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:
      SCREAMING_SNAKE_CASE__		:       Dict       						=       False


SCREAMING_SNAKE_CASE__		:       str       						=       logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__		:       int       						=       "ybelkada/fonts"







def      _a					(   ):







     '''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      _a					(   lowercase__	:      Union[str, Any]  ,	lowercase__	:      List[Any]  ,	lowercase__	:      Any			):







     '''simple docstring'''


     requires_backends(lowercase__  ,	['torch']			)
     _check_torch_version()

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







     '''simple docstring'''


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

     SCREAMING_SNAKE_CASE__					:					List[str]      =		textwrap.TextWrapper(width=80			)
     SCREAMING_SNAKE_CASE__					:					int      =		wrapper.wrap(text=lowercase__			)
     SCREAMING_SNAKE_CASE__					:					str      =		'\n'.join(lowercase__			)

     if font_bytes is not None and font_path is None:
          SCREAMING_SNAKE_CASE__					:					str      =		io.BytesIO(lowercase__			)
     elif font_path is not None:
          SCREAMING_SNAKE_CASE__					:					int      =		font_path
     else:
          SCREAMING_SNAKE_CASE__					:					Tuple      =		hf_hub_download(lowercase__  ,	'Arial.TTF'			)
     SCREAMING_SNAKE_CASE__					:					Dict      =		ImageFont.truetype(lowercase__  ,	encoding='UTF-8'  ,	size=lowercase__			)

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

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







def      _a					(   lowercase__	:      np.ndarray  ,	lowercase__	:      str  ,	**lowercase__	:      Union[str, Any]			):







     '''simple docstring'''


     requires_backends(lowercase__  ,	'vision'			)

     # Convert to PIL image if necessary
     SCREAMING_SNAKE_CASE__					:					int      =		to_pil_image(lowercase__			)

     SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		render_text(lowercase__  ,	**lowercase__			)
     SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		max(header_image.width  ,	image.width			)

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

     SCREAMING_SNAKE_CASE__					:					int      =		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
     SCREAMING_SNAKE_CASE__					:					List[str]      =		to_numpy_array(lowercase__			)

     if infer_channel_dimension_format(lowercase__			) == ChannelDimension.LAST:
          SCREAMING_SNAKE_CASE__					:					Dict      =		to_channel_dimension_format(lowercase__  ,	ChannelDimension.LAST			)

     return new_image







class    snake_case       (   UpperCamelCase_    ):
  lowercase_								=  ['flattened_patches']




  def __init__(   self  :						List[Any]  ,			a_  :						bool = True  ,			a_  :						bool = True  ,			a_  :						Dict[str, int] = None  ,			a_  :						int = 2048  ,			a_  :						bool = False  ,			**a_  :						List[Any]  ,			)->							None:




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




  def 						__lowercase(   self  :						Union[str, Any]  ,			a_  :						np.ndarray  ,			a_  :						int  ,			a_  :						dict  ,			**a_  :						Dict					)->							np.ndarray:




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

       # convert to torch
       SCREAMING_SNAKE_CASE__					:					Any      =		to_channel_dimension_format(a_  ,			ChannelDimension.FIRST					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		torch.from_numpy(a_					)

       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					int      =		patch_size['height'], patch_size['width']
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					List[Any]      =		get_image_size(a_					)

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

       SCREAMING_SNAKE_CASE__					:					str      =		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]
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		torch_extract_patches(a_  ,			a_  ,			a_					)

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		patches.shape
       SCREAMING_SNAKE_CASE__					:					Dict      =		patches_shape[1]
       SCREAMING_SNAKE_CASE__					:					List[str]      =		patches_shape[2]
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		patches_shape[3]

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

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

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

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

       SCREAMING_SNAKE_CASE__					:					int      =		to_numpy_array(a_					)

       return result




  def 						__lowercase(   self  :						Dict  ,			a_  :						np.ndarray  ,			a_  :						Optional[Union[str, ChannelDimension]] = None  ,			**a_  :						Optional[int]					)->							np.ndarray:




       """simple docstring"""
       if image.dtype == np.uinta:
            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		image.astype(np.floataa					)

