
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    BartForSequenceClassification,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    TapexTokenizer,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.17.0.dev0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")

lowerCamelCase_  :	Tuple 				=					logging.getLogger(__name__)

@dataclass
class     _UpperCamelCase			:






			'''simple docstring'''

			__UpperCamelCase :  Optional[str]    =	field(
			    default="""tab_fact"""       ,    metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}					)
			__UpperCamelCase :  Optional[str]    =	field(
			    default="""tab_fact"""       ,    metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}       ,    )
			__UpperCamelCase :  int    =	field(
			    default=1_024       ,    metadata={
			        """help""": (
			            """The maximum total input sequence length after tokenization. Sequences longer """
			            """than this will be truncated, sequences shorter will be padded."""
			        )
			    }       ,    )
			__UpperCamelCase :  bool    =	field(
			    default=_A       ,    metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}					)
			__UpperCamelCase :  bool    =	field(
			    default=_A       ,    metadata={
			        """help""": (
			            """Whether to pad all samples to `max_seq_length`. """
			            """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
			        )
			    }       ,    )
			__UpperCamelCase :  Optional[int]    =	field(
			    default=_A       ,    metadata={
			        """help""": (
			            """For debugging purposes or quicker training, truncate the number of training examples to this """
			            """value if set."""
			        )
			    }       ,    )
			__UpperCamelCase :  Optional[int]    =	field(
			    default=_A       ,    metadata={
			        """help""": (
			            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
			            """value if set."""
			        )
			    }       ,    )
			__UpperCamelCase :  Optional[int]    =	field(
			    default=_A       ,    metadata={
			        """help""": (
			            """For debugging purposes or quicker training, truncate the number of prediction examples to this """
			            """value if set."""
			        )
			    }       ,    )
			__UpperCamelCase :  Optional[str]    =	field(
			    default=_A       ,    metadata={"""help""": """A csv or a json file containing the training data."""}					)
			__UpperCamelCase :  Optional[str]    =	field(
			    default=_A       ,    metadata={"""help""": """A csv or a json file containing the validation data."""}					)
			__UpperCamelCase :  Optional[str]    =	field(default=_A       ,    metadata={"""help""": """A csv or a json file containing the test data."""}					)

			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							if self.dataset_name is not None:
											pass
							elif self.train_file is None or self.validation_file is None:
											raise ValueError("""Need either a GLUE task, a training/validation file or a dataset name.""" )
							else:
											UpperCamelCase_:							Optional[Any]		=  self.train_file.split(""".""" )[-1]
											assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
											UpperCamelCase_:							Dict		=  self.validation_file.split(""".""" )[-1]
											assert (
											    validation_extension == train_extension
											), "`validation_file` should have the same extension (csv or json) as `train_file`."




@dataclass
class     _UpperCamelCase			:






			'''simple docstring'''

			__UpperCamelCase :  str    =	field(
			    default=_A       ,    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}					)
			__UpperCamelCase :  Optional[str]    =	field(
			    default=_A       ,    metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}					)
			__UpperCamelCase :  Optional[str]    =	field(
			    default=_A       ,    metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}					)
			__UpperCamelCase :  Optional[str]    =	field(
			    default=_A       ,    metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}       ,    )
			__UpperCamelCase :  bool    =	field(
			    default=_A       ,    metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}       ,    )
			__UpperCamelCase :  str    =	field(
			    default="""main"""       ,    metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}       ,    )
			__UpperCamelCase :  bool    =	field(
			    default=_A       ,    metadata={
			        """help""": (
			            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
			            """with private models)."""
			        )
			    }       ,    )







def     A__   (  )					->		Union[str, Any]:
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				UpperCamelCase_:							Dict		=  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
				if len(sys.argv  ) == 2 and sys.argv[1].endswith(""".json"""  ):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								UpperCamelCase_,				UpperCamelCase_,				UpperCamelCase_:							str		=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
				else:
								UpperCamelCase_,				UpperCamelCase_,				UpperCamelCase_:							str		=  parser.parse_args_into_dataclasses()

