






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   UpperCamelCase  (       ):




     '''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   UpperCamelCase  (       snake_case__     :   Union[str, Any]	,snake_case__     :   Optional[Any]	,snake_case__     :   Dict  ):




     '''simple docstring'''






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

     __snake_case			:int          =						image_tensor.unsqueeze(0  )
     __snake_case			:Tuple          =						torch.nn.functional.unfold(snake_case__	,(patch_height, patch_width)	,stride=(patch_height, patch_width)  )
     __snake_case			:Tuple          =						patches.reshape(image_tensor.size(0  )	,image_tensor.size(1  )	,snake_case__	,snake_case__	,-1  )
     __snake_case			:List[Any]          =						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   UpperCamelCase  (       snake_case__     :   str	,snake_case__     :   int = 36	,snake_case__     :   str = "black"	,snake_case__     :   str = "white"	,snake_case__     :   int = 5	,snake_case__     :   int = 5	,snake_case__     :   int = 5	,snake_case__     :   int = 5	,snake_case__     :   Optional[bytes] = None	,snake_case__     :   Optional[str] = None	,):




     '''simple docstring'''






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

     __snake_case			:Dict          =						textwrap.TextWrapper(width=80  )
     __snake_case			:Optional[int]          =						wrapper.wrap(text=snake_case__  )
     __snake_case			:Any          =						"""\n""".join(snake_case__  )

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

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

     # Create the actual image with a bit of padding around the text.
     __snake_case			:str          =						text_width + left_padding + right_padding
     __snake_case			:Optional[Any]          =						text_height + top_padding + bottom_padding
     __snake_case			:List[str]          =						Image.new("""RGB"""	,(image_width, image_height)	,snake_case__  )
     __snake_case			:Union[str, Any]          =						ImageDraw.Draw(snake_case__  )
     draw.text(xy=(left_padding, top_padding)	,text=snake_case__	,fill=snake_case__	,font=snake_case__  )
     return image






def   UpperCamelCase  (       snake_case__     :   np.ndarray	,snake_case__     :   str	,**snake_case__     :   Optional[Any]  ):




     '''simple docstring'''






     requires_backends(snake_case__	,"""vision"""  )

     # Convert to PIL image if necessary
     __snake_case			:Optional[Any]          =						to_pil_image(snake_case__  )

     __snake_case			:Union[str, Any]          =						render_text(snake_case__	,**snake_case__  )
     __snake_case			:Union[str, Any]          =						max(header_image.width	,image.width  )

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

     __snake_case			:List[str]          =						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			:Optional[Any]          =						to_numpy_array(snake_case__  )

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

     return new_image




class 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				Tuple 				= ["flattened_patches"]



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






            '''simple docstring'''





            super().__init__(**a__    )
            __snake_case			:int          =						patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
            __snake_case			:Optional[int]          =						do_normalize
            __snake_case			:Optional[Any]          =						do_convert_rgb
            __snake_case			:str          =						max_patches
            __snake_case			:Any          =						is_vqa



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






            '''simple docstring'''





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

            # convert to torch
            __snake_case			:str          =						to_channel_dimension_format(a__			,						ChannelDimension.FIRST    )
            __snake_case			:Union[str, Any]          =						torch.from_numpy(a__    )

            __snake_case       , __snake_case			:int          =						patch_size["""height"""], patch_size["""width"""]
            __snake_case       , __snake_case			:Tuple          =						get_image_size(a__    )

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

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

            # [1, rows, columns, patch_height * patch_width * image_channels]
            __snake_case			:Optional[int]          =						torch_extract_patches(a__			,						a__			,						a__    )

            __snake_case			:List[Any]          =						patches.shape
            __snake_case			:List[str]          =						patches_shape[1]
            __snake_case			:List[Any]          =						patches_shape[2]
            __snake_case			:int          =						patches_shape[3]

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

            # [rows * columns, 1]
            __snake_case			:int          =						torch.arange(a__    ).reshape([rows, 1]    ).repeat(1			,						a__    ).reshape([rows * columns, 1]    )
            __snake_case			:Union[str, Any]          =						torch.arange(a__    ).reshape([1, columns]    ).repeat(a__			,						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			:str          =						row_ids.to(torch.floataa    )
            __snake_case			:Any          =						col_ids.to(torch.floataa    )

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

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

            __snake_case			:List[Any]          =						to_numpy_array(a__    )

            return result



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






            '''simple docstring'''





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

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

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



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






            '''simple docstring'''





