


'''simple docstring'''






import argparse
import json
import subprocess


def  _snake_case       (					A			,   A	)						->   Tuple:
							lowerCAmelCase__	       =     []

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

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

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

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


if __name__ == "__main__":


			def  _snake_case       (					A	)						->   Optional[Any]:
										return values.split(''','''	)

			__UpperCAmelCase       =							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.'''
			)
			__UpperCAmelCase       =							parser.parse_args()






			get_runner_status(args.target_runners, args.token)



'''simple docstring'''






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

__UpperCAmelCase       =							logging.get_logger(__name__)


def  _snake_case       (					A			,   A			,   A	)						->   Optional[Any]:
							return [
							    int(1000 * (box[0] / width)	),
							    int(1000 * (box[1] / height)	),
							    int(1000 * (box[2] / width)	),
							    int(1000 * (box[3] / height)	),
							]


def  _snake_case       (					A			,   A			,   A	)						->   Union[str, Any]:
							lowerCAmelCase__	       =     to_pil_image(A	)
							lowerCAmelCase__       , lowerCAmelCase__	       =     pil_image.size
							lowerCAmelCase__	       =     pytesseract.image_to_data(A			,   lang=A			,   output_type='''dict'''			,   config=A	)
							lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__	       =     data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

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

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

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

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

							return words, normalized_boxes






class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       Any     						=				["pixel_values"]







							def __init__(		self ,   lowerCamelCase_ = True ,   lowerCamelCase_ = None ,   lowerCamelCase_ = PILImageResampling.BILINEAR ,   lowerCamelCase_ = True ,   lowerCamelCase_ = 1 / 2_55 ,   lowerCamelCase_ = True ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = True ,   lowerCamelCase_ = None ,   lowerCamelCase_ = "" ,   **lowerCamelCase_ ,   )	->							None:
														super().__init__(**lowerCamelCase_       )
														lowerCAmelCase__	       =     size if size is not None else {'''height''': 2_24, '''width''': 2_24}
														lowerCAmelCase__	       =     get_size_dict(lowerCamelCase_       )

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







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ = PILImageResampling.BILINEAR ,   lowerCamelCase_ = None ,   **lowerCamelCase_ ,   )	->							np.ndarray:
														lowerCAmelCase__	       =     get_size_dict(lowerCamelCase_       )
														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()}"""       )
														lowerCAmelCase__	       =     (size['''height'''], size['''width'''])
														return resize(lowerCamelCase_ ,   size=lowerCamelCase_ ,   resample=lowerCamelCase_ ,   data_format=lowerCamelCase_ ,   **lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ = None ,   **lowerCamelCase_ ,   )	->							np.ndarray:
														return rescale(lowerCamelCase_ ,   scale=lowerCamelCase_ ,   data_format=lowerCamelCase_ ,   **lowerCamelCase_       )







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







							def   __SCREAMING_SNAKE_CASE							(		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_ = None ,   lowerCamelCase_ = ChannelDimension.FIRST ,   **lowerCamelCase_ ,   )	->							PIL.Image.Image:
														lowerCAmelCase__	       =     do_resize if do_resize is not None else self.do_resize
														lowerCAmelCase__	       =     size if size is not None else self.size
														lowerCAmelCase__	       =     get_size_dict(lowerCamelCase_       )
														lowerCAmelCase__	       =     resample if resample is not None else self.resample
														lowerCAmelCase__	       =     do_rescale if do_rescale is not None else self.do_rescale
														lowerCAmelCase__	       =     rescale_factor if rescale_factor is not None else self.rescale_factor
														lowerCAmelCase__	       =     do_normalize if do_normalize is not None else self.do_normalize
														lowerCAmelCase__	       =     image_mean if image_mean is not None else self.image_mean
														lowerCAmelCase__	       =     image_std if image_std is not None else self.image_std
														lowerCAmelCase__	       =     apply_ocr if apply_ocr is not None else self.apply_ocr
														lowerCAmelCase__	       =     ocr_lang if ocr_lang is not None else self.ocr_lang
														lowerCAmelCase__	       =     tesseract_config if tesseract_config is not None else self.tesseract_config

														lowerCAmelCase__	       =     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:
																					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.
														lowerCAmelCase__	       =     [to_numpy_array(lowerCamelCase_       ) for image in images]

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

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

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

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

														lowerCAmelCase__	       =     [to_channel_dimension_format(lowerCamelCase_ ,   lowerCamelCase_       ) for image in images]

														lowerCAmelCase__	       =     BatchFeature(data={'''pixel_values''': images} ,   tensor_type=lowerCamelCase_       )

														if apply_ocr:
																					lowerCAmelCase__	       =     words_batch
																					lowerCAmelCase__	       =     boxes_batch
														return data