



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:
   UpperCAmelCase_		 =					False


UpperCAmelCase_		 =					logging.get_logger(__name__)
UpperCAmelCase_		 =					"""ybelkada/fonts"""

def 	SCREAMING_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 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Dict	,   _snake_case	:Optional[int]	,   _snake_case	:int				)  ->     Any:
   requires_backends(_snake_case	,   ['''torch''']				)
   _check_torch_version()

   _A      =       image_tensor.unsqueeze(0				)
   _A      =       torch.nn.functional.unfold(_snake_case	,   (patch_height, patch_width)	,   stride=(patch_height, patch_width)				)
   _A      =       patches.reshape(image_tensor.size(0				)	,   image_tensor.size(1				)	,   _snake_case	,   _snake_case	,   -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 	SCREAMING_SNAKE_CASE_							(	_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	,   )  ->     Image.Image:
   requires_backends(_snake_case	,   '''vision'''				)
   # Add new lines so that each line is no more than 80 characters.

   _A      =       textwrap.TextWrapper(width=80				)
   _A      =       wrapper.wrap(text=_snake_case				)
   _A      =       '''\n'''.join(_snake_case				)

   if font_bytes is not None and font_path is None:
      _A      =       io.BytesIO(_snake_case				)
   elif font_path is not None:
      _A      =       font_path
   else:
      _A      =       hf_hub_download(_snake_case	,   '''Arial.TTF'''				)
   _A      =       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.
   _A      =       ImageDraw.Draw(Image.new('''RGB'''	,   (1, 1)	,   _snake_case				)				)
   _A  ,    _A  ,    _A  ,    _A      =       temp_draw.textbbox((0, 0)	,   _snake_case	,   _snake_case				)

   # 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)	,   _snake_case				)
   _A      =       ImageDraw.Draw(_snake_case				)
   draw.text(xy=(left_padding, top_padding)	,   text=_snake_case	,   fill=_snake_case	,   font=_snake_case				)
   return image

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:np.ndarray	,   _snake_case	:str	,   **_snake_case	:Optional[int]				)  ->     Any:
   requires_backends(_snake_case	,   '''vision'''				)

   # Convert to PIL image if necessary
   _A      =       to_pil_image(_snake_case				)

   _A      =       render_text(_snake_case	,   **_snake_case				)
   _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(_snake_case				)

   if infer_channel_dimension_format(_snake_case				) == ChannelDimension.LAST:
      _A      =       to_channel_dimension_format(_snake_case	,   ChannelDimension.LAST				)

   return new_image


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: Optional[Any] =  ["flattened_patches"]

 def __init__(    self					:					Any			,						__lowerCAmelCase					:					bool = True			,						__lowerCAmelCase					:					bool = True			,						__lowerCAmelCase					:					Dict[str, int] = None			,						__lowerCAmelCase					:					int = 20_48			,						__lowerCAmelCase					:					bool = False			,						**__lowerCAmelCase					:					Any			,						)   ->	None:
    super().__init__(**__lowerCAmelCase      )
    _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     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					np.ndarray			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					dict			,						**__lowerCAmelCase					:					str      )   ->	np.ndarray:
    requires_backends(self.extract_flattened_patches			,						'''torch'''      )
    _check_torch_version()

    # convert to torch
    _A      =       to_channel_dimension_format(__lowerCAmelCase			,						ChannelDimension.FIRST      )
    _A      =       torch.from_numpy(__lowerCAmelCase      )

    _A  ,    _A      =       patch_size['''height'''], patch_size['''width''']
    _A  ,    _A      =       get_image_size(__lowerCAmelCase      )

    # 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      )			,						__lowerCAmelCase      )			,						1      )
    _A      =       max(min(math.floor(scale * image_width / patch_width      )			,						__lowerCAmelCase      )			,						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=__lowerCAmelCase			,						antialias=__lowerCAmelCase			,						).squeeze(0      )

    # [1, rows, columns, patch_height * patch_width * image_channels]
    _A      =       torch_extract_patches(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase      )

    _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(__lowerCAmelCase      ).reshape([rows, 1]      ).repeat(1			,						__lowerCAmelCase      ).reshape([rows * columns, 1]      )
    _A      =       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]
    _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(__lowerCAmelCase			,						[0, 0, 0, max_patches - (rows * columns)]      ).float()

    _A      =       to_numpy_array(__lowerCAmelCase      )

    return result

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					np.ndarray			,						__lowerCAmelCase					:					Optional[Union[str, ChannelDimension]] = None			,						**__lowerCAmelCase					:					str      )   ->	np.ndarray:
    if image.dtype == np.uinta:
       _A      =       image.astype(np.floataa      )

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

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

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					ImageInput			,						__lowerCAmelCase					:					Optional[str] = None			,						__lowerCAmelCase					:					bool = None			,						__lowerCAmelCase					:					Optional[bool] = None			,						__lowerCAmelCase					:					Optional[int] = None			,						__lowerCAmelCase					:					Optional[Dict[str, int]] = None			,						__lowerCAmelCase					:					Optional[Union[str, TensorType]] = None			,						__lowerCAmelCase					:					ChannelDimension = ChannelDimension.FIRST			,						**__lowerCAmelCase					:					int			,						)   ->	ImageInput:
    _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'''			,						__lowerCAmelCase      ) is not None:
       raise ValueError('''data_format is not an accepted input as the outputs are '''      )

    _A      =       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:
       _A      =       [convert_to_rgb(__lowerCAmelCase      ) for image in images]

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

       if isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ):
          _A      =       [header_text] * len(__lowerCAmelCase      )

