




import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase    :		Tuple			   =       get_tests_dir("fixtures/test_sentencepiece.model")

if is_torch_available():
	from transformers.models.mbart.modeling_mbart import shift_tokens_right

lowerCamelCase    :		Any			   =       250_004
lowerCamelCase    :		str			   =       250_020







@require_sentencepiece
@require_tokenizers
class   A(						UpperCamelCase							,				unittest.TestCase       ):



	'''simple docstring'''


	UpperCamelCase						       =						MBartaaTokenizer
	UpperCamelCase						       =						MBartaaTokenizerFast
	UpperCamelCase						       =						True
	UpperCamelCase						       =						True

	def     a__			(    self :  Dict  )       ->     Optional[Any]:

							"""simple docstring"""






							super().setUp()

							# We have a SentencePiece fixture for testing
							lowerCamelCase_							=   MBartaaTokenizer(A_						,							src_lang='en_XX'						,							tgt_lang='ro_RO'						,							keep_accents=A_  )
							tokenizer.save_pretrained(self.tmpdirname  )

	def     a__			(    self :  int  )       ->     Any:

							"""simple docstring"""






							lowerCamelCase_							=   '<s>'
							lowerCamelCase_							=   0

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_  )						,							A_  )
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_  )						,							A_  )

	def     a__			(    self :  Optional[int]  )       ->     int:

							"""simple docstring"""






							lowerCamelCase_							=   list(self.get_tokenizer().get_vocab().keys()  )

							self.assertEqual(vocab_keys[0]						,							'<s>'  )
							self.assertEqual(vocab_keys[1]						,							'<pad>'  )
							self.assertEqual(vocab_keys[-1]						,							'<mask>'  )
							self.assertEqual(len(A_  )						,							1054  )

	def     a__			(    self :  List[str]  )       ->     Dict:

							"""simple docstring"""






							self.assertEqual(self.get_tokenizer().vocab_size						,							1054  )

	def     a__			(    self :  List[str]  )       ->     List[str]:

							"""simple docstring"""






							lowerCamelCase_							=   MBartaaTokenizer(A_						,							src_lang='en_XX'						,							tgt_lang='ro_RO'						,							keep_accents=A_  )

							lowerCamelCase_							=   tokenizer.tokenize('This is a test'  )
							self.assertListEqual(A_						,							['▁This', '▁is', '▁a', '▁t', 'est']  )

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(A_  )						,							[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]						,							)

							lowerCamelCase_							=   tokenizer.tokenize('I was born in 92000, and this is falsé.'  )
							self.assertListEqual(
							    A_						,							[SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '9', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', 'é', '.']						,							)
							lowerCamelCase_							=   tokenizer.convert_tokens_to_ids(A_  )
							self.assertListEqual(
							    A_						,							[
							        value + tokenizer.fairseq_offset
							        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
							    ]						,							)

							lowerCamelCase_							=   tokenizer.convert_ids_to_tokens(A_  )
							self.assertListEqual(
							    A_						,							[SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '<unk>', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', '<unk>', '.']						,							)

	@slow
	def     a__			(    self :  int  )       ->     Union[str, Any]:

							"""simple docstring"""






							lowerCamelCase_							=   {'input_ids': [[250004, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [250004, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 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], [250004, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 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]], '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, 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, 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, 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, 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=A_						,							model_name='facebook/mbart-large-50'						,							revision='d3913889c59cd5c9e456b269c376325eabad57e2'						,							)





	def     a__			(    self :  Union[str, Any]  )       ->     Dict:

							"""simple docstring"""






							if not self.test_slow_tokenizer:
													# as we don't have a slow version, we can't compare the outputs between slow and fast versions
													return

							lowerCamelCase_							=   (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart50', {})
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
																			lowerCamelCase_							=   self.rust_tokenizer_class.from_pretrained(A_						,							**A_  )
																			lowerCamelCase_							=   self.tokenizer_class.from_pretrained(A_						,							**A_  )

																			lowerCamelCase_							=   tempfile.mkdtemp()

																			lowerCamelCase_							=   tokenizer_r.save_pretrained(A_  )
																			lowerCamelCase_							=   tokenizer_p.save_pretrained(A_  )

																			# Checks it save with the same files + the tokenizer.json file for the fast one
																			self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files  )  )
																			lowerCamelCase_							=   tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f  )
																			self.assertSequenceEqual(A_						,							A_  )

