


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

_UpperCAmelCase			:  int     =logging.get_logger(__name__)



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> Dict:
				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 (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> List[Any]:
				lowerCAmelCase_ :      str    				=				to_pil_image(lowerCAmelCase_ )
				lowerCAmelCase_				,  lowerCAmelCase_ :      Tuple    				=				pil_image.size
				lowerCAmelCase_ :      Any    				=				pytesseract.image_to_data(lowerCAmelCase_ ,   lang=lowerCAmelCase_ ,   output_type='''dict''' ,   config=lowerCAmelCase_ )
				lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ :      Dict    				=				data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

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

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

				# finally, normalize the bounding boxes
				lowerCAmelCase_ :      Optional[Any]    				=				[]
				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 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  List[Any]					=							["""pixel_values"""]




						def __init__(				self   ,						__lowercase = True   ,						__lowercase = None   ,						__lowercase = PILImageResampling.BILINEAR   ,						__lowercase = True   ,						__lowercase = 1 / 2_5_5   ,						__lowercase = True   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = True   ,						__lowercase = None   ,						__lowercase = ""   ,						**__lowercase   ,						)					->    None:
										super().__init__(**__lowercase							)
										lowerCAmelCase_ :      List[str]    				=				size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
										lowerCAmelCase_ :      Any    				=				get_size_dict(__lowercase							)

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




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase = PILImageResampling.BILINEAR   ,						__lowercase = None   ,						**__lowercase   ,						)					->    np.ndarray:
										lowerCAmelCase_ :      Union[str, Any]    				=				get_size_dict(__lowercase							)
										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()}"""							)
										lowerCAmelCase_ :      List[str]    				=				(size['''height'''], size['''width'''])
										return resize(__lowercase   ,						size=__lowercase   ,						resample=__lowercase   ,						data_format=__lowercase   ,						**__lowercase							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase = None   ,						**__lowercase   ,						)					->    np.ndarray:
										return rescale(__lowercase   ,						scale=__lowercase   ,						data_format=__lowercase   ,						**__lowercase							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase = None   ,						**__lowercase   ,						)					->    np.ndarray:
										return normalize(__lowercase   ,						mean=__lowercase   ,						std=__lowercase   ,						data_format=__lowercase   ,						**__lowercase							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase=None   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = ChannelDimension.FIRST   ,						**__lowercase   ,						)					->    PIL.Image.Image:
										lowerCAmelCase_ :      List[Any]    				=				do_resize if do_resize is not None else self.do_resize
										lowerCAmelCase_ :      List[str]    				=				size if size is not None else self.size
										lowerCAmelCase_ :      str    				=				get_size_dict(__lowercase							)
										lowerCAmelCase_ :      str    				=				resample if resample is not None else self.resample
										lowerCAmelCase_ :      Tuple    				=				do_rescale if do_rescale is not None else self.do_rescale
										lowerCAmelCase_ :      Union[str, Any]    				=				rescale_factor if rescale_factor is not None else self.rescale_factor
										lowerCAmelCase_ :      Any    				=				do_normalize if do_normalize is not None else self.do_normalize
										lowerCAmelCase_ :      Union[str, Any]    				=				image_mean if image_mean is not None else self.image_mean
										lowerCAmelCase_ :      Optional[int]    				=				image_std if image_std is not None else self.image_std
										lowerCAmelCase_ :      Optional[Any]    				=				apply_ocr if apply_ocr is not None else self.apply_ocr
										lowerCAmelCase_ :      Dict    				=				ocr_lang if ocr_lang is not None else self.ocr_lang
										lowerCAmelCase_ :      int    				=				tesseract_config if tesseract_config is not None else self.tesseract_config

										lowerCAmelCase_ :      int    				=				make_list_of_images(__lowercase							)

										if not valid_images(__lowercase							):
														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.
										lowerCAmelCase_ :      List[Any]    				=				[to_numpy_array(__lowercase							) for image in images]

										# Tesseract OCR to get words + normalized bounding boxes
										if apply_ocr:
														requires_backends(self   ,						'''pytesseract'''							)
														lowerCAmelCase_ :      Union[str, Any]    				=				[]
														lowerCAmelCase_ :      Any    				=				[]
														for image in images:
																		lowerCAmelCase_				,  lowerCAmelCase_ :      List[str]    				=				apply_tesseract(__lowercase   ,						__lowercase   ,						__lowercase							)
																		words_batch.append(__lowercase							)
																		boxes_batch.append(__lowercase							)

										if do_resize:
														lowerCAmelCase_ :      Union[str, Any]    				=				[self.resize(image=__lowercase   ,						size=__lowercase   ,						resample=__lowercase							) for image in images]

										if do_rescale:
														lowerCAmelCase_ :      List[Any]    				=				[self.rescale(image=__lowercase   ,						scale=__lowercase							) for image in images]

										if do_normalize:
														lowerCAmelCase_ :      List[str]    				=				[self.normalize(image=__lowercase   ,						mean=__lowercase   ,						std=__lowercase							) for image in images]

										lowerCAmelCase_ :      Union[str, Any]    				=				[to_channel_dimension_format(__lowercase   ,						__lowercase							) for image in images]

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

										if apply_ocr:
														lowerCAmelCase_ :      str    				=				words_batch
														lowerCAmelCase_ :      str    				=				boxes_batch
										return data



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase			:  Optional[int]     =logging.get_logger(__name__)

_UpperCAmelCase			:  Union[str, Any]     ={
    """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json""",
}







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  str					=							"""gpt_neox_japanese"""




						def __init__(				self   ,						__lowercase=3_2_0_0_0   ,						__lowercase=2_5_6_0   ,						__lowercase=3_2   ,						__lowercase=3_2   ,						__lowercase=4   ,						__lowercase="gelu"   ,						__lowercase=1.00   ,						__lowercase=1_0_0_0_0   ,						__lowercase=2_0_4_8   ,						__lowercase=0.02   ,						__lowercase=1e-5   ,						__lowercase=True   ,						__lowercase=3_1_9_9_6   ,						__lowercase=3_1_9_9_9   ,						__lowercase=0.1   ,						__lowercase=0.0   ,						**__lowercase   ,						)					->    str:
										super().__init__(bos_token_id=__lowercase   ,						eos_token_id=__lowercase   ,						**__lowercase							)
										lowerCAmelCase_ :      Optional[Any]    				=				vocab_size
										lowerCAmelCase_ :      Tuple    				=				max_position_embeddings
										lowerCAmelCase_ :      Optional[Any]    				=				hidden_size
										lowerCAmelCase_ :      Optional[Any]    				=				num_hidden_layers
										lowerCAmelCase_ :      str    				=				num_attention_heads
										lowerCAmelCase_ :      str    				=				intermediate_multiple_size
										lowerCAmelCase_ :      str    				=				hidden_act
										lowerCAmelCase_ :      Dict    				=				rotary_pct
										lowerCAmelCase_ :      Union[str, Any]    				=				rotary_emb_base
										lowerCAmelCase_ :      int    				=				initializer_range
										lowerCAmelCase_ :      Any    				=				layer_norm_eps
										lowerCAmelCase_ :      Optional[Any]    				=				use_cache
										lowerCAmelCase_ :      Tuple    				=				attention_dropout
										lowerCAmelCase_ :      Dict    				=				hidden_dropout