"""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

SCREAMING_SNAKE_CASE__   =					logging.get_logger(__name__)





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Tuple	,     _UpperCamelCase    :			int	,     _UpperCamelCase    :			str   )   ->			Dict:





						"""simple docstring"""






						return [
						    int(1_0_0_0 * (box[0] / width)   ),
						    int(1_0_0_0 * (box[1] / height)   ),
						    int(1_0_0_0 * (box[2] / width)   ),
						    int(1_0_0_0 * (box[3] / height)   ),
						]





def 				lowerCAmelCase__      (     _UpperCamelCase    :			np.ndarray	,     _UpperCamelCase    :			Optional[str]	,     _UpperCamelCase    :			Optional[str] = None   )   ->			Any:





						"""simple docstring"""






						snake_case        =     tesseract_config if tesseract_config is not None else ''

						# apply OCR
						snake_case        =     to_pil_image(_UpperCamelCase   )
						snake_case   ,snake_case        =     pil_image.size
						snake_case        =     pytesseract.image_to_data(_UpperCamelCase	,     lang=_UpperCamelCase	,     output_type='dict'	,     config=_UpperCamelCase   )
						snake_case   ,snake_case   ,snake_case   ,snake_case   ,snake_case        =     data['text'], data['left'], data['top'], data['width'], data['height']

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

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

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

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

						return words, normalized_boxes



class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Union[str, Any]     					=  ["""pixel_values"""]

				def __init__(			self						,      lowerCAmelCase = True						,      lowerCAmelCase = None						,      lowerCAmelCase = PILImageResampling.BILINEAR						,      lowerCAmelCase = True						,      lowerCAmelCase = None						,      lowerCAmelCase = ""						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										super().__init__(**lowerCAmelCase      )
										snake_case        =     size if size is not None else {'height': 2_24, 'width': 2_24}
										snake_case        =     get_size_dict(lowerCAmelCase      )

										snake_case        =     do_resize
										snake_case        =     size
										snake_case        =     resample
										snake_case        =     apply_ocr
										snake_case        =     ocr_lang
										snake_case        =     tesseract_config

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = PILImageResampling.BILINEAR						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										snake_case        =     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()}"""      )
										snake_case        =     (size['height'], size['width'])
										return resize(lowerCAmelCase						,      size=lowerCAmelCase						,      resample=lowerCAmelCase						,      data_format=lowerCAmelCase						,      **lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = ChannelDimension.FIRST						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										snake_case        =     do_resize if do_resize is not None else self.do_resize
										snake_case        =     size if size is not None else self.size
										snake_case        =     get_size_dict(lowerCAmelCase      )
										snake_case        =     resample if resample is not None else self.resample
										snake_case        =     apply_ocr if apply_ocr is not None else self.apply_ocr
										snake_case        =     ocr_lang if ocr_lang is not None else self.ocr_lang
										snake_case        =     tesseract_config if tesseract_config is not None else self.tesseract_config

										snake_case        =     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.'      )

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

										if apply_ocr:
																requires_backends(self						,      'pytesseract'      )
																snake_case        =     []
																snake_case        =     []
																for image in images:
																						snake_case   ,snake_case        =     apply_tesseract(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      )
																						words_batch.append(lowerCAmelCase      )
																						boxes_batch.append(lowerCAmelCase      )

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

										# flip color channels from RGB to BGR (as Detectron2 requires this)
										snake_case        =     [flip_channel_order(lowerCAmelCase      ) for image in images]
										snake_case        =     [to_channel_dimension_format(lowerCAmelCase						,      lowerCAmelCase      ) for image in images]

										snake_case        =     BatchFeature(data={'pixel_values': images}						,      tensor_type=lowerCAmelCase      )

										if apply_ocr:
																snake_case        =     words_batch
																snake_case        =     boxes_batch
										return data



"""simple docstring"""
import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu



@require_cpu
class 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     torch.nn.Linear(10						,      10      )
										snake_case        =     torch.optim.SGD(model.parameters()						,      0.1      )
										snake_case        =     Accelerator()
										snake_case        =     accelerator.prepare(lowerCAmelCase      )
										try:
																pickle.loads(pickle.dumps(lowerCAmelCase      )      )
										except Exception as e:
																self.fail(F"""Accelerated optimizer pickling failed with {e}"""      )
										AcceleratorState._reset_state()