																			# Checks everything loads correctly in the same way
																			lowerCamelCase_							=   tokenizer_r.from_pretrained(A_  )
																			lowerCamelCase_							=   tokenizer_p.from_pretrained(A_  )

																			# Check special tokens are set accordingly on Rust and Python
																			for key in tokenizer_pp.special_tokens_map:
																									self.assertTrue(hasattr(A_						,							A_  )  )
																									# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																									# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																			shutil.rmtree(A_  )

																			# Save tokenizer rust, legacy_format=True
																			lowerCamelCase_							=   tempfile.mkdtemp()

																			lowerCamelCase_							=   tokenizer_r.save_pretrained(A_						,							legacy_format=A_  )
																			lowerCamelCase_							=   tokenizer_p.save_pretrained(A_  )

																			# Checks it save with the same files
																			self.assertSequenceEqual(A_						,							A_  )

																			# Checks everything loads correctly in the same way
																			lowerCamelCase_							=   tokenizer_r.from_pretrained(A_  )
																			lowerCamelCase_							=   tokenizer_p.from_pretrained(A_  )

																			# Check special tokens are set accordingly on Rust and Python
																			for key in tokenizer_pp.special_tokens_map:
																									self.assertTrue(hasattr(A_						,							A_  )  )

																			shutil.rmtree(A_  )

																			# Save tokenizer rust, legacy_format=False
																			lowerCamelCase_							=   tempfile.mkdtemp()

																			lowerCamelCase_							=   tokenizer_r.save_pretrained(A_						,							legacy_format=A_  )
																			lowerCamelCase_							=   tokenizer_p.save_pretrained(A_  )

																			# Checks it saved the tokenizer.json file
																			self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files  )  )

																			# Checks everything loads correctly in the same way
																			lowerCamelCase_							=   tokenizer_r.from_pretrained(A_  )
																			lowerCamelCase_							=   tokenizer_p.from_pretrained(A_  )

																			# Check special tokens are set accordingly on Rust and Python
																			for key in tokenizer_pp.special_tokens_map:
																									self.assertTrue(hasattr(A_						,							A_  )  )

																			shutil.rmtree(A_  )







@require_torch
@require_sentencepiece
@require_tokenizers
class   A(						unittest.TestCase       ):



	'''simple docstring'''


	UpperCamelCase						       =						'''facebook/mbart-large-50-one-to-many-mmt'''
	UpperCamelCase						       =						[
	    ''' UN Chief Says There Is No Military Solution in Syria''',
	    ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
	]
	UpperCamelCase						       =						[
	    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
	    '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
	    ''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
	    ''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
	]
	UpperCamelCase						       =						[EN_CODE, 8274, 12_7873, 2_5916, 7, 8622, 2071, 438, 6_7485, 53, 18_7895, 23, 5_1712, 2]

	@classmethod
	def     a__			(    cls :  Tuple  )       ->     str:

							"""simple docstring"""






							lowerCamelCase_							=   MBartaaTokenizer.from_pretrained(
							    cls.checkpoint_name						,							src_lang='en_XX'						,							tgt_lang='ro_RO'  )
							lowerCamelCase_							=   1
							return cls

	def     a__			(    self :  Union[str, Any]  )       ->     Dict:

							"""simple docstring"""






							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']						,							250001  )
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']						,							250004  )
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']						,							250020  )
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['mr_IN']						,							250038  )

	def     a__			(    self :  int  )       ->     Dict:

							"""simple docstring"""






							lowerCamelCase_							=   self.tokenizer.batch_encode_plus(self.src_text  ).input_ids[0]
							self.assertListEqual(self.expected_src_tokens						,							A_  )

	def     a__			(    self :  Dict  )       ->     Optional[int]:

							"""simple docstring"""






							self.assertIn(A_						,							self.tokenizer.all_special_ids  )
							lowerCamelCase_							=   [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
							lowerCamelCase_							=   self.tokenizer.decode(A_						,							skip_special_tokens=A_  )
							lowerCamelCase_							=   self.tokenizer.decode(generated_ids[1:]						,							skip_special_tokens=A_  )
							self.assertEqual(A_						,							A_  )
							self.assertNotIn(self.tokenizer.eos_token						,							A_  )

	def     a__			(    self :  int  )       ->     str:

							"""simple docstring"""






							lowerCamelCase_							=   ['this is gunna be a long sentence ' * 20]
							assert isinstance(src_text[0]						,							A_  )
							lowerCamelCase_							=   10
							lowerCamelCase_							=   self.tokenizer(A_						,							max_length=A_						,							truncation=A_  ).input_ids[0]
							self.assertEqual(ids[0]						,							A_  )
							self.assertEqual(ids[-1]						,							2  )
							self.assertEqual(len(A_  )						,							A_  )

	def     a__			(    self :  Any  )       ->     str:

							"""simple docstring"""






							self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']  )						,							[250053, 250001]  )

	def     a__			(    self :  Any  )       ->     Union[str, Any]:

							"""simple docstring"""






							lowerCamelCase_							=   tempfile.mkdtemp()
							lowerCamelCase_							=   self.tokenizer.fairseq_tokens_to_ids
							self.tokenizer.save_pretrained(A_  )
							lowerCamelCase_							=   MBartaaTokenizer.from_pretrained(A_  )
							self.assertDictEqual(new_tok.fairseq_tokens_to_ids						,							A_  )

	@require_torch
	def     a__			(    self :  Tuple  )       ->     Optional[int]:

							"""simple docstring"""






							lowerCamelCase_							=   self.tokenizer(self.src_text						,							text_target=self.tgt_text						,							padding=A_						,							return_tensors='pt'  )
							lowerCamelCase_							=   shift_tokens_right(batch['labels']						,							self.tokenizer.pad_token_id  )

							# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
							assert batch.input_ids[1][0] == EN_CODE
							assert batch.input_ids[1][-1] == 2
							assert batch.labels[1][0] == RO_CODE
							assert batch.labels[1][-1] == 2
							assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]

	@require_torch
	def     a__			(    self :  Optional[int]  )       ->     Union[str, Any]:

							"""simple docstring"""






							lowerCamelCase_							=   self.tokenizer(
							    self.src_text						,							text_target=self.tgt_text						,							padding=A_						,							truncation=A_						,							max_length=len(self.expected_src_tokens  )						,							return_tensors='pt'						,							)
							lowerCamelCase_							=   shift_tokens_right(batch['labels']						,							self.tokenizer.pad_token_id  )

							self.assertIsInstance(A_						,							A_  )

							self.assertEqual((2, 14)						,							batch.input_ids.shape  )
							self.assertEqual((2, 14)						,							batch.attention_mask.shape  )
							lowerCamelCase_							=   batch.input_ids.tolist()[0]
							self.assertListEqual(self.expected_src_tokens						,							A_  )
							self.assertEqual(2						,							batch.decoder_input_ids[0, 0]  )  # decoder_start_token_id
							# Test that special tokens are reset
							self.assertEqual(self.tokenizer.prefix_tokens						,							[EN_CODE]  )
							self.assertEqual(self.tokenizer.suffix_tokens						,							[self.tokenizer.eos_token_id]  )

	def     a__			(    self :  Optional[Any]  )       ->     Any:

							"""simple docstring"""






							lowerCamelCase_							=   self.tokenizer(self.src_text						,							padding=A_						,							truncation=A_						,							max_length=3						,							return_tensors='pt'  )
							lowerCamelCase_							=   self.tokenizer(
							    text_target=self.tgt_text						,							padding=A_						,							truncation=A_						,							max_length=10						,							return_tensors='pt'  )
							lowerCamelCase_							=   targets['input_ids']
							lowerCamelCase_							=   shift_tokens_right(A_						,							self.tokenizer.pad_token_id  )

							self.assertEqual(batch.input_ids.shape[1]						,							3  )
							self.assertEqual(batch.decoder_input_ids.shape[1]						,							10  )





	@require_torch
	def     a__			(    self :  List[str]  )       ->     Union[str, Any]:

							"""simple docstring"""






							lowerCamelCase_							=   self.tokenizer._build_translation_inputs(
							    'A test'						,							return_tensors='pt'						,							src_lang='en_XX'						,							tgt_lang='ar_AR'  )

							self.assertEqual(
							    nested_simplify(A_  )						,							{
							        # en_XX, A, test, EOS
							        'input_ids': [[250004, 62, 3034, 2]],
							        'attention_mask': [[1, 1, 1, 1]],
							        # ar_AR
							        'forced_bos_token_id': 250001,
							    }						,							)









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



