



import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCamelCase						    =  get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")

@require_sentencepiece
@require_tokenizers
class       _SCREAMING_SNAKE_CASE	(UpperCamelCase			,    unittest.TestCase ):
     lowerCAmelCase					=			PegasusTokenizer
     lowerCAmelCase					=			PegasusTokenizerFast
     lowerCAmelCase					=			True
     lowerCAmelCase					=			True
     def 	__snake_case    (       self :							List[Any]				)->Union[str, Any]:
      super().setUp()

      # We have a SentencePiece fixture for testing
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	PegasusTokenizer(UpperCamelCase				)
      tokenizer.save_pretrained(self.tmpdirname				)
     @cached_property
     def 	__snake_case    (       self :							Union[str, Any]				)->Any:
      return PegasusTokenizer.from_pretrained("google/pegasus-large"				)
     def 	__snake_case    (       self :							Optional[int]     ,  **UpperCamelCase :							str				)->PegasusTokenizer:
      return PegasusTokenizer.from_pretrained(self.tmpdirname     ,  **UpperCamelCase				)
     def 	__snake_case    (       self :							int     ,  UpperCamelCase :							int				)->List[str]:
      return ("This is a test", "This is a test")
     def 	__snake_case    (       self :							Any				)->Any:
      __SCREAMING_SNAKE_CASE       :    Dict       =	"</s>"
      __SCREAMING_SNAKE_CASE       :    Tuple       =	1

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase				)     ,  UpperCamelCase				)
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase				)     ,  UpperCamelCase				)
     def 	__snake_case    (       self :							Any				)->str:
      __SCREAMING_SNAKE_CASE       :    Any       =	list(self.get_tokenizer().get_vocab().keys()				)

      self.assertEqual(vocab_keys[0]     ,  "<pad>"				)
      self.assertEqual(vocab_keys[1]     ,  "</s>"				)
      self.assertEqual(vocab_keys[-1]     ,  "v"				)
      self.assertEqual(len(UpperCamelCase				)     ,  1_1_0_3				)
     def 	__snake_case    (       self :							List[Any]				)->Dict:
      self.assertEqual(self.get_tokenizer().vocab_size     ,  1_1_0_3				)
     def 	__snake_case    (       self :							str				)->List[Any]:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.rust_tokenizer_class.from_pretrained(self.tmpdirname				)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.tokenizer_class.from_pretrained(self.tmpdirname				)
      __SCREAMING_SNAKE_CASE       :    int       =	(
          "Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"
          " </s> <pad> <pad> <pad>"
      )
      __SCREAMING_SNAKE_CASE       :    str       =	rust_tokenizer([raw_input_str]     ,  return_tensors=UpperCamelCase     ,  add_special_tokens=UpperCamelCase				).input_ids[0]
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	py_tokenizer([raw_input_str]     ,  return_tensors=UpperCamelCase     ,  add_special_tokens=UpperCamelCase				).input_ids[0]
      self.assertListEqual(UpperCamelCase     ,  UpperCamelCase				)
     def 	__snake_case    (       self :							int				)->List[Any]:
      __SCREAMING_SNAKE_CASE       :    Dict       =	self._large_tokenizer
      # <mask_1> masks whole sentence while <mask_2> masks single word
      __SCREAMING_SNAKE_CASE       :    List[str]       =	"<mask_1> To ensure a <mask_2> flow of bank resolutions."
      __SCREAMING_SNAKE_CASE       :    Tuple       =	[2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
      __SCREAMING_SNAKE_CASE       :    Any       =	tokenizer([raw_input_str]     ,  return_tensors=UpperCamelCase				).input_ids[0]
      self.assertListEqual(UpperCamelCase     ,  UpperCamelCase				)
     def 	__snake_case    (       self :							Optional[int]				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    int       =	self._large_tokenizer
      # The tracebacks for the following asserts are **better** without messages or self.assertEqual
      assert tokenizer.vocab_size == 9_6_1_0_3
      assert tokenizer.pad_token_id == 0
      assert tokenizer.eos_token_id == 1
      assert tokenizer.offset == 1_0_3
      assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
      assert tokenizer.unk_token == "<unk>"
      assert tokenizer.model_max_length == 1_0_2_4
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	"To ensure a smooth flow of bank resolutions."
      __SCREAMING_SNAKE_CASE       :    Any       =	[4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	tokenizer([raw_input_str]     ,  return_tensors=UpperCamelCase				).input_ids[0]
      self.assertListEqual(UpperCamelCase     ,  UpperCamelCase				)
      assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]				) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
     @require_torch
     def 	__snake_case    (       self :							str				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    Any       =	["This is going to be way too long." * 1_5_0, "short example"]
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	["not super long but more than 5 tokens", "tiny"]
      __SCREAMING_SNAKE_CASE       :    Tuple       =	self._large_tokenizer(UpperCamelCase     ,  padding=UpperCamelCase     ,  truncation=UpperCamelCase     ,  return_tensors="pt"				)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self._large_tokenizer(
          text_target=UpperCamelCase     ,  max_length=5     ,  padding=UpperCamelCase     ,  truncation=UpperCamelCase     ,  return_tensors="pt"				)

