


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    snake_case_     (  )  ->				int:
       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    snake_case_     (  snake_case			,							snake_case			,							snake_case     )  ->				Optional[int]:
       requires_backends(__lowerCamelCase			,							['torch']     )
       _check_torch_version()

       lowercase__:   Union[str, Any]  			=   image_tensor.unsqueeze(0     )
       lowercase__:   Optional[Any]  			=   torch.nn.functional.unfold(__lowerCamelCase			,							(patch_height, patch_width)			,							stride=(patch_height, patch_width)     )
       lowercase__:   str  			=   patches.reshape(image_tensor.size(0     )			,							image_tensor.size(1     )			,							__lowerCamelCase			,							__lowerCamelCase			,							-1     )
       lowercase__:   Union[str, 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    snake_case_     (  snake_case			,							snake_case = 36			,							snake_case = "black"			,							snake_case = "white"			,							snake_case = 5			,							snake_case = 5			,							snake_case = 5			,							snake_case = 5			,							snake_case = None			,							snake_case = None			,							)  ->				List[str]:
       requires_backends(__lowerCamelCase			,							'vision'     )
       # Add new lines so that each line is no more than 80 characters.

       lowercase__:   List[Any]  			=   textwrap.TextWrapper(width=80     )
       lowercase__:   List[Any]  			=   wrapper.wrap(text=__lowerCamelCase     )
       lowercase__:   Any  			=   "\n".join(__lowerCamelCase     )

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

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

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




def    snake_case_     (  snake_case			,							snake_case			,							**snake_case     )  ->				Optional[Any]:
       requires_backends(__lowerCamelCase			,							'vision'     )

       # Convert to PIL image if necessary
       lowercase__:   List[str]  			=   to_pil_image(__lowerCamelCase     )

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

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

       lowercase__:   Tuple  			=   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
       lowercase__:   Any  			=   to_numpy_array(__lowerCamelCase     )

       if infer_channel_dimension_format(__lowerCamelCase     ) == ChannelDimension.LAST:
              lowercase__:   Any  			=   to_channel_dimension_format(__lowerCamelCase			,							ChannelDimension.LAST     )

       return new_image





class   __a			(    _lowerCAmelCase      ):
 __lowercase :							Dict        = ['flattened_patches']
 def __init__(   self				, lowerCAmelCase__ = True				, lowerCAmelCase__ = True				, lowerCAmelCase__ = None				, lowerCAmelCase__ = 2_048				, lowerCAmelCase__ = False				, **lowerCAmelCase__				, )					->				Dict:




        '''simple docstring'''
        super().__init__(**lowerCAmelCase__ )
        lowercase__:   Any  			=   patch_size if patch_size is not None else {"height": 16, "width": 16}
        lowercase__:   Dict  			=   do_normalize
        lowercase__:   Optional[int]  			=   do_convert_rgb
        lowercase__:   str  			=   max_patches
        lowercase__:   Optional[int]  			=   is_vqa
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, **lowerCAmelCase__ )					->				Union[str, Any]:




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

        # convert to torch
        lowercase__:   Dict  			=   to_channel_dimension_format(lowerCAmelCase__				, ChannelDimension.FIRST )
        lowercase__:   int  			=   torch.from_numpy(lowerCAmelCase__ )

        lowercase__:   Tuple  			=   patch_size["height"], patch_size["width"]
        lowercase__:   Optional[int]  			=   get_image_size(lowerCAmelCase__ )

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

        lowercase__:   List[str]  			=   torch.nn.functional.interpolate(
            image.unsqueeze(0 )				, size=(resized_height, resized_width)				, mode='bilinear'				, align_corners=lowerCAmelCase__				, antialias=lowerCAmelCase__				, ).squeeze(0 )

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

        lowercase__:   Dict  			=   patches.shape
        lowercase__:   List[str]  			=   patches_shape[1]
        lowercase__:   Union[str, Any]  			=   patches_shape[2]
        lowercase__:   Any  			=   patches_shape[3]

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

        # [rows * columns, 1]
        lowercase__:   str  			=   torch.arange(lowerCAmelCase__ ).reshape([rows, 1] ).repeat(1				, lowerCAmelCase__ ).reshape([rows * columns, 1] )
        lowercase__:   Union[str, Any]  			=   torch.arange(lowerCAmelCase__ ).reshape([1, columns] ).repeat(lowerCAmelCase__				, 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]
        lowercase__:   Optional[Any]  			=   row_ids.to(torch.floataa )
        lowercase__:   Tuple  			=   col_ids.to(torch.floataa )

