




import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


lowerCamelCase    :		str			   =       logging.get_logger(__name__)

lowerCamelCase    :		Dict			   =       {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

lowerCamelCase    :		Any			   =       {
    "vocab_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
lowerCamelCase    :		Optional[Any]			   =       {
    "vocab_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
lowerCamelCase    :		Any			   =       {
    "vocab_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}

lowerCamelCase    :		Tuple			   =       {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
lowerCamelCase    :		Optional[int]			   =       {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
lowerCamelCase    :		List[Any]			   =       {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


lowerCamelCase    :		Union[str, Any]			   =       {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
lowerCamelCase    :		str			   =       {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
lowerCamelCase    :		List[str]			   =       {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						VOCAB_FILES_NAMES
 UpperCamelCase						       =						CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase						       =						CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase						       =						CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
 UpperCamelCase						       =						DPRContextEncoderTokenizer







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						VOCAB_FILES_NAMES
 UpperCamelCase						       =						QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase						       =						QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase						       =						QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
 UpperCamelCase						       =						DPRQuestionEncoderTokenizer

lowerCamelCase    :		Union[str, Any]			   =       collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

lowerCamelCase    :		str			   =       collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


lowerCamelCase    :		int			   =       r"\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `'tf'`: Return TensorFlow `tf.constant` objects.\n                - `'pt'`: Return PyTorch `torch.Tensor` objects.\n                - `'np'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer's default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    "







@add_start_docstrings(UpperCamelCase       )
class   A:



 '''simple docstring'''


 def __call__(    self :  List[str]						,							A_ :  List[str]						,							A_ :  Optional[str] = None						,							A_ :  Optional[str] = None						,							A_ :  Union[bool, str] = False						,							A_ :  Union[bool, str] = False						,							A_ :  Optional[int] = None						,							A_ :  Optional[Union[str, TensorType]] = None						,							A_ :  Optional[bool] = None						,							**A_ :  str						,							)       ->     BatchEncoding:

       """simple docstring"""






       if titles is None and texts is None:
             return super().__call__(
                 A_						,							padding=A_						,							truncation=A_						,							max_length=A_						,							return_tensors=A_						,							return_attention_mask=A_						,							**A_						,							)
       elif titles is None or texts is None:
             lowerCamelCase_							=   titles if texts is None else texts
             return super().__call__(
                 A_						,							A_						,							padding=A_						,							truncation=A_						,							max_length=A_						,							return_tensors=A_						,							return_attention_mask=A_						,							**A_						,							)
       lowerCamelCase_							=   titles if not isinstance(A_						,							A_  ) else [titles]
       lowerCamelCase_							=   texts if not isinstance(A_						,							A_  ) else [texts]
       lowerCamelCase_							=   len(A_  )
       lowerCamelCase_							=   questions if not isinstance(A_						,							A_  ) else [questions] * n_passages
       assert len(A_  ) == len(
           A_  ), f"""There should be as many titles than texts but got {len(A_  )} titles and {len(A_  )} texts."""
       lowerCamelCase_							=   super().__call__(A_						,							A_						,							padding=A_						,							truncation=A_  )['input_ids']
       lowerCamelCase_							=   super().__call__(A_						,							add_special_tokens=A_						,							padding=A_						,							truncation=A_  )['input_ids']
       lowerCamelCase_							=   {
           'input_ids': [
               (encoded_question_and_title + encoded_text)[:max_length]
               if max_length is not None and truncation
               else encoded_question_and_title + encoded_text
               for encoded_question_and_title, encoded_text in zip(A_						,							A_  )
           ]
       }
       if return_attention_mask is not False:
             lowerCamelCase_							=   []
             for input_ids in encoded_inputs["input_ids"]:
                   attention_mask.append([int(input_id != self.pad_token_id  ) for input_id in input_ids]  )
             lowerCamelCase_							=   attention_mask
       return self.pad(A_						,							padding=A_						,							max_length=A_						,							return_tensors=A_  )

 def     a__			(    self :  Optional[int]						,							A_ :  BatchEncoding						,							A_ :  DPRReaderOutput						,							A_ :  int = 16						,							A_ :  int = 64						,							A_ :  int = 4						,							)       ->     List[DPRSpanPrediction]:

       """simple docstring"""






       lowerCamelCase_							=   reader_input['input_ids']
       lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_							=   reader_output[:3]
       lowerCamelCase_							=   len(A_  )
       lowerCamelCase_							=   sorted(range(A_  )						,							reverse=A_						,							key=relevance_logits.__getitem__  )
       lowerCamelCase_							=   []
       for doc_id in sorted_docs:
             lowerCamelCase_							=   list(input_ids[doc_id]  )
             # assuming question & title information is at the beginning of the sequence
             lowerCamelCase_							=   sequence_ids.index(self.sep_token_id						,							2  ) + 1  # second sep id
             if sequence_ids[-1] == self.pad_token_id:
                   lowerCamelCase_							=   sequence_ids.index(self.pad_token_id  )
             else:
                   lowerCamelCase_							=   len(A_  )

             lowerCamelCase_							=   self._get_best_spans(
                 start_logits=start_logits[doc_id][passage_offset:sequence_len]						,							end_logits=end_logits[doc_id][passage_offset:sequence_len]						,							max_answer_length=A_						,							top_spans=A_						,							)
             for start_index, end_index in best_spans:
                   start_index += passage_offset
                   end_index += passage_offset
                   nbest_spans_predictions.append(
                       DPRSpanPrediction(
                           span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]						,							relevance_score=relevance_logits[doc_id]						,							doc_id=A_						,							start_index=A_						,							end_index=A_						,							text=self.decode(sequence_ids[start_index : end_index + 1]  )						,							)  )
             if len(A_  ) >= num_spans:
                   break
       return nbest_spans_predictions[:num_spans]





 def     a__			(    self :  List[str]						,							A_ :  List[int]						,							A_ :  List[int]						,							A_ :  int						,							A_ :  int						,							)       ->     List[DPRSpanPrediction]:

       """simple docstring"""






       lowerCamelCase_							=   []
       for start_index, start_score in enumerate(A_  ):
             for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]  ):
                   scores.append(((start_index, start_index + answer_length), start_score + end_score)  )
       lowerCamelCase_							=   sorted(A_						,							key=lambda A_  : x[1]						,							reverse=A_  )
       lowerCamelCase_							=   []
       for (start_index, end_index), score in scores:
             assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
             lowerCamelCase_							=   end_index - start_index + 1
             assert length <= max_answer_length, f"""Span is too long: {length} > {max_answer_length}"""
             if any(
                 start_index <= prev_start_index <= prev_end_index <= end_index
                 or prev_start_index <= start_index <= end_index <= prev_end_index
                 for (prev_start_index, prev_end_index) in chosen_span_intervals  ):
                   continue
             chosen_span_intervals.append((start_index, end_index)  )

             if len(A_  ) == top_spans:
                   break
       return chosen_span_intervals







@add_end_docstrings(UpperCamelCase       )
class   A(						UpperCamelCase							,				UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						VOCAB_FILES_NAMES
 UpperCamelCase						       =						READER_PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase						       =						READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase						       =						READER_PRETRAINED_INIT_CONFIGURATION
 UpperCamelCase						       =						['''input_ids''', '''attention_mask''']
 UpperCamelCase						       =						DPRReaderTokenizer









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    :		Union[str, Any]			   =       logging.get_logger(__name__)
lowerCamelCase    :		Any			   =       "ybelkada/fonts"

def       _SCREAMING_SNAKE_CASE      (							):


      '''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       _SCREAMING_SNAKE_CASE      (							lowercase     :	Dict     ,							lowercase     :	List[str]     ,							lowercase     :	List[Any]					):


      '''simple docstring'''



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

      lowerCamelCase_							=   image_tensor.unsqueeze(0					)
      lowerCamelCase_							=   torch.nn.functional.unfold(lowercase     ,							(patch_height, patch_width)     ,							stride=(patch_height, patch_width)					)
      lowerCamelCase_							=   patches.reshape(image_tensor.size(0					)     ,							image_tensor.size(1					)     ,							lowercase     ,							lowercase     ,							-1					)
      lowerCamelCase_							=   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      (							lowercase     :	str     ,							lowercase     :	int = 36     ,							lowercase     :	str = "black"     ,							lowercase     :	str = "white"     ,							lowercase     :	int = 5     ,							lowercase     :	int = 5     ,							lowercase     :	int = 5     ,							lowercase     :	int = 5     ,							lowercase     :	Optional[bytes] = None     ,							lowercase     :	Optional[str] = None     ,							):