      assert batch.input_ids.shape == (2, 1_0_2_4)
      assert batch.attention_mask.shape == (2, 1_0_2_4)
      assert targets["input_ids"].shape == (2, 5)
      assert len(UpperCamelCase				) == 2  # input_ids, attention_mask.






     @slow
     def 	__snake_case    (       self :							List[str]				)->Optional[Any]:
      # fmt: off
      __SCREAMING_SNAKE_CASE       :    Dict       =	{"input_ids": [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=UpperCamelCase     ,  model_name="google/bigbird-pegasus-large-arxiv"     ,  revision="ba85d0851d708441f91440d509690f1ab6353415"     ,  )






@require_sentencepiece
@require_tokenizers
class       _SCREAMING_SNAKE_CASE	(UpperCamelCase			,    unittest.TestCase ):
     lowerCAmelCase					=			PegasusTokenizer
     lowerCAmelCase					=			PegasusTokenizerFast
     lowerCAmelCase					=			True
     lowerCAmelCase					=			True
     def 	__snake_case    (       self :							Tuple				)->Dict:
      super().setUp()

      # We have a SentencePiece fixture for testing
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	PegasusTokenizer(UpperCamelCase     ,  offset=0     ,  mask_token_sent=UpperCamelCase     ,  mask_token="[MASK]"				)
      tokenizer.save_pretrained(self.tmpdirname				)
     @cached_property
     def 	__snake_case    (       self :							Optional[int]				)->List[str]:
      return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv"				)
     def 	__snake_case    (       self :							Optional[int]     ,  **UpperCamelCase :							List[str]				)->PegasusTokenizer:
      return PegasusTokenizer.from_pretrained(self.tmpdirname     ,  **UpperCamelCase				)
     def 	__snake_case    (       self :							Any     ,  UpperCamelCase :							Union[str, Any]				)->List[Any]:
      return ("This is a test", "This is a test")
     def 	__snake_case    (       self :							List[str]				)->Any:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.rust_tokenizer_class.from_pretrained(self.tmpdirname				)
      __SCREAMING_SNAKE_CASE       :    Any       =	self.tokenizer_class.from_pretrained(self.tmpdirname				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	(
          "Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
          " <pad> <pad> <pad>"
      )
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	rust_tokenizer([raw_input_str]     ,  return_tensors=UpperCamelCase     ,  add_special_tokens=UpperCamelCase				).input_ids[0]
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	py_tokenizer([raw_input_str]     ,  return_tensors=UpperCamelCase     ,  add_special_tokens=UpperCamelCase				).input_ids[0]
      self.assertListEqual(UpperCamelCase     ,  UpperCamelCase				)
     @require_torch
     def 	__snake_case    (       self :							Tuple				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	["This is going to be way too long." * 1_0_0_0, "short example"]
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	["not super long but more than 5 tokens", "tiny"]
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self._large_tokenizer(UpperCamelCase     ,  padding=UpperCamelCase     ,  truncation=UpperCamelCase     ,  return_tensors="pt"				)
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self._large_tokenizer(
          text_target=UpperCamelCase     ,  max_length=5     ,  padding=UpperCamelCase     ,  truncation=UpperCamelCase     ,  return_tensors="pt"				)

      assert batch.input_ids.shape == (2, 4_0_9_6)
      assert batch.attention_mask.shape == (2, 4_0_9_6)
      assert targets["input_ids"].shape == (2, 5)
      assert len(UpperCamelCase				) == 2  # input_ids, attention_mask.






     def 	__snake_case    (       self :							List[Any]				)->Tuple:
      __SCREAMING_SNAKE_CASE       :    int       =	(
          "This is an example string that is used to test the original TF implementation against the HF"
          " implementation"
      )

      __SCREAMING_SNAKE_CASE       :    Tuple       =	self._large_tokenizer(UpperCamelCase				).input_ids

      self.assertListEqual(
          UpperCamelCase     ,  [1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 1]     ,  )











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						    =  False


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






def 	_lowerCAmelCase						(				):



 """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   :     List[str]		,	__lowerCamelCase   :     int		,	__lowerCamelCase   :     Dict     ):



 """simple docstring"""





