


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			: Optional[int]				   =					False


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



def 							UpperCamelCase_( )       ->		str:
    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_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Any:
    requires_backends(lowerCamelCase_				,					['torch']					)
    _check_torch_version()

    _lowercase			:				Tuple 						=						image_tensor.unsqueeze(0					)
    _lowercase			:				str 						=						torch.nn.functional.unfold(lowerCamelCase_				,					(patch_height, patch_width)				,					stride=(patch_height, patch_width)					)
    _lowercase			:				Dict 						=						patches.reshape(image_tensor.size(0					)				,					image_tensor.size(1					)				,					lowerCamelCase_				,					lowerCamelCase_				,					-1					)
    _lowercase			:				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 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_ = 36				,					lowerCamelCase_ = "black"				,					lowerCamelCase_ = "white"				,					lowerCamelCase_ = 5				,					lowerCamelCase_ = 5				,					lowerCamelCase_ = 5				,					lowerCamelCase_ = 5				,					lowerCamelCase_ = None				,					lowerCamelCase_ = None				,					)       ->		Image.Image:
    requires_backends(lowerCamelCase_				,					'vision'					)
    # Add new lines so that each line is no more than 80 characters.

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

    if font_bytes is not None and font_path is None:
        _lowercase			:				Union[str, Any] 						=						io.BytesIO(lowerCamelCase_					)
    elif font_path is not None:
        _lowercase			:				str 						=						font_path
    else:
        _lowercase			:				Optional[int] 						=						hf_hub_download(lowerCamelCase_				,					'Arial.TTF'					)
    _lowercase			:				Tuple 						=						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			:				Union[str, Any] 						=						ImageDraw.Draw(Image.new('RGB'				,					(1, 1)				,					lowerCamelCase_					)					)
    _lowercase						,							_lowercase						,							_lowercase						,							_lowercase			:				Union[str, Any] 						=						temp_draw.textbbox((0, 0)				,					lowerCamelCase_				,					lowerCamelCase_					)

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



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					**lowerCamelCase_					)       ->		Any:
    requires_backends(lowerCamelCase_				,					'vision'					)

    # Convert to PIL image if necessary
    _lowercase			:				str 						=						to_pil_image(lowerCamelCase_					)

    _lowercase			:				Optional[int] 						=						render_text(lowerCamelCase_				,					**lowerCamelCase_					)
    _lowercase			:				str 						=						max(header_image.width				,					image.width					)

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

    _lowercase			:				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
    _lowercase			:				str 						=						to_numpy_array(lowerCamelCase_					)

    if infer_channel_dimension_format(lowerCamelCase_					) == ChannelDimension.LAST:
        _lowercase			:				int 						=						to_channel_dimension_format(lowerCamelCase_				,					ChannelDimension.LAST					)

    return new_image






class _lowerCamelCase(		_a  ):
       lowercase_					:      List[Any]       					=     ["""flattened_patches"""]
       def __init__(   self, lowerCamelCase = True, lowerCamelCase = True, lowerCamelCase = None, lowerCamelCase = 20_48, lowerCamelCase = False, **lowerCamelCase, )			->  None:




           """simple docstring"""

           super().__init__(**lowerCamelCase)
           _lowercase			:				Optional[int] 						=						patch_size if patch_size is not None else {'height': 16, 'width': 16}
           _lowercase			:				Union[str, Any] 						=						do_normalize
           _lowercase			:				Optional[int] 						=						do_convert_rgb
           _lowercase			:				int 						=						max_patches
           _lowercase			:				List[str] 						=						is_vqa
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, **lowerCamelCase)			->  np.ndarray:




           """simple docstring"""

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

           # convert to torch
           _lowercase			:				List[Any] 						=						to_channel_dimension_format(lowerCamelCase, ChannelDimension.FIRST)
           _lowercase			:				int 						=						torch.from_numpy(lowerCamelCase)

           _lowercase						,							_lowercase			:				Union[str, Any] 						=						patch_size['height'], patch_size['width']
           _lowercase						,							_lowercase			:				List[str] 						=						get_image_size(lowerCamelCase)

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

           _lowercase			:				Tuple 						=						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			:				Dict 						=						torch_extract_patches(lowerCamelCase, lowerCamelCase, lowerCamelCase)

           _lowercase			:				Tuple 						=						patches.shape
           _lowercase			:				List[str] 						=						patches_shape[1]
           _lowercase			:				Optional[Any] 						=						patches_shape[2]
           _lowercase			:				Any 						=						patches_shape[3]

