

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:
    UpperCamelCase__		 =     False


UpperCamelCase__		 =     logging.get_logger(__name__)
UpperCamelCase__		 =     '''ybelkada/fonts'''







def 						lowerCamelCase__      ( ):


       """simple docstring"""




       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 						lowerCamelCase__      ( __A    :Dict	,__A    :int	,__A    :str		):


       """simple docstring"""




       requires_backends(__A	,["""torch"""]		)
       _check_torch_version()

       __snake_case       	=							image_tensor.unsqueeze(0		)
       __snake_case       	=							torch.nn.functional.unfold(__A	,(patch_height, patch_width)	,stride=(patch_height, patch_width)		)
       __snake_case       	=							patches.reshape(image_tensor.size(0		)	,image_tensor.size(1		)	,__A	,__A	,-1		)
       __snake_case       	=							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 						lowerCamelCase__      ( __A    :str	,__A    :int = 3_6	,__A    :str = "black"	,__A    :str = "white"	,__A    :int = 5	,__A    :int = 5	,__A    :int = 5	,__A    :int = 5	,__A    :Optional[bytes] = None	,__A    :Optional[str] = None	,):


       """simple docstring"""




       requires_backends(__A	,"""vision"""		)
       # Add new lines so that each line is no more than 80 characters.

       __snake_case       	=							textwrap.TextWrapper(width=8_0		)
       __snake_case       	=							wrapper.wrap(text=__A		)
       __snake_case       	=							"""\n""".join(__A		)

       if font_bytes is not None and font_path is None:
              __snake_case       	=							io.BytesIO(__A		)
       elif font_path is not None:
              __snake_case       	=							font_path
       else:
              __snake_case       	=							hf_hub_download(__A	,"""Arial.TTF"""		)
       __snake_case       	=							ImageFont.truetype(__A	,encoding="""UTF-8"""	,size=__A		)

       # Use a temporary canvas to determine the width and height in pixels when
       # rendering the text.
       __snake_case       	=							ImageDraw.Draw(Image.new("""RGB"""	,(1, 1)	,__A		)		)
       __snake_case	,    __snake_case	,    __snake_case	,    __snake_case       	=							temp_draw.textbbox((0, 0)	,__A	,__A		)

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







def 						lowerCamelCase__      ( __A    :np.ndarray	,__A    :str	,**__A    :Optional[Any]		):


       """simple docstring"""




       requires_backends(__A	,"""vision"""		)

       # Convert to PIL image if necessary
       __snake_case       	=							to_pil_image(__A		)

       __snake_case       	=							render_text(__A	,**__A		)
       __snake_case       	=							max(header_image.width	,image.width		)

       __snake_case       	=							int(image.height * (new_width / image.width)		)
       __snake_case       	=							int(header_image.height * (new_width / header_image.width)		)

       __snake_case       	=							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
       __snake_case       	=							to_numpy_array(__A		)

       if infer_channel_dimension_format(__A		) == ChannelDimension.LAST:
              __snake_case       	=							to_channel_dimension_format(__A	,ChannelDimension.LAST		)

       return new_image




class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							["flattened_patches"]


       def __init__(		self      , _UpperCamelCase = True      , _UpperCamelCase = True      , _UpperCamelCase = None      , _UpperCamelCase = 20_48      , _UpperCamelCase = False      , **_UpperCamelCase      , )      ->							None:






              """simple docstring"""


              super().__init__(**_UpperCamelCase   )
              __snake_case       	=							patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
              __snake_case       	=							do_normalize
              __snake_case       	=							do_convert_rgb
              __snake_case       	=							max_patches
              __snake_case       	=							is_vqa


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






              """simple docstring"""


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

              # convert to torch
              __snake_case       	=							to_channel_dimension_format(_UpperCamelCase      , ChannelDimension.FIRST   )
              __snake_case       	=							torch.from_numpy(_UpperCamelCase   )

              __snake_case	,    __snake_case       	=							patch_size["""height"""], patch_size["""width"""]
              __snake_case	,    __snake_case       	=							get_image_size(_UpperCamelCase   )

              # maximize scale s.t.
              __snake_case       	=							math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width)   )
              __snake_case       	=							max(min(math.floor(scale * image_height / patch_height   )      , _UpperCamelCase   )      , 1   )
              __snake_case       	=							max(min(math.floor(scale * image_width / patch_width   )      , _UpperCamelCase   )      , 1   )
              __snake_case       	=							max(num_feasible_rows * patch_height      , 1   )
              __snake_case       	=							max(num_feasible_cols * patch_width      , 1   )

