



'''simple docstring'''






import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
						import faiss

@require_faiss
class 							SCREAMING_SNAKE_CASE(				__A     ):







	"""simple docstring"""






	def 	A      (		self     :	Dict						)     ->						int:
								UpperCAmelCase					:			str									=						tempfile.mkdtemp()
								UpperCAmelCase					:			Dict									=						8

								# DPR tok
								UpperCAmelCase					:			Dict									=						[
								    "[UNK]",
								    "[CLS]",
								    "[SEP]",
								    "[PAD]",
								    "[MASK]",
								    "want",
								    "##want",
								    "##ed",
								    "wa",
								    "un",
								    "runn",
								    "##ing",
								    ",",
								    "low",
								    "lowest",
								]
								UpperCAmelCase					:			str									=						os.path.join(self.tmpdirname						,     '''dpr_tokenizer'''						)
								os.makedirs(lowerCAmelCase_						,     exist_ok=lowerCAmelCase_						)
								UpperCAmelCase					:			int									=						os.path.join(lowerCAmelCase_						,     DPR_VOCAB_FILES_NAMES['''vocab_file''']						)
								with open(self.vocab_file						,     '''w'''						,     encoding='''utf-8'''						) as vocab_writer:
															vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]						)						)

								# BART tok
								UpperCAmelCase					:			List[str]									=						[
								    "l",
								    "o",
								    "w",
								    "e",
								    "r",
								    "s",
								    "t",
								    "i",
								    "d",
								    "n",
								    "\u0120",
								    "\u0120l",
								    "\u0120n",
								    "\u0120lo",
								    "\u0120low",
								    "er",
								    "\u0120lowest",
								    "\u0120newer",
								    "\u0120wider",
								    "<unk>",
								]
								UpperCAmelCase					:			Union[str, Any]									=						dict(zip(lowerCAmelCase_						,     range(len(lowerCAmelCase_						)						)						)						)
								UpperCAmelCase					:			Dict									=						["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
								UpperCAmelCase					:			Tuple									=						{"unk_token": "<unk>"}

								UpperCAmelCase					:			int									=						os.path.join(self.tmpdirname						,     '''bart_tokenizer'''						)
								os.makedirs(lowerCAmelCase_						,     exist_ok=lowerCAmelCase_						)
								UpperCAmelCase					:			Tuple									=						os.path.join(lowerCAmelCase_						,     BART_VOCAB_FILES_NAMES['''vocab_file''']						)
								UpperCAmelCase					:			Tuple									=						os.path.join(lowerCAmelCase_						,     BART_VOCAB_FILES_NAMES['''merges_file''']						)
								with open(self.vocab_file						,     '''w'''						,     encoding='''utf-8'''						) as fp:
															fp.write(json.dumps(lowerCAmelCase_						) + '''\n'''						)
								with open(self.merges_file						,     '''w'''						,     encoding='''utf-8'''						) as fp:
															fp.write('''\n'''.join(lowerCAmelCase_						)						)







	def 	A      (		self     :	Any						)     ->						DPRQuestionEncoderTokenizer:
								return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname						,     '''dpr_tokenizer'''						)						)







	def 	A      (		self     :	int						)     ->						DPRContextEncoderTokenizer:
								return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname						,     '''dpr_tokenizer'''						)						)







	def 	A      (		self     :	List[str]						)     ->						BartTokenizer:
								return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname						,     '''bart_tokenizer'''						)						)







	def 	A      (		self     :	Any						)     ->						Tuple:
								shutil.rmtree(self.tmpdirname						)







	def 	A      (		self     :	Union[str, Any]						)     ->						Tuple:
								UpperCAmelCase					:			Union[str, Any]									=						Dataset.from_dict(
								    {
								        '''id''': ['''0''', '''1'''],
								        '''text''': ['''foo''', '''bar'''],
								        '''title''': ['''Foo''', '''Bar'''],
								        '''embeddings''': [np.ones(self.retrieval_vector_size						), 2 * np.ones(self.retrieval_vector_size						)],
								    }						)
								dataset.add_faiss_index('''embeddings'''						,     string_factory='''Flat'''						,     metric_type=faiss.METRIC_INNER_PRODUCT						)
								return dataset







