


import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
							    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
							    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
							    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
							    LayoutLMvaConfig,
							    LayoutLMvaForQuestionAnswering,
							    LayoutLMvaForSequenceClassification,
							    LayoutLMvaForTokenClassification,
							    LayoutLMvaModel,
							)
							from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
							from PIL import Image

							from transformers import LayoutLMvaImageProcessor







class        UpperCamelCase__   :

					def __init__(     self       :   int							,					UpperCamelCase__       :   Tuple							,					UpperCamelCase__       :   str=2							,					UpperCamelCase__       :   List[str]=3							,					UpperCamelCase__       :   Optional[int]=4							,					UpperCamelCase__       :   Any=2							,					UpperCamelCase__       :   Dict=7							,					UpperCamelCase__       :   Optional[Any]=True							,					UpperCamelCase__       :   int=True							,					UpperCamelCase__       :   List[Any]=True							,					UpperCamelCase__       :   List[str]=True							,					UpperCamelCase__       :   Union[str, Any]=99							,					UpperCamelCase__       :   int=36							,					UpperCamelCase__       :   Optional[int]=3							,					UpperCamelCase__       :   str=4							,					UpperCamelCase__       :   Any=37							,					UpperCamelCase__       :   Dict="gelu"							,					UpperCamelCase__       :   Optional[int]=0.1							,					UpperCamelCase__       :   Union[str, Any]=0.1							,					UpperCamelCase__       :   Optional[Any]=512							,					UpperCamelCase__       :   str=16							,					UpperCamelCase__       :   Dict=2							,					UpperCamelCase__       :   Optional[int]=0.02							,					UpperCamelCase__       :   List[Any]=6							,					UpperCamelCase__       :   Tuple=6							,					UpperCamelCase__       :   Dict=3							,					UpperCamelCase__       :   Optional[Any]=4							,					UpperCamelCase__       :   Optional[Any]=None							,					UpperCamelCase__       :   Union[str, Any]=1_000							,					):






										'''simple docstring'''






										lowercase_  							=			parent
										lowercase_  							=			batch_size
										lowercase_  							=			num_channels
										lowercase_  							=			image_size
										lowercase_  							=			patch_size
										lowercase_  							=			text_seq_length
										lowercase_  							=			is_training
										lowercase_  							=			use_input_mask
										lowercase_  							=			use_token_type_ids
										lowercase_  							=			use_labels
										lowercase_  							=			vocab_size
										lowercase_  							=			hidden_size
										lowercase_  							=			num_hidden_layers
										lowercase_  							=			num_attention_heads
										lowercase_  							=			intermediate_size
										lowercase_  							=			hidden_act
										lowercase_  							=			hidden_dropout_prob
										lowercase_  							=			attention_probs_dropout_prob
										lowercase_  							=			max_position_embeddings
										lowercase_  							=			type_vocab_size
										lowercase_  							=			type_sequence_label_size
										lowercase_  							=			initializer_range
										lowercase_  							=			coordinate_size
										lowercase_  							=			shape_size
										lowercase_  							=			num_labels
										lowercase_  							=			num_choices
										lowercase_  							=			scope
										lowercase_  							=			range_bbox

										# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
										lowercase_  							=			text_seq_length
										lowercase_  							=			(image_size // patch_size) ** 2 + 1
										lowercase_  							=			self.text_seq_length + self.image_seq_length

					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										lowercase_  							=			ids_tensor([self.batch_size, self.text_seq_length]							,					self.vocab_size       )

										lowercase_  							=			ids_tensor([self.batch_size, self.text_seq_length, 4]							,					self.range_bbox       )
										# Ensure that bbox is legal
										for i in range(bbox.shape[0]       ):
															for j in range(bbox.shape[1]       ):
																				if bbox[i, j, 3] < bbox[i, j, 1]:
																									lowercase_  							=			bbox[i, j, 3]
																									lowercase_  							=			bbox[i, j, 1]
																									lowercase_  							=			t
																				if bbox[i, j, 2] < bbox[i, j, 0]:
																									lowercase_  							=			bbox[i, j, 2]
																									lowercase_  							=			bbox[i, j, 0]
																									lowercase_  							=			t

										lowercase_  							=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

										lowercase_  							=			None
										if self.use_input_mask:
															lowercase_  							=			random_attention_mask([self.batch_size, self.text_seq_length]       )