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

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

        lowercase__:   Dict  			=   to_numpy_array(lowerCAmelCase__ )

        return result
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ = None				, **lowerCAmelCase__ )					->				Tuple:




        '''simple docstring'''




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

        # take mean across the whole `image`
        lowercase__:   int  			=   np.mean(lowerCAmelCase__ )
        lowercase__:   Any  			=   np.std(lowerCAmelCase__ )
        lowercase__:   Tuple  			=   max(lowerCAmelCase__				, 1.0 / math.sqrt(np.prod(image.shape ) ) )

        return normalize(lowerCAmelCase__				, mean=lowerCAmelCase__				, std=lowerCAmelCase__				, **lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__ = ChannelDimension.FIRST				, **lowerCAmelCase__				, )					->				Optional[int]:




        '''simple docstring'''
        lowercase__:   str  			=   do_normalize if do_normalize is not None else self.do_normalize
        lowercase__:   str  			=   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
        lowercase__:   List[str]  			=   patch_size if patch_size is not None else self.patch_size
        lowercase__:   Optional[int]  			=   max_patches if max_patches is not None else self.max_patches
        lowercase__:   int  			=   self.is_vqa

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

        lowercase__:   Any  			=   make_list_of_images(lowerCAmelCase__ )

        if not valid_images(lowerCAmelCase__ ):
               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:
               lowercase__:   Tuple  			=   [convert_to_rgb(lowerCAmelCase__ ) for image in images]

        # All transformations expect numpy arrays.
        lowercase__:   Dict  			=   [to_numpy_array(lowerCAmelCase__ ) for image in images]

        if is_vqa:
               if header_text is None:
                      raise ValueError('A header text must be provided for VQA models.' )
               lowercase__:   Optional[Any]  			=   kwargs.pop('font_bytes'				, lowerCAmelCase__ )
               lowercase__:   str  			=   kwargs.pop('font_path'				, lowerCAmelCase__ )

               if isinstance(lowerCAmelCase__				, lowerCAmelCase__ ):
                      lowercase__:   Union[str, Any]  			=   [header_text] * len(lowerCAmelCase__ )

               lowercase__:   Optional[int]  			=   [
                   render_header(lowerCAmelCase__				, header_text[i]				, font_bytes=lowerCAmelCase__				, font_path=lowerCAmelCase__ )
                   for i, image in enumerate(lowerCAmelCase__ )
               ]

        if do_normalize:
               lowercase__:   Optional[int]  			=   [self.normalize(image=lowerCAmelCase__ ) for image in images]

        # convert to torch tensor and permute
        lowercase__:   List[Any]  			=   [
            self.extract_flattened_patches(image=lowerCAmelCase__				, max_patches=lowerCAmelCase__				, patch_size=lowerCAmelCase__ )
            for image in images
        ]

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

        lowercase__:   Tuple  			=   BatchFeature(
            data={'flattened_patches': images, 'attention_mask': attention_masks}				, tensor_type=lowerCAmelCase__ )

        return encoded_outputs









'''simple docstring'''


import pytest

from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def 							UpperCAmelCase_  (						__lowerCamelCase					:					List[str] ):
   monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"	,set() )
@pytest.fixture
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Any ):
   class       a_   :





        def __init__( self		:					int	,      lowercase		:					int						):





           """simple docstring"""




           lowercase_			:Optional[Any]    			= metric_id







   class       a_   :
        __A      =			[MetricMock(_lowerCAmelCase							) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]





        def 							lowercase__      ( self		:					Union[str, Any]						):





           """simple docstring"""




           return self._metrics







   monkeypatch.setattr("datasets.inspect.huggingface_hub"	,HfhMock() )


@pytest.mark.parametrize(
    "func, args"	,[(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))] )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Union[str, Any]	,__lowerCamelCase					:					int	,__lowerCamelCase					:					Optional[int]	,__lowerCamelCase					:					Union[str, Any]	,__lowerCamelCase					:					Tuple ):
   if "tmp_path" in args:
      lowercase_			:Union[str, Any]    			= tuple(arg if arg != "tmp_path" else tmp_path for arg in args )
   with pytest.warns(__lowerCamelCase	,match="https://huggingface.co/docs/evaluate" ):
      func(*__lowerCamelCase )