      '''simple docstring'''



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

      lowerCamelCase_							=   textwrap.TextWrapper(width=80					)
      lowerCamelCase_							=   wrapper.wrap(text=lowercase					)
      lowerCamelCase_							=   '\n'.join(lowercase					)

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

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

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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	np.ndarray     ,							lowercase     :	str     ,							**lowercase     :	List[Any]					):


      '''simple docstring'''



      requires_backends(lowercase     ,							'vision'					)

      # Convert to PIL image if necessary
      lowerCamelCase_							=   to_pil_image(lowercase					)

      lowerCamelCase_							=   render_text(lowercase     ,							**lowercase					)
      lowerCamelCase_							=   max(header_image.width     ,							image.width					)

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

      lowerCamelCase_							=   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
      lowerCamelCase_							=   to_numpy_array(lowercase					)

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

      return new_image







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						['''flattened_patches''']

 def __init__(    self :  Dict						,							A_ :  bool = True						,							A_ :  bool = True						,							A_ :  Dict[str, int] = None						,							A_ :  int = 2048						,							A_ :  bool = False						,							**A_ :  str						,							)       ->     None:

       """simple docstring"""






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

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

       """simple docstring"""






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

       # convert to torch
       lowerCamelCase_							=   to_channel_dimension_format(A_						,							ChannelDimension.FIRST  )
       lowerCamelCase_							=   torch.from_numpy(A_  )

       lowerCamelCase_					,    lowerCamelCase_							=   patch_size['height'], patch_size['width']
       lowerCamelCase_					,    lowerCamelCase_							=   get_image_size(A_  )

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

       lowerCamelCase_							=   torch.nn.functional.interpolate(
           image.unsqueeze(0  )						,							size=(resized_height, resized_width)						,							mode='bilinear'						,							align_corners=A_						,							antialias=A_						,							).squeeze(0  )

       # [1, rows, columns, patch_height * patch_width * image_channels]
       lowerCamelCase_							=   torch_extract_patches(A_						,							A_						,							A_  )

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

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

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

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

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

       lowerCamelCase_							=   to_numpy_array(A_  )

       return result

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

       """simple docstring"""






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

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

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





 def     a__			(    self :  Optional[Any]						,							A_ :  ImageInput						,							A_ :  Optional[str] = None						,							A_ :  bool = None						,							A_ :  Optional[bool] = None						,							A_ :  Optional[int] = None						,							A_ :  Optional[Dict[str, int]] = None						,							A_ :  Optional[Union[str, TensorType]] = None						,							A_ :  ChannelDimension = ChannelDimension.FIRST						,							**A_ :  Optional[int]						,							)       ->     ImageInput:

       """simple docstring"""






       lowerCamelCase_							=   do_normalize if do_normalize is not None else self.do_normalize
       lowerCamelCase_							=   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
       lowerCamelCase_							=   patch_size if patch_size is not None else self.patch_size
       lowerCamelCase_							=   max_patches if max_patches is not None else self.max_patches
       lowerCamelCase_							=   self.is_vqa

       if kwargs.get('data_format'						,							A_  ) is not None:
             raise ValueError('data_format is not an accepted input as the outputs are '  )

       lowerCamelCase_							=   make_list_of_images(A_  )

       if not valid_images(A_  ):
             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:
             lowerCamelCase_							=   [convert_to_rgb(A_  ) for image in images]

       # All transformations expect numpy arrays.
       lowerCamelCase_							=   [to_numpy_array(A_  ) for image in images]

       if is_vqa:
             if header_text is None:
                   raise ValueError('A header text must be provided for VQA models.'  )
             lowerCamelCase_							=   kwargs.pop('font_bytes'						,							A_  )
             lowerCamelCase_							=   kwargs.pop('font_path'						,							A_  )

             if isinstance(A_						,							A_  ):
                   lowerCamelCase_							=   [header_text] * len(A_  )

             lowerCamelCase_							=   [
                 render_header(A_						,							header_text[i]						,							font_bytes=A_						,							font_path=A_  )
                 for i, image in enumerate(A_  )
             ]

       if do_normalize:
             lowerCamelCase_							=   [self.normalize(image=A_  ) for image in images]

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

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

       lowerCamelCase_							=   BatchFeature(
           data={'flattened_patches': images, 'attention_mask': attention_masks}						,							tensor_type=A_  )

       return encoded_outputs