	def 	A      (		self     :	Optional[Any]						)     ->						str:
								UpperCAmelCase					:			int									=						self.get_dummy_dataset()
								UpperCAmelCase					:			Dict									=						RagConfig(
								    retrieval_vector_size=self.retrieval_vector_size						,     question_encoder=DPRConfig().to_dict()						,     generator=BartConfig().to_dict()						,     )
								with patch('''transformers.models.rag.retrieval_rag.load_dataset'''						) as mock_load_dataset:
															UpperCAmelCase					:			List[str]									=						dataset
															UpperCAmelCase					:			Dict									=						RagRetriever(
															    lowerCAmelCase_						,     question_encoder_tokenizer=self.get_dpr_tokenizer()						,     generator_tokenizer=self.get_bart_tokenizer()						,     )
								return retriever







	def 	A      (		self     :	Optional[int]						,     __snake_case     :	bool						)     ->						List[Any]:
								UpperCAmelCase					:			Optional[Any]									=						self.get_dummy_dataset()
								UpperCAmelCase					:			str									=						RagConfig(
								    retrieval_vector_size=self.retrieval_vector_size						,     question_encoder=DPRConfig().to_dict()						,     generator=BartConfig().to_dict()						,     index_name='''custom'''						,     )
								if from_disk:
															UpperCAmelCase					:			List[str]									=						os.path.join(self.tmpdirname						,     '''dataset'''						)
															UpperCAmelCase					:			List[Any]									=						os.path.join(self.tmpdirname						,     '''index.faiss'''						)
															dataset.get_index('''embeddings'''						).save(os.path.join(self.tmpdirname						,     '''index.faiss'''						)						)
															dataset.drop_index('''embeddings'''						)
															dataset.save_to_disk(os.path.join(self.tmpdirname						,     '''dataset'''						)						)
															del dataset
															UpperCAmelCase					:			Optional[Any]									=						RagRetriever(
															    lowerCAmelCase_						,     question_encoder_tokenizer=self.get_dpr_tokenizer()						,     generator_tokenizer=self.get_bart_tokenizer()						,     )
								else:
															UpperCAmelCase					:			List[Any]									=						RagRetriever(
															    lowerCAmelCase_						,     question_encoder_tokenizer=self.get_dpr_tokenizer()						,     generator_tokenizer=self.get_bart_tokenizer()						,     index=CustomHFIndex(config.retrieval_vector_size						,     lowerCAmelCase_						)						,     )
								return retriever







	def 	A      (		self     :	Optional[int]						)     ->						int:
								UpperCAmelCase					:			Any									=						Dataset.from_dict(
								    {
								        '''id''': ['''0''', '''1'''],
								        '''text''': ['''foo''', '''bar'''],
								        '''title''': ['''Foo''', '''Bar'''],
								        '''embeddings''': [np.ones(self.retrieval_vector_size + 1						), 2 * np.ones(self.retrieval_vector_size + 1						)],
								    }						)
								dataset.add_faiss_index('''embeddings'''						,     string_factory='''Flat'''						,     metric_type=faiss.METRIC_INNER_PRODUCT						)

								UpperCAmelCase					:			Union[str, Any]									=						os.path.join(self.tmpdirname						,     '''hf_bert_base.hnswSQ8_correct_phi_128.c_index'''						)
								dataset.save_faiss_index('''embeddings'''						,     index_file_name + '''.index.dpr'''						)
								pickle.dump(dataset['''id''']						,     open(index_file_name + '''.index_meta.dpr'''						,     '''wb'''						)						)

								UpperCAmelCase					:			Tuple									=						os.path.join(self.tmpdirname						,     '''psgs_w100.tsv.pkl'''						)
								UpperCAmelCase					:			Optional[Any]									=						{sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
								pickle.dump(lowerCAmelCase_						,     open(lowerCAmelCase_						,     '''wb'''						)						)

