"""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

_A   					=    logging.get_logger(__name__)




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   str:
				return [
				    int(1_000 * (box[0] / width)       ),
				    int(1_000 * (box[1] / height)       ),
				    int(1_000 * (box[2] / width)       ),
				    int(1_000 * (box[3] / height)       ),
				]




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = None       )							->   List[Any]:
				SCREAMING_SNAKE_CASE__		  =				tesseract_config if tesseract_config is not None else ""

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

				# filter empty words and corresponding coordinates
				SCREAMING_SNAKE_CASE__		  =				[idx for idx, word in enumerate(__UpperCAmelCase       ) if not word.strip()]
				SCREAMING_SNAKE_CASE__		  =				[word for idx, word in enumerate(__UpperCAmelCase       ) if idx not in irrelevant_indices]
				SCREAMING_SNAKE_CASE__		  =				[coord for idx, coord in enumerate(__UpperCAmelCase       ) if idx not in irrelevant_indices]
				SCREAMING_SNAKE_CASE__		  =				[coord for idx, coord in enumerate(__UpperCAmelCase       ) if idx not in irrelevant_indices]
				SCREAMING_SNAKE_CASE__		  =				[coord for idx, coord in enumerate(__UpperCAmelCase       ) if idx not in irrelevant_indices]
				SCREAMING_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
				SCREAMING_SNAKE_CASE__		  =				[]
				for x, y, w, h in zip(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       ):
								SCREAMING_SNAKE_CASE__		  =				[x, y, x + w, y + h]
								actual_boxes.append(__UpperCAmelCase       )

				# finally, normalize the bounding boxes
				SCREAMING_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		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		["pixel_values"]



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

									SCREAMING_SNAKE_CASE__		  =				do_resize
									SCREAMING_SNAKE_CASE__		  =				size
									SCREAMING_SNAKE_CASE__		  =				resample
									SCREAMING_SNAKE_CASE__		  =				apply_ocr
									SCREAMING_SNAKE_CASE__		  =				ocr_lang
									SCREAMING_SNAKE_CASE__		  =				tesseract_config



					def 			lowerCAmelCase_					(						self			:				str   , _snake_case			:				np.ndarray   , _snake_case			:				Dict[str, int]   , _snake_case			:				PILImageResampling = PILImageResampling.BILINEAR   , _snake_case			:				Optional[Union[str, ChannelDimension]] = None   , **_snake_case			:				List[Any]   , )							->       np.ndarray:
									SCREAMING_SNAKE_CASE__		  =				get_size_dict(_snake_case		)
									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()}"""		)
									SCREAMING_SNAKE_CASE__		  =				(size["height"], size["width"])
									return resize(_snake_case   , size=_snake_case   , resample=_snake_case   , data_format=_snake_case   , **_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				ImageInput   , _snake_case			:				bool = None   , _snake_case			:				Dict[str, int] = None   , _snake_case			:				PILImageResampling = None   , _snake_case			:				bool = None   , _snake_case			:				Optional[str] = None   , _snake_case			:				Optional[str] = None   , _snake_case			:				Optional[Union[str, TensorType]] = None   , _snake_case			:				ChannelDimension = ChannelDimension.FIRST   , **_snake_case			:				Optional[Any]   , )							->       PIL.Image.Image:
									SCREAMING_SNAKE_CASE__		  =				do_resize if do_resize is not None else self.do_resize
									SCREAMING_SNAKE_CASE__		  =				size if size is not None else self.size
									SCREAMING_SNAKE_CASE__		  =				get_size_dict(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				resample if resample is not None else self.resample
									SCREAMING_SNAKE_CASE__		  =				apply_ocr if apply_ocr is not None else self.apply_ocr
									SCREAMING_SNAKE_CASE__		  =				ocr_lang if ocr_lang is not None else self.ocr_lang
									SCREAMING_SNAKE_CASE__		  =				tesseract_config if tesseract_config is not None else self.tesseract_config

									SCREAMING_SNAKE_CASE__		  =				make_list_of_images(_snake_case		)

									if not valid_images(_snake_case		):
													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.
									SCREAMING_SNAKE_CASE__		  =				[to_numpy_array(_snake_case		) for image in images]

									if apply_ocr:
													requires_backends(self   , "pytesseract"		)
													SCREAMING_SNAKE_CASE__		  =				[]
													SCREAMING_SNAKE_CASE__		  =				[]
													for image in images:
																	SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				apply_tesseract(_snake_case   , _snake_case   , _snake_case		)
																	words_batch.append(_snake_case		)
																	boxes_batch.append(_snake_case		)

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

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

									SCREAMING_SNAKE_CASE__		  =				BatchFeature(data={"pixel_values": images}   , tensor_type=_snake_case		)

									if apply_ocr:
													SCREAMING_SNAKE_CASE__		  =				words_batch
													SCREAMING_SNAKE_CASE__		  =				boxes_batch
									return data





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import _LazyModule


_A   					=    {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}


if TYPE_CHECKING:
						from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
						import sys

						_A   					=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




