





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

UpperCAmelCase_      							=     logging.get_logger(__name__)





def 						lowerCamelCase__     (     UpperCamelCase__			:       int       ,    UpperCamelCase__			:       Optional[Any]       ,    UpperCamelCase__			:       Tuple						)			-> Optional[int]:



					'''simple docstring'''




					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 						lowerCamelCase__     (     UpperCamelCase__			:       np.ndarray       ,    UpperCamelCase__			:       Optional[str]       ,    UpperCamelCase__			:       Optional[str] = None						)			-> int:



					'''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 			UpperCamelCase_		(  _lowerCamelCase					):
					lowerCAmelCase_							=	['''pixel_values''']




					def __init__(		self				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = PILImageResampling.BILINEAR				,						lowerCAmelCase_ = True				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = ""				,						**lowerCAmelCase_				,						)		->							None:
										super().__init__(**lowerCAmelCase_  )
										_snake_case									=				size if size is not None else {'height': 224, 'width': 224}
										_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 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_ = PILImageResampling.BILINEAR				,						lowerCAmelCase_ = None				,						**lowerCAmelCase_				,						)		->							np.ndarray:
										_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 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = ChannelDimension.FIRST				,						**lowerCAmelCase_				,						)		->							PIL.Image.Image:
										_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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_      							=     {
    """configuration_bigbird_pegasus""": [
        """BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BigBirdPegasusConfig""",
        """BigBirdPegasusOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase_      							=     [
					    """BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BigBirdPegasusForCausalLM""",
					    """BigBirdPegasusForConditionalGeneration""",
					    """BigBirdPegasusForQuestionAnswering""",
					    """BigBirdPegasusForSequenceClassification""",
					    """BigBirdPegasusModel""",
					    """BigBirdPegasusPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_bigbird_pegasus import (
					    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BigBirdPegasusConfig,
					    BigBirdPegasusOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bigbird_pegasus import (
										    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BigBirdPegasusForCausalLM,
										    BigBirdPegasusForConditionalGeneration,
										    BigBirdPegasusForQuestionAnswering,
										    BigBirdPegasusForSequenceClassification,
										    BigBirdPegasusModel,
										    BigBirdPegasusPreTrainedModel,
										)


else:
					import sys

					UpperCAmelCase_      							=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