										lowercase_  							=			None
										if self.use_token_type_ids:
															lowercase_  							=			ids_tensor([self.batch_size, self.text_seq_length]							,					self.type_vocab_size       )

										lowercase_  							=			None
										lowercase_  							=			None
										if self.use_labels:
															lowercase_  							=			ids_tensor([self.batch_size]							,					self.type_sequence_label_size       )
															lowercase_  							=			ids_tensor([self.batch_size, self.text_seq_length]							,					self.num_labels       )

										lowercase_  							=			LayoutLMvaConfig(
										    vocab_size=self.vocab_size							,					hidden_size=self.hidden_size							,					num_hidden_layers=self.num_hidden_layers							,					num_attention_heads=self.num_attention_heads							,					intermediate_size=self.intermediate_size							,					hidden_act=self.hidden_act							,					hidden_dropout_prob=self.hidden_dropout_prob							,					attention_probs_dropout_prob=self.attention_probs_dropout_prob							,					max_position_embeddings=self.max_position_embeddings							,					type_vocab_size=self.type_vocab_size							,					initializer_range=self.initializer_range							,					coordinate_size=self.coordinate_size							,					shape_size=self.shape_size							,					input_size=self.image_size							,					patch_size=self.patch_size							,					)

										return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels

					def   UpperCAmelCase__       (     self       :   Optional[int]							,					UpperCamelCase__       :   Tuple							,					UpperCamelCase__       :   Any							,					UpperCamelCase__       :   Tuple							,					UpperCamelCase__       :   Tuple							,					UpperCamelCase__       :   Tuple							,					UpperCamelCase__       :   Tuple							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   List[str]       ):






										'''simple docstring'''






										lowercase_  							=			LayoutLMvaModel(config=UpperCamelCase__       )
										model.to(UpperCamelCase__       )
										model.eval()

										# text + image
										lowercase_  							=			model(UpperCamelCase__							,					pixel_values=UpperCamelCase__       )
										lowercase_  							=			model(
										    UpperCamelCase__							,					bbox=UpperCamelCase__							,					pixel_values=UpperCamelCase__							,					attention_mask=UpperCamelCase__							,					token_type_ids=UpperCamelCase__       )
										lowercase_  							=			model(UpperCamelCase__							,					bbox=UpperCamelCase__							,					pixel_values=UpperCamelCase__							,					token_type_ids=UpperCamelCase__       )
										lowercase_  							=			model(UpperCamelCase__							,					bbox=UpperCamelCase__							,					pixel_values=UpperCamelCase__       )

										self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, self.seq_length, self.hidden_size)       )

										# text only
										lowercase_  							=			model(UpperCamelCase__       )
										self.parent.assertEqual(
										    result.last_hidden_state.shape							,					(self.batch_size, self.text_seq_length, self.hidden_size)       )

										# image only
										lowercase_  							=			model(pixel_values=UpperCamelCase__       )
										self.parent.assertEqual(
										    result.last_hidden_state.shape							,					(self.batch_size, self.image_seq_length, self.hidden_size)       )

					def   UpperCAmelCase__       (     self       :   Union[str, Any]							,					UpperCamelCase__       :   Optional[Any]							,					UpperCamelCase__       :   Tuple							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   Any							,					UpperCamelCase__       :   List[Any]							,					UpperCamelCase__       :   Optional[Any]							,					UpperCamelCase__       :   List[Any]							,					UpperCamelCase__       :   Optional[Any]       ):






										'''simple docstring'''






										lowercase_  							=			self.num_labels
										lowercase_  							=			LayoutLMvaForSequenceClassification(UpperCamelCase__       )
										model.to(UpperCamelCase__       )
										model.eval()
										lowercase_  							=			model(
										    UpperCamelCase__							,					bbox=UpperCamelCase__							,					pixel_values=UpperCamelCase__							,					attention_mask=UpperCamelCase__							,					token_type_ids=UpperCamelCase__							,					labels=UpperCamelCase__							,					)
										self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_labels)       )

					def   UpperCAmelCase__       (     self       :   Any							,					UpperCamelCase__       :   Union[str, Any]							,					UpperCamelCase__       :   Tuple							,					UpperCamelCase__       :   Union[str, Any]							,					UpperCamelCase__       :   Optional[int]							,					UpperCamelCase__       :   Any							,					UpperCamelCase__       :   Union[str, Any]							,					UpperCamelCase__       :   Tuple							,					UpperCamelCase__       :   int       ):






