
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  UpperCamelCase__		:







						'''simple docstring'''





						@staticmethod
						def   __snake_case				(			*UpperCamelCase__				,       **UpperCamelCase__		):
										pass




					def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]       ) ->       Optional[int]:

									"""simple docstring"""







									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.
_SCREAMING_SNAKE_CASE    :      Dict          =			(
    'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)







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







	'''simple docstring'''
	_lowerCAmelCase							       =				MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING





	@require_pytesseract
	@require_vision
	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	Dict         =							pipeline(
					    '''document-question-answering'''				,       model=UpperCamelCase__				,       tokenizer=UpperCamelCase__				,       image_processor=UpperCamelCase__		)

					A__  :	int         =							INVOICE_URL
					A__  :	Union[str, Any]         =							list(zip(*apply_tesseract(load_image(UpperCamelCase__		)				,       UpperCamelCase__				,       ''''''		)		)		)
					A__  :	Dict         =							'''What is the placebo?'''
					A__  :	int         =							[
					    {
					        '''image''': load_image(UpperCamelCase__		),
					        '''question''': question,
					    },
					    {
					        '''image''': image,
					        '''question''': question,
					    },
					    {
					        '''image''': image,
					        '''question''': question,
					        '''word_boxes''': word_boxes,
					    },
					]
					return dqa_pipeline, examples





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	List[Any]         =							dqa_pipeline(UpperCamelCase__				,       top_k=2		)
					self.assertEqual(
					    UpperCamelCase__				,       [
					        [
					            {'''score''': ANY(UpperCamelCase__		), '''answer''': ANY(UpperCamelCase__		), '''start''': ANY(UpperCamelCase__		), '''end''': ANY(UpperCamelCase__		)},
					            {'''score''': ANY(UpperCamelCase__		), '''answer''': ANY(UpperCamelCase__		), '''start''': ANY(UpperCamelCase__		), '''end''': ANY(UpperCamelCase__		)},
					        ]
					    ]
					    * 3				,       )





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

					A__  :	str         =							[
					    {'''score''': 0.0_0_0_1, '''answer''': '''oy 2312/2019''', '''start''': 38, '''end''': 39},
					    {'''score''': 0.0_0_0_1, '''answer''': '''oy 2312/2019 DUE''', '''start''': 38, '''end''': 40},
					]
					A__  :	str         =							dqa_pipeline(image=UpperCamelCase__				,       question=UpperCamelCase__				,       top_k=2		)
					self.assertEqual(nested_simplify(UpperCamelCase__				,       decimals=4		)				,       UpperCamelCase__		)

					A__  :	List[str]         =							dqa_pipeline({'''image''': image, '''question''': question}				,       top_k=2		)
					self.assertEqual(nested_simplify(UpperCamelCase__				,       decimals=4		)				,       UpperCamelCase__		)

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

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





	@slow
	@require_torch
	@require_detectrona
	@require_pytesseract
	def   __snake_case				(			self		):
					A__  :	Any         =							pipeline(
					    '''document-question-answering'''				,       model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'''				,       revision='''9977165'''				,       )
					A__  :	Any         =							INVOICE_URL
					A__  :	Dict         =							'''What is the invoice number?'''

					A__  :	Any         =							dqa_pipeline(image=UpperCamelCase__				,       question=UpperCamelCase__				,       top_k=2		)
					self.assertEqual(
					    nested_simplify(UpperCamelCase__				,       decimals=4		)				,       [
					        {'''score''': 0.9_9_4_4, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
					        {'''score''': 0.0_0_0_9, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
					    ]				,       )

					A__  :	int         =							dqa_pipeline({'''image''': image, '''question''': question}				,       top_k=2		)
					self.assertEqual(
					    nested_simplify(UpperCamelCase__				,       decimals=4		)				,       [
					        {'''score''': 0.9_9_4_4, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
					        {'''score''': 0.0_0_0_9, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
					    ]				,       )

					A__  :	List[str]         =							dqa_pipeline(
					    [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]				,       top_k=2		)
					self.assertEqual(
					    nested_simplify(UpperCamelCase__				,       decimals=4		)				,       [
					        [
					            {'''score''': 0.9_9_4_4, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
					            {'''score''': 0.0_0_0_9, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
					        ],
					    ]
					    * 2				,       )





	@slow
	@require_torch
	@require_detectrona
	@require_pytesseract
	def   __snake_case				(			self		):
					A__  :	str         =							pipeline(
					    '''document-question-answering'''				,       model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'''				,       revision='''9977165'''				,       max_seq_len=50				,       )
					A__  :	Dict         =							INVOICE_URL
					A__  :	int         =							'''What is the invoice number?'''

