





from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
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_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
      import PIL

# soft dependency
if is_pytesseract_available():
      import pytesseract

lowercase_					     =	logging.get_logger(__name__)




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     List[Any]:
  return [
      int(1000 * (box[0] / width)	),
      int(1000 * (box[1] / height)	),
      int(1000 * (box[2] / width)	),
      int(1000 * (box[3] / height)	),
  ]




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     List[str]:
  lowercase__			    =						to_pil_image(_SCREAMING_SNAKE_CASE	)
  lowercase__    ,					lowercase__			    =						pil_image.size
  lowercase__			    =						pytesseract.image_to_data(_SCREAMING_SNAKE_CASE	,		lang=_SCREAMING_SNAKE_CASE	,		output_type='dict'	,		config=_SCREAMING_SNAKE_CASE	)
  lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__			    =						data['text'], data['left'], data['top'], data['width'], data['height']

  # filter empty words and corresponding coordinates
  lowercase__			    =						[idx for idx, word in enumerate(_SCREAMING_SNAKE_CASE	) if not word.strip()]
  lowercase__			    =						[word for idx, word in enumerate(_SCREAMING_SNAKE_CASE	) if idx not in irrelevant_indices]
  lowercase__			    =						[coord for idx, coord in enumerate(_SCREAMING_SNAKE_CASE	) if idx not in irrelevant_indices]
  lowercase__			    =						[coord for idx, coord in enumerate(_SCREAMING_SNAKE_CASE	) if idx not in irrelevant_indices]
  lowercase__			    =						[coord for idx, coord in enumerate(_SCREAMING_SNAKE_CASE	) if idx not in irrelevant_indices]
  lowercase__			    =						[coord for idx, coord in enumerate(_SCREAMING_SNAKE_CASE	) if idx not in irrelevant_indices]

  # turn coordinates into (left, top, left+width, top+height) format
  lowercase__			    =						[]
  for x, y, w, h in zip(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	):
    lowercase__			    =						[x, y, x + w, y + h]
    actual_boxes.append(_SCREAMING_SNAKE_CASE	)

  # finally, normalize the bounding boxes
  lowercase__			    =						[]
  for box in actual_boxes:
    normalized_boxes.append(normalize_box(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)	)

  assert len(_SCREAMING_SNAKE_CASE	) == len(_SCREAMING_SNAKE_CASE	), "Not as many words as there are bounding boxes"

  return words, normalized_boxes


class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	List[str]				      = ['pixel_values']


   def __init__(					self    :       List[str]    ,  a    :       bool = True    ,  a    :       Dict[str, int] = None    ,  a    :       PILImageResampling = PILImageResampling.BILINEAR    ,  a    :       bool = True    ,  a    :       float = 1 / 255    ,  a    :       bool = True    ,  a    :       Union[float, Iterable[float]] = None    ,  a    :       Union[float, Iterable[float]] = None    ,  a    :       bool = True    ,  a    :       Optional[str] = None    ,  a    :       Optional[str] = ""    ,  **a    :       Any    ,  )->     None:





     """simple docstring"""







     super().__init__(**a )
     lowercase__			    =						size if size is not None else {'height': 224, 'width': 224}
     lowercase__			    =						get_size_dict(a )

     lowercase__			    =						do_resize
     lowercase__			    =						size
     lowercase__			    =						resample
     lowercase__			    =						do_rescale
     lowercase__			    =						rescale_value
     lowercase__			    =						do_normalize
     lowercase__			    =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     lowercase__			    =						image_std if image_std is not None else IMAGENET_STANDARD_STD
     lowercase__			    =						apply_ocr
     lowercase__			    =						ocr_lang
     lowercase__			    =						tesseract_config


   def  SCREAMING_SNAKE_CASE_							(					self    :       str    ,  a    :       np.ndarray    ,  a    :       Dict[str, int]    ,  a    :       PILImageResampling = PILImageResampling.BILINEAR    ,  a    :       Optional[Union[str, ChannelDimension]] = None    ,  **a    :       List[Any]    ,  )->     np.ndarray:





     """simple docstring"""







     lowercase__			    =						get_size_dict(a )
     if "height" not in size or "width" not in size:
       raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
     lowercase__			    =						(size['height'], size['width'])
     return resize(a    ,  size=a    ,  resample=a    ,  data_format=a    ,  **a )


