpperCamelCase__			)
 elif task == "MLM":
  __A  :		Any       =			TapasForMaskedLM(config=UpperCamelCase__			)
 elif task == "INTERMEDIATE_PRETRAINING":
  __A  :		List[Any]       =			TapasModel(config=UpperCamelCase__			)
 else:
  raise ValueError(f"""Task {task} not supported."""			)

 print(f"""Building PyTorch model from configuration: {config}"""			)
 # Load weights from tf checkpoint
 load_tf_weights_in_tapas(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)

 # Save pytorch-model (weights and configuration)
 print(f"""Save PyTorch model to {pytorch_dump_path}"""			)
 model.save_pretrained(UpperCamelCase__			)

 # Save tokenizer files
 print(f"""Save tokenizer files to {pytorch_dump_path}"""			)
 __A  :		int       =			TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt',				model_max_length=512			)
 tokenizer.save_pretrained(UpperCamelCase__			)

 print('Used relative position embeddings:',				model.config.reset_position_index_per_cell			)


if __name__ == "__main__":
 UpperCAmelCase_ :     Dict								=      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
 )
 parser.add_argument(
     '--reset_position_index_per_cell',
     default=False,
     action='store_true',
     help='Whether to use relative position embeddings or not. Defaults to True.',
 )
 parser.add_argument(
     '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
 )
 parser.add_argument(
     '--tapas_config_file',
     default=None,
     type=str,
     required=True,
     help=(
         'The config json file corresponding to the pre-trained TAPAS model. \n'
         'This specifies the model architecture.'
     ),
 )
 parser.add_argument(
     '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
 )
 UpperCAmelCase_ :     Any								=      parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.task,
     args.reset_position_index_per_cell,
     args.tf_checkpoint_path,
     args.tapas_config_file,
     args.pytorch_dump_path,
 )