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

           # [rows * columns, 1]
           _lowercase			:				str 						=						torch.arange(lowerCamelCase).reshape([rows, 1]).repeat(1, lowerCamelCase).reshape([rows * columns, 1])
           _lowercase			:				str 						=						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			:				Any 						=						row_ids.to(torch.floataa)
           _lowercase			:				List[str] 						=						col_ids.to(torch.floataa)

           # [rows * columns, 2 + patch_height * patch_width * image_channels]
           _lowercase			:				int 						=						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			:				Tuple 						=						to_numpy_array(lowerCamelCase)

           return result
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None, **lowerCamelCase)			->  np.ndarray:




           """simple docstring"""

           if image.dtype == np.uinta:
               _lowercase			:				Optional[int] 						=						image.astype(np.floataa)

           # take mean across the whole `image`
           _lowercase			:				Dict 						=						np.mean(lowerCamelCase)
           _lowercase			:				Dict 						=						np.std(lowerCamelCase)
           _lowercase			:				str 						=						max(lowerCamelCase, 1.0 / math.sqrt(np.prod(image.shape)))

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




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = ChannelDimension.FIRST, **lowerCamelCase, )			->  ImageInput:




           """simple docstring"""

           _lowercase			:				List[str] 						=						do_normalize if do_normalize is not None else self.do_normalize
           _lowercase			:				Optional[Any] 						=						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			:				Tuple 						=						max_patches if max_patches is not None else self.max_patches
           _lowercase			:				List[str] 						=						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			:				Optional[int] 						=						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			:				int 						=						[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[int] 						=						kwargs.pop('font_bytes', lowerCamelCase)
               _lowercase			:				Dict 						=						kwargs.pop('font_path', lowerCamelCase)

               if isinstance(lowerCamelCase, lowerCamelCase):
                   _lowercase			:				List[str] 						=						[header_text] * len(lowerCamelCase)

               _lowercase			:				Tuple 						=						[
                   render_header(lowerCamelCase, header_text[i], font_bytes=lowerCamelCase, font_path=lowerCamelCase)
                   for i, image in enumerate(lowerCamelCase)
               ]

           if do_normalize:
               _lowercase			:				Any 						=						[self.normalize(image=lowerCamelCase) for image in images]

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

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

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

           return encoded_outputs



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowercase:	int       			=		logging.get_logger(__name__)

_lowercase:	Union[str, Any]       			=		{'''vocab_file''': '''sentencepiece.bpe.model'''}

_lowercase:	Dict       			=		{
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    }
}

_lowercase:	List[Any]       			=		{
    '''camembert-base''': 5_1_2,
}

_lowercase:	Dict       			=		'''▁'''



class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							=VOCAB_FILES_NAMES
			UpperCamelCase__    							=PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__    							=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__    							=["input_ids", "attention_mask"]





			def __init__(       self	:	str  ,  lowercase__	:	int  ,  lowercase__	:	Tuple="<s>"  ,  lowercase__	:	Optional[int]="</s>"  ,  lowercase__	:	Optional[Any]="</s>"  ,  lowercase__	:	Any="<s>"  ,  lowercase__	:	Union[str, Any]="<unk>"  ,  lowercase__	:	Union[str, Any]="<pad>"  ,  lowercase__	:	Optional[int]="<mask>"  ,  lowercase__	:	str=["<s>NOTUSED", "</s>NOTUSED"]  ,  lowercase__	:	Optional[Dict[str, Any]] = None  ,  **lowercase__	:	Union[str, Any]  ,  ):
										# Mask token behave like a normal word, i.e. include the space before it
										_lowerCAmelCase							=							AddedToken(lowercase__  ,  lstrip=lowercase__  ,  rstrip=lowercase__						) if isinstance(lowercase__  ,  lowercase__						) else mask_token

										_lowerCAmelCase							=							{} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    bos_token=lowercase__  ,  eos_token=lowercase__  ,  unk_token=lowercase__  ,  sep_token=lowercase__  ,  cls_token=lowercase__  ,  pad_token=lowercase__  ,  mask_token=lowercase__  ,  additional_special_tokens=lowercase__  ,  sp_model_kwargs=self.sp_model_kwargs  ,  **lowercase__  ,  )
										_lowerCAmelCase							=							spm.SentencePieceProcessor(**self.sp_model_kwargs						)
										self.sp_model.Load(str(lowercase__						)						)
										_lowerCAmelCase							=							vocab_file
										# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
										# sentencepiece vocabulary (this is the case for <s> and </s>
										_lowerCAmelCase							=							{'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
										_lowerCAmelCase							=							len(self.fairseq_tokens_to_ids						)
										_lowerCAmelCase							=							len(self.sp_model						) + len(self.fairseq_tokens_to_ids						)
										_lowerCAmelCase							=							{v: k for k, v in self.fairseq_tokens_to_ids.items()}





