
import io
import math
from typing import Dict, Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    get_image_size,
    infer_channel_dimension_format,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends


if is_vision_available():
    import textwrap

    from PIL import Image, ImageDraw, ImageFont

if is_torch_available():
    import torch

    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
    lowerCamelCase  =False


lowerCamelCase  =logging.get_logger(__name__)
lowerCamelCase  ="ybelkada/fonts"


def 				SCREAMING_SNAKE_CASE_   (      ):
      if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
            raise ImportError(
                f'''You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use '''
                '''Pix2StructImageProcessor. Please upgrade torch.'''		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		):
      requires_backends(UpperCamelCase__			,		['''torch''']		)
      _check_torch_version()

      UpperCamelCase__     : Optional[Any]											=					image_tensor.unsqueeze(0		)
      UpperCamelCase__     : List[Any]											=					torch.nn.functional.unfold(UpperCamelCase__			,		(patch_height, patch_width)			,		stride=(patch_height, patch_width)		)
      UpperCamelCase__     : int											=					patches.reshape(image_tensor.size(0		)			,		image_tensor.size(1		)			,		UpperCamelCase__			,		UpperCamelCase__			,		-1		)
      UpperCamelCase__     : int											=					patches.permute(0			,		4			,		2			,		3			,		1		).reshape(
          image_tensor.size(2		) // patch_height			,		image_tensor.size(3		) // patch_width			,		image_tensor.size(1		) * patch_height * patch_width			,		)
      return patches.unsqueeze(0		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__ = 3_6			,		UpperCamelCase__ = "black"			,		UpperCamelCase__ = "white"			,		UpperCamelCase__ = 5			,		UpperCamelCase__ = 5			,		UpperCamelCase__ = 5			,		UpperCamelCase__ = 5			,		UpperCamelCase__ = None			,		UpperCamelCase__ = None			,		):
      requires_backends(UpperCamelCase__			,		'''vision'''		)
      # Add new lines so that each line is no more than 80 characters.

      UpperCamelCase__     : List[str]											=					textwrap.TextWrapper(width=8_0		)
      UpperCamelCase__     : List[str]											=					wrapper.wrap(text=UpperCamelCase__		)
      UpperCamelCase__     : Optional[int]											=					'''\n'''.join(UpperCamelCase__		)

      if font_bytes is not None and font_path is None:
            UpperCamelCase__     : Optional[int]											=					io.BytesIO(UpperCamelCase__		)
      elif font_path is not None:
            UpperCamelCase__     : Tuple											=					font_path
      else:
            UpperCamelCase__     : str											=					hf_hub_download(UpperCamelCase__			,		'''Arial.TTF'''		)
      UpperCamelCase__     : List[Any]											=					ImageFont.truetype(UpperCamelCase__			,		encoding='''UTF-8'''			,		size=UpperCamelCase__		)

      # Use a temporary canvas to determine the width and height in pixels when
      # rendering the text.
      UpperCamelCase__     : Any											=					ImageDraw.Draw(Image.new('''RGB'''			,		(1, 1)			,		UpperCamelCase__		)		)
      UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__     : Union[str, Any]											=					temp_draw.textbbox((0, 0)			,		UpperCamelCase__			,		UpperCamelCase__		)

      # Create the actual image with a bit of padding around the text.
      UpperCamelCase__     : str											=					text_width + left_padding + right_padding
      UpperCamelCase__     : Tuple											=					text_height + top_padding + bottom_padding
      UpperCamelCase__     : str											=					Image.new('''RGB'''			,		(image_width, image_height)			,		UpperCamelCase__		)
      UpperCamelCase__     : str											=					ImageDraw.Draw(UpperCamelCase__		)
      draw.text(xy=(left_padding, top_padding)			,		text=UpperCamelCase__			,		fill=UpperCamelCase__			,		font=UpperCamelCase__		)
      return image


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		**UpperCamelCase__		):
      requires_backends(UpperCamelCase__			,		'''vision'''		)

      # Convert to PIL image if necessary
      UpperCamelCase__     : Any											=					to_pil_image(UpperCamelCase__		)

      UpperCamelCase__     : List[str]											=					render_text(UpperCamelCase__			,		**UpperCamelCase__		)
      UpperCamelCase__     : Dict											=					max(header_image.width			,		image.width		)

      UpperCamelCase__     : Tuple											=					int(image.height * (new_width / image.width)		)
      UpperCamelCase__     : Optional[Any]											=					int(header_image.height * (new_width / header_image.width)		)

      UpperCamelCase__     : Optional[Any]											=					Image.new('''RGB'''			,		(new_width, new_height + new_header_height)			,		'''white'''		)
      new_image.paste(header_image.resize((new_width, new_header_height)		)			,		(0, 0)		)
      new_image.paste(image.resize((new_width, new_height)		)			,		(0, new_header_height)		)