										'''simple docstring'''






										lowercase_  							=			self.num_labels
										lowercase_  							=			LayoutLMvaForTokenClassification(config=UpperCamelCase__       )
										model.to(UpperCamelCase__       )
										model.eval()
										lowercase_  							=			model(
										    UpperCamelCase__							,					bbox=UpperCamelCase__							,					pixel_values=UpperCamelCase__							,					attention_mask=UpperCamelCase__							,					token_type_ids=UpperCamelCase__							,					labels=UpperCamelCase__							,					)
										self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.text_seq_length, self.num_labels)       )

					def   UpperCAmelCase__       (     self       :   Any							,					UpperCamelCase__       :   Dict							,					UpperCamelCase__       :   List[Any]							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   Any							,					UpperCamelCase__       :   Dict							,					UpperCamelCase__       :   Tuple							,					UpperCamelCase__       :   Tuple       ):






										'''simple docstring'''






										lowercase_  							=			LayoutLMvaForQuestionAnswering(config=UpperCamelCase__       )
										model.to(UpperCamelCase__       )
										model.eval()
										lowercase_  							=			model(
										    UpperCamelCase__							,					bbox=UpperCamelCase__							,					pixel_values=UpperCamelCase__							,					attention_mask=UpperCamelCase__							,					token_type_ids=UpperCamelCase__							,					start_positions=UpperCamelCase__							,					end_positions=UpperCamelCase__							,					)
										self.parent.assertEqual(result.start_logits.shape							,					(self.batch_size, self.seq_length)       )
										self.parent.assertEqual(result.end_logits.shape							,					(self.batch_size, self.seq_length)       )







					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										lowercase_  							=			self.prepare_config_and_inputs()
										(
										    (
										    lowercase_
										)							,					(
										    lowercase_
										)							,					(
										    lowercase_
										)							,					(
										    lowercase_
										)							,					(
										    lowercase_
										)							,					(
										    lowercase_
										)							,					(
										    lowercase_
										)							,					(
										    lowercase_
										)							,					
										)  							=			config_and_inputs
										lowercase_  							=			{
										    """input_ids""": input_ids,
										    """bbox""": bbox,
										    """pixel_values""": pixel_values,
										    """token_type_ids""": token_type_ids,
										    """attention_mask""": input_mask,
										}
										return config, inputs_dict







@require_torch
class        UpperCamelCase__   (	__magic_name__							,				__magic_name__							,				unittest.TestCase   ):
					__SCREAMING_SNAKE_CASE       :  Tuple						=   False
					__SCREAMING_SNAKE_CASE       :  str						=   False
					__SCREAMING_SNAKE_CASE       :  Dict						=   False

					__SCREAMING_SNAKE_CASE       :  Tuple						=   (
					    (
					        LayoutLMvaModel,
					        LayoutLMvaForSequenceClassification,
					        LayoutLMvaForTokenClassification,
					        LayoutLMvaForQuestionAnswering,
					    )
					    if is_torch_available()
					    else ()
					)
					__SCREAMING_SNAKE_CASE       :  Union[str, Any]						=   (
					    {'document-question-answering': LayoutLMvaForQuestionAnswering, 'feature-extraction': LayoutLMvaModel}
					    if is_torch_available()
					    else {}
					)

					def   UpperCAmelCase__       (     self       :   Any							,					UpperCamelCase__       :   Tuple							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   Any							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   Dict       ):






										'''simple docstring'''






										return True

					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										lowercase_  							=			LayoutLMvaModelTester(self       )
										lowercase_  							=			ConfigTester(self							,					config_class=UpperCamelCase__							,					hidden_size=37       )

					def   UpperCAmelCase__       (     self       :   Any							,					UpperCamelCase__       :   Dict							,					UpperCamelCase__       :   Any							,					UpperCamelCase__       :   Optional[int]=False       ):