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

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




  def 						__lowercase(   self  :						Optional[Any]  ,			a_  :						ImageInput  ,			a_  :						Optional[str] = None  ,			a_  :						bool = None  ,			a_  :						Optional[bool] = None  ,			a_  :						Optional[int] = None  ,			a_  :						Optional[Dict[str, int]] = None  ,			a_  :						Optional[Union[str, TensorType]] = None  ,			a_  :						ChannelDimension = ChannelDimension.FIRST  ,			**a_  :						str  ,			)->							ImageInput:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
       SCREAMING_SNAKE_CASE__					:					str      =		patch_size if patch_size is not None else self.patch_size
       SCREAMING_SNAKE_CASE__					:					List[str]      =		max_patches if max_patches is not None else self.max_patches
       SCREAMING_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 '					)

       SCREAMING_SNAKE_CASE__					:					Any      =		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:
            SCREAMING_SNAKE_CASE__					:					str      =		[convert_to_rgb(a_					) for image in images]

       # All transformations expect numpy arrays.
       SCREAMING_SNAKE_CASE__					:					Tuple      =		[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.'					)
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		kwargs.pop('font_bytes'  ,			a_					)
            SCREAMING_SNAKE_CASE__					:					Tuple      =		kwargs.pop('font_path'  ,			a_					)

            if isinstance(a_  ,			a_					):
                 SCREAMING_SNAKE_CASE__					:					List[Any]      =		[header_text] * len(a_					)

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

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

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

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

       SCREAMING_SNAKE_CASE__					:					List[str]      =		BatchFeature(
           data={'flattened_patches': images, 'attention_mask': attention_masks}  ,			tensor_type=a_					)

       return encoded_outputs










'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							["""image_processor""", """tokenizer"""]
 __lowercase  						=							"""CLIPImageProcessor"""
 __lowercase  						=							("""CLIPTokenizer""", """CLIPTokenizerFast""")
 def __init__(							self :List[Any]		,			lowercase_ :List[str]=None		,			lowercase_ :Any=None		,			**lowercase_ :Union[str, Any]				)->				Optional[Any]:
     A__							=     None
     if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."		,			lowercase_		,			)
         A__							=     kwargs.pop("feature_extractor"				)

     A__							=     image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
         raise ValueError("You need to specify an `image_processor`."				)
     if tokenizer is None:
         raise ValueError("You need to specify a `tokenizer`."				)

     super().__init__(lowercase_		,			lowercase_				)
 def __call__(							self :Optional[Any]		,			lowercase_ :Optional[int]=None		,			lowercase_ :Dict=None		,			lowercase_ :List[Any]=None		,			**lowercase_ :Optional[Any]				)->				Union[str, Any]:

     if text is None and images is None:
         raise ValueError("You have to specify either text or images. Both cannot be none."				)

     if text is not None:
         A__							=     self.tokenizer(lowercase_		,			return_tensors=lowercase_		,			**lowercase_				)

     if images is not None:
         A__							=     self.image_processor(lowercase_		,			return_tensors=lowercase_		,			**lowercase_				)

     if text is not None and images is not None:
         A__							=     image_features.pixel_values
         return encoding
     elif text is not None:
         return encoding
     else:
         return BatchEncoding(data=dict(**lowercase_				)		,			tensor_type=lowercase_				)
 def 	UpperCAmelCase_				(							self :int		,			*lowercase_ :Tuple		,			**lowercase_ :Any				)->				Dict:
     return self.tokenizer.batch_decode(*lowercase_		,			**lowercase_				)
 def 	UpperCAmelCase_				(							self :Any		,			*lowercase_ :Any		,			**lowercase_ :Any				)->				List[str]:
     return self.tokenizer.decode(*lowercase_		,			**lowercase_				)
 @property
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Optional[int]:
     A__							=     self.tokenizer.model_input_names
     A__							=     self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)
 @property
 def 	UpperCAmelCase_				(							self :List[Any]				)->				str:
     warnings.warn(
         "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,			lowercase_		,			)
     return self.image_processor_class



 @property
 def 	UpperCAmelCase_				(							self :List[str]				)->				List[Any]:
     warnings.warn(
         "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,			lowercase_		,			)
     return self.image_processor