              __snake_case       	=							torch.nn.functional.interpolate(
                  image.unsqueeze(0   )      , size=(resized_height, resized_width)      , mode="""bilinear"""      , align_corners=_UpperCamelCase      , antialias=_UpperCamelCase      , ).squeeze(0   )

              # [1, rows, columns, patch_height * patch_width * image_channels]
              __snake_case       	=							torch_extract_patches(_UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )

              __snake_case       	=							patches.shape
              __snake_case       	=							patches_shape[1]
              __snake_case       	=							patches_shape[2]
              __snake_case       	=							patches_shape[3]

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

              # [rows * columns, 1]
              __snake_case       	=							torch.arange(_UpperCamelCase   ).reshape([rows, 1]   ).repeat(1      , _UpperCamelCase   ).reshape([rows * columns, 1]   )
              __snake_case       	=							torch.arange(_UpperCamelCase   ).reshape([1, columns]   ).repeat(_UpperCamelCase      , 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]
              __snake_case       	=							row_ids.to(torch.floataa   )
              __snake_case       	=							col_ids.to(torch.floataa   )

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

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

              __snake_case       	=							to_numpy_array(_UpperCamelCase   )

              return result


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






              """simple docstring"""


              if image.dtype == np.uinta:
                     __snake_case       	=							image.astype(np.floataa   )

              # take mean across the whole `image`
              __snake_case       	=							np.mean(_UpperCamelCase   )
              __snake_case       	=							np.std(_UpperCamelCase   )
              __snake_case       	=							max(_UpperCamelCase      , 1.0 / math.sqrt(np.prod(image.shape   )   )   )

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






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






              """simple docstring"""


              __snake_case       	=							do_normalize if do_normalize is not None else self.do_normalize
              __snake_case       	=							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
              __snake_case       	=							patch_size if patch_size is not None else self.patch_size
              __snake_case       	=							max_patches if max_patches is not None else self.max_patches
              __snake_case       	=							self.is_vqa

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

              __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."""   )

              # PIL RGBA images are converted to RGB
              if do_convert_rgb:
                     __snake_case       	=							[convert_to_rgb(_UpperCamelCase   ) for image in images]

              # All transformations expect numpy arrays.
              __snake_case       	=							[to_numpy_array(_UpperCamelCase   ) for image in images]

              if is_vqa:
                     if header_text is None:
                            raise ValueError("""A header text must be provided for VQA models."""   )
                     __snake_case       	=							kwargs.pop("""font_bytes"""      , _UpperCamelCase   )
                     __snake_case       	=							kwargs.pop("""font_path"""      , _UpperCamelCase   )

                     if isinstance(_UpperCamelCase      , _UpperCamelCase   ):
                            __snake_case       	=							[header_text] * len(_UpperCamelCase   )

                     __snake_case       	=							[
                         render_header(_UpperCamelCase      , header_text[i]      , font_bytes=_UpperCamelCase      , font_path=_UpperCamelCase   )
                         for i, image in enumerate(_UpperCamelCase   )
                     ]

              if do_normalize:
                     __snake_case       	=							[self.normalize(image=_UpperCamelCase   ) for image in images]

              # convert to torch tensor and permute
              __snake_case       	=							[
                  self.extract_flattened_patches(image=_UpperCamelCase      , max_patches=_UpperCamelCase      , patch_size=_UpperCamelCase   )
                  for image in images
              ]

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

              __snake_case       	=							BatchFeature(
                  data={"""flattened_patches""": images, """attention_mask""": attention_masks}      , tensor_type=_UpperCamelCase   )

              return encoded_outputs




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase__		 =     {
    '''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvNextConfig''', '''ConvNextOnnxConfig''']
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		 =     ['''ConvNextFeatureExtractor''']
    UpperCamelCase__		 =     ['''ConvNextImageProcessor''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		 =     [
        '''CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''ConvNextForImageClassification''',
        '''ConvNextModel''',
        '''ConvNextPreTrainedModel''',
        '''ConvNextBackbone''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		 =     [
        '''TFConvNextForImageClassification''',
        '''TFConvNextModel''',
        '''TFConvNextPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_convnext import ConvNextFeatureExtractor
        from .image_processing_convnext import ConvNextImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_convnext import (
            CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
            ConvNextBackbone,
            ConvNextForImageClassification,
            ConvNextModel,
            ConvNextPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
    import sys

    UpperCamelCase__		 =     _LazyModule(__name__, globals()['''__file__'''], _import_structure)

