





'''simple docstring'''


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					:     Optional[Any]												='''ybelkada/fonts'''
def 							UpperCAmelCase_  (						):
   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					:					List[Any]	,__lowerCamelCase					:					Union[str, Any]	,__lowerCamelCase					:					Optional[Any] ):
   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 							UpperCAmelCase_  (						__lowerCamelCase					:					str	,__lowerCamelCase					:					int = 36	,__lowerCamelCase					:					str = "black"	,__lowerCamelCase					:					str = "white"	,__lowerCamelCase					:					int = 5	,__lowerCamelCase					:					int = 5	,__lowerCamelCase					:					int = 5	,__lowerCamelCase					:					int = 5	,__lowerCamelCase					:					Optional[bytes] = None	,__lowerCamelCase					:					Optional[str] = None	,):
   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_			,							lowercase_			,							lowercase_			,							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 							UpperCAmelCase_  (						__lowerCamelCase					:					np.ndarray	,__lowerCamelCase					:					str	,**__lowerCamelCase					:					str ):
   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							):
     __A      =			["flattened_patches"]





     def __init__( self		:					Optional[int]	,      lowercase		:					bool = True	,      lowercase		:					bool = True	,      lowercase		:					Dict[str, int] = None	,      lowercase		:					int = 2_048	,      lowercase		:					bool = False	,      **lowercase		:					List[str]	,      ):





        """simple docstring"""




        super().__init__(**lowercase						)
        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 							lowercase__      ( self		:					Tuple	,      lowercase		:					np.ndarray	,      lowercase		:					int	,      lowercase		:					dict	,      **lowercase		:					Optional[Any]						):





        """simple docstring"""




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

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

        lowercase_			,							lowercase_			:Tuple    			= patch_size["height"], patch_size["width"]
        lowercase_			,							lowercase_			:Optional[int]    			= get_image_size(lowercase						)

        # 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						)	,      lowercase						)	,      1						)
        lowercase_			:Any    			= max(min(math.floor(scale * image_width / patch_width						)	,      lowercase						)	,      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=lowercase	,      antialias=lowercase	,      ).squeeze(0						)

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

        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(lowercase						).reshape([rows, 1]						).repeat(1	,      lowercase						).reshape([rows * columns, 1]						)
        lowercase_			:Union[str, Any]    			= torch.arange(lowercase						).reshape([1, columns]						).repeat(lowercase	,      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(lowercase	,      [0, 0, 0, max_patches - (rows * columns)]						).float()

        lowercase_			:Dict    			= to_numpy_array(lowercase						)

        return result





     def 							lowercase__      ( self		:					Union[str, Any]	,      lowercase		:					np.ndarray	,      lowercase		:					Optional[Union[str, ChannelDimension]] = None	,      **lowercase		:					List[Any]						):





        """simple docstring"""




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

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

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







     def 							lowercase__      ( self		:					Any	,      lowercase		:					ImageInput	,      lowercase		:					Optional[str] = None	,      lowercase		:					bool = None	,      lowercase		:					Optional[bool] = None	,      lowercase		:					Optional[int] = None	,      lowercase		:					Optional[Dict[str, int]] = None	,      lowercase		:					Optional[Union[str, TensorType]] = None	,      lowercase		:					ChannelDimension = ChannelDimension.FIRST	,      **lowercase		:					Union[str, Any]	,      ):





        """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"	,      lowercase						) is not None:
           raise ValueError("data_format is not an accepted input as the outputs are "						)

        lowercase_			:Any    			= make_list_of_images(lowercase						)

        if not valid_images(lowercase						):
           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(lowercase						) for image in images]

        # All transformations expect numpy arrays.
        lowercase_			:Dict    			= [to_numpy_array(lowercase						) 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"	,      lowercase						)
           lowercase_			:str    			= kwargs.pop("font_path"	,      lowercase						)

           if isinstance(lowercase	,      lowercase						):
              lowercase_			:Union[str, Any]    			= [header_text] * len(lowercase						)

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

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

        # convert to torch tensor and permute
        lowercase_			:List[Any]    			= [
            self.extract_flattened_patches(image=lowercase	,      max_patches=lowercase	,      patch_size=lowercase						)
            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=lowercase						)

        return encoded_outputs









'''simple docstring'''