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

 __SCREAMING_SNAKE_CASE       :    List[Any]       =	image_tensor.unsqueeze(0     )
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	torch.nn.functional.unfold(__lowerCamelCase		,	(patch_height, patch_width)		,	stride=(patch_height, patch_width)     )
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	patches.reshape(image_tensor.size(0     )		,	image_tensor.size(1     )		,	__lowerCamelCase		,	__lowerCamelCase		,	-1     )
 __SCREAMING_SNAKE_CASE       :    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 	_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		,	):



 """simple docstring"""





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

 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	textwrap.TextWrapper(width=80     )
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	wrapper.wrap(text=__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    str       =	"\n".join(__lowerCamelCase     )

 if font_bytes is not None and font_path is None:
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	io.BytesIO(__lowerCamelCase     )
 elif font_path is not None:
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	font_path
 else:
  __SCREAMING_SNAKE_CASE       :    Tuple       =	hf_hub_download(__lowerCamelCase		,	"Arial.TTF"     )
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	ImageFont.truetype(__lowerCamelCase		,	encoding="UTF-8"		,	size=__lowerCamelCase     )

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

 # Create the actual image with a bit of padding around the text.
 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	text_width + left_padding + right_padding
 __SCREAMING_SNAKE_CASE       :    Tuple       =	text_height + top_padding + bottom_padding
 __SCREAMING_SNAKE_CASE       :    Tuple       =	Image.new("RGB"		,	(image_width, image_height)		,	__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	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   :     Union[str, Any]     ):



 """simple docstring"""





 requires_backends(__lowerCamelCase		,	"vision"     )

 # Convert to PIL image if necessary
 __SCREAMING_SNAKE_CASE       :    int       =	to_pil_image(__lowerCamelCase     )

 __SCREAMING_SNAKE_CASE       :    int       =	render_text(__lowerCamelCase		,	**__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    Tuple       =	max(header_image.width		,	image.width     )

 __SCREAMING_SNAKE_CASE       :    Tuple       =	int(image.height * (new_width / image.width)     )
 __SCREAMING_SNAKE_CASE       :    Any       =	int(header_image.height * (new_width / header_image.width)     )

 __SCREAMING_SNAKE_CASE       :    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
 __SCREAMING_SNAKE_CASE       :    Dict       =	to_numpy_array(__lowerCamelCase     )

 if infer_channel_dimension_format(__lowerCamelCase     ) == ChannelDimension.LAST:
  __SCREAMING_SNAKE_CASE       :    Any       =	to_channel_dimension_format(__lowerCamelCase		,	ChannelDimension.LAST     )

 return new_image






class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):
     lowerCAmelCase					=			["""flattened_patches"""]





     def __init__(       self :							Tuple     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							Dict[str, int] = None     ,  UpperCamelCase :							int = 2_0_4_8     ,  UpperCamelCase :							bool = False     ,  **UpperCamelCase :							Optional[int]     ,  )->None:
      super().__init__(**UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	patch_size if patch_size is not None else {"height": 1_6, "width": 1_6}
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	do_normalize
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	do_convert_rgb
      __SCREAMING_SNAKE_CASE       :    List[str]       =	max_patches
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	is_vqa





     def 	__snake_case    (       self :							Tuple     ,  UpperCamelCase :							np.ndarray     ,  UpperCamelCase :							int     ,  UpperCamelCase :							dict     ,  **UpperCamelCase :							Tuple				)->np.ndarray:
      requires_backends(self.extract_flattened_patches     ,  "torch"				)
      _check_torch_version()

      # convert to torch
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	to_channel_dimension_format(UpperCamelCase     ,  ChannelDimension.FIRST				)
      __SCREAMING_SNAKE_CASE       :    int       =	torch.from_numpy(UpperCamelCase				)

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[str]       =	patch_size["height"], patch_size["width"]
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Optional[Any]       =	get_image_size(UpperCamelCase				)

      # maximize scale s.t.
      __SCREAMING_SNAKE_CASE       :    List[str]       =	math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width)				)
      __SCREAMING_SNAKE_CASE       :    List[str]       =	max(min(math.floor(scale * image_height / patch_height				)     ,  UpperCamelCase				)     ,  1				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	max(min(math.floor(scale * image_width / patch_width				)     ,  UpperCamelCase				)     ,  1				)
      __SCREAMING_SNAKE_CASE       :    List[str]       =	max(num_feasible_rows * patch_height     ,  1				)
      __SCREAMING_SNAKE_CASE       :    int       =	max(num_feasible_cols * patch_width     ,  1				)