										'''simple docstring'''






										lowercase_  							=			copy.deepcopy(UpperCamelCase__       )
										if model_class in get_values(UpperCamelCase__       ):
															lowercase_  							=			{
															    k: v.unsqueeze(1       ).expand(-1							,					self.model_tester.num_choices							,					-1       ).contiguous()
															    if isinstance(UpperCamelCase__							,					torch.Tensor       ) and v.ndim > 1
															    else v
															    for k, v in inputs_dict.items()
															}
										if return_labels:
															if model_class in get_values(UpperCamelCase__       ):
																				lowercase_  							=			torch.ones(self.model_tester.batch_size							,					dtype=torch.long							,					device=UpperCamelCase__       )
															elif model_class in get_values(UpperCamelCase__       ):
																				lowercase_  							=			torch.zeros(
																				    self.model_tester.batch_size							,					dtype=torch.long							,					device=UpperCamelCase__       )
																				lowercase_  							=			torch.zeros(
																				    self.model_tester.batch_size							,					dtype=torch.long							,					device=UpperCamelCase__       )
															elif model_class in [
															    *get_values(UpperCamelCase__       ),
															]:
																				lowercase_  							=			torch.zeros(
																				    self.model_tester.batch_size							,					dtype=torch.long							,					device=UpperCamelCase__       )
															elif model_class in [
															    *get_values(UpperCamelCase__       ),
															]:
																				lowercase_  							=			torch.zeros(
																				    (self.model_tester.batch_size, self.model_tester.text_seq_length)							,					dtype=torch.long							,					device=UpperCamelCase__							,					)

										return inputs_dict

					def   UpperCAmelCase__       (     self       :   Union[str, Any]       ):






										'''simple docstring'''






										self.config_tester.run_common_tests()

					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										lowercase_  							=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										lowercase_  							=			self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
															lowercase_  							=			type
															self.model_tester.create_and_check_model(*UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   str       ):






										'''simple docstring'''






										lowercase_  							=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										lowercase_  							=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Dict       ):






										'''simple docstring'''






										lowercase_  							=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__       )







					@slow
					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															lowercase_  							=			LayoutLMvaModel.from_pretrained(UpperCamelCase__       )
															self.assertIsNotNone(UpperCamelCase__       )



def 			UpperCAmelCase_				(      ):
					lowercase_  							=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
					return image







@require_torch
class        UpperCamelCase__   (	unittest.TestCase   ):

					@cached_property
					def   UpperCAmelCase__       (     self       :   List[Any]       ):






										'''simple docstring'''






										return LayoutLMvaImageProcessor(apply_ocr=UpperCamelCase__       ) if is_vision_available() else None







					@slow
					def   UpperCAmelCase__       (     self       :   str       ):






										'''simple docstring'''






										lowercase_  							=			LayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""       ).to(UpperCamelCase__       )

										lowercase_  							=			self.default_image_processor
										lowercase_  							=			prepare_img()
										lowercase_  							=			image_processor(images=UpperCamelCase__							,					return_tensors="""pt"""       ).pixel_values.to(UpperCamelCase__       )

										lowercase_  							=			torch.tensor([[1, 2]]       )
										lowercase_  							=			torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]       ).unsqueeze(0       )

										# forward pass
										lowercase_  							=			model(
										    input_ids=input_ids.to(UpperCamelCase__       )							,					bbox=bbox.to(UpperCamelCase__       )							,					pixel_values=pixel_values.to(UpperCamelCase__       )							,					)

										# verify the logits
										lowercase_  							=			torch.Size((1, 199, 768)       )
										self.assertEqual(outputs.last_hidden_state.shape							,					UpperCamelCase__       )

										lowercase_  							=			torch.tensor(
										    [[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]]       ).to(UpperCamelCase__       )

										self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]							,					UpperCamelCase__							,					atol=1e-4       )       )






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:
							a					  =     False


a					  =     logging.get_logger(__name__)
a					  =     'ybelkada/fonts'


def 			UpperCAmelCase_				(      ):
					if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
										raise ImportError(
										    F'''You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use '''
										    """Pix2StructImageProcessor. Please upgrade torch."""  )


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  ):
					requires_backends(UpperCAmelCase__						,							["""torch"""]  )
					_check_torch_version()

					lowercase_  							=			image_tensor.unsqueeze(0  )
					lowercase_  							=			torch.nn.functional.unfold(UpperCAmelCase__						,							(patch_height, patch_width)						,							stride=(patch_height, patch_width)  )
					lowercase_  							=			patches.reshape(image_tensor.size(0  )						,							image_tensor.size(1  )						,							UpperCAmelCase__						,							UpperCAmelCase__						,							-1  )
					lowercase_  							=			patches.permute(0						,							4						,							2						,							3						,							1  ).reshape(
					    image_tensor.size(2  ) // patch_height						,							image_tensor.size(3  ) // patch_width						,							image_tensor.size(1  ) * patch_height * patch_width						,							)
					return patches.unsqueeze(0  )