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


lowerCAmelCase					:     Tuple												=logging.get_logger(__name__)


lowerCAmelCase					:     List[str]												={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all MVP models at https://huggingface.co/models?filter=mvp
lowerCAmelCase					:     Optional[int]												={
    '''vocab_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
    },
    '''added_tokens.json''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
    },
    '''merges_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json''',
    },
}

lowerCAmelCase					:     List[Any]												={
    '''RUCAIBox/mvp''': 1_024,
}
class       a_   (				_lowerCAmelCase							):
     __A      =			VOCAB_FILES_NAMES
     __A      =			PRETRAINED_VOCAB_FILES_MAP
     __A      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __A      =			["input_ids", "attention_mask"]
     __A      =			MvpTokenizer





     def __init__( self		:					Optional[Any]	,      lowercase		:					Any=None	,      lowercase		:					List[Any]=None	,      lowercase		:					Dict=None	,      lowercase		:					int="replace"	,      lowercase		:					int="<s>"	,      lowercase		:					List[str]="</s>"	,      lowercase		:					Optional[Any]="</s>"	,      lowercase		:					List[str]="<s>"	,      lowercase		:					List[str]="<unk>"	,      lowercase		:					List[str]="<pad>"	,      lowercase		:					Tuple="<mask>"	,      lowercase		:					Tuple=False	,      lowercase		:					Dict=True	,      **lowercase		:					List[str]	,      ):





        """simple docstring"""




        super().__init__(
            lowercase	,      lowercase	,      tokenizer_file=lowercase	,      errors=lowercase	,      bos_token=lowercase	,      eos_token=lowercase	,      sep_token=lowercase	,      cls_token=lowercase	,      unk_token=lowercase	,      pad_token=lowercase	,      mask_token=lowercase	,      add_prefix_space=lowercase	,      trim_offsets=lowercase	,      **lowercase	,      )

        lowercase_			:Dict    			= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
        if pre_tok_state.get("add_prefix_space"	,      lowercase						) != add_prefix_space:
           lowercase_			:List[str]    			= getattr(lowercase	,      pre_tok_state.pop("type"						)						)
           lowercase_			:int    			= add_prefix_space
           lowercase_			:Optional[int]    			= pre_tok_class(**lowercase						)

        lowercase_			:Any    			= add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        lowercase_			:List[Any]    			= "post_processor"
        lowercase_			:str    			= getattr(self.backend_tokenizer	,      lowercase	,      lowercase						)
        if tokenizer_component_instance:
           lowercase_			:Any    			= json.loads(tokenizer_component_instance.__getstate__()						)

           # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
           if "sep" in state:
              lowercase_			:int    			= tuple(state["sep"]						)
           if "cls" in state:
              lowercase_			:Any    			= tuple(state["cls"]						)

           lowercase_			:int    			= False

           if state.get("add_prefix_space"	,      lowercase						) != add_prefix_space:
              lowercase_			:Union[str, Any]    			= add_prefix_space
              lowercase_			:int    			= True

           if state.get("trim_offsets"	,      lowercase						) != trim_offsets:
              lowercase_			:Any    			= trim_offsets
              lowercase_			:int    			= True

           if changes_to_apply:
              lowercase_			:Tuple    			= getattr(lowercase	,      state.pop("type"						)						)
              lowercase_			:Any    			= component_class(**lowercase						)
              setattr(self.backend_tokenizer	,      lowercase	,      lowercase						)





     @property
     def 							lowercase__      ( self		:					Optional[int]						):





        """simple docstring"""




        if self._mask_token is None:
           if self.verbose:
              logger.error("Using mask_token, but it is not set yet."						)
           return None
        return str(self._mask_token						)





     @mask_token.setter
     def 							lowercase__      ( self		:					int	,      lowercase		:					Dict						):





        """simple docstring"""




        lowercase_			:List[str]    			= AddedToken(lowercase	,      lstrip=lowercase	,      rstrip=lowercase						) if isinstance(lowercase	,      lowercase						) else value
        lowercase_			:Union[str, Any]    			= value





     def 							lowercase__      ( self		:					Optional[Any]	,      *lowercase		:					List[Any]	,      **lowercase		:					Any						):





        """simple docstring"""




        lowercase_			:Any    			= kwargs.get("is_split_into_words"	,      lowercase						)

        if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
               "to use it with pretokenized inputs."						)

        return super()._batch_encode_plus(*lowercase	,      **lowercase						)





     def 							lowercase__      ( self		:					Optional[Any]	,      *lowercase		:					Optional[int]	,      **lowercase		:					int						):





        """simple docstring"""




        lowercase_			:Any    			= kwargs.get("is_split_into_words"	,      lowercase						)

        if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
               "to use it with pretokenized inputs."						)

        return super()._encode_plus(*lowercase	,      **lowercase						)





     def 							lowercase__      ( self		:					Dict	,      lowercase		:					str	,      lowercase		:					Optional[str] = None						):





        """simple docstring"""




        lowercase_			:str    			= self._tokenizer.model.save(lowercase	,      name=lowercase						)
        return tuple(lowercase						)





     def 							lowercase__      ( self		:					Tuple	,      lowercase		:					Dict	,      lowercase		:					int=None						):





        """simple docstring"""




        lowercase_			:List[str]    			= [self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
           return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







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





        """simple docstring"""




        lowercase_			:Union[str, Any]    			= [self.sep_token_id]
        lowercase_			:Tuple    			= [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]


