

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   :     Optional[Any]    							=     False


_lowerCAmelCase   :     str    							=     logging.get_logger(__name__)
_lowerCAmelCase   :     Union[str, Any]    							=     '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  :    Dict			,	_snake_case  :    Dict			,	_snake_case  :    Dict      ):




			"""simple docstring"""






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

			__a     =image_tensor.unsqueeze(0      )
			__a     =torch.nn.functional.unfold(lowerCAmelCase__			,	(patch_height, patch_width)			,	stride=(patch_height, patch_width)      )
			__a     =patches.reshape(image_tensor.size(0      )			,	image_tensor.size(1      )			,	lowerCAmelCase__			,	lowerCAmelCase__			,	-1      )
			__a     =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(lowerCAmelCase__			,	'vision'      )
			# Add new lines so that each line is no more than 80 characters.

			__a     =textwrap.TextWrapper(width=80      )
			__a     =wrapper.wrap(text=lowerCAmelCase__      )
			__a     ='\n'.join(lowerCAmelCase__      )

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

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

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






def 			UpperCamelCase_(				_snake_case  :    np.ndarray			,	_snake_case  :    str			,	**_snake_case  :    Tuple      ):




			"""simple docstring"""






			requires_backends(lowerCAmelCase__			,	'vision'      )

			# Convert to PIL image if necessary
			__a     =to_pil_image(lowerCAmelCase__      )

			__a     =render_text(lowerCAmelCase__			,	**lowerCAmelCase__      )
			__a     =max(header_image.width			,	image.width      )

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

			__a     =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
			__a     =to_numpy_array(lowerCAmelCase__      )

			if infer_channel_dimension_format(lowerCAmelCase__      ) == ChannelDimension.LAST:
						__a     =to_channel_dimension_format(lowerCAmelCase__			,	ChannelDimension.LAST      )

			return new_image






class       __magic_name__       (     _lowerCamelCase ):
			SCREAMING_SNAKE_CASE				  =		['flattened_patches']





			def __init__(						self   ,    __snake_case = True   ,    __snake_case = True   ,    __snake_case = None   ,    __snake_case = 2048   ,    __snake_case = False   ,    **__snake_case   ,    )  ->					None:




						'''simple docstring'''
						super().__init__(**__UpperCamelCase   )
						__a     =patch_size if patch_size is not None else {'height': 16, 'width': 16}
						__a     =do_normalize
						__a     =do_convert_rgb
						__a     =max_patches
						__a     =is_vqa





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




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

						# convert to torch
						__a     =to_channel_dimension_format(__UpperCamelCase   ,    ChannelDimension.FIRST   )
						__a     =torch.from_numpy(__UpperCamelCase   )

						__a ,       __a     =patch_size['height'], patch_size['width']
						__a ,       __a     =get_image_size(__UpperCamelCase   )

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

						__a     =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]
						__a     =torch_extract_patches(__UpperCamelCase   ,    __UpperCamelCase   ,    __UpperCamelCase   )

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

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

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

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

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

						__a     =to_numpy_array(__UpperCamelCase   )

						return result





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




						'''simple docstring'''
						if image.dtype == np.uinta:
									__a     =image.astype(np.floataa   )

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

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





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




						'''simple docstring'''
						__a     =do_normalize if do_normalize is not None else self.do_normalize
						__a     =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
						__a     =patch_size if patch_size is not None else self.patch_size
						__a     =max_patches if max_patches is not None else self.max_patches
						__a     =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 '   )

						__a     =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:
									__a     =[convert_to_rgb(__UpperCamelCase   ) for image in images]

						# All transformations expect numpy arrays.
						__a     =[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.'   )
									__a     =kwargs.pop('font_bytes'   ,    __UpperCamelCase   )
									__a     =kwargs.pop('font_path'   ,    __UpperCamelCase   )

									if isinstance(__UpperCamelCase   ,    __UpperCamelCase   ):
												__a     =[header_text] * len(__UpperCamelCase   )

									__a     =[
									    render_header(__UpperCamelCase   ,    header_text[i]   ,    font_bytes=__UpperCamelCase   ,    font_path=__UpperCamelCase   )
									    for i, image in enumerate(__UpperCamelCase   )
									]

						if do_normalize:
									__a     =[self.normalize(image=__UpperCamelCase   ) for image in images]

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

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

						__a     =BatchFeature(
						    data={'flattened_patches': images, 'attention_mask': attention_masks}   ,    tensor_type=__UpperCamelCase   )

						return encoded_outputs











from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case     :List[str]			       =logging.get_logger(__name__)

__snake_case     :int			       ={'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}



class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     Optional[Any]								=     'openai-gpt'
				A_   :     Any								=     {
				    'max_position_embeddings': 'n_positions',
				    'hidden_size': 'n_embd',
				    'num_attention_heads': 'n_head',
				    'num_hidden_layers': 'n_layer',
				}


				def __init__(							self      :  List[Any]						, __UpperCamelCase      :  List[str]=40_478						, __UpperCamelCase      :  List[Any]=512						, __UpperCamelCase      :  List[Any]=768						, __UpperCamelCase      :  Optional[int]=12						, __UpperCamelCase      :  Dict=12						, __UpperCamelCase      :  List[str]="gelu"						, __UpperCamelCase      :  str=0.1						, __UpperCamelCase      :  Optional[Any]=0.1						, __UpperCamelCase      :  Union[str, Any]=0.1						, __UpperCamelCase      :  List[str]=1e-5						, __UpperCamelCase      :  List[Any]=0.0_2						, __UpperCamelCase      :  str="cls_index"						, __UpperCamelCase      :  int=True						, __UpperCamelCase      :  int=None						, __UpperCamelCase      :  Optional[int]=True						, __UpperCamelCase      :  Tuple=0.1						, **__UpperCamelCase      :  List[Any]						, )		->     List[str]:
											A      						=    vocab_size
											A      						=    n_positions
											A      						=    n_embd
											A      						=    n_layer
											A      						=    n_head
											A      						=    afn
											A      						=    resid_pdrop
											A      						=    embd_pdrop
											A      						=    attn_pdrop
											A      						=    layer_norm_epsilon
											A      						=    initializer_range
											A      						=    summary_type
											A      						=    summary_use_proj
											A      						=    summary_activation
											A      						=    summary_first_dropout
											A      						=    summary_proj_to_labels
											super().__init__(**__UpperCamelCase       )