


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						:int			=  False


SCREAMING_SNAKE_CASE						:Union[str, Any]			=  logging.get_logger(__name__)
SCREAMING_SNAKE_CASE						:Optional[Any]			=  '''ybelkada/fonts'''



def       _lowerCAmelCase    (				)->Optional[Any]:
   '''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       _lowerCAmelCase    (				lowerCAmelCase_				:str		,    lowerCAmelCase_				:Tuple		,    lowerCAmelCase_				:Optional[int]				)->str:
   '''simple docstring'''






   requires_backends(lowerCAmelCase_		,    ["torch"]				)
   _check_torch_version()

   snake_case_            =       image_tensor.unsqueeze(0				)
   snake_case_            =       torch.nn.functional.unfold(lowerCAmelCase_		,    (patch_height, patch_width)		,    stride=(patch_height, patch_width)				)
   snake_case_            =       patches.reshape(image_tensor.size(0				)		,    image_tensor.size(1				)		,    lowerCAmelCase_		,    lowerCAmelCase_		,    -1				)
   snake_case_            =       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       _lowerCAmelCase    (				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		,    )->Image.Image:
   '''simple docstring'''






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

   snake_case_            =       textwrap.TextWrapper(width=80				)
   snake_case_            =       wrapper.wrap(text=lowerCAmelCase_				)
   snake_case_            =       "\n".join(lowerCAmelCase_				)

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

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

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



def       _lowerCAmelCase    (				lowerCAmelCase_				:np.ndarray		,    lowerCAmelCase_				:str		,    **lowerCAmelCase_				:Tuple				)->List[Any]:
   '''simple docstring'''






   requires_backends(lowerCAmelCase_		,    "vision"				)

   # Convert to PIL image if necessary
   snake_case_            =       to_pil_image(lowerCAmelCase_				)

   snake_case_            =       render_text(lowerCAmelCase_		,    **lowerCAmelCase_				)
   snake_case_            =       max(header_image.width		,    image.width				)

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

   snake_case_            =       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
   snake_case_            =       to_numpy_array(lowerCAmelCase_				)

   if infer_channel_dimension_format(lowerCAmelCase_				) == ChannelDimension.LAST:
      snake_case_            =       to_channel_dimension_format(lowerCAmelCase_		,    ChannelDimension.LAST				)

   return new_image



class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							['flattened_patches']



  def __init__(  self							:							Tuple     ,					_lowerCAmelCase							:							bool = True     ,					_lowerCAmelCase							:							bool = True     ,					_lowerCAmelCase							:							Dict[str, int] = None     ,					_lowerCAmelCase							:							int = 2_0_4_8     ,					_lowerCAmelCase							:							bool = False     ,					**_lowerCAmelCase							:							Optional[int]     ,					) ->			None:




     """simple docstring"""
     super().__init__(**_lowerCAmelCase	)
     snake_case_            =       patch_size if patch_size is not None else {"height": 1_6, "width": 1_6}
     snake_case_            =       do_normalize
     snake_case_            =       do_convert_rgb
     snake_case_            =       max_patches
     snake_case_            =       is_vqa



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							np.ndarray     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							dict     ,					**_lowerCAmelCase							:							Union[str, Any]	) ->			np.ndarray:




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

     # convert to torch
     snake_case_            =       to_channel_dimension_format(_lowerCAmelCase     ,					ChannelDimension.FIRST	)
     snake_case_            =       torch.from_numpy(_lowerCAmelCase	)

     snake_case_     ,     snake_case_            =       patch_size["height"], patch_size["width"]
     snake_case_     ,     snake_case_            =       get_image_size(_lowerCAmelCase	)

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

     snake_case_            =       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]
     snake_case_            =       torch_extract_patches(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase	)

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

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

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

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

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

     snake_case_            =       to_numpy_array(_lowerCAmelCase	)

     return result



  def        lowerCAmelCase__	(  self							:							Optional[int]     ,					_lowerCAmelCase							:							np.ndarray     ,					_lowerCAmelCase							:							Optional[Union[str, ChannelDimension]] = None     ,					**_lowerCAmelCase							:							Any	) ->			np.ndarray:




     """simple docstring"""
     if image.dtype == np.uinta:
        snake_case_            =       image.astype(np.floataa	)

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

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



  def        lowerCAmelCase__	(  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							:							Tuple     ,					) ->			ImageInput:




     """simple docstring"""
     snake_case_            =       do_normalize if do_normalize is not None else self.do_normalize
     snake_case_            =       do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
     snake_case_            =       patch_size if patch_size is not None else self.patch_size
     snake_case_            =       max_patches if max_patches is not None else self.max_patches
     snake_case_            =       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 "	)

     snake_case_            =       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:
        snake_case_            =       [convert_to_rgb(_lowerCAmelCase	) for image in images]

     # All transformations expect numpy arrays.
     snake_case_            =       [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."	)
        snake_case_            =       kwargs.pop("font_bytes"     ,					_lowerCAmelCase	)
        snake_case_            =       kwargs.pop("font_path"     ,					_lowerCAmelCase	)

        if isinstance(_lowerCAmelCase     ,					_lowerCAmelCase	):
           snake_case_            =       [header_text] * len(_lowerCAmelCase	)

        snake_case_            =       [
            render_header(_lowerCAmelCase     ,					header_text[i]     ,					font_bytes=_lowerCAmelCase     ,					font_path=_lowerCAmelCase	)
            for i, image in enumerate(_lowerCAmelCase	)
        ]

     if do_normalize:
        snake_case_            =       [self.normalize(image=_lowerCAmelCase	) for image in images]

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

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

     snake_case_            =       BatchFeature(
         data={"flattened_patches": images, "attention_mask": attention_masks}     ,					tensor_type=_lowerCAmelCase	)

     return encoded_outputs








import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE						:Union[str, Any]			=  logging.get_logger(__name__)


SCREAMING_SNAKE_CASE						:Dict			=  {
    '''vocab_file''': '''vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
    '''merges_file''': '''merges.txt''',
}

SCREAMING_SNAKE_CASE						:Optional[int]			=  {
    '''vocab_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'''
        ),
    },
    '''tokenizer_config_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'''
        ),
    },
    '''merges_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'''
        ),
    },
}

SCREAMING_SNAKE_CASE						:List[str]			=  '''</w>'''
SCREAMING_SNAKE_CASE						:int			=  '''@@ '''



def       _lowerCAmelCase    (				lowerCAmelCase_				:int				)->int:
   '''simple docstring'''






   snake_case_            =       set()
   snake_case_            =       word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)				)
      snake_case_            =       char
   return pairs


# Speech2Text2 has no max input length
SCREAMING_SNAKE_CASE						:Tuple			=  {'''facebook/s2t-wav2vec2-large-en-de''': 10_24}



class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							VOCAB_FILES_NAMES
  _SCREAMING_SNAKE_CASE		     =							PRETRAINED_VOCAB_FILES_MAP
  _SCREAMING_SNAKE_CASE		     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _SCREAMING_SNAKE_CASE		     =							['input_ids', 'attention_mask']



  def __init__(  self							:							List[Any]     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							Union[str, Any]="<s>"     ,					_lowerCAmelCase							:							Tuple="<pad>"     ,					_lowerCAmelCase							:							Union[str, Any]="</s>"     ,					_lowerCAmelCase							:							str="<unk>"     ,					_lowerCAmelCase							:							Union[str, Any]=False     ,					_lowerCAmelCase							:							List[str]=None     ,					**_lowerCAmelCase							:							List[str]     ,					) ->			List[str]:




     """simple docstring"""
     super().__init__(
         unk_token=_lowerCAmelCase     ,					bos_token=_lowerCAmelCase     ,					eos_token=_lowerCAmelCase     ,					pad_token=_lowerCAmelCase     ,					do_lower_case=_lowerCAmelCase     ,					**_lowerCAmelCase     ,					)

     snake_case_            =       do_lower_case

     with open(_lowerCAmelCase     ,					encoding="utf-8"	) as vocab_handle:
        snake_case_            =       json.load(_lowerCAmelCase	)
     snake_case_            =       {v: k for k, v in self.encoder.items()}

     if merges_file is None:
        logger.info(F'''No merges files provided. {self.__class__.__name__} can only be used for decoding.'''	)

        snake_case_            =       None
        snake_case_            =       None
     else:
        with open(_lowerCAmelCase     ,					encoding="utf-8"	) as merges_handle:
           snake_case_            =       merges_handle.read().split("\n"	)[:-1]

        snake_case_            =       [tuple(merge.split()[:2]	) for merge in merges]
        snake_case_            =       dict(zip(_lowerCAmelCase     ,					range(len(_lowerCAmelCase	)	)	)	)
        snake_case_            =       {}



  @property
  def        lowerCAmelCase__	(  self							:							Union[str, Any]	) ->			int:




     """simple docstring"""
     return len(self.decoder	)



  def        lowerCAmelCase__	(  self							:							Any	) ->			Dict:




     """simple docstring"""
     return dict(self.encoder     ,					**self.added_tokens_encoder	)



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							Any	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       tuple(token[:-1]	) + (token[-1] + BPE_TOKEN_MERGES,)
     if token in self.cache:
        return self.cache[token]
     snake_case_            =       get_pairs(_lowerCAmelCase	)

     if not pairs:
        return token

     while True:
        snake_case_            =       min(_lowerCAmelCase     ,					key=lambda _lowerCAmelCase	: self.bpe_ranks.get(_lowerCAmelCase     ,					float("inf"	)	)	)
        if bigram not in self.bpe_ranks:
           break
        snake_case_     ,     snake_case_            =       bigram
        snake_case_            =       []
        snake_case_            =       0
        while i < len(_lowerCAmelCase	):
           try:
              snake_case_            =       word.index(_lowerCAmelCase     ,					_lowerCAmelCase	)
           except ValueError:
              new_word.extend(word[i:]	)
              break
           else:
              new_word.extend(word[i:j]	)
              snake_case_            =       j

           if word[i] == first and i < len(_lowerCAmelCase	) - 1 and word[i + 1] == second:
              new_word.append(first + second	)
              i += 2
           else:
              new_word.append(word[i]	)
              i += 1
        snake_case_            =       tuple(_lowerCAmelCase	)
        snake_case_            =       new_word
        if len(_lowerCAmelCase	) == 1:
           break
        else:
           snake_case_            =       get_pairs(_lowerCAmelCase	)
     snake_case_            =       " ".join(_lowerCAmelCase	)
     if word == "\n  " + BPE_TOKEN_MERGES:
        snake_case_            =       "\n" + BPE_TOKEN_MERGES

     if word.endswith(_lowerCAmelCase	):
        snake_case_            =       word.replace(_lowerCAmelCase     ,					""	)

     snake_case_            =       word.replace(" "     ,					_lowerCAmelCase	)
     snake_case_            =       word
     return word



  def        lowerCAmelCase__	(  self							:							List[Any]     ,					_lowerCAmelCase							:							List[str]	) ->			List[Any]:




     """simple docstring"""

     if self.bpe_ranks is None:
        raise ValueError(
            "This tokenizer was instantiated without a `merges.txt` file, so"
            " that it can only be used for decoding, not for encoding."
            "Make sure to provide `merges.txt` file at instantiation to enable "
            "encoding."	)

     if self.do_lower_case:
        snake_case_            =       text.lower()

     snake_case_            =       text.split()

     snake_case_            =       []
     for token in text:
        if token:
           split_tokens.extend(list(self.bpe(_lowerCAmelCase	).split(" "	)	)	)

     return split_tokens



  def        lowerCAmelCase__	(  self							:							str     ,					_lowerCAmelCase							:							str	) ->			int:




     """simple docstring"""
     return self.encoder.get(_lowerCAmelCase     ,					self.encoder.get(self.unk_token	)	)



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							int	) ->			str:




     """simple docstring"""
     snake_case_            =       self.decoder.get(_lowerCAmelCase     ,					self.unk_token	)
     return result



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							List[str]	) ->			str:




     """simple docstring"""
     snake_case_            =       " ".join(_lowerCAmelCase	)

     # make sure @@ tokens are concatenated
     snake_case_            =       "".join(string.split(_lowerCAmelCase	)	)

     return string



  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							Optional[str] = None	) ->			Tuple[str]:




     """simple docstring"""
     if not os.path.isdir(_lowerCAmelCase	):
        logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
        return
     snake_case_            =       os.path.join(
         _lowerCAmelCase     ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
     snake_case_            =       os.path.join(
         _lowerCAmelCase     ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]	)

     with open(_lowerCAmelCase     ,					"w"     ,					encoding="utf-8"	) as f:
        f.write(json.dumps(self.encoder     ,					indent=2     ,					sort_keys=_lowerCAmelCase     ,					ensure_ascii=_lowerCAmelCase	) + "\n"	)

     snake_case_            =       0
     if self.bpe_ranks is None:
        return (vocab_file,)

     with open(_lowerCAmelCase     ,					"w"     ,					encoding="utf-8"	) as writer:
        for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,					key=lambda _lowerCAmelCase	: kv[1]	):
           if index != token_index:
              logger.warning(
                  F'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'''
                  " Please check that the tokenizer is not corrupted!"	)
              snake_case_            =       token_index
           writer.write(" ".join(_lowerCAmelCase	) + "\n"	)
           index += 1

     return (vocab_file, merges_file)