					A__  :	List[Any]         =							dqa_pipeline(image=UpperCamelCase__				,       question=UpperCamelCase__				,       top_k=2		)
					self.assertEqual(
					    nested_simplify(UpperCamelCase__				,       decimals=4		)				,       [
					        {'''score''': 0.9_9_7_4, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
					        {'''score''': 0.9_9_4_8, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
					    ]				,       )

					A__  :	Union[str, Any]         =							dqa_pipeline({'''image''': image, '''question''': question}				,       top_k=2		)
					self.assertEqual(
					    nested_simplify(UpperCamelCase__				,       decimals=4		)				,       [
					        {'''score''': 0.9_9_7_4, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
					        {'''score''': 0.9_9_4_8, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
					    ]				,       )

					A__  :	Optional[int]         =							dqa_pipeline(
					    [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]				,       top_k=2		)
					self.assertEqual(
					    nested_simplify(UpperCamelCase__				,       decimals=4		)				,       [
					        [
					            {'''score''': 0.9_9_7_4, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
					            {'''score''': 0.9_9_4_8, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
					        ]
					    ]
					    * 2				,       )





	@slow
	@require_torch
	@require_pytesseract
	@require_vision
	def   __snake_case				(			self		):
					A__  :	Tuple         =							AutoTokenizer.from_pretrained(
					    '''impira/layoutlm-document-qa'''				,       revision='''3dc6de3'''				,       add_prefix_space=UpperCamelCase__		)
					A__  :	Dict         =							pipeline(
					    '''document-question-answering'''				,       model='''impira/layoutlm-document-qa'''				,       tokenizer=UpperCamelCase__				,       revision='''3dc6de3'''				,       )
					A__  :	Tuple         =							INVOICE_URL
					A__  :	Tuple         =							'''What is the invoice number?'''

					A__  :	Union[str, Any]         =							dqa_pipeline(image=UpperCamelCase__				,       question=UpperCamelCase__				,       top_k=2		)
					self.assertEqual(
					    nested_simplify(UpperCamelCase__				,       decimals=4		)				,       [
					        {'''score''': 0.4_2_5_1, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
					        {'''score''': 0.0_8_1_9, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
					    ]				,       )

					A__  :	str         =							dqa_pipeline({'''image''': image, '''question''': question}				,       top_k=2		)
					self.assertEqual(
					    nested_simplify(UpperCamelCase__				,       decimals=4		)				,       [
					        {'''score''': 0.4_2_5_1, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
					        {'''score''': 0.0_8_1_9, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
					    ]				,       )

					A__  :	Optional[int]         =							dqa_pipeline(
					    [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]				,       top_k=2		)
					self.assertEqual(
					    nested_simplify(UpperCamelCase__				,       decimals=4		)				,       [
					        [
					            {'''score''': 0.4_2_5_1, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
					            {'''score''': 0.0_8_1_9, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
					        ]
					    ]
					    * 2				,       )

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

					# This model should also work if `image` is set to None
					A__  :	Tuple         =							dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question}				,       top_k=2		)
					self.assertEqual(
					    nested_simplify(UpperCamelCase__				,       decimals=4		)				,       [
					        {'''score''': 0.4_2_5_1, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
					        {'''score''': 0.0_8_1_9, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
					    ]				,       )





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

					A__  :	Optional[int]         =							dqa_pipeline(image=UpperCamelCase__				,       question=UpperCamelCase__				,       top_k=2		)
					self.assertEqual(
					    nested_simplify(UpperCamelCase__				,       decimals=4		)				,       [
					        {'''score''': 0.9_9_9_9, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
					        {'''score''': 0.9_9_9_8, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
					    ]				,       )

					A__  :	List[str]         =							dqa_pipeline(
					    [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]				,       top_k=2		)
					self.assertEqual(
					    nested_simplify(UpperCamelCase__				,       decimals=4		)				,       [
					        [
					            {'''score''': 0.9_9_9_9, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
					            {'''score''': 0.9_9_9_8, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
					        ]
					    ]
					    * 2				,       )

					A__  :	int         =							list(zip(*apply_tesseract(load_image(UpperCamelCase__		)				,       UpperCamelCase__				,       ''''''		)		)		)