								UpperCAmelCase					:			int									=						RagConfig(
								    retrieval_vector_size=self.retrieval_vector_size						,     question_encoder=DPRConfig().to_dict()						,     generator=BartConfig().to_dict()						,     index_name='''legacy'''						,     index_path=self.tmpdirname						,     )
								UpperCAmelCase					:			Optional[int]									=						RagRetriever(
								    lowerCAmelCase_						,     question_encoder_tokenizer=self.get_dpr_tokenizer()						,     generator_tokenizer=self.get_bart_tokenizer()						)
								return retriever







	def 	A      (		self     :	Dict						)     ->						Tuple:
								UpperCAmelCase					:			Dict									=						1
								UpperCAmelCase					:			Dict									=						self.get_dummy_canonical_hf_index_retriever()
								UpperCAmelCase					:			Dict									=						np.array(
								    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]						,     dtype=np.floataa						)
								UpperCAmelCase					:			Dict									=						retriever.retrieve(lowerCAmelCase_						,     n_docs=lowerCAmelCase_						)
								self.assertEqual(retrieved_doc_embeds.shape						,     (2, n_docs, self.retrieval_vector_size)						)
								self.assertEqual(len(lowerCAmelCase_						)						,     2						)
								self.assertEqual(sorted(doc_dicts[0]						)						,     ['''embeddings''', '''id''', '''text''', '''title''']						)
								self.assertEqual(len(doc_dicts[0]['''id''']						)						,     lowerCAmelCase_						)
								self.assertEqual(doc_dicts[0]['''id'''][0]						,     '''1'''						)  # max inner product is reached with second doc
								self.assertEqual(doc_dicts[1]['''id'''][0]						,     '''0'''						)  # max inner product is reached with first doc
								self.assertListEqual(doc_ids.tolist()						,     [[1], [0]]						)







	def 	A      (		self     :	int						)     ->						Any:
								UpperCAmelCase					:			Optional[int]									=						self.get_dummy_canonical_hf_index_retriever()
								with tempfile.TemporaryDirectory() as tmp_dirname:
															with patch('''transformers.models.rag.retrieval_rag.load_dataset'''						) as mock_load_dataset:
																						UpperCAmelCase					:			Dict									=						self.get_dummy_dataset()
																						retriever.save_pretrained(lowerCAmelCase_						)
																						UpperCAmelCase					:			Dict									=						RagRetriever.from_pretrained(lowerCAmelCase_						)
																						self.assertIsInstance(lowerCAmelCase_						,     lowerCAmelCase_						)
																						UpperCAmelCase					:			Tuple									=						np.array(
																						    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]						,     dtype=np.floataa						)
																						UpperCAmelCase					:			Tuple									=						retriever.retrieve(lowerCAmelCase_						,     n_docs=1						)
																						self.assertTrue(out is not None						)







	def 	A      (		self     :	Any						)     ->						str:
								UpperCAmelCase					:			Union[str, Any]									=						1
								UpperCAmelCase					:			Optional[int]									=						self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase_						)
								UpperCAmelCase					:			Tuple									=						np.array(
								    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]						,     dtype=np.floataa						)
								UpperCAmelCase					:			Union[str, Any]									=						retriever.retrieve(lowerCAmelCase_						,     n_docs=lowerCAmelCase_						)
								self.assertEqual(retrieved_doc_embeds.shape						,     (2, n_docs, self.retrieval_vector_size)						)
								self.assertEqual(len(lowerCAmelCase_						)						,     2						)
								self.assertEqual(sorted(doc_dicts[0]						)						,     ['''embeddings''', '''id''', '''text''', '''title''']						)
								self.assertEqual(len(doc_dicts[0]['''id''']						)						,     lowerCAmelCase_						)
								self.assertEqual(doc_dicts[0]['''id'''][0]						,     '''1'''						)  # max inner product is reached with second doc
								self.assertEqual(doc_dicts[1]['''id'''][0]						,     '''0'''						)  # max inner product is reached with first doc
								self.assertListEqual(doc_ids.tolist()						,     [[1], [0]]						)







	def 	A      (		self     :	List[Any]						)     ->						Tuple:
								UpperCAmelCase					:			Union[str, Any]									=						self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase_						)
								with tempfile.TemporaryDirectory() as tmp_dirname:
															retriever.save_pretrained(lowerCAmelCase_						)
															UpperCAmelCase					:			Dict									=						RagRetriever.from_pretrained(lowerCAmelCase_						)
															self.assertIsInstance(lowerCAmelCase_						,     lowerCAmelCase_						)
															UpperCAmelCase					:			List[Any]									=						np.array(
															    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]						,     dtype=np.floataa						)
															UpperCAmelCase					:			Any									=						retriever.retrieve(lowerCAmelCase_						,     n_docs=1						)
															self.assertTrue(out is not None						)