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]  ,  lowercase__	:	List[int]  ,  lowercase__	:	Optional[List[int]] = None						):

										if token_ids_a is None:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										_lowerCAmelCase							=							[self.cls_token_id]
										_lowerCAmelCase							=							[self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any  ,  lowercase__	:	List[int]  ,  lowercase__	:	Optional[List[int]] = None  ,  lowercase__	:	bool = False						):
										if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=lowercase__  ,  token_ids_a=lowercase__  ,  already_has_special_tokens=lowercase__						)

										if token_ids_a is None:
																	return [1] + ([0] * len(lowercase__						)) + [1]
										return [1] + ([0] * len(lowercase__						)) + [1, 1] + ([0] * len(lowercase__						)) + [1]





			def   SCREAMING_SNAKE_CASE__     (       self	:	int  ,  lowercase__	:	List[int]  ,  lowercase__	:	Optional[List[int]] = None						):
										_lowerCAmelCase							=							[self.sep_token_id]
										_lowerCAmelCase							=							[self.cls_token_id]

										if token_ids_a is None:
																	return len(cls + token_ids_a + sep						) * [0]
										return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]						):
										return len(self.fairseq_tokens_to_ids						) + len(self.sp_model						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										_lowerCAmelCase							=							{self.convert_ids_to_tokens(lowercase__						): i for i in range(self.vocab_size						)}
										vocab.update(self.added_tokens_encoder						)
										return vocab





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]  ,  lowercase__	:	str						):
										return self.sp_model.encode(lowercase__  ,  out_type=lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]  ,  lowercase__	:	Optional[Any]						):
										if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
										elif self.sp_model.PieceToId(lowercase__						) == 0:
																	# Convert sentence piece unk token to fairseq unk token index
																	return self.unk_token_id
										return self.fairseq_offset + self.sp_model.PieceToId(lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]  ,  lowercase__	:	Tuple						):
										if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
										return self.sp_model.IdToPiece(index - self.fairseq_offset						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	int  ,  lowercase__	:	Optional[int]						):
										_lowerCAmelCase							=							[]
										_lowerCAmelCase							=							''
										_lowerCAmelCase							=							False
										for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																								if not prev_is_special:
																															out_string += " "
																								out_string += self.sp_model.decode(lowercase__						) + token
																								_lowerCAmelCase							=							True
																								_lowerCAmelCase							=							[]
																	else:
																								current_sub_tokens.append(lowercase__						)
																								_lowerCAmelCase							=							False
										out_string += self.sp_model.decode(lowercase__						)
										return out_string.strip()





			def __getstate__(       self	:	Any						):
										_lowerCAmelCase							=							self.__dict__.copy()
										_lowerCAmelCase							=							None
										return state





			def __setstate__(       self	:	Optional[Any]  ,  lowercase__	:	Union[str, Any]						):
										_lowerCAmelCase							=							d

										# for backward compatibility
										if not hasattr(self  ,  'sp_model_kwargs'						):
																	_lowerCAmelCase							=							{}

										_lowerCAmelCase							=							spm.SentencePieceProcessor(**self.sp_model_kwargs						)
										self.sp_model.Load(self.vocab_file						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]  ,  lowercase__	:	str  ,  lowercase__	:	Optional[str] = None						):
										if not os.path.isdir(lowercase__						):
																	logger.error(f'Vocabulary path ({save_directory}) should be a directory'						)
																	return
										_lowerCAmelCase							=							os.path.join(
										    lowercase__  ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

										if os.path.abspath(self.vocab_file						) != os.path.abspath(lowercase__						) and os.path.isfile(self.vocab_file						):
																	copyfile(self.vocab_file  ,  lowercase__						)
										elif not os.path.isfile(self.vocab_file						):
																	with open(lowercase__  ,  'wb'						) as fi:
																								_lowerCAmelCase							=							self.sp_model.serialized_model_proto()
																								fi.write(lowercase__						)

										return (out_vocab_file,)






