



'''simple docstring'''

from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    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

__lowerCAmelCase						      =       logging.get_logger(__name__)



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



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_ = None  )      ->				Union[str, Any]:
		_a					:				Any				=							tesseract_config if tesseract_config is not None else ''

		# apply OCR
		_a					:				List[str]				=							to_pil_image(lowerCAmelCase_  )
		_a	, _a					:				List[Any]				=							pil_image.size
		_a					:				Any				=							pytesseract.image_to_data(lowerCAmelCase_						,      lang=lowerCAmelCase_						,      output_type='dict'						,      config=lowerCAmelCase_  )
		_a	, _a	, _a	, _a	, _a					:				str				=							data['text'], data['left'], data['top'], data['width'], data['height']

		# filter empty words and corresponding coordinates
		_a					:				List[Any]				=							[idx for idx, word in enumerate(lowerCAmelCase_  ) if not word.strip()]
		_a					:				Tuple				=							[word for idx, word in enumerate(lowerCAmelCase_  ) if idx not in irrelevant_indices]
		_a					:				List[str]				=							[coord for idx, coord in enumerate(lowerCAmelCase_  ) if idx not in irrelevant_indices]
		_a					:				Tuple				=							[coord for idx, coord in enumerate(lowerCAmelCase_  ) if idx not in irrelevant_indices]
		_a					:				int				=							[coord for idx, coord in enumerate(lowerCAmelCase_  ) if idx not in irrelevant_indices]
		_a					:				Tuple				=							[coord for idx, coord in enumerate(lowerCAmelCase_  ) if idx not in irrelevant_indices]

		# turn coordinates into (left, top, left+width, top+height) format
		_a					:				Dict				=							[]
		for x, y, w, h in zip(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  ):
				_a					:				Optional[Any]				=							[x, y, x + w, y + h]
				actual_boxes.append(lowerCAmelCase_  )

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

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

		return words, normalized_boxes

class      __magic_name__						(   _UpperCamelCase       ):
							lowerCAmelCase	:   List[Any]           = ['pixel_values']







							def __init__(							self :	List[str]     ,_UpperCAmelCase :	bool = True     ,_UpperCAmelCase :	Dict[str, int] = None     ,_UpperCAmelCase :	PILImageResampling = PILImageResampling.BILINEAR     ,_UpperCAmelCase :	bool = True     ,_UpperCAmelCase :	Optional[str] = None     ,_UpperCAmelCase :	Optional[str] = ""     ,**_UpperCAmelCase :	Dict     ,):
									super().__init__(**_UpperCAmelCase		)
									_a					:				Dict				=							size if size is not None else {'height': 224, 'width': 224}
									_a					:				Union[str, Any]				=							get_size_dict(_UpperCAmelCase		)

									_a					:				List[Any]				=							do_resize
									_a					:				Dict				=							size
									_a					:				str				=							resample
									_a					:				Dict				=							apply_ocr
									_a					:				Tuple				=							ocr_lang
									_a					:				Optional[int]				=							tesseract_config







							def 							__lowercase      (							self :	Union[str, Any]     ,_UpperCAmelCase :	np.ndarray     ,_UpperCAmelCase :	Dict[str, int]     ,_UpperCAmelCase :	PILImageResampling = PILImageResampling.BILINEAR     ,_UpperCAmelCase :	Optional[Union[str, ChannelDimension]] = None     ,**_UpperCAmelCase :	List[str]     ,):
									_a					:				Optional[Any]				=							get_size_dict(_UpperCAmelCase		)
									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()}"""		)
									_a					:				List[Any]				=							(size['height'], size['width'])
									return resize(_UpperCAmelCase     ,size=_UpperCAmelCase     ,resample=_UpperCAmelCase     ,data_format=_UpperCAmelCase     ,**_UpperCAmelCase		)







							def 							__lowercase      (							self :	Dict     ,_UpperCAmelCase :	ImageInput     ,_UpperCAmelCase :	bool = None     ,_UpperCAmelCase :	Dict[str, int] = None     ,_UpperCAmelCase :	PILImageResampling = None     ,_UpperCAmelCase :	bool = None     ,_UpperCAmelCase :	Optional[str] = None     ,_UpperCAmelCase :	Optional[str] = None     ,_UpperCAmelCase :	Optional[Union[str, TensorType]] = None     ,_UpperCAmelCase :	ChannelDimension = ChannelDimension.FIRST     ,**_UpperCAmelCase :	Dict     ,):
									_a					:				Any				=							do_resize if do_resize is not None else self.do_resize
									_a					:				Optional[Any]				=							size if size is not None else self.size
									_a					:				List[Any]				=							get_size_dict(_UpperCAmelCase		)
									_a					:				Optional[Any]				=							resample if resample is not None else self.resample
									_a					:				Optional[Any]				=							apply_ocr if apply_ocr is not None else self.apply_ocr
									_a					:				List[Any]				=							ocr_lang if ocr_lang is not None else self.ocr_lang
									_a					:				List[str]				=							tesseract_config if tesseract_config is not None else self.tesseract_config

									_a					:				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.'		)

									# All transformations expect numpy arrays.
									_a					:				Any				=							[to_numpy_array(_UpperCAmelCase		) for image in images]

									if apply_ocr:
											requires_backends(self     ,'pytesseract'		)
											_a					:				Tuple				=							[]
											_a					:				str				=							[]
											for image in images:
													_a	, _a					:				Dict				=							apply_tesseract(_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase		)
													words_batch.append(_UpperCAmelCase		)
													boxes_batch.append(_UpperCAmelCase		)

									if do_resize:
											_a					:				Any				=							[self.resize(image=_UpperCAmelCase     ,size=_UpperCAmelCase     ,resample=_UpperCAmelCase		) for image in images]

									# flip color channels from RGB to BGR (as Detectron2 requires this)
									_a					:				Union[str, Any]				=							[flip_channel_order(_UpperCAmelCase		) for image in images]
									_a					:				int				=							[to_channel_dimension_format(_UpperCAmelCase     ,_UpperCAmelCase		) for image in images]

									_a					:				Union[str, Any]				=							BatchFeature(data={'pixel_values': images}     ,tensor_type=_UpperCAmelCase		)

									if apply_ocr:
											_a					:				Any				=							words_batch
											_a					:				Tuple				=							boxes_batch
									return data









'''simple docstring'''

from __future__ import annotations

import os
from typing import Any

import requests

__lowerCAmelCase						      =       '''https://api.github.com'''

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
__lowerCAmelCase						      =       BASE_URL + '''/user'''

# https://github.com/settings/tokens
__lowerCAmelCase						      =       os.environ.get('''USER_TOKEN''', '''''')



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				dict[Any, Any]:
		_a					:				Union[str, Any]				=							{
		    'Authorization': f"""token {auth_token}""",
		    'Accept': 'application/vnd.github.v3+json',
		}
		return requests.get(lowerCAmelCase_						,      headers=lowerCAmelCase_  ).json()


if __name__ == "__main__":  # pragma: no cover
							if USER_TOKEN:
														for key, value in fetch_github_info(USER_TOKEN).items():
																					print(f"""{key}: {value}""")
							else:
														raise ValueError('''\'USER_TOKEN\' field cannot be empty.''')