				# Setup logging
				logging.basicConfig(
				    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""       ,      datefmt="""%m/%d/%Y %H:%M:%S"""       ,      handlers=[logging.StreamHandler(sys.stdout  )]       ,      )

				UpperCamelCase_:							List[str]		=  training_args.get_process_log_level()
				logger.setLevel(lowerCamelCase  )
				datasets.utils.logging.set_verbosity(lowerCamelCase  )
				transformers.utils.logging.set_verbosity(lowerCamelCase  )
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

				# Log on each process the small summary:
				logger.warning(
				    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
				    + F'''distributed training: {bool(training_args.local_rank != -1  )}, 16-bits training: {training_args.fpaa}'''  )
				logger.info(F'''Training/evaluation parameters {training_args}'''  )

				# Detecting last checkpoint.
				UpperCamelCase_:							Any		=  None
				if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
								UpperCamelCase_:							str		=  get_last_checkpoint(training_args.output_dir  )
								if last_checkpoint is None and len(os.listdir(training_args.output_dir  )  ) > 0:
												raise ValueError(
												    F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
												    """Use --overwrite_output_dir to overcome."""  )
								elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
												logger.info(
												    F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
												    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""  )

    # Set seed before initializing model.
				set_seed(training_args.seed  )

				# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
				# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
				#
				# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
				#
				# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
				# single column. You can easily tweak this behavior (see below)
				#
				# In distributed training, the load_dataset function guarantee that only one local process can concurrently
				# download the dataset.
				if data_args.dataset_name is not None:
								# Downloading and loading a dataset from the hub.
								UpperCamelCase_:							List[Any]		=  load_dataset(
								    data_args.dataset_name       ,      data_args.dataset_config_name       ,      cache_dir=model_args.cache_dir  )
				else:
								# Loading a dataset from your local files.
								# CSV/JSON training and evaluation files are needed.
								UpperCamelCase_:							Union[str, Any]		=  {"""train""": data_args.train_file, """validation""": data_args.validation_file}

								# Get the test dataset: you can provide your own CSV/JSON test file (see below)
								# when you use `do_predict` without specifying a GLUE benchmark task.
								if training_args.do_predict:
												if data_args.test_file is not None:
																UpperCamelCase_:							Optional[Any]		=  data_args.train_file.split("""."""  )[-1]
																UpperCamelCase_:							Union[str, Any]		=  data_args.test_file.split("""."""  )[-1]
																assert (
																    test_extension == train_extension
																), "`test_file` should have the same extension (csv or json) as `train_file`."
																UpperCamelCase_:							int		=  data_args.test_file
												else:
																raise ValueError("""Need either a GLUE task or a test file for `do_predict`."""  )

								for key in data_files.keys():
												logger.info(F'''load a local file for {key}: {data_files[key]}'''  )

								if data_args.train_file.endswith(""".csv"""  ):
												# Loading a dataset from local csv files
												UpperCamelCase_:							List[Any]		=  load_dataset("""csv"""       ,      data_files=lowerCamelCase       ,      cache_dir=model_args.cache_dir  )
								else:
												# Loading a dataset from local json files
												UpperCamelCase_:							Dict		=  load_dataset("""json"""       ,      data_files=lowerCamelCase       ,      cache_dir=model_args.cache_dir  )
    # See more about loading any type of standard or custom dataset at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Labels
				UpperCamelCase_:							Union[str, Any]		=  raw_datasets["""train"""].features["""label"""].names
				UpperCamelCase_:							Dict		=  len(lowerCamelCase  )

				# Load pretrained model and tokenizer
				#
				# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				UpperCamelCase_:							Union[str, Any]		=  AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,      num_labels=lowerCamelCase       ,      cache_dir=model_args.cache_dir       ,      revision=model_args.model_revision       ,      use_auth_token=True if model_args.use_auth_token else None       ,      )
				# load tapex tokenizer
				UpperCamelCase_:							Dict		=  TapexTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path       ,      cache_dir=model_args.cache_dir       ,      use_fast=model_args.use_fast_tokenizer       ,      revision=model_args.model_revision       ,      use_auth_token=True if model_args.use_auth_token else None       ,      add_prefix_space=lowerCamelCase       ,      )
				UpperCamelCase_:							List[str]		=  BartForSequenceClassification.from_pretrained(
				    model_args.model_name_or_path       ,      from_tf=bool(""".ckpt""" in model_args.model_name_or_path  )       ,      config=lowerCamelCase       ,      cache_dir=model_args.cache_dir       ,      revision=model_args.model_revision       ,      use_auth_token=True if model_args.use_auth_token else None       ,      )

				# Padding strategy
				if data_args.pad_to_max_length:
								UpperCamelCase_:							List[str]		=  """max_length"""
				else:
								# We will pad later, dynamically at batch creation, to the max sequence length in each batch
								UpperCamelCase_:							Dict		=  False

				# Some models have set the order of the labels to use, so let's make sure we do use it.
				UpperCamelCase_:							str		=  {"""Refused""": 0, """Entailed""": 1}
				UpperCamelCase_:							Tuple		=  {0: """Refused""", 1: """Entailed"""}

				if data_args.max_seq_length > tokenizer.model_max_length:
								logger.warning(
								    F'''The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'''
								    F'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'''  )
				UpperCamelCase_:							List[str]		=  min(data_args.max_seq_length       ,      tokenizer.model_max_length  )

				def preprocess_tabfact_function(lowerCamelCase  ):
								# Tokenize the texts
								def _convert_table_text_to_pandas(lowerCamelCase  ):
												UpperCamelCase_:							Dict		=  [_table_row.split("""#"""  ) for _table_row in _table_text.strip("""\n"""  ).split("""\n"""  )]
												UpperCamelCase_:							str		=  pd.DataFrame.from_records(_table_content[1:]       ,      columns=_table_content[0]  )
												return _table_pd

								UpperCamelCase_:							int		=  examples["""statement"""]
								UpperCamelCase_:							Optional[int]		=  list(map(_convert_table_text_to_pandas       ,      examples["""table_text"""]  )  )
								UpperCamelCase_:							int		=  tokenizer(lowerCamelCase       ,      lowerCamelCase       ,      padding=lowerCamelCase       ,      max_length=lowerCamelCase       ,      truncation=lowerCamelCase  )

								UpperCamelCase_:							List[Any]		=  examples["""label"""]
								return result

				with training_args.main_process_first(desc="""dataset map pre-processing"""  ):
								UpperCamelCase_:							Dict		=  raw_datasets.map(
								    lowerCamelCase       ,      batched=lowerCamelCase       ,      load_from_cache_file=not data_args.overwrite_cache       ,      desc="""Running tokenizer on dataset"""       ,      )
				if training_args.do_train:
								if "train" not in raw_datasets:
												raise ValueError("""--do_train requires a train dataset"""  )
								UpperCamelCase_:							Dict		=  raw_datasets["""train"""]
								if data_args.max_train_samples is not None:
												UpperCamelCase_:							Union[str, Any]		=  train_dataset.select(range(data_args.max_train_samples  )  )

				if training_args.do_eval:
								if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
												raise ValueError("""--do_eval requires a validation dataset"""  )
								UpperCamelCase_:							List[str]		=  raw_datasets["""validation"""]
								if data_args.max_eval_samples is not None:
												UpperCamelCase_:							List[str]		=  eval_dataset.select(range(data_args.max_eval_samples  )  )

				if training_args.do_predict or data_args.test_file is not None:
								if "test" not in raw_datasets and "test_matched" not in raw_datasets:
												raise ValueError("""--do_predict requires a test dataset"""  )
								UpperCamelCase_:							str		=  raw_datasets["""test"""]
								if data_args.max_predict_samples is not None:
												UpperCamelCase_:							int		=  predict_dataset.select(range(data_args.max_predict_samples  )  )

    # Log a few random samples from the training set:
				if training_args.do_train:
								for index in random.sample(range(len(lowerCamelCase  )  )       ,      3  ):
												logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.'''  )

    # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
    # predictions and label_ids field) and has to return a dictionary string to float.
				def compute_metrics(lowerCamelCase  ):
								UpperCamelCase_:							Optional[int]		=  p.predictions[0] if isinstance(p.predictions       ,      lowerCamelCase  ) else p.predictions
								UpperCamelCase_:							Optional[Any]		=  np.argmax(lowerCamelCase       ,      axis=1  )
								return {"accuracy": (preds == p.label_ids).astype(np.floataa  ).mean().item()}

				# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
				if data_args.pad_to_max_length:
								UpperCamelCase_:							Any		=  default_data_collator
				elif training_args.fpaa:
								UpperCamelCase_:							int		=  DataCollatorWithPadding(lowerCamelCase       ,      pad_to_multiple_of=8  )
				else:
								UpperCamelCase_:							int		=  None

				# Initialize our Trainer
				UpperCamelCase_:							int		=  Trainer(
				    model=lowerCamelCase       ,      args=lowerCamelCase       ,      train_dataset=train_dataset if training_args.do_train else None       ,      eval_dataset=eval_dataset if training_args.do_eval else None       ,      compute_metrics=lowerCamelCase       ,      tokenizer=lowerCamelCase       ,      data_collator=lowerCamelCase       ,      )

				# Training
				if training_args.do_train:
								UpperCamelCase_:							List[Any]		=  None
								if training_args.resume_from_checkpoint is not None:
												UpperCamelCase_:							Any		=  training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												UpperCamelCase_:							Any		=  last_checkpoint
								UpperCamelCase_:							Optional[int]		=  trainer.train(resume_from_checkpoint=lowerCamelCase  )
								UpperCamelCase_:							List[str]		=  train_result.metrics
								UpperCamelCase_:							Dict		=  (
								    data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCamelCase  )
								)
								UpperCamelCase_:							int		=  min(lowerCamelCase       ,      len(lowerCamelCase  )  )

								trainer.save_model()  # Saves the tokenizer too for easy upload

								trainer.log_metrics("""train"""       ,      lowerCamelCase  )
								trainer.save_metrics("""train"""       ,      lowerCamelCase  )
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								logger.info("""*** Evaluate ***"""  )

								UpperCamelCase_:							List[str]		=  trainer.evaluate(eval_dataset=lowerCamelCase  )
								UpperCamelCase_:							int		=  data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCamelCase  )
								UpperCamelCase_:							int		=  min(lowerCamelCase       ,      len(lowerCamelCase  )  )

								trainer.log_metrics("""eval"""       ,      lowerCamelCase  )
								trainer.save_metrics("""eval"""       ,      lowerCamelCase  )

				if training_args.do_predict:
								logger.info("""*** Predict ***"""  )

								# Removing the `label` columns because it contains -1 and Trainer won't like that.
								UpperCamelCase_:							List[str]		=  predict_dataset.remove_columns("""label"""  )
								UpperCamelCase_:							Union[str, Any]		=  trainer.predict(lowerCamelCase       ,      metric_key_prefix="""predict"""  ).predictions
								UpperCamelCase_:							Optional[int]		=  np.argmax(lowerCamelCase       ,      axis=1  )

								UpperCamelCase_:							int		=  os.path.join(training_args.output_dir       ,      """predict_results_tabfact.txt"""  )
								if trainer.is_world_process_zero():
												with open(lowerCamelCase       ,      """w"""  ) as writer:
																logger.info("""***** Predict Results *****"""  )
																writer.write("""index\tprediction\n"""  )
																for index, item in enumerate(lowerCamelCase  ):
																				UpperCamelCase_:							str		=  label_list[item]
																				writer.write(F'''{index}\t{item}\n'''  )

				UpperCamelCase_:							Tuple		=  {"""finetuned_from""": model_args.model_name_or_path, """tasks""": """text-classification"""}

				if training_args.push_to_hub:
								trainer.push_to_hub(**lowerCamelCase  )
				else:
								trainer.create_model_card(**lowerCamelCase  )







def     A__   (  lowerCamelCase  )					->		Optional[int]:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
							main()








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_  :	List[str] 				=					False


lowerCamelCase_  :	int 				=					logging.get_logger(__name__)
lowerCamelCase_  :	Optional[int] 				=					"""ybelkada/fonts"""







def     A__   (  )					->		Dict:
				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     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Optional[int]:
				requires_backends(lowerCamelCase       ,      ["""torch"""]  )
				_check_torch_version()

				UpperCamelCase_:							Tuple		=  image_tensor.unsqueeze(0  )
				UpperCamelCase_:							Any		=  torch.nn.functional.unfold(lowerCamelCase       ,      (patch_height, patch_width)       ,      stride=(patch_height, patch_width)  )
				UpperCamelCase_:							int		=  patches.reshape(image_tensor.size(0  )       ,      image_tensor.size(1  )       ,      lowerCamelCase       ,      lowerCamelCase       ,      -1  )
				UpperCamelCase_:							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     A__   (  lowerCamelCase       ,      lowerCamelCase = 36       ,      lowerCamelCase = "black"       ,      lowerCamelCase = "white"       ,      lowerCamelCase = 5       ,      lowerCamelCase = 5       ,      lowerCamelCase = 5       ,      lowerCamelCase = 5       ,      lowerCamelCase = None       ,      lowerCamelCase = None       ,      )					->		Image.Image:
				requires_backends(lowerCamelCase       ,      """vision"""  )
				# Add new lines so that each line is no more than 80 characters.

				UpperCamelCase_:							List[str]		=  textwrap.TextWrapper(width=80  )
				UpperCamelCase_:							Optional[int]		=  wrapper.wrap(text=lowerCamelCase  )
				UpperCamelCase_:							List[str]		=  """\n""".join(lowerCamelCase  )

				if font_bytes is not None and font_path is None:
								UpperCamelCase_:							List[Any]		=  io.BytesIO(lowerCamelCase  )
				elif font_path is not None:
								UpperCamelCase_:							List[Any]		=  font_path
				else:
								UpperCamelCase_:							Tuple		=  hf_hub_download(lowerCamelCase       ,      """Arial.TTF"""  )
				UpperCamelCase_:							Optional[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.
				UpperCamelCase_:							str		=  ImageDraw.Draw(Image.new("""RGB"""       ,      (1, 1)       ,      lowerCamelCase  )  )
				UpperCamelCase_,				UpperCamelCase_,				UpperCamelCase_,				UpperCamelCase_:							Optional[int]		=  temp_draw.textbbox((0, 0)       ,      lowerCamelCase       ,      lowerCamelCase  )

				# Create the actual image with a bit of padding around the text.
				UpperCamelCase_:							Optional[int]		=  text_width + left_padding + right_padding
				UpperCamelCase_:							List[str]		=  text_height + top_padding + bottom_padding
				UpperCamelCase_:							Union[str, Any]		=  Image.new("""RGB"""       ,      (image_width, image_height)       ,      lowerCamelCase  )
				UpperCamelCase_:							Optional[Any]		=  ImageDraw.Draw(lowerCamelCase  )
				draw.text(xy=(left_padding, top_padding)       ,      text=lowerCamelCase       ,      fill=lowerCamelCase       ,      font=lowerCamelCase  )
				return image







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      **lowerCamelCase  )					->		List[str]:
				requires_backends(lowerCamelCase       ,      """vision"""  )

				# Convert to PIL image if necessary
				UpperCamelCase_:							List[str]		=  to_pil_image(lowerCamelCase  )

				UpperCamelCase_:							Union[str, Any]		=  render_text(lowerCamelCase       ,      **lowerCamelCase  )
				UpperCamelCase_:							Tuple		=  max(header_image.width       ,      image.width  )

				UpperCamelCase_:							Tuple		=  int(image.height * (new_width / image.width)  )
				UpperCamelCase_:							Dict		=  int(header_image.height * (new_width / header_image.width)  )

				UpperCamelCase_:							str		=  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
				UpperCamelCase_:							Optional[Any]		=  to_numpy_array(lowerCamelCase  )

				if infer_channel_dimension_format(lowerCamelCase  ) == ChannelDimension.LAST:
								UpperCamelCase_:							Tuple		=  to_channel_dimension_format(lowerCamelCase       ,      ChannelDimension.LAST  )

				return new_image





class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  Optional[int]    =	["""flattened_patches"""]

			def __init__(    self	:    int  ,		snake_case_	:    bool = True  ,		snake_case_	:    bool = True  ,		snake_case_	:    Dict[str, int] = None  ,		snake_case_	:    int = 2048  ,		snake_case_	:    bool = False  ,		**snake_case_	:    Any  ,		):
							super().__init__(**snake_case_ )
							UpperCamelCase_:							int		=  patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
							UpperCamelCase_:							Tuple		=  do_normalize
							UpperCamelCase_:							List[Any]		=  do_convert_rgb
							UpperCamelCase_:							Tuple		=  max_patches
							UpperCamelCase_:							Tuple		=  is_vqa

			def  lowerCAmelCase__		(    self	:    int  ,		snake_case_	:    np.ndarray  ,		snake_case_	:    int  ,		snake_case_	:    dict  ,		**snake_case_	:    Tuple ):
							requires_backends(self.extract_flattened_patches  ,		"""torch""" )
							_check_torch_version()

							# convert to torch
							UpperCamelCase_:							int		=  to_channel_dimension_format(snake_case_  ,		ChannelDimension.FIRST )
							UpperCamelCase_:							List[str]		=  torch.from_numpy(snake_case_ )

							UpperCamelCase_,				UpperCamelCase_:							List[Any]		=  patch_size["""height"""], patch_size["""width"""]
							UpperCamelCase_,				UpperCamelCase_:							Tuple		=  get_image_size(snake_case_ )

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

							UpperCamelCase_:							str		=  torch.nn.functional.interpolate(
							    image.unsqueeze(0 )  ,		size=(resized_height, resized_width)  ,		mode="""bilinear"""  ,		align_corners=snake_case_  ,		antialias=snake_case_  ,		).squeeze(0 )

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

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

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

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

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

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

							UpperCamelCase_:							List[Any]		=  to_numpy_array(snake_case_ )

							return result

			def  lowerCAmelCase__		(    self	:    List[Any]  ,		snake_case_	:    np.ndarray  ,		snake_case_	:    Optional[Union[str, ChannelDimension]] = None  ,		**snake_case_	:    Tuple ):
							if image.dtype == np.uinta:
											UpperCamelCase_:							List[str]		=  image.astype(np.floataa )

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

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




			def  lowerCAmelCase__		(    self	:    str  ,		snake_case_	:    ImageInput  ,		snake_case_	:    Optional[str] = None  ,		snake_case_	:    bool = None  ,		snake_case_	:    Optional[bool] = None  ,		snake_case_	:    Optional[int] = None  ,		snake_case_	:    Optional[Dict[str, int]] = None  ,		snake_case_	:    Optional[Union[str, TensorType]] = None  ,		snake_case_	:    ChannelDimension = ChannelDimension.FIRST  ,		**snake_case_	:    Union[str, Any]  ,		):
							UpperCamelCase_:							Tuple		=  do_normalize if do_normalize is not None else self.do_normalize
							UpperCamelCase_:							Tuple		=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
							UpperCamelCase_:							Optional[Any]		=  patch_size if patch_size is not None else self.patch_size
							UpperCamelCase_:							Optional[int]		=  max_patches if max_patches is not None else self.max_patches
							UpperCamelCase_:							Tuple		=  self.is_vqa

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

							UpperCamelCase_:							Dict		=  make_list_of_images(snake_case_ )

							if not valid_images(snake_case_ ):
											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:
											UpperCamelCase_:							str		=  [convert_to_rgb(snake_case_ ) for image in images]

							# All transformations expect numpy arrays.
							UpperCamelCase_:							Union[str, Any]		=  [to_numpy_array(snake_case_ ) for image in images]

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

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

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

							if do_normalize:
											UpperCamelCase_:							Union[str, Any]		=  [self.normalize(image=snake_case_ ) for image in images]

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

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

							UpperCamelCase_:							Optional[Any]		=  BatchFeature(
							    data={"""flattened_patches""": images, """attention_mask""": attention_masks}  ,		tensor_type=snake_case_ )

							return encoded_outputs