	def 	A      (		self     :	Any						)     ->						Any:
								UpperCAmelCase					:			Optional[int]									=						1
								UpperCAmelCase					:			Dict									=						self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase_						)
								UpperCAmelCase					:			Optional[Any]									=						np.array(
								    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]						,     dtype=np.floataa						)
								UpperCAmelCase					:			Optional[int]									=						retriever.retrieve(lowerCAmelCase_						,     n_docs=lowerCAmelCase_						)
								self.assertEqual(retrieved_doc_embeds.shape						,     (2, n_docs, self.retrieval_vector_size)						)
								self.assertEqual(len(lowerCAmelCase_						)						,     2						)
								self.assertEqual(sorted(doc_dicts[0]						)						,     ['''embeddings''', '''id''', '''text''', '''title''']						)
								self.assertEqual(len(doc_dicts[0]['''id''']						)						,     lowerCAmelCase_						)
								self.assertEqual(doc_dicts[0]['''id'''][0]						,     '''1'''						)  # max inner product is reached with second doc
								self.assertEqual(doc_dicts[1]['''id'''][0]						,     '''0'''						)  # max inner product is reached with first doc
								self.assertListEqual(doc_ids.tolist()						,     [[1], [0]]						)







	def 	A      (		self     :	Dict						)     ->						Tuple:
								UpperCAmelCase					:			Optional[int]									=						self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase_						)
								with tempfile.TemporaryDirectory() as tmp_dirname:
															retriever.save_pretrained(lowerCAmelCase_						)
															UpperCAmelCase					:			List[Any]									=						RagRetriever.from_pretrained(lowerCAmelCase_						)
															self.assertIsInstance(lowerCAmelCase_						,     lowerCAmelCase_						)
															UpperCAmelCase					:			Optional[int]									=						np.array(
															    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]						,     dtype=np.floataa						)
															UpperCAmelCase					:			str									=						retriever.retrieve(lowerCAmelCase_						,     n_docs=1						)
															self.assertTrue(out is not None						)







	def 	A      (		self     :	str						)     ->						Any:
								UpperCAmelCase					:			str									=						1
								UpperCAmelCase					:			int									=						self.get_dummy_legacy_index_retriever()
								UpperCAmelCase					:			Any									=						np.array(
								    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]						,     dtype=np.floataa						)
								UpperCAmelCase					:			int									=						retriever.retrieve(lowerCAmelCase_						,     n_docs=lowerCAmelCase_						)
								self.assertEqual(retrieved_doc_embeds.shape						,     (2, n_docs, self.retrieval_vector_size)						)
								self.assertEqual(len(lowerCAmelCase_						)						,     2						)
								self.assertEqual(sorted(doc_dicts[0]						)						,     ['''text''', '''title''']						)
								self.assertEqual(len(doc_dicts[0]['''text''']						)						,     lowerCAmelCase_						)
								self.assertEqual(doc_dicts[0]['''text'''][0]						,     '''bar'''						)  # max inner product is reached with second doc
								self.assertEqual(doc_dicts[1]['''text'''][0]						,     '''foo'''						)  # max inner product is reached with first doc
								self.assertListEqual(doc_ids.tolist()						,     [[1], [0]]						)







	def 	A      (		self     :	Dict						)     ->						int:
								UpperCAmelCase					:			Optional[int]									=						self.get_dummy_legacy_index_retriever()
								with tempfile.TemporaryDirectory() as tmp_dirname:
															retriever.save_pretrained(lowerCAmelCase_						)
															UpperCAmelCase					:			Dict									=						RagRetriever.from_pretrained(lowerCAmelCase_						)
															self.assertIsInstance(lowerCAmelCase_						,     lowerCAmelCase_						)
															UpperCAmelCase					:			List[str]									=						np.array(
															    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]						,     dtype=np.floataa						)
															UpperCAmelCase					:			int									=						retriever.retrieve(lowerCAmelCase_						,     n_docs=1						)
															self.assertTrue(out is not None						)







	@require_torch
	@require_tokenizers
	@require_sentencepiece
	def 	A      (		self     :	Optional[int]						)     ->						List[Any]:
								import torch