					# This model should also work if `image` is set to None
					A__  :	int         =							dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question}				,       top_k=2		)
					self.assertEqual(
					    nested_simplify(UpperCamelCase__				,       decimals=4		)				,       [
					        {'''score''': 0.9_9_9_9, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
					        {'''score''': 0.9_9_9_8, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
					    ]				,       )





	@slow
	@require_torch
	def   __snake_case				(			self		):
					A__  :	Any         =							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'''				,       )

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





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

import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''





	def __init__(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__=1024				,       UpperCamelCase__=1024				,       UpperCamelCase__=3.6		):
					A__  :	str         =							tokenizer
					A__  :	int         =							tokenizer.bos_token_id
					A__  :	List[Any]         =							dataset
					A__  :	Tuple         =							seq_length
					A__  :	Any         =							seq_length * chars_per_token * num_of_sequences





	def __iter__(			self		):
					A__  :	Dict         =							iter(self.dataset		)
					A__  :	Tuple         =							True
					while more_examples:
									A__       ,							A__  :	Optional[Any]         =							[], 0
									while True:
													if buffer_len >= self.input_characters:
																	break
													try:
																	buffer.append(next(UpperCamelCase__		)['''content''']		)
																	buffer_len += len(buffer[-1]		)
													except StopIteration:
																	A__  :	Dict         =							False
																	break
									A__  :	str         =							tokenizer(UpperCamelCase__				,       truncation=UpperCamelCase__		)['''input_ids''']
									A__  :	Optional[int]         =							[]
									for tokenized_input in tokenized_inputs:
													all_token_ids.extend(tokenized_input + [self.concat_token_id]		)
									for i in range(0				,       len(UpperCamelCase__		)				,       self.seq_length		):
													A__  :	Optional[int]         =							all_token_ids[i : i + self.seq_length]
													if len(UpperCamelCase__		) == self.seq_length:
																	yield torch.tensor(UpperCamelCase__		)




def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[Any]       ) ->       Any:

				"""simple docstring"""







				A__  :	Any         =							{'''streaming''': True}
				A__  :	List[str]         =							load_dataset(args.dataset_name							,						split='''train'''							,						**__UpperCamelCase       )
				A__  :	List[str]         =							ConstantLengthDataset(__UpperCamelCase							,						__UpperCamelCase							,						seq_length=args.seq_length       )
				A__  :	int         =							DataLoader(__UpperCamelCase							,						batch_size=args.batch_size       )
				return eval_dataloader

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]       ) ->       Dict:

				"""simple docstring"""







				model.eval()
				A__  :	Dict         =							[]
				for step, batch in enumerate(__UpperCamelCase       ):
								with torch.no_grad():
												A__  :	Any         =							model(__UpperCamelCase							,						labels=__UpperCamelCase       )
								A__  :	Tuple         =							outputs.loss.repeat(args.batch_size       )
								losses.append(accelerator.gather(__UpperCamelCase       )       )

								if args.max_eval_steps > 0 and step >= args.max_eval_steps:
												break
				A__  :	Tuple         =							torch.mean(torch.cat(__UpperCamelCase       )       )
				try:
								A__  :	Optional[Any]         =							torch.exp(__UpperCamelCase       )
				except OverflowError:
								A__  :	Union[str, Any]         =							float('''inf'''       )
				return loss.item(), perplexity.item()


# Setup Accelerator
_SCREAMING_SNAKE_CASE    :      List[Any]          =			Accelerator()

# Parse configuration
_SCREAMING_SNAKE_CASE    :      Optional[int]          =			HfArgumentParser(EvaluationArguments)
_SCREAMING_SNAKE_CASE    :      Union[str, Any]          =			parser.parse_args()
set_seed(args.seed)

# Logging
_SCREAMING_SNAKE_CASE    :      Dict          =			logging.getLogger(__name__)
logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)

# Load model and tokenizer
_SCREAMING_SNAKE_CASE    :      Optional[int]          =			AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_SCREAMING_SNAKE_CASE    :      List[str]          =			AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			create_dataloader(args)

# Prepare everything with our `accelerator`.
_SCREAMING_SNAKE_CASE   ,     _SCREAMING_SNAKE_CASE    :      Optional[Any]          =			accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
_SCREAMING_SNAKE_CASE   ,     _SCREAMING_SNAKE_CASE    :      Optional[int]          =			evaluate(args)



logger.info(f"""loss/eval: {eval_loss}, perplexity: {perplexity}""")