






'''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

__lowerCamelCase   :					int   							=					logging.get_logger(__name__)

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    Optional[int]:



			"""simple docstring"""



			return [
			    int(1000 * (box[0] / width)						),
			    int(1000 * (box[1] / height)						),
			    int(1000 * (box[2] / width)						),
			    int(1000 * (box[3] / height)						),
			]

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					int   =       to_pil_image(__A						)
			snake_case_				:					Tuple   =       pil_image.size
			snake_case_				:					Optional[Any]   =       pytesseract.image_to_data(__A     ,lang=__A     ,output_type="dict"     ,config=__A						)
			snake_case_				:					Optional[Any]   =       data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

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

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

			# finally, normalize the bounding boxes
			snake_case_				:					List[str]   =       []
			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"""




							a__   		=	['''pixel_values''']

							def __init__(		self			:List[Any]       ,							lowerCAmelCase__			:bool = True       ,							lowerCAmelCase__			:Dict[str, int] = None       ,							lowerCAmelCase__			:PILImageResampling = PILImageResampling.BILINEAR       ,							lowerCAmelCase__			:bool = True       ,							lowerCAmelCase__			:float = 1 / 255       ,							lowerCAmelCase__			:bool = True       ,							lowerCAmelCase__			:Union[float, Iterable[float]] = None       ,							lowerCAmelCase__			:Union[float, Iterable[float]] = None       ,							lowerCAmelCase__			:bool = True       ,							lowerCAmelCase__			:Optional[str] = None       ,							lowerCAmelCase__			:Optional[str] = ""       ,							**lowerCAmelCase__			:Tuple       ,							)				->  None:


										'''simple docstring'''




										super().__init__(**lowerCAmelCase__				)
										snake_case_				:					Tuple   =       size if size is not None else {'''height''': 224, '''width''': 224}
										snake_case_				:					Optional[Any]   =       get_size_dict(lowerCAmelCase__				)

										snake_case_				:					Optional[Any]   =       do_resize
										snake_case_				:					List[Any]   =       size
										snake_case_				:					List[str]   =       resample
										snake_case_				:					Dict   =       do_rescale
										snake_case_				:					str   =       rescale_value
										snake_case_				:					Optional[int]   =       do_normalize
										snake_case_				:					Any   =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										snake_case_				:					Union[str, Any]   =       image_std if image_std is not None else IMAGENET_STANDARD_STD
										snake_case_				:					List[Any]   =       apply_ocr
										snake_case_				:					Union[str, Any]   =       ocr_lang
										snake_case_				:					str   =       tesseract_config

							def 							_A	(		self			:Dict       ,							lowerCAmelCase__			:np.ndarray       ,							lowerCAmelCase__			:Dict[str, int]       ,							lowerCAmelCase__			:PILImageResampling = PILImageResampling.BILINEAR       ,							lowerCAmelCase__			:Optional[Union[str, ChannelDimension]] = None       ,							**lowerCAmelCase__			:Optional[Any]       ,							)				->  np.ndarray:


										'''simple docstring'''




										snake_case_				:					Tuple   =       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_				:					Optional[Any]   =       (size['''height'''], size['''width'''])
										return resize(lowerCAmelCase__       ,							size=lowerCAmelCase__       ,							resample=lowerCAmelCase__       ,							data_format=lowerCAmelCase__       ,							**lowerCAmelCase__				)

							def 							_A	(		self			:Union[str, Any]       ,							lowerCAmelCase__			:np.ndarray       ,							lowerCAmelCase__			:Union[int, float]       ,							lowerCAmelCase__			:Optional[Union[str, ChannelDimension]] = None       ,							**lowerCAmelCase__			:Optional[Any]       ,							)				->  np.ndarray:


										'''simple docstring'''




										return rescale(lowerCAmelCase__       ,							scale=lowerCAmelCase__       ,							data_format=lowerCAmelCase__       ,							**lowerCAmelCase__				)

							def 							_A	(		self			:Union[str, Any]       ,							lowerCAmelCase__			:np.ndarray       ,							lowerCAmelCase__			:Union[float, Iterable[float]]       ,							lowerCAmelCase__			:Union[float, Iterable[float]]       ,							lowerCAmelCase__			:Optional[Union[str, ChannelDimension]] = None       ,							**lowerCAmelCase__			:Optional[int]       ,							)				->  np.ndarray:


										'''simple docstring'''




										return normalize(lowerCAmelCase__       ,							mean=lowerCAmelCase__       ,							std=lowerCAmelCase__       ,							data_format=lowerCAmelCase__       ,							**lowerCAmelCase__				)






							def 							_A	(		self			:Any       ,							lowerCAmelCase__			:ImageInput       ,							lowerCAmelCase__			:bool = None       ,							lowerCAmelCase__			:Dict[str, int] = None       ,							lowerCAmelCase__			:Union[str, Any]=None       ,							lowerCAmelCase__			:bool = None       ,							lowerCAmelCase__			:float = None       ,							lowerCAmelCase__			:bool = None       ,							lowerCAmelCase__			:Union[float, Iterable[float]] = None       ,							lowerCAmelCase__			:Union[float, Iterable[float]] = None       ,							lowerCAmelCase__			:bool = None       ,							lowerCAmelCase__			:Optional[str] = None       ,							lowerCAmelCase__			:Optional[str] = None       ,							lowerCAmelCase__			:Optional[Union[str, TensorType]] = None       ,							lowerCAmelCase__			:ChannelDimension = ChannelDimension.FIRST       ,							**lowerCAmelCase__			:List[str]       ,							)				->  PIL.Image.Image:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       do_resize if do_resize is not None else self.do_resize
										snake_case_				:					Tuple   =       size if size is not None else self.size
										snake_case_				:					Optional[int]   =       get_size_dict(lowerCAmelCase__				)
										snake_case_				:					List[str]   =       resample if resample is not None else self.resample
										snake_case_				:					List[Any]   =       do_rescale if do_rescale is not None else self.do_rescale
										snake_case_				:					List[Any]   =       rescale_factor if rescale_factor is not None else self.rescale_factor
										snake_case_				:					Optional[int]   =       do_normalize if do_normalize is not None else self.do_normalize
										snake_case_				:					List[Any]   =       image_mean if image_mean is not None else self.image_mean
										snake_case_				:					Optional[int]   =       image_std if image_std is not None else self.image_std
										snake_case_				:					Any   =       apply_ocr if apply_ocr is not None else self.apply_ocr
										snake_case_				:					Any   =       ocr_lang if ocr_lang is not None else self.ocr_lang
										snake_case_				:					Dict   =       tesseract_config if tesseract_config is not None else self.tesseract_config

										snake_case_				:					str   =       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.
										snake_case_				:					Tuple   =       [to_numpy_array(lowerCAmelCase__				) for image in images]

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

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

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

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

										snake_case_				:					Optional[Any]   =       [to_channel_dimension_format(lowerCAmelCase__       ,							lowerCAmelCase__				) for image in images]

										snake_case_				:					Dict   =       BatchFeature(data={"pixel_values": images}       ,							tensor_type=lowerCAmelCase__				)

										if apply_ocr:
													snake_case_				:					int   =       words_batch
													snake_case_				:					List[str]   =       boxes_batch
										return data












'''simple docstring'''




from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__lowerCamelCase   :					Dict   							=					{
    '''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
    '''tokenization_gpt_neox_japanese''': ['''GPTNeoXJapaneseTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase   :					int   							=					[
		    '''GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''GPTNeoXJapaneseForCausalLM''',
		    '''GPTNeoXJapaneseLayer''',
		    '''GPTNeoXJapaneseModel''',
		    '''GPTNeoXJapanesePreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
		from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_gpt_neox_japanese import (
				    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GPTNeoXJapaneseForCausalLM,
				    GPTNeoXJapaneseLayer,
				    GPTNeoXJapaneseModel,
				    GPTNeoXJapanesePreTrainedModel,
				)


else:
		import sys

		__lowerCamelCase   :					Optional[int]   							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