								UpperCAmelCase					:			Any									=						1
								UpperCAmelCase					:			List[Any]									=						self.get_dummy_canonical_hf_index_retriever()
								UpperCAmelCase					:			str									=						[[5, 7], [10, 11]]
								UpperCAmelCase					:			Any									=						np.array(
								    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]						,     dtype=np.floataa						)
								UpperCAmelCase					:			Optional[Any]									=						retriever(lowerCAmelCase_						,     lowerCAmelCase_						,     prefix=retriever.config.generator.prefix						,     n_docs=lowerCAmelCase_						)
								UpperCAmelCase					:			str									=						(
								    out["context_input_ids"],
								    out["context_attention_mask"],
								    out["retrieved_doc_embeds"],
								)
								self.assertEqual(retrieved_doc_embeds.shape						,     (2, n_docs, self.retrieval_vector_size)						)
								self.assertIsInstance(lowerCAmelCase_						,     lowerCAmelCase_						)
								self.assertIsInstance(lowerCAmelCase_						,     lowerCAmelCase_						)
								self.assertIsInstance(lowerCAmelCase_						,     np.ndarray						)

								UpperCAmelCase					:			List[str]									=						retriever(
								    lowerCAmelCase_						,     lowerCAmelCase_						,     prefix=retriever.config.generator.prefix						,     n_docs=lowerCAmelCase_						,     return_tensors='''pt'''						,     )
								UpperCAmelCase					:			Dict									=						(  # noqa: F841
								    out["context_input_ids"],
								    out["context_attention_mask"],
								    out["retrieved_doc_embeds"],
								    out["doc_ids"],
								)
								self.assertEqual(retrieved_doc_embeds.shape						,     (2, n_docs, self.retrieval_vector_size)						)
								self.assertIsInstance(lowerCAmelCase_						,     torch.Tensor						)
								self.assertIsInstance(lowerCAmelCase_						,     torch.Tensor						)
								self.assertIsInstance(lowerCAmelCase_						,     torch.Tensor						)







	@require_torch
	@require_tokenizers
	@require_sentencepiece
	def 	A      (		self     :	List[Any]						)     ->						Tuple:
								UpperCAmelCase					:			Optional[Any]									=						self.get_dpr_ctx_encoder_tokenizer()
								UpperCAmelCase					:			List[Any]									=						1
								UpperCAmelCase					:			Union[str, Any]									=						self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase_						)
								retriever.set_ctx_encoder_tokenizer(lowerCAmelCase_						)

								UpperCAmelCase					:			Optional[Any]									=						[[5, 7], [10, 11]]
								UpperCAmelCase					:			Union[str, Any]									=						np.array(
								    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]						,     dtype=np.floataa						)
								UpperCAmelCase					:			List[str]									=						retriever(lowerCAmelCase_						,     lowerCAmelCase_						,     prefix=retriever.config.generator.prefix						,     n_docs=lowerCAmelCase_						)

								self.assertEqual(
								    len(lowerCAmelCase_						)						,     6						)  # check whether the retriever output consist of 6 attributes including tokenized docs
								self.assertEqual(
								    all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''')						)						,     lowerCAmelCase_						)  # check for doc token related keys in dictionary.













"""simple docstring"""
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_			:





				@staticmethod
				def 	_SCREAMING_SNAKE_CASE ( *lowerCAmelCase_   :					Any      ,      **lowerCAmelCase_   :					Dict		)		->     str:
							pass





			def 							snake_case (  A__		):
						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_							      = (
    '''https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'''
)







@is_pipeline_test
@require_torch
@require_vision
class      UpperCamelCase_			(unittest.TestCase	):
	__magic_name__       =							MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING





	@require_pytesseract
	@require_vision
	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					Any      ,      lowerCAmelCase_   :					Optional[int]		)		->     Union[str, Any]:
				UpperCAmelCase_	:					int     	=   pipeline(
				    "document-question-answering"      ,      model=lowerCAmelCase_      ,      tokenizer=lowerCAmelCase_      ,      image_processor=lowerCAmelCase_		)

