
import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

__lowerCAmelCase					:			Union[str, Any]			      =		16
__lowerCAmelCase					:			str			      =		32


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase = 16	) ->						List[str]:
    __lowercase		:			List[str]	    =       AutoTokenizer.from_pretrained('''bert-base-cased'''	)
    __lowercase		:			Any	    =       load_dataset('''glue'''	,					'''mrpc'''	)

    def tokenize_function(__lowerCAmelCase	):
        # max_length=None => use the model max length (it's actually the default)
        __lowercase		:			Union[str, Any]	    =       tokenizer(examples['''sentence1''']	,					examples['''sentence2''']	,					truncation=__lowerCAmelCase	,					max_length=__lowerCAmelCase	)
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    # starting with the main process first:
    with accelerator.main_process_first():
        __lowercase		:			List[str]	    =       datasets.map(
            __lowerCAmelCase	,					batched=__lowerCAmelCase	,					remove_columns=['''idx''', '''sentence1''', '''sentence2''']	,					)

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    __lowercase		:			List[Any]	    =       tokenized_datasets.rename_column('''label'''	,					'''labels'''	)

    def collate_fn(__lowerCAmelCase	):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        __lowercase		:			Optional[Any]	    =       128 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            __lowercase		:			List[Any]	    =       16
        elif accelerator.mixed_precision != "no":
            __lowercase		:			Any	    =       8
        else:
            __lowercase		:			Union[str, Any]	    =       None

        return tokenizer.pad(
            __lowerCAmelCase	,					padding='''longest'''	,					max_length=__lowerCAmelCase	,					pad_to_multiple_of=__lowerCAmelCase	,					return_tensors='''pt'''	,					)

    # Instantiate dataloaders.
    __lowercase		:			int	    =       DataLoader(
        tokenized_datasets['''train''']	,					shuffle=__lowerCAmelCase	,					collate_fn=__lowerCAmelCase	,					batch_size=__lowerCAmelCase	)
    __lowercase		:			str	    =       DataLoader(
        tokenized_datasets['''validation''']	,					shuffle=__lowerCAmelCase	,					collate_fn=__lowerCAmelCase	,					batch_size=__lowerCAmelCase	)

    return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
 from accelerate.test_utils.training import mocked_dataloaders

 __lowerCAmelCase					:			Optional[int]			      =		mocked_dataloaders  # noqa: F811


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	) ->						Optional[Any]:
    # For testing only
    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''	,					__lowerCAmelCase	) == "1":
        __lowercase		:			Optional[int]	    =       2
    # Initialize Accelerator

    # New Code #
    # We pass in "all" to `log_with` to grab all available trackers in the environment
    # Note: If using a custom `Tracker` class, should be passed in here such as:
    # >>> log_with = ["all", MyCustomTrackerClassInstance()]
    if args.with_tracking:
        __lowercase		:			List[Any]	    =       Accelerator(
            cpu=args.cpu	,					mixed_precision=args.mixed_precision	,					log_with='''all'''	,					project_dir=args.project_dir	)
    else:
        __lowercase		:			List[str]	    =       Accelerator(cpu=args.cpu	,					mixed_precision=args.mixed_precision	)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    __lowercase		:			List[Any]	    =       config['''lr''']
    __lowercase		:			Tuple	    =       int(config['''num_epochs''']	)
    __lowercase		:			Optional[int]	    =       int(config['''seed''']	)
    __lowercase		:			List[Any]	    =       int(config['''batch_size''']	)
    set_seed(__lowerCAmelCase	)

    __lowercase		:			Dict	    =       get_dataloaders(__lowerCAmelCase	,					__lowerCAmelCase	)
    __lowercase		:			int	    =       evaluate.load('''glue'''	,					'''mrpc'''	)

    # If the batch size is too big we use gradient accumulation
    __lowercase		:			Optional[int]	    =       1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        __lowercase		:			str	    =       batch_size // MAX_GPU_BATCH_SIZE
        __lowercase		:			Optional[int]	    =       MAX_GPU_BATCH_SIZE

