


"""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

a__    :							int            =					logging.get_logger(__name__)




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




		'''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__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




		'''simple docstring'''


		__SCREAMING_SNAKE_CASE						       =    to_pil_image(lowerCAmelCase_       )
		__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    pil_image.size
		__SCREAMING_SNAKE_CASE						       =    pytesseract.image_to_data(lowerCAmelCase_  ,						lang=lowerCAmelCase_  ,						output_type="dict"  ,						config=lowerCAmelCase_       )
		__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(lowerCAmelCase_       ) if not word.strip()]
		__SCREAMING_SNAKE_CASE						       =    [word for idx, word in enumerate(lowerCAmelCase_       ) if idx not in irrelevant_indices]
		__SCREAMING_SNAKE_CASE						       =    [coord for idx, coord in enumerate(lowerCAmelCase_       ) if idx not in irrelevant_indices]
		__SCREAMING_SNAKE_CASE						       =    [coord for idx, coord in enumerate(lowerCAmelCase_       ) if idx not in irrelevant_indices]
		__SCREAMING_SNAKE_CASE						       =    [coord for idx, coord in enumerate(lowerCAmelCase_       ) if idx not in irrelevant_indices]
		__SCREAMING_SNAKE_CASE						       =    [coord for idx, coord in enumerate(lowerCAmelCase_       ) 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(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):
				__SCREAMING_SNAKE_CASE						       =    [x, y, x + w, y + h]
				actual_boxes.append(lowerCAmelCase_       )

		# finally, normalize the bounding boxes
		__SCREAMING_SNAKE_CASE						       =    []
		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 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  Optional[Any]    =      ["pixel_values"]




	def __init__(			self      :    Dict      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    Dict[str, int] = None      ,      UpperCAmelCase__      :    PILImageResampling = PILImageResampling.BILINEAR      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    float = 1 / 2_5_5      ,      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__      :    Optional[Any]      ,      )	->     None:
			super().__init__(**UpperCAmelCase__       )
			__SCREAMING_SNAKE_CASE						       =    size if size is not None else {"height": 2_2_4, "width": 2_2_4}
			__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__       )

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




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




	def   UpperCAmelCase_				(			self      :    Optional[Any]      ,      UpperCAmelCase__      :    np.ndarray      ,      UpperCAmelCase__      :    Union[int, float]      ,      UpperCAmelCase__      :    Optional[Union[str, ChannelDimension]] = None      ,      **UpperCAmelCase__      :    List[Any]      ,      )	->     np.ndarray:
			return rescale(UpperCAmelCase__      ,      scale=UpperCAmelCase__      ,      data_format=UpperCAmelCase__      ,      **UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    str      ,      UpperCAmelCase__      :    np.ndarray      ,      UpperCAmelCase__      :    Union[float, Iterable[float]]      ,      UpperCAmelCase__      :    Union[float, Iterable[float]]      ,      UpperCAmelCase__      :    Optional[Union[str, ChannelDimension]] = None      ,      **UpperCAmelCase__      :    Optional[int]      ,      )	->     np.ndarray:
			return normalize(UpperCAmelCase__      ,      mean=UpperCAmelCase__      ,      std=UpperCAmelCase__      ,      data_format=UpperCAmelCase__      ,      **UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Union[str, Any]      ,      UpperCAmelCase__      :    ImageInput      ,      UpperCAmelCase__      :    bool = None      ,      UpperCAmelCase__      :    Dict[str, int] = None      ,      UpperCAmelCase__      :    List[str]=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__      :    List[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(UpperCAmelCase__       )
			__SCREAMING_SNAKE_CASE						       =    resample if resample is not None else self.resample
			__SCREAMING_SNAKE_CASE						       =    do_rescale if do_rescale is not None else self.do_rescale
			__SCREAMING_SNAKE_CASE						       =    rescale_factor if rescale_factor is not None else self.rescale_factor
			__SCREAMING_SNAKE_CASE						       =    do_normalize if do_normalize is not None else self.do_normalize
			__SCREAMING_SNAKE_CASE						       =    image_mean if image_mean is not None else self.image_mean
			__SCREAMING_SNAKE_CASE						       =    image_std if image_std is not None else self.image_std
			__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(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.
			__SCREAMING_SNAKE_CASE						       =    [to_numpy_array(UpperCAmelCase__       ) for image in images]

			# Tesseract OCR to get words + normalized bounding boxes
			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(UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__       )
							words_batch.append(UpperCAmelCase__       )
							boxes_batch.append(UpperCAmelCase__       )

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

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

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

			__SCREAMING_SNAKE_CASE						       =    [to_channel_dimension_format(UpperCAmelCase__      ,      UpperCAmelCase__       ) for image in images]

			__SCREAMING_SNAKE_CASE						       =    BatchFeature(data={"pixel_values": images}      ,      tensor_type=UpperCAmelCase__       )

			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 (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
		from ..ta.tokenization_ta import TaTokenizer
else:
		from ...utils.dummy_sentencepiece_objects import TaTokenizer

a__    :							Dict            =					TaTokenizer

if is_tokenizers_available():
		from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
		from ...utils.dummy_tokenizers_objects import TaTokenizerFast

a__    :							Any            =					TaTokenizerFast

a__    :							Tuple            =					{'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							int            =					[
		    '''MT5EncoderModel''',
		    '''MT5ForConditionalGeneration''',
		    '''MT5ForQuestionAnswering''',
		    '''MT5Model''',
		    '''MT5PreTrainedModel''',
		    '''MT5Stack''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							str            =					['''TFMT5EncoderModel''', '''TFMT5ForConditionalGeneration''', '''TFMT5Model''']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							Union[str, Any]            =					['''FlaxMT5EncoderModel''', '''FlaxMT5ForConditionalGeneration''', '''FlaxMT5Model''']


if TYPE_CHECKING:
		from .configuration_mta import MTaConfig, MTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mta import (
				    MTaEncoderModel,
				    MTaForConditionalGeneration,
				    MTaForQuestionAnswering,
				    MTaModel,
				    MTaPreTrainedModel,
				    MTaStack,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
		import sys

		a__    :							str            =					_LazyModule(
		    __name__,
		    globals()['''__file__'''],
		    _import_structure,
		    extra_objects={'''MT5Tokenizer''': MTaTokenizer, '''MT5TokenizerFast''': MTaTokenizerFast},
		    module_spec=__spec__,
		)