				UpperCAmelCase_	:					int     	=   INVOICE_URL
				UpperCAmelCase_	:					Union[str, Any]     	=   list(zip(*apply_tesseract(load_image(lowerCAmelCase_		)      ,      lowerCAmelCase_      ,      ""		)		)		)
				UpperCAmelCase_	:					Optional[Any]     	=   "What is the placebo?"
				UpperCAmelCase_	:					Tuple     	=   [
				    {
				        "image": load_image(lowerCAmelCase_		),
				        "question": question,
				    },
				    {
				        "image": image,
				        "question": question,
				    },
				    {
				        "image": image,
				        "question": question,
				        "word_boxes": word_boxes,
				    },
				]
				return dqa_pipeline, examples





	def 	_SCREAMING_SNAKE_CASE ( self   :					int      ,      lowerCAmelCase_   :					Any      ,      lowerCAmelCase_   :					List[Any]		)		->     int:
				UpperCAmelCase_	:					Union[str, Any]     	=   dqa_pipeline(lowerCAmelCase_      ,      top_k=2		)
				self.assertEqual(
				    lowerCAmelCase_      ,      [
				        [
				            {"score": ANY(lowerCAmelCase_		), "answer": ANY(lowerCAmelCase_		), "start": ANY(lowerCAmelCase_		), "end": ANY(lowerCAmelCase_		)},
				            {"score": ANY(lowerCAmelCase_		), "answer": ANY(lowerCAmelCase_		), "start": ANY(lowerCAmelCase_		), "end": ANY(lowerCAmelCase_		)},
				        ]
				    ]
				    * 3      ,      )





	@require_torch
	@require_detectrona
	@require_pytesseract
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     Any:
				UpperCAmelCase_	:					Tuple     	=   pipeline("document-question-answering"      ,      model="hf-internal-testing/tiny-random-layoutlmv2"		)
				UpperCAmelCase_	:					Dict     	=   INVOICE_URL
				UpperCAmelCase_	:					int     	=   "How many cats are there?"

				UpperCAmelCase_	:					Any     	=   [
				    {"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},
				]
				UpperCAmelCase_	:					List[str]     	=   dqa_pipeline(image=lowerCAmelCase_      ,      question=lowerCAmelCase_      ,      top_k=2		)
				self.assertEqual(nested_simplify(lowerCAmelCase_      ,      decimals=4		)      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					Dict     	=   dqa_pipeline({"image": image, "question": question}      ,      top_k=2		)
				self.assertEqual(nested_simplify(lowerCAmelCase_      ,      decimals=4		)      ,      lowerCAmelCase_		)

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

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





	@slow
	@require_torch
	@require_detectrona
	@require_pytesseract
	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     int:
				UpperCAmelCase_	:					Dict     	=   pipeline(
				    "document-question-answering"      ,      model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"      ,      revision="9977165"      ,      )
				UpperCAmelCase_	:					Optional[Any]     	=   INVOICE_URL
				UpperCAmelCase_	:					Dict     	=   "What is the invoice number?"

				UpperCAmelCase_	:					int     	=   dqa_pipeline(image=lowerCAmelCase_      ,      question=lowerCAmelCase_      ,      top_k=2		)
				self.assertEqual(
				    nested_simplify(lowerCAmelCase_      ,      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},
				    ]      ,      )

				UpperCAmelCase_	:					int     	=   dqa_pipeline({"image": image, "question": question}      ,      top_k=2		)
				self.assertEqual(
				    nested_simplify(lowerCAmelCase_      ,      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},
				    ]      ,      )

				UpperCAmelCase_	:					Any     	=   dqa_pipeline(
				    [{"image": image, "question": question}, {"image": image, "question": question}]      ,      top_k=2		)
				self.assertEqual(
				    nested_simplify(lowerCAmelCase_      ,      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 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     Tuple:
				UpperCAmelCase_	:					Tuple     	=   pipeline(
				    "document-question-answering"      ,      model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"      ,      revision="9977165"      ,      max_seq_len=50      ,      )
				UpperCAmelCase_	:					Tuple     	=   INVOICE_URL
				UpperCAmelCase_	:					Any     	=   "What is the invoice number?"

				UpperCAmelCase_	:					str     	=   dqa_pipeline(image=lowerCAmelCase_      ,      question=lowerCAmelCase_      ,      top_k=2		)
				self.assertEqual(
				    nested_simplify(lowerCAmelCase_      ,      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},
				    ]      ,      )

				UpperCAmelCase_	:					Optional[int]     	=   dqa_pipeline({"image": image, "question": question}      ,      top_k=2		)
				self.assertEqual(
				    nested_simplify(lowerCAmelCase_      ,      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},
				    ]      ,      )