       _A      =       [
           render_header(__lowerCAmelCase			,						header_text[i]			,						font_bytes=__lowerCAmelCase			,						font_path=__lowerCAmelCase      )
           for i, image in enumerate(__lowerCAmelCase      )
       ]

    if do_normalize:
       _A      =       [self.normalize(image=__lowerCAmelCase      ) for image in images]

    # convert to torch tensor and permute
    _A      =       [
        self.extract_flattened_patches(image=__lowerCAmelCase			,						max_patches=__lowerCAmelCase			,						patch_size=__lowerCAmelCase      )
        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=__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


UpperCAmelCase_		 =					logging.get_logger(__name__)

UpperCAmelCase_		 =					"""▁"""

UpperCAmelCase_		 =					{"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}

UpperCAmelCase_		 =					{
    """vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
    },
    """monolingual_vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
    },
}

UpperCAmelCase_		 =					{"""vinai/bartpho-syllable""": 1_0_2_4}


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: int =  VOCAB_FILES_NAMES
 a__					: Tuple =  PRETRAINED_VOCAB_FILES_MAP
 a__					: Optional[int] =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 a__					: Tuple =  ["input_ids", "attention_mask"]

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

    _A      =       {} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        bos_token=__lowerCAmelCase			,						eos_token=__lowerCAmelCase			,						unk_token=__lowerCAmelCase			,						sep_token=__lowerCAmelCase			,						cls_token=__lowerCAmelCase			,						pad_token=__lowerCAmelCase			,						mask_token=__lowerCAmelCase			,						sp_model_kwargs=self.sp_model_kwargs			,						**__lowerCAmelCase			,						)

    _A      =       vocab_file
    _A      =       monolingual_vocab_file
    _A      =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
    self.sp_model.Load(str(__lowerCAmelCase      )      )

    # Load the reduced vocab

    # Keep order of special tokens for backward compatibility
    _A      =       {}
    _A      =       0
    for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
       if str(__lowerCAmelCase      ) not in self.fairseq_tokens_to_ids:
          _A      =       cnt
          cnt += 1
    with open(__lowerCAmelCase			,						'''r'''			,						encoding='''utf-8'''      ) as f:
       for line in f.readlines():
          _A      =       line.strip().split()[0]
          _A      =       len(self.fairseq_tokens_to_ids      )
    if str(__lowerCAmelCase      ) not in self.fairseq_tokens_to_ids:
       _A      =       len(self.fairseq_tokens_to_ids      )

    _A      =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}

 def __getstate__(    self					:					Any      )   ->	List[Any]:
    _A      =       self.__dict__.copy()
    _A      =       None
    _A      =       self.sp_model.serialized_model_proto()
    return state

 def __setstate__(    self					:					Union[str, Any]			,						__lowerCAmelCase					:					Dict      )   ->	List[Any]:
    _A      =       d

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

    _A      =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
    self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					List[int]			,						__lowerCAmelCase					:					Optional[List[int]] = None      )   ->	List[int]:

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

 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					List[int]			,						__lowerCAmelCase					:					Optional[List[int]] = None			,						__lowerCAmelCase					:					bool = False      )   ->	List[int]:

    if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=__lowerCAmelCase			,						token_ids_a=__lowerCAmelCase			,						already_has_special_tokens=__lowerCAmelCase      )

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

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					List[int]			,						__lowerCAmelCase					:					Optional[List[int]] = None      )   ->	List[int]:
    _A      =       [self.sep_token_id]
    _A      =       [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     snake_case_      (    self					:					Optional[int]      )   ->	Union[str, Any]:
    return len(self.fairseq_ids_to_tokens      )

 def     snake_case_      (    self					:					Dict      )   ->	Optional[Any]:
    _A      =       {self.convert_ids_to_tokens(__lowerCAmelCase      ): i for i in range(self.vocab_size      )}
    vocab.update(self.added_tokens_encoder      )
    return vocab

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					str      )   ->	List[str]:
    return self.sp_model.encode(__lowerCAmelCase			,						out_type=__lowerCAmelCase      )

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					Optional[Any]      )   ->	Dict:
    if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
    else:
       return self.unk_token_id

 def     snake_case_      (    self					:					int			,						__lowerCAmelCase					:					Optional[int]      )   ->	List[str]:
    return self.fairseq_ids_to_tokens[index]

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					Union[str, Any]      )   ->	Tuple:
    _A      =       ''''''.join(__lowerCAmelCase      ).replace(__lowerCAmelCase			,						''' '''      ).strip()
    return out_string

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Optional[str] = None      )   ->	Tuple[str]:
    if not os.path.isdir(__lowerCAmelCase      ):
       logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
       return
    _A      =       os.path.join(
        __lowerCAmelCase			,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )
    _A      =       os.path.join(
        __lowerCAmelCase			,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file''']			,						)

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

    if os.path.abspath(self.monolingual_vocab_file      ) != os.path.abspath(
        __lowerCAmelCase      ) and os.path.isfile(self.monolingual_vocab_file      ):
       copyfile(self.monolingual_vocab_file			,						__lowerCAmelCase      )
    elif not os.path.isfile(self.monolingual_vocab_file      ):
       with open(__lowerCAmelCase			,						'''w'''			,						encoding='''utf-8'''      ) as fp:
          for token in self.fairseq_tokens_to_ids:
             if token not in self.all_special_tokens:
                fp.write(f'''{str(__lowerCAmelCase      )} \n'''      )

    return out_vocab_file, out_monolingual_vocab_file