      # Convert back to the original framework if necessary
      UpperCamelCase__     : List[Any]											=					to_numpy_array(UpperCamelCase__		)

      if infer_channel_dimension_format(UpperCamelCase__		) == ChannelDimension.LAST:
            UpperCamelCase__     : str											=					to_channel_dimension_format(UpperCamelCase__			,		ChannelDimension.LAST		)

      return new_image




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	['''flattened_patches''']






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = 2_0_4_8    ,    __SCREAMING_SNAKE_CASE = False    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> None:




           """simple docstring"""







           super().__init__(**__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Tuple											=					patch_size if patch_size is not None else {'''height''': 1_6, '''width''': 1_6}
           UpperCamelCase__     : List[str]											=					do_normalize
           UpperCamelCase__     : Union[str, Any]											=					do_convert_rgb
           UpperCamelCase__     : Tuple											=					max_patches
           UpperCamelCase__     : Union[str, Any]											=					is_vqa






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)	-> np.ndarray:




           """simple docstring"""







           requires_backends(self.extract_flattened_patches    ,    '''torch'''	)
           _check_torch_version()

           # convert to torch
           UpperCamelCase__     : Tuple											=					to_channel_dimension_format(__SCREAMING_SNAKE_CASE    ,    ChannelDimension.FIRST	)
           UpperCamelCase__     : Dict											=					torch.from_numpy(__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__					,UpperCamelCase__     : Optional[Any]											=					patch_size['''height'''], patch_size['''width''']
           UpperCamelCase__					,UpperCamelCase__     : Union[str, Any]											=					get_image_size(__SCREAMING_SNAKE_CASE	)

           # maximize scale s.t.
           UpperCamelCase__     : List[str]											=					math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width)	)
           UpperCamelCase__     : List[Any]											=					max(min(math.floor(scale * image_height / patch_height	)    ,    __SCREAMING_SNAKE_CASE	)    ,    1	)
           UpperCamelCase__     : Optional[int]											=					max(min(math.floor(scale * image_width / patch_width	)    ,    __SCREAMING_SNAKE_CASE	)    ,    1	)
           UpperCamelCase__     : Optional[int]											=					max(num_feasible_rows * patch_height    ,    1	)
           UpperCamelCase__     : Dict											=					max(num_feasible_cols * patch_width    ,    1	)

           UpperCamelCase__     : str											=					torch.nn.functional.interpolate(
               image.unsqueeze(0	)    ,    size=(resized_height, resized_width)    ,    mode='''bilinear'''    ,    align_corners=__SCREAMING_SNAKE_CASE    ,    antialias=__SCREAMING_SNAKE_CASE    ,    ).squeeze(0	)

           # [1, rows, columns, patch_height * patch_width * image_channels]
           UpperCamelCase__     : Union[str, Any]											=					torch_extract_patches(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[str]											=					patches.shape
           UpperCamelCase__     : List[str]											=					patches_shape[1]
           UpperCamelCase__     : Optional[Any]											=					patches_shape[2]
           UpperCamelCase__     : str											=					patches_shape[3]

           # [rows * columns, patch_height * patch_width * image_channels]
           UpperCamelCase__     : int											=					patches.reshape([rows * columns, depth]	)

           # [rows * columns, 1]
           UpperCamelCase__     : int											=					torch.arange(__SCREAMING_SNAKE_CASE	).reshape([rows, 1]	).repeat(1    ,    __SCREAMING_SNAKE_CASE	).reshape([rows * columns, 1]	)
           UpperCamelCase__     : Tuple											=					torch.arange(__SCREAMING_SNAKE_CASE	).reshape([1, columns]	).repeat(__SCREAMING_SNAKE_CASE    ,    1	).reshape([rows * columns, 1]	)

           # Offset by 1 so the ids do not contain zeros, which represent padding.
           row_ids += 1
           col_ids += 1

           # Prepare additional patch features.
           # [rows * columns, 1]
           UpperCamelCase__     : Dict											=					row_ids.to(torch.floataa	)
           UpperCamelCase__     : Optional[Any]											=					col_ids.to(torch.floataa	)

           # [rows * columns, 2 + patch_height * patch_width * image_channels]
           UpperCamelCase__     : Any											=					torch.cat([row_ids, col_ids, patches]    ,    -1	)

           # [max_patches, 2 + patch_height * patch_width * image_channels]
           UpperCamelCase__     : str											=					torch.nn.functional.pad(__SCREAMING_SNAKE_CASE    ,    [0, 0, 0, max_patches - (rows * columns)]	).float()

           UpperCamelCase__     : Optional[Any]											=					to_numpy_array(__SCREAMING_SNAKE_CASE	)

           return result






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE	)	-> np.ndarray:




           """simple docstring"""







           if image.dtype == np.uinta:
                 UpperCamelCase__     : Union[str, Any]											=					image.astype(np.floataa	)

           # take mean across the whole `image`
           UpperCamelCase__     : Optional[Any]											=					np.mean(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Dict											=					np.std(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : List[str]											=					max(__SCREAMING_SNAKE_CASE    ,    1.0 / math.sqrt(np.prod(image.shape	)	)	)

           return normalize(__SCREAMING_SNAKE_CASE    ,    mean=__SCREAMING_SNAKE_CASE    ,    std=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = ChannelDimension.FIRST    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> ImageInput:




           """simple docstring"""







           UpperCamelCase__     : str											=					do_normalize if do_normalize is not None else self.do_normalize
           UpperCamelCase__     : Dict											=					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
           UpperCamelCase__     : Any											=					patch_size if patch_size is not None else self.patch_size
           UpperCamelCase__     : Any											=					max_patches if max_patches is not None else self.max_patches
           UpperCamelCase__     : Tuple											=					self.is_vqa

           if kwargs.get('''data_format'''    ,    __SCREAMING_SNAKE_CASE	) is not None:
                 raise ValueError('''data_format is not an accepted input as the outputs are '''	)

           UpperCamelCase__     : List[Any]											=					make_list_of_images(__SCREAMING_SNAKE_CASE	)

           if not valid_images(__SCREAMING_SNAKE_CASE	):
                 raise ValueError(
                     '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                     '''torch.Tensor, tf.Tensor or jax.ndarray.'''	)

           # PIL RGBA images are converted to RGB
           if do_convert_rgb:
                 UpperCamelCase__     : Optional[Any]											=					[convert_to_rgb(__SCREAMING_SNAKE_CASE	) for image in images]

           # All transformations expect numpy arrays.
           UpperCamelCase__     : List[str]											=					[to_numpy_array(__SCREAMING_SNAKE_CASE	) for image in images]

           if is_vqa:
                 if header_text is None:
                       raise ValueError('''A header text must be provided for VQA models.'''	)
                 UpperCamelCase__     : Dict											=					kwargs.pop('''font_bytes'''    ,    __SCREAMING_SNAKE_CASE	)
                 UpperCamelCase__     : List[str]											=					kwargs.pop('''font_path'''    ,    __SCREAMING_SNAKE_CASE	)

                 if isinstance(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	):
                       UpperCamelCase__     : Tuple											=					[header_text] * len(__SCREAMING_SNAKE_CASE	)

                 UpperCamelCase__     : Any											=					[
                     render_header(__SCREAMING_SNAKE_CASE    ,    header_text[i]    ,    font_bytes=__SCREAMING_SNAKE_CASE    ,    font_path=__SCREAMING_SNAKE_CASE	)
                     for i, image in enumerate(__SCREAMING_SNAKE_CASE	)
                 ]

           if do_normalize:
                 UpperCamelCase__     : List[str]											=					[self.normalize(image=__SCREAMING_SNAKE_CASE	) for image in images]

           # convert to torch tensor and permute
           UpperCamelCase__     : str											=					[
               self.extract_flattened_patches(image=__SCREAMING_SNAKE_CASE    ,    max_patches=__SCREAMING_SNAKE_CASE    ,    patch_size=__SCREAMING_SNAKE_CASE	)
               for image in images
           ]

           # create attention mask in numpy
           UpperCamelCase__     : List[str]											=					[(image.sum(axis=-1	) != 0).astype(np.floataa	) for image in images]

           UpperCamelCase__     : str											=					BatchFeature(
               data={'''flattened_patches''': images, '''attention_mask''': attention_masks}    ,    tensor_type=__SCREAMING_SNAKE_CASE	)

           return encoded_outputs




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase  ={
    "configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
    "tokenization_luke": ["LukeTokenizer"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase  =[
        "LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
        "LukeForEntityClassification",
        "LukeForEntityPairClassification",
        "LukeForEntitySpanClassification",
        "LukeForMultipleChoice",
        "LukeForQuestionAnswering",
        "LukeForSequenceClassification",
        "LukeForTokenClassification",
        "LukeForMaskedLM",
        "LukeModel",
        "LukePreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
    from .tokenization_luke import LukeTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_luke import (
            LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
            LukeForEntityClassification,
            LukeForEntityPairClassification,
            LukeForEntitySpanClassification,
            LukeForMaskedLM,
            LukeForMultipleChoice,
            LukeForQuestionAnswering,
            LukeForSequenceClassification,
            LukeForTokenClassification,
            LukeModel,
            LukePreTrainedModel,
        )

else:
    import sys

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