      __SCREAMING_SNAKE_CASE       :    Any       =	torch.nn.functional.interpolate(
          image.unsqueeze(0				)     ,  size=(resized_height, resized_width)     ,  mode="bilinear"     ,  align_corners=UpperCamelCase     ,  antialias=UpperCamelCase     ,  ).squeeze(0				)

      # [1, rows, columns, patch_height * patch_width * image_channels]
      __SCREAMING_SNAKE_CASE       :    List[str]       =	torch_extract_patches(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	patches.shape
      __SCREAMING_SNAKE_CASE       :    int       =	patches_shape[1]
      __SCREAMING_SNAKE_CASE       :    List[str]       =	patches_shape[2]
      __SCREAMING_SNAKE_CASE       :    List[str]       =	patches_shape[3]

      # [rows * columns, patch_height * patch_width * image_channels]
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	patches.reshape([rows * columns, depth]				)

      # [rows * columns, 1]
      __SCREAMING_SNAKE_CASE       :    Any       =	torch.arange(UpperCamelCase				).reshape([rows, 1]				).repeat(1     ,  UpperCamelCase				).reshape([rows * columns, 1]				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	torch.arange(UpperCamelCase				).reshape([1, columns]				).repeat(UpperCamelCase     ,  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]
      __SCREAMING_SNAKE_CASE       :    str       =	row_ids.to(torch.floataa				)
      __SCREAMING_SNAKE_CASE       :    int       =	col_ids.to(torch.floataa				)

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

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

      __SCREAMING_SNAKE_CASE       :    str       =	to_numpy_array(UpperCamelCase				)

      return result





     def 	__snake_case    (       self :							Optional[Any]     ,  UpperCamelCase :							np.ndarray     ,  UpperCamelCase :							Optional[Union[str, ChannelDimension]] = None     ,  **UpperCamelCase :							Optional[int]				)->np.ndarray:
      if image.dtype == np.uinta:
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	image.astype(np.floataa				)

      # take mean across the whole `image`
      __SCREAMING_SNAKE_CASE       :    int       =	np.mean(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    List[str]       =	np.std(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	max(UpperCamelCase     ,  1.0 / math.sqrt(np.prod(image.shape				)				)				)

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






     def 	__snake_case    (       self :							Union[str, Any]     ,  UpperCamelCase :							ImageInput     ,  UpperCamelCase :							Optional[str] = None     ,  UpperCamelCase :							bool = None     ,  UpperCamelCase :							Optional[bool] = None     ,  UpperCamelCase :							Optional[int] = None     ,  UpperCamelCase :							Optional[Dict[str, int]] = None     ,  UpperCamelCase :							Optional[Union[str, TensorType]] = None     ,  UpperCamelCase :							ChannelDimension = ChannelDimension.FIRST     ,  **UpperCamelCase :							Dict     ,  )->ImageInput:
      __SCREAMING_SNAKE_CASE       :    int       =	do_normalize if do_normalize is not None else self.do_normalize
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	patch_size if patch_size is not None else self.patch_size
      __SCREAMING_SNAKE_CASE       :    List[str]       =	max_patches if max_patches is not None else self.max_patches
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.is_vqa

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

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	make_list_of_images(UpperCamelCase				)

      if not valid_images(UpperCamelCase				):
       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:
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	[convert_to_rgb(UpperCamelCase				) for image in images]

      # All transformations expect numpy arrays.
      __SCREAMING_SNAKE_CASE       :    str       =	[to_numpy_array(UpperCamelCase				) for image in images]

      if is_vqa:
       if header_text is None:
        raise ValueError("A header text must be provided for VQA models."				)
       __SCREAMING_SNAKE_CASE       :    Any       =	kwargs.pop("font_bytes"     ,  UpperCamelCase				)
       __SCREAMING_SNAKE_CASE       :    Any       =	kwargs.pop("font_path"     ,  UpperCamelCase				)

       if isinstance(UpperCamelCase     ,  UpperCamelCase				):
        __SCREAMING_SNAKE_CASE       :    Dict       =	[header_text] * len(UpperCamelCase				)

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

      if do_normalize:
       __SCREAMING_SNAKE_CASE       :    List[str]       =	[self.normalize(image=UpperCamelCase				) for image in images]

      # convert to torch tensor and permute
      __SCREAMING_SNAKE_CASE       :    str       =	[
          self.extract_flattened_patches(image=UpperCamelCase     ,  max_patches=UpperCamelCase     ,  patch_size=UpperCamelCase				)
          for image in images
      ]

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

      __SCREAMING_SNAKE_CASE       :    Dict       =	BatchFeature(
          data={"flattened_patches": images, "attention_mask": attention_masks}     ,  tensor_type=UpperCamelCase				)

      return encoded_outputs