				UpperCAmelCase_	:					str     	=   dqa_pipeline(
				    [{"image": image, "question": question}, {"image": image, "question": question}]      ,      top_k=2		)
				self.assertEqual(
				    nested_simplify(lowerCAmelCase_      ,      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 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     Union[str, Any]:
				UpperCAmelCase_	:					Any     	=   AutoTokenizer.from_pretrained(
				    "impira/layoutlm-document-qa"      ,      revision="3dc6de3"      ,      add_prefix_space=lowerCAmelCase_		)
				UpperCAmelCase_	:					str     	=   pipeline(
				    "document-question-answering"      ,      model="impira/layoutlm-document-qa"      ,      tokenizer=lowerCAmelCase_      ,      revision="3dc6de3"      ,      )
				UpperCAmelCase_	:					Any     	=   INVOICE_URL
				UpperCAmelCase_	:					List[str]     	=   "What is the invoice number?"

				UpperCAmelCase_	:					str     	=   dqa_pipeline(image=lowerCAmelCase_      ,      question=lowerCAmelCase_      ,      top_k=2		)
				self.assertEqual(
				    nested_simplify(lowerCAmelCase_      ,      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},
				    ]      ,      )

				UpperCAmelCase_	:					Dict     	=   dqa_pipeline({"image": image, "question": question}      ,      top_k=2		)
				self.assertEqual(
				    nested_simplify(lowerCAmelCase_      ,      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},
				    ]      ,      )

				UpperCAmelCase_	:					Union[str, Any]     	=   dqa_pipeline(
				    [{"image": image, "question": question}, {"image": image, "question": question}]      ,      top_k=2		)
				self.assertEqual(
				    nested_simplify(lowerCAmelCase_      ,      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      ,      )

				UpperCAmelCase_	:					Dict     	=   list(zip(*apply_tesseract(load_image(lowerCAmelCase_		)      ,      lowerCAmelCase_      ,      ""		)		)		)

				# This model should also work if `image` is set to None
				UpperCAmelCase_	:					List[str]     	=   dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}      ,      top_k=2		)
				self.assertEqual(
				    nested_simplify(lowerCAmelCase_      ,      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 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     Tuple:
				UpperCAmelCase_	:					List[Any]     	=   AutoTokenizer.from_pretrained(
				    "impira/layoutlm-document-qa"      ,      revision="3dc6de3"      ,      add_prefix_space=lowerCAmelCase_		)
				UpperCAmelCase_	:					str     	=   pipeline(
				    "document-question-answering"      ,      model="impira/layoutlm-document-qa"      ,      tokenizer=lowerCAmelCase_      ,      revision="3dc6de3"      ,      max_seq_len=50      ,      )
				UpperCAmelCase_	:					List[Any]     	=   INVOICE_URL
				UpperCAmelCase_	:					Optional[int]     	=   "What is the invoice number?"

				UpperCAmelCase_	:					int     	=   dqa_pipeline(image=lowerCAmelCase_      ,      question=lowerCAmelCase_      ,      top_k=2		)
				self.assertEqual(
				    nested_simplify(lowerCAmelCase_      ,      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},
				    ]      ,      )

				UpperCAmelCase_	:					Tuple     	=   dqa_pipeline(
				    [{"image": image, "question": question}, {"image": image, "question": question}]      ,      top_k=2		)
				self.assertEqual(
				    nested_simplify(lowerCAmelCase_      ,      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      ,      )

				UpperCAmelCase_	:					Optional[int]     	=   list(zip(*apply_tesseract(load_image(lowerCAmelCase_		)      ,      lowerCAmelCase_      ,      ""		)		)		)

				# This model should also work if `image` is set to None
				UpperCAmelCase_	:					Dict     	=   dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}      ,      top_k=2		)
				self.assertEqual(
				    nested_simplify(lowerCAmelCase_      ,      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 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     List[str]:
				UpperCAmelCase_	:					List[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"      ,      )

				UpperCAmelCase_	:					Optional[int]     	=   INVOICE_URL
				UpperCAmelCase_	:					int     	=   "What is the invoice number?"
				UpperCAmelCase_	:					List[str]     	=   dqa_pipeline(image=lowerCAmelCase_      ,      question=lowerCAmelCase_      ,      top_k=2		)
				self.assertEqual(nested_simplify(lowerCAmelCase_      ,      decimals=4		)      ,      [{"answer": "us-001"}]		)





	@require_tf
	@unittest.skip("Document question answering not implemented in TF"		)
	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     List[str]:
				pass