            __snake_case			:List[str]          =						do_normalize if do_normalize is not None else self.do_normalize
            __snake_case			:List[Any]          =						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
            __snake_case			:Any          =						patch_size if patch_size is not None else self.patch_size
            __snake_case			:str          =						max_patches if max_patches is not None else self.max_patches
            __snake_case			:Any          =						self.is_vqa

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

            __snake_case			:Dict          =						make_list_of_images(a__    )

            if not valid_images(a__    ):
                 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			:Optional[int]          =						[convert_to_rgb(a__    ) for image in images]

            # All transformations expect numpy arrays.
            __snake_case			:Optional[Any]          =						[to_numpy_array(a__    ) 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			:Optional[int]          =						kwargs.pop("""font_bytes"""			,						a__    )
                 __snake_case			:Optional[int]          =						kwargs.pop("""font_path"""			,						a__    )

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

                 __snake_case			:Union[str, Any]          =						[
                     render_header(a__			,						header_text[i]			,						font_bytes=a__			,						font_path=a__    )
                     for i, image in enumerate(a__    )
                 ]

            if do_normalize:
                 __snake_case			:Union[str, Any]          =						[self.normalize(image=a__    ) for image in images]

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

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

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

            return encoded_outputs










import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import GLPNImageProcessor




class 			snake_case__   (							unittest.TestCase):

       '''simple docstring'''



       def __init__(							self			,						a__			,						a__=7			,						a__=3			,						a__=18			,						a__=30			,						a__=4_00			,						a__=True			,						a__=32			,						a__=True			,						) ->      List[Any]:






            '''simple docstring'''





            __snake_case			:List[Any]          =						parent
            __snake_case			:Dict          =						batch_size
            __snake_case			:Optional[Any]          =						num_channels
            __snake_case			:Dict          =						image_size
            __snake_case			:Dict          =						min_resolution
            __snake_case			:Dict          =						max_resolution
            __snake_case			:List[Any]          =						do_resize
            __snake_case			:Dict          =						size_divisor
            __snake_case			:Union[str, Any]          =						do_rescale



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            return {
                "do_resize": self.do_resize,
                "size_divisor": self.size_divisor,
                "do_rescale": self.do_rescale,
            }




@require_torch
@require_vision
class 			snake_case__   (							lowercase_	,    unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase							:				Tuple 				= GLPNImageProcessor if is_vision_available() else None



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:Dict          =						GLPNImageProcessingTester(self    )



       @property
       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            return self.image_processor_tester.prepare_image_processor_dict()



       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						self.image_processing_class(**self.image_processor_dict    )
            self.assertTrue(hasattr(a__			,						"""do_resize"""    )    )
            self.assertTrue(hasattr(a__			,						"""size_divisor"""    )    )
            self.assertTrue(hasattr(a__			,						"""resample"""    )    )
            self.assertTrue(hasattr(a__			,						"""do_rescale"""    )    )



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            pass



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            __snake_case			:int          =						self.image_processing_class(**self.image_processor_dict    )
            # create random PIL images
            __snake_case			:Optional[Any]          =						prepare_image_inputs(self.image_processor_tester			,						equal_resolution=a__    )
            for image in image_inputs:
                 self.assertIsInstance(a__			,						Image.Image    )

            # Test not batched input (GLPNImageProcessor doesn't support batching)
            __snake_case			:Union[str, Any]          =						image_processing(image_inputs[0]			,						return_tensors="""pt"""    ).pixel_values
            self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
            self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:int          =						self.image_processing_class(**self.image_processor_dict    )
            # create random numpy tensors
            __snake_case			:List[Any]          =						prepare_image_inputs(self.image_processor_tester			,						equal_resolution=a__			,						numpify=a__    )
            for image in image_inputs:
                 self.assertIsInstance(a__			,						np.ndarray    )

            # Test not batched input (GLPNImageProcessor doesn't support batching)
            __snake_case			:int          =						image_processing(image_inputs[0]			,						return_tensors="""pt"""    ).pixel_values
            self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
            self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )



       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            __snake_case			:Tuple          =						self.image_processing_class(**self.image_processor_dict    )
            # create random PyTorch tensors
            __snake_case			:Optional[int]          =						prepare_image_inputs(self.image_processor_tester			,						equal_resolution=a__			,						torchify=a__    )
            for image in image_inputs:
                 self.assertIsInstance(a__			,						torch.Tensor    )

            # Test not batched input (GLPNImageProcessor doesn't support batching)
            __snake_case			:List[Any]          =						image_processing(image_inputs[0]			,						return_tensors="""pt"""    ).pixel_values
            self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
            self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )