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    __lowercase		:			Optional[int]	    =       AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''	,					return_dict=__lowerCAmelCase	)

    # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
    # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
    # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
    __lowercase		:			Tuple	    =       model.to(accelerator.device	)

    # Instantiate optimizer
    __lowercase		:			Optional[Any]	    =       AdamW(params=model.parameters()	,					lr=__lowerCAmelCase	)

    # Instantiate scheduler
    __lowercase		:			str	    =       get_linear_schedule_with_warmup(
        optimizer=__lowerCAmelCase	,					num_warmup_steps=100	,					num_training_steps=(len(__lowerCAmelCase	) * num_epochs) // gradient_accumulation_steps	,					)

    # Prepare everything
    # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
    # prepare method.
    __lowercase		:			List[str]	    =       accelerator.prepare(
        __lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	)

    # New Code #
    # We need to initialize the trackers we use. Overall configurations can also be stored
    if args.with_tracking:
        __lowercase		:			str	    =       os.path.split(__lowerCAmelCase	)[-1].split('''.'''	)[0]
        accelerator.init_trackers(__lowerCAmelCase	,					__lowerCAmelCase	)

    # Now we train the model
    for epoch in range(__lowerCAmelCase	):
        model.train()
        # New Code #
        # For our tracking example, we will log the total loss of each epoch
        if args.with_tracking:
            __lowercase		:			Any	    =       0
        for step, batch in enumerate(__lowerCAmelCase	):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device	)
            __lowercase		:			List[str]	    =       model(**__lowerCAmelCase	)
            __lowercase		:			List[str]	    =       outputs.loss
            # New Code #
            if args.with_tracking:
                total_loss += loss.detach().float()
            __lowercase		:			Tuple	    =       loss / gradient_accumulation_steps
            accelerator.backward(__lowerCAmelCase	)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

        model.eval()
        for step, batch in enumerate(__lowerCAmelCase	):
            # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
            batch.to(accelerator.device	)
            with torch.no_grad():
                __lowercase		:			int	    =       model(**__lowerCAmelCase	)
            __lowercase		:			Optional[Any]	    =       outputs.logits.argmax(dim=-1	)
            __lowercase		:			Dict	    =       accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
            metric.add_batch(
                predictions=__lowerCAmelCase	,					references=__lowerCAmelCase	,					)

        __lowercase		:			Union[str, Any]	    =       metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'epoch {epoch}:'	,					__lowerCAmelCase	)

        # New Code #
        # To actually log, we call `Accelerator.log`
        # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
        if args.with_tracking:
            accelerator.log(
                {
                    '''accuracy''': eval_metric['''accuracy'''],
                    '''f1''': eval_metric['''f1'''],
                    '''train_loss''': total_loss.item() / len(__lowerCAmelCase	),
                    '''epoch''': epoch,
                }	,					step=__lowerCAmelCase	,					)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
    if args.with_tracking:
        accelerator.end_training()


def     UpperCAmelCase_  (						) ->						Tuple:
    __lowercase		:			Tuple	    =       argparse.ArgumentParser(description='''Simple example of training script.'''	)
    parser.add_argument(
        '''--mixed_precision'''	,					type=__lowerCAmelCase	,					default=__lowerCAmelCase	,					choices=['''no''', '''fp16''', '''bf16''', '''fp8''']	,					help='''Whether to use mixed precision. Choose'''
        '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
        '''and an Nvidia Ampere GPU.'''	,					)
    parser.add_argument('''--cpu'''	,					action='''store_true'''	,					help='''If passed, will train on the CPU.'''	)
    parser.add_argument(
        '''--with_tracking'''	,					action='''store_true'''	,					help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''	,					)
    parser.add_argument(
        '''--project_dir'''	,					type=__lowerCAmelCase	,					default='''logs'''	,					help='''Location on where to store experiment tracking logs` and relevent project information'''	,					)
    __lowercase		:			List[str]	    =       parser.parse_args()
    __lowercase		:			int	    =       {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(__lowerCAmelCase	,					__lowerCAmelCase	)


if __name__ == "__main__":
 main()




import unittest

from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_detectrona,
    require_pytesseract,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image

 from transformers.image_utils import load_image
else:


 class 						__lowerCAmelCase	:



       """simple docstring"""







       @staticmethod
       def 		snake_case_			(      *_snake_case		:       Union[str, Any]				,       **_snake_case		:       str				):
           pass






 def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						Union[str, Any]:
     return None


# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
__lowerCAmelCase					:			Dict			      =		(
    "https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"
)


@is_pipeline_test
@require_torch
@require_vision
class 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      A__	:			int        =    MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING




      @require_pytesseract
      @require_vision
      def 		snake_case_			(      self		:       List[Any]				,       _snake_case		:       Any				,       _snake_case		:       Dict				,       _snake_case		:       Any				):
          __lowercase		:			List[str]	    =       pipeline(
              '''document-question-answering'''				,       model=_snake_case				,       tokenizer=_snake_case				,       image_processor=_snake_case				)

          __lowercase		:			int	    =       INVOICE_URL
          __lowercase		:			Optional[int]	    =       list(zip(*apply_tesseract(load_image(_snake_case				)				,       _snake_case				,       ''''''				)				)				)
          __lowercase		:			List[str]	    =       '''What is the placebo?'''
          __lowercase		:			Tuple	    =       [
              {
                  '''image''': load_image(_snake_case				),
                  '''question''': question,
              },
              {
                  '''image''': image,
                  '''question''': question,
              },
              {
                  '''image''': image,
                  '''question''': question,
                  '''word_boxes''': word_boxes,
              },
          ]
          return dqa_pipeline, examples




      def 		snake_case_			(      self		:       List[str]				,       _snake_case		:       int				,       _snake_case		:       List[Any]				):
          __lowercase		:			Optional[Any]	    =       dqa_pipeline(_snake_case				,       top_k=2				)
          self.assertEqual(
              _snake_case				,       [
                  [
                      {'''score''': ANY(_snake_case				), '''answer''': ANY(_snake_case				), '''start''': ANY(_snake_case				), '''end''': ANY(_snake_case				)},
                      {'''score''': ANY(_snake_case				), '''answer''': ANY(_snake_case				), '''start''': ANY(_snake_case				), '''end''': ANY(_snake_case				)},
                  ]
              ]
              * 3				,       )




      @require_torch
      @require_detectrona
      @require_pytesseract
      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			List[str]	    =       pipeline('''document-question-answering'''				,       model='''hf-internal-testing/tiny-random-layoutlmv2'''				)
          __lowercase		:			Any	    =       INVOICE_URL
          __lowercase		:			str	    =       '''How many cats are there?'''

          __lowercase		:			Optional[int]	    =       [
              {'''score''': 0.00_01, '''answer''': '''oy 2312/2019''', '''start''': 38, '''end''': 39},
              {'''score''': 0.00_01, '''answer''': '''oy 2312/2019 DUE''', '''start''': 38, '''end''': 40},
          ]
          __lowercase		:			Optional[Any]	    =       dqa_pipeline(image=_snake_case				,       question=_snake_case				,       top_k=2				)
          self.assertEqual(nested_simplify(_snake_case				,       decimals=4				)				,       _snake_case				)

          __lowercase		:			Optional[Any]	    =       dqa_pipeline({'''image''': image, '''question''': question}				,       top_k=2				)
          self.assertEqual(nested_simplify(_snake_case				,       decimals=4				)				,       _snake_case				)

          # This image does not detect ANY text in it, meaning layoutlmv2 should fail.
          # Empty answer probably
          __lowercase		:			Optional[int]	    =       '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
          __lowercase		:			str	    =       dqa_pipeline(image=_snake_case				,       question=_snake_case				,       top_k=2				)
          self.assertEqual(_snake_case				,       []				)

          # We can optionnally pass directly the words and bounding boxes
          __lowercase		:			List[Any]	    =       '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
          __lowercase		:			Dict	    =       []
          __lowercase		:			List[str]	    =       []
          __lowercase		:			Tuple	    =       dqa_pipeline(image=_snake_case				,       question=_snake_case				,       words=_snake_case				,       boxes=_snake_case				,       top_k=2				)
          self.assertEqual(_snake_case				,       []				)




      @slow
      @require_torch
      @require_detectrona
      @require_pytesseract
      def 		snake_case_			(      self		:       int				):
          __lowercase		:			List[Any]	    =       pipeline(
              '''document-question-answering'''				,       model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'''				,       revision='''9977165'''				,       )
          __lowercase		:			List[str]	    =       INVOICE_URL
          __lowercase		:			Optional[Any]	    =       '''What is the invoice number?'''

          __lowercase		:			int	    =       dqa_pipeline(image=_snake_case				,       question=_snake_case				,       top_k=2				)
          self.assertEqual(
              nested_simplify(_snake_case				,       decimals=4				)				,       [
                  {'''score''': 0.99_44, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                  {'''score''': 0.00_09, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
              ]				,       )

          __lowercase		:			Union[str, Any]	    =       dqa_pipeline({'''image''': image, '''question''': question}				,       top_k=2				)
          self.assertEqual(
              nested_simplify(_snake_case				,       decimals=4				)				,       [
                  {'''score''': 0.99_44, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                  {'''score''': 0.00_09, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
              ]				,       )

          __lowercase		:			List[str]	    =       dqa_pipeline(
              [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]				,       top_k=2				)
          self.assertEqual(
              nested_simplify(_snake_case				,       decimals=4				)				,       [
                  [
                      {'''score''': 0.99_44, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                      {'''score''': 0.00_09, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                  ],
              ]
              * 2				,       )




      @slow
      @require_torch
      @require_detectrona
      @require_pytesseract
      def 		snake_case_			(      self		:       int				):
          __lowercase		:			Optional[Any]	    =       pipeline(
              '''document-question-answering'''				,       model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'''				,       revision='''9977165'''				,       max_seq_len=50				,       )
          __lowercase		:			Union[str, Any]	    =       INVOICE_URL
          __lowercase		:			Any	    =       '''What is the invoice number?'''

          __lowercase		:			Any	    =       dqa_pipeline(image=_snake_case				,       question=_snake_case				,       top_k=2				)
          self.assertEqual(
              nested_simplify(_snake_case				,       decimals=4				)				,       [
                  {'''score''': 0.99_74, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
                  {'''score''': 0.99_48, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
              ]				,       )

          __lowercase		:			Dict	    =       dqa_pipeline({'''image''': image, '''question''': question}				,       top_k=2				)
          self.assertEqual(
              nested_simplify(_snake_case				,       decimals=4				)				,       [
                  {'''score''': 0.99_74, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
                  {'''score''': 0.99_48, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
              ]				,       )

          __lowercase		:			List[Any]	    =       dqa_pipeline(
              [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]				,       top_k=2				)
          self.assertEqual(
              nested_simplify(_snake_case				,       decimals=4				)				,       [
                  [
                      {'''score''': 0.99_74, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
                      {'''score''': 0.99_48, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                  ]
              ]
              * 2				,       )




      @slow
      @require_torch
      @require_pytesseract
      @require_vision
      def 		snake_case_			(      self		:       str				):
          __lowercase		:			List[Any]	    =       AutoTokenizer.from_pretrained(
              '''impira/layoutlm-document-qa'''				,       revision='''3dc6de3'''				,       add_prefix_space=_snake_case				)
          __lowercase		:			Union[str, Any]	    =       pipeline(
              '''document-question-answering'''				,       model='''impira/layoutlm-document-qa'''				,       tokenizer=_snake_case				,       revision='''3dc6de3'''				,       )
          __lowercase		:			Tuple	    =       INVOICE_URL
          __lowercase		:			Any	    =       '''What is the invoice number?'''

          __lowercase		:			Optional[Any]	    =       dqa_pipeline(image=_snake_case				,       question=_snake_case				,       top_k=2				)
          self.assertEqual(
              nested_simplify(_snake_case				,       decimals=4				)				,       [
                  {'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                  {'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
              ]				,       )

          __lowercase		:			List[Any]	    =       dqa_pipeline({'''image''': image, '''question''': question}				,       top_k=2				)
          self.assertEqual(
              nested_simplify(_snake_case				,       decimals=4				)				,       [
                  {'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                  {'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
              ]				,       )

          __lowercase		:			int	    =       dqa_pipeline(
              [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]				,       top_k=2				)
          self.assertEqual(
              nested_simplify(_snake_case				,       decimals=4				)				,       [
                  [
                      {'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                      {'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
                  ]
              ]
              * 2				,       )

          __lowercase		:			Union[str, Any]	    =       list(zip(*apply_tesseract(load_image(_snake_case				)				,       _snake_case				,       ''''''				)				)				)

          # This model should also work if `image` is set to None
          __lowercase		:			Union[str, Any]	    =       dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question}				,       top_k=2				)
          self.assertEqual(
              nested_simplify(_snake_case				,       decimals=4				)				,       [
                  {'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                  {'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
              ]				,       )




      @slow
      @require_torch
      @require_pytesseract
      @require_vision
      def 		snake_case_			(      self		:       List[str]				):
          __lowercase		:			Optional[int]	    =       AutoTokenizer.from_pretrained(
              '''impira/layoutlm-document-qa'''				,       revision='''3dc6de3'''				,       add_prefix_space=_snake_case				)
          __lowercase		:			List[str]	    =       pipeline(
              '''document-question-answering'''				,       model='''impira/layoutlm-document-qa'''				,       tokenizer=_snake_case				,       revision='''3dc6de3'''				,       max_seq_len=50				,       )
          __lowercase		:			Tuple	    =       INVOICE_URL
          __lowercase		:			Optional[int]	    =       '''What is the invoice number?'''

          __lowercase		:			Any	    =       dqa_pipeline(image=_snake_case				,       question=_snake_case				,       top_k=2				)
          self.assertEqual(
              nested_simplify(_snake_case				,       decimals=4				)				,       [
                  {'''score''': 0.99_99, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                  {'''score''': 0.99_98, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
              ]				,       )

          __lowercase		:			Any	    =       dqa_pipeline(
              [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]				,       top_k=2				)
          self.assertEqual(
              nested_simplify(_snake_case				,       decimals=4				)				,       [
                  [
                      {'''score''': 0.99_99, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                      {'''score''': 0.99_98, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                  ]
              ]
              * 2				,       )

          __lowercase		:			int	    =       list(zip(*apply_tesseract(load_image(_snake_case				)				,       _snake_case				,       ''''''				)				)				)

          # This model should also work if `image` is set to None
          __lowercase		:			Tuple	    =       dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question}				,       top_k=2				)
          self.assertEqual(
              nested_simplify(_snake_case				,       decimals=4				)				,       [
                  {'''score''': 0.99_99, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
                  {'''score''': 0.99_98, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
              ]				,       )




      @slow
      @require_torch
      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			int	    =       pipeline(
              '''document-question-answering'''				,       model='''naver-clova-ix/donut-base-finetuned-docvqa'''				,       tokenizer=AutoTokenizer.from_pretrained('''naver-clova-ix/donut-base-finetuned-docvqa'''				)				,       feature_extractor='''naver-clova-ix/donut-base-finetuned-docvqa'''				,       )

          __lowercase		:			List[Any]	    =       INVOICE_URL
          __lowercase		:			List[Any]	    =       '''What is the invoice number?'''
          __lowercase		:			int	    =       dqa_pipeline(image=_snake_case				,       question=_snake_case				,       top_k=2				)
          self.assertEqual(nested_simplify(_snake_case				,       decimals=4				)				,       [{'''answer''': '''us-001'''}]				)




      @require_tf
      @unittest.skip('''Document question answering not implemented in TF'''				)
      def 		snake_case_			(      self		:       int				):
          pass