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__ = 3_6						,							UpperCAmelCase__ = "black"						,							UpperCAmelCase__ = "white"						,							UpperCAmelCase__ = 5						,							UpperCAmelCase__ = 5						,							UpperCAmelCase__ = 5						,							UpperCAmelCase__ = 5						,							UpperCAmelCase__ = None						,							UpperCAmelCase__ = None						,							):
					requires_backends(UpperCAmelCase__						,							"""vision"""  )
					# Add new lines so that each line is no more than 80 characters.

					lowercase_  							=			textwrap.TextWrapper(width=8_0  )
					lowercase_  							=			wrapper.wrap(text=UpperCAmelCase__  )
					lowercase_  							=			"""\n""".join(UpperCAmelCase__  )

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

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

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


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							**UpperCAmelCase__  ):
					requires_backends(UpperCAmelCase__						,							"""vision"""  )

					# Convert to PIL image if necessary
					lowercase_  							=			to_pil_image(UpperCAmelCase__  )

					lowercase_  							=			render_text(UpperCAmelCase__						,							**UpperCAmelCase__  )
					lowercase_  							=			max(header_image.width						,							image.width  )

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

					lowercase_  							=			Image.new("""RGB"""						,							(new_width, new_height + new_header_height)						,							"""white"""  )
					new_image.paste(header_image.resize((new_width, new_header_height)  )						,							(0, 0)  )
					new_image.paste(image.resize((new_width, new_height)  )						,							(0, new_header_height)  )

					# Convert back to the original framework if necessary
					lowercase_  							=			to_numpy_array(UpperCAmelCase__  )

					if infer_channel_dimension_format(UpperCAmelCase__  ) == ChannelDimension.LAST:
										lowercase_  							=			to_channel_dimension_format(UpperCAmelCase__						,							ChannelDimension.LAST  )

					return new_image







class        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  Tuple						=   ['flattened_patches']

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






										'''simple docstring'''






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

					def   UpperCAmelCase__       (     self       :   Optional[Any]							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   dict							,					**UpperCamelCase__       :   Optional[int]       ):






										'''simple docstring'''






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

										# convert to torch
										lowercase_  							=			to_channel_dimension_format(UpperCamelCase__							,					ChannelDimension.FIRST       )
										lowercase_  							=			torch.from_numpy(UpperCamelCase__       )

										lowercase_							,					lowercase_  							=			patch_size["""height"""], patch_size["""width"""]
										lowercase_							,					lowercase_  							=			get_image_size(UpperCamelCase__       )

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

										lowercase_  							=			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]
										lowercase_  							=			torch_extract_patches(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__       )

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

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

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

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

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

										lowercase_  							=			to_numpy_array(UpperCamelCase__       )

										return result

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






										'''simple docstring'''






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

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

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







					def   UpperCAmelCase__       (     self       :   str							,					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__       :   Union[str, Any]							,					):






										'''simple docstring'''






										lowercase_  							=			do_normalize if do_normalize is not None else self.do_normalize
										lowercase_  							=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
										lowercase_  							=			patch_size if patch_size is not None else self.patch_size
										lowercase_  							=			max_patches if max_patches is not None else self.max_patches
										lowercase_  							=			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 """       )

										lowercase_  							=			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:
															lowercase_  							=			[convert_to_rgb(UpperCamelCase__       ) for image in images]

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

															if isinstance(UpperCamelCase__							,					UpperCamelCase__       ):
																				lowercase_  							=			[header_text] * len(UpperCamelCase__       )

															lowercase_  							=			[
															    render_header(UpperCamelCase__							,					header_text[i]							,					font_bytes=UpperCamelCase__							,					font_path=UpperCamelCase__       )
															    for i, image in enumerate(UpperCamelCase__       )
															]

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

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

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

										lowercase_  							=			BatchFeature(
										    data={"""flattened_patches""": images, """attention_mask""": attention_masks}							,					tensor_type=UpperCamelCase__       )

										return encoded_outputs


