

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

A__						=							logging.get_logger(__name__)




def 					_lowercase     (				a_				:					str					,a_				:					List[Any]					,a_				:					Tuple				)				->	Union[str, Any]:




					'''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 					_lowercase     (				a_				:					np.ndarray					,a_				:					Optional[str]					,a_				:					Optional[str]				)				->	List[str]:




					'''simple docstring'''







					__magic_name__							=    to_pil_image(a_				)
					__magic_name__,	__magic_name__							=    pil_image.size
					__magic_name__							=    pytesseract.image_to_data(a_					,lang=a_					,output_type='dict'					,config=a_				)
					__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							=    data['text'], data['left'], data['top'], data['width'], data['height']

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

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

					# finally, normalize the bounding boxes
					__magic_name__							=    []
					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 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Dict       				=  ["pixel_values"]







	def __init__(   self: Any  , __UpperCamelCase: bool = True  , __UpperCamelCase: Dict[str, int] = None  , __UpperCamelCase: PILImageResampling = PILImageResampling.BILINEAR  , __UpperCamelCase: bool = True  , __UpperCamelCase: float = 1 / 2_55  , __UpperCamelCase: bool = True  , __UpperCamelCase: Union[float, Iterable[float]] = None  , __UpperCamelCase: Union[float, Iterable[float]] = None  , __UpperCamelCase: bool = True  , __UpperCamelCase: Optional[str] = None  , __UpperCamelCase: Optional[str] = ""  , **__UpperCamelCase: List[str]  , ):

						'''simple docstring'''





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

						__magic_name__							=    do_resize
						__magic_name__							=    size
						__magic_name__							=    resample
						__magic_name__							=    do_rescale
						__magic_name__							=    rescale_value
						__magic_name__							=    do_normalize
						__magic_name__							=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						__magic_name__							=    image_std if image_std is not None else IMAGENET_STANDARD_STD
						__magic_name__							=    apply_ocr
						__magic_name__							=    ocr_lang
						__magic_name__							=    tesseract_config







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]  , __UpperCamelCase: np.ndarray  , __UpperCamelCase: Dict[str, int]  , __UpperCamelCase: PILImageResampling = PILImageResampling.BILINEAR  , __UpperCamelCase: Optional[Union[str, ChannelDimension]] = None  , **__UpperCamelCase: Tuple  , ):

						'''simple docstring'''





						__magic_name__							=    get_size_dict(__UpperCamelCase      )
						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()}'      )
						__magic_name__							=    (size['height'], size['width'])
						return resize(__UpperCamelCase  , size=__UpperCamelCase  , resample=__UpperCamelCase  , data_format=__UpperCamelCase  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple  , __UpperCamelCase: np.ndarray  , __UpperCamelCase: Union[int, float]  , __UpperCamelCase: Optional[Union[str, ChannelDimension]] = None  , **__UpperCamelCase: Tuple  , ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict  , __UpperCamelCase: np.ndarray  , __UpperCamelCase: Union[float, Iterable[float]]  , __UpperCamelCase: Union[float, Iterable[float]]  , __UpperCamelCase: Optional[Union[str, ChannelDimension]] = None  , **__UpperCamelCase: Optional[Any]  , ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]  , __UpperCamelCase: ImageInput  , __UpperCamelCase: bool = None  , __UpperCamelCase: Dict[str, int] = None  , __UpperCamelCase: List[Any]=None  , __UpperCamelCase: bool = None  , __UpperCamelCase: float = None  , __UpperCamelCase: bool = None  , __UpperCamelCase: Union[float, Iterable[float]] = None  , __UpperCamelCase: Union[float, Iterable[float]] = None  , __UpperCamelCase: bool = None  , __UpperCamelCase: Optional[str] = None  , __UpperCamelCase: Optional[str] = None  , __UpperCamelCase: Optional[Union[str, TensorType]] = None  , __UpperCamelCase: ChannelDimension = ChannelDimension.FIRST  , **__UpperCamelCase: Optional[int]  , ):

						'''simple docstring'''





						__magic_name__							=    do_resize if do_resize is not None else self.do_resize
						__magic_name__							=    size if size is not None else self.size
						__magic_name__							=    get_size_dict(__UpperCamelCase      )
						__magic_name__							=    resample if resample is not None else self.resample
						__magic_name__							=    do_rescale if do_rescale is not None else self.do_rescale
						__magic_name__							=    rescale_factor if rescale_factor is not None else self.rescale_factor
						__magic_name__							=    do_normalize if do_normalize is not None else self.do_normalize
						__magic_name__							=    image_mean if image_mean is not None else self.image_mean
						__magic_name__							=    image_std if image_std is not None else self.image_std
						__magic_name__							=    apply_ocr if apply_ocr is not None else self.apply_ocr
						__magic_name__							=    ocr_lang if ocr_lang is not None else self.ocr_lang
						__magic_name__							=    tesseract_config if tesseract_config is not None else self.tesseract_config