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





     """simple docstring"""







     return rescale(a    ,  scale=a    ,  data_format=a    ,  **a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any]    ,  a    :       np.ndarray    ,  a    :       Union[float, Iterable[float]]    ,  a    :       Union[float, Iterable[float]]    ,  a    :       Optional[Union[str, ChannelDimension]] = None    ,  **a    :       Tuple    ,  )->     np.ndarray:





     """simple docstring"""







     return normalize(a    ,  mean=a    ,  std=a    ,  data_format=a    ,  **a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any]    ,  a    :       ImageInput    ,  a    :       bool = None    ,  a    :       Dict[str, int] = None    ,  a    :       List[str]=None    ,  a    :       bool = None    ,  a    :       float = None    ,  a    :       bool = None    ,  a    :       Union[float, Iterable[float]] = None    ,  a    :       Union[float, Iterable[float]] = None    ,  a    :       bool = None    ,  a    :       Optional[str] = None    ,  a    :       Optional[str] = None    ,  a    :       Optional[Union[str, TensorType]] = None    ,  a    :       ChannelDimension = ChannelDimension.FIRST    ,  **a    :       int    ,  )->     PIL.Image.Image:





     """simple docstring"""







     lowercase__			    =						do_resize if do_resize is not None else self.do_resize
     lowercase__			    =						size if size is not None else self.size
     lowercase__			    =						get_size_dict(a )
     lowercase__			    =						resample if resample is not None else self.resample
     lowercase__			    =						do_rescale if do_rescale is not None else self.do_rescale
     lowercase__			    =						rescale_factor if rescale_factor is not None else self.rescale_factor
     lowercase__			    =						do_normalize if do_normalize is not None else self.do_normalize
     lowercase__			    =						image_mean if image_mean is not None else self.image_mean
     lowercase__			    =						image_std if image_std is not None else self.image_std
     lowercase__			    =						apply_ocr if apply_ocr is not None else self.apply_ocr
     lowercase__			    =						ocr_lang if ocr_lang is not None else self.ocr_lang
     lowercase__			    =						tesseract_config if tesseract_config is not None else self.tesseract_config

     lowercase__			    =						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_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('If do_normalize is True, image_mean and image_std must be specified.' )

     # All transformations expect numpy arrays.
     lowercase__			    =						[to_numpy_array(a ) for image in images]

     # Tesseract OCR to get words + normalized bounding boxes
     if apply_ocr:
       requires_backends(self    ,  'pytesseract' )
       lowercase__			    =						[]
       lowercase__			    =						[]
       for image in images:
         lowercase__    ,					lowercase__			    =						apply_tesseract(a    ,  a    ,  a )
         words_batch.append(a )
         boxes_batch.append(a )

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

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

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

     lowercase__			    =						[to_channel_dimension_format(a    ,  a ) for image in images]

     lowercase__			    =						BatchFeature(data={'pixel_values': images}    ,  tensor_type=a )

     if apply_ocr:
       lowercase__			    =						words_batch
       lowercase__			    =						boxes_batch
     return data









import argparse
import json
import subprocess




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Optional[int]:
  lowercase__			    =						[]

  lowercase__			    =						(
      F"""curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: Bearer {token}\""""
      ' https://api.github.com/repos/huggingface/transformers/actions/runners'
  )
  lowercase__			    =						subprocess.run(_SCREAMING_SNAKE_CASE	,		shell=_SCREAMING_SNAKE_CASE	,		stdout=subprocess.PIPE	)
  lowercase__			    =						output.stdout.decode('utf-8'	)
  lowercase__			    =						json.loads(_SCREAMING_SNAKE_CASE	)

  lowercase__			    =						status['runners']
  for runner in runners:
    if runner["name"] in target_runners:
      if runner["status"] == "offline":
        offline_runners.append(_SCREAMING_SNAKE_CASE	)

    # save the result so we can report them on Slack
  with open('offline_runners.txt'	,		'w'	) as fp:
    fp.write(json.dumps(_SCREAMING_SNAKE_CASE	)	)

  if len(_SCREAMING_SNAKE_CASE	) > 0:
    lowercase__			    =						'\n'.join([x['name'] for x in offline_runners]	)
    raise ValueError(F"""The following runners are offline:\n{failed}"""	)


if __name__ == "__main__":




      def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     str:
        return values.split(','	)

      lowercase_					     =	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--target_runners""",
          default=None,
          type=list_str,
          required=True,
          help="""Comma-separated list of runners to check status.""",
      )

      parser.add_argument(
          """--token""", default=None, type=str, required=True, help="""A token that has actions:read permission."""
      )
      lowercase_					     =	parser.parse_args()


      get_runner_status(args.target_runners, args.token)