						__magic_name__							=    make_list_of_images(__UpperCamelCase      )

						if not valid_images(__UpperCamelCase      ):
											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.
						__magic_name__							=    [to_numpy_array(__UpperCamelCase      ) for image in images]

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

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

						if do_rescale:
											__magic_name__							=    [self.rescale(image=__UpperCamelCase  , scale=__UpperCamelCase      ) for image in images]

						if do_normalize:
											__magic_name__							=    [self.normalize(image=__UpperCamelCase  , mean=__UpperCamelCase  , std=__UpperCamelCase      ) for image in images]

						__magic_name__							=    [to_channel_dimension_format(__UpperCamelCase  , __UpperCamelCase      ) for image in images]

						__magic_name__							=    BatchFeature(data={'pixel_values': images}  , tensor_type=__UpperCamelCase      )

						if apply_ocr:
											__magic_name__							=    words_batch
											__magic_name__							=    boxes_batch
						return data







import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
						import jax

						from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
						from transformers.models.bert.modeling_flax_bert import FlaxBertModel
						from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel

@require_flax
class 				__UpperCamelCase				(					unittest.TestCase			):







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						for model_name in ["bert-base-cased", "bert-large-uncased"]:
											with self.subTest(__UpperCamelCase      ):
																__magic_name__							=    AutoConfig.from_pretrained(__UpperCamelCase      )
																self.assertIsNotNone(__UpperCamelCase      )
																self.assertIsInstance(__UpperCamelCase  , __UpperCamelCase      )

																__magic_name__							=    FlaxAutoModel.from_pretrained(__UpperCamelCase      )
																self.assertIsNotNone(__UpperCamelCase      )
																self.assertIsInstance(__UpperCamelCase  , __UpperCamelCase      )







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						for model_name in ["roberta-base", "roberta-large"]:
											with self.subTest(__UpperCamelCase      ):
																__magic_name__							=    AutoConfig.from_pretrained(__UpperCamelCase      )
																self.assertIsNotNone(__UpperCamelCase      )
																self.assertIsInstance(__UpperCamelCase  , __UpperCamelCase      )

																__magic_name__							=    FlaxAutoModel.from_pretrained(__UpperCamelCase      )
																self.assertIsNotNone(__UpperCamelCase      )
																self.assertIsInstance(__UpperCamelCase  , __UpperCamelCase      )







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						for model_name in ["bert-base-cased", "bert-large-uncased"]:
											__magic_name__							=    AutoTokenizer.from_pretrained(__UpperCamelCase      )
											__magic_name__							=    FlaxBertModel.from_pretrained(__UpperCamelCase      )
											__magic_name__							=    tokenizer('Do you support jax jitted function?'  , return_tensors=TensorType.JAX      )

											@jax.jit
											def eval(**__UpperCamelCase: Optional[Any]      ):
																return model(**__UpperCamelCase      )

											eval(**__UpperCamelCase      ).block_until_ready()







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						for model_name in ["roberta-base", "roberta-large"]:
											__magic_name__							=    AutoTokenizer.from_pretrained(__UpperCamelCase      )
											__magic_name__							=    FlaxRobertaModel.from_pretrained(__UpperCamelCase      )
											__magic_name__							=    tokenizer('Do you support jax jitted function?'  , return_tensors=TensorType.JAX      )

											@jax.jit
											def eval(**__UpperCamelCase: Any      ):
																return model(**__UpperCamelCase      )

											eval(**__UpperCamelCase      ).block_until_ready()







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						with self.assertRaisesRegex(
						    __UpperCamelCase  , 'bert-base is not a local folder and is not a valid model identifier'      ):
											__magic_name__							=    FlaxAutoModel.from_pretrained('bert-base'      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						with self.assertRaisesRegex(
						    __UpperCamelCase  , r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'      ):
											__magic_name__							=    FlaxAutoModel.from_pretrained(__UpperCamelCase  , revision='aaaaaa'      )







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						with self.assertRaisesRegex(
						    __UpperCamelCase  , 'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'  , ):
											__magic_name__							=    FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model'      )







	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						with self.assertRaisesRegex(__UpperCamelCase  , 'Use `from_pt=True` to load this model'      ):
											__magic_name__							=    FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'      )




