


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        _lowercase      :






										'''simple docstring'''



										@staticmethod
										def 		__magic_name__(       *lowerCAmelCase__			:Union[str, Any]			,			**lowerCAmelCase__			:str	)		->			Union[str, Any]:
																pass




						def        _UpperCamelCase							(      lowercase__      ):
												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	: str  				=(
    'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)
@is_pipeline_test
@require_torch
@require_vision
class        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Tuple						=		MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING


				@require_pytesseract
				@require_vision
				def 		__magic_name__(       self			:Any			,			lowerCAmelCase__			:List[str]			,			lowerCAmelCase__			:Optional[Any]			,			lowerCAmelCase__			:Any	)		->			Any:
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		pipeline(
										    '''document-question-answering'''			,			model=lowerCAmelCase__			,			tokenizer=lowerCAmelCase__			,			image_processor=lowerCAmelCase__	)

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


				def 		__magic_name__(       self			:Optional[Any]			,			lowerCAmelCase__			:Tuple			,			lowerCAmelCase__			:Tuple	)		->			str:
										__SCREAMING_SNAKE_CASE						:     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 		__magic_name__(       self			:Dict	)		->			List[str]:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		pipeline('''document-question-answering'''			,			model='''hf-internal-testing/tiny-random-layoutlmv2'''	)
										__SCREAMING_SNAKE_CASE						:     Dict					=		INVOICE_URL
										__SCREAMING_SNAKE_CASE						:     int					=		'''How many cats are there?'''

										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		[
										    {'''score''': 0.0001, '''answer''': '''oy 2312/2019''', '''start''': 38, '''end''': 39},
										    {'''score''': 0.0001, '''answer''': '''oy 2312/2019 DUE''', '''start''': 38, '''end''': 40},
										]
										__SCREAMING_SNAKE_CASE						:     Tuple					=		dqa_pipeline(image=lowerCAmelCase__			,			question=lowerCAmelCase__			,			top_k=2	)
										self.assertEqual(nested_simplify(lowerCAmelCase__			,			decimals=4	)			,			lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     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
										__SCREAMING_SNAKE_CASE						:     Any					=		'''./tests/fixtures/tests_samples/COCO/000000039769.png'''
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		dqa_pipeline(image=lowerCAmelCase__			,			question=lowerCAmelCase__			,			top_k=2	)
										self.assertEqual(lowerCAmelCase__			,			[]	)

										# We can optionnally pass directly the words and bounding boxes
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		'''./tests/fixtures/tests_samples/COCO/000000039769.png'''
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		[]
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		[]
										__SCREAMING_SNAKE_CASE						:     Union[str, 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 		__magic_name__(       self			:int	)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		pipeline(
										    '''document-question-answering'''			,			model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'''			,			revision='''9977165'''			,			)
										__SCREAMING_SNAKE_CASE						:     Dict					=		INVOICE_URL
										__SCREAMING_SNAKE_CASE						:     Any					=		'''What is the invoice number?'''

										__SCREAMING_SNAKE_CASE						:     Any					=		dqa_pipeline(image=lowerCAmelCase__			,			question=lowerCAmelCase__			,			top_k=2	)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__			,			decimals=4	)			,			[
										        {'''score''': 0.9944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
										        {'''score''': 0.0009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
										    ]			,			)

										__SCREAMING_SNAKE_CASE						:     Tuple					=		dqa_pipeline({'''image''': image, '''question''': question}			,			top_k=2	)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__			,			decimals=4	)			,			[
										        {'''score''': 0.9944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
										        {'''score''': 0.0009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
										    ]			,			)

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		dqa_pipeline(
										    [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]			,			top_k=2	)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__			,			decimals=4	)			,			[
										        [
										            {'''score''': 0.9944, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
										            {'''score''': 0.0009, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
										        ],
										    ]
										    * 2			,			)


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

										__SCREAMING_SNAKE_CASE						:     List[str]					=		dqa_pipeline(image=lowerCAmelCase__			,			question=lowerCAmelCase__			,			top_k=2	)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__			,			decimals=4	)			,			[
										        {'''score''': 0.9974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
										        {'''score''': 0.9948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
										    ]			,			)

										__SCREAMING_SNAKE_CASE						:     int					=		dqa_pipeline({'''image''': image, '''question''': question}			,			top_k=2	)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__			,			decimals=4	)			,			[
										        {'''score''': 0.9974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
										        {'''score''': 0.9948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
										    ]			,			)

										__SCREAMING_SNAKE_CASE						:     str					=		dqa_pipeline(
										    [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]			,			top_k=2	)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__			,			decimals=4	)			,			[
										        [
										            {'''score''': 0.9974, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
										            {'''score''': 0.9948, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
										        ]
										    ]
										    * 2			,			)


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

										__SCREAMING_SNAKE_CASE						:     Dict					=		dqa_pipeline(image=lowerCAmelCase__			,			question=lowerCAmelCase__			,			top_k=2	)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__			,			decimals=4	)			,			[
										        {'''score''': 0.4251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
										        {'''score''': 0.0819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
										    ]			,			)

										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		dqa_pipeline({'''image''': image, '''question''': question}			,			top_k=2	)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__			,			decimals=4	)			,			[
										        {'''score''': 0.4251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
										        {'''score''': 0.0819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
										    ]			,			)

										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		dqa_pipeline(
										    [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]			,			top_k=2	)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__			,			decimals=4	)			,			[
										        [
										            {'''score''': 0.4251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
										            {'''score''': 0.0819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
										        ]
										    ]
										    * 2			,			)

										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		list(zip(*apply_tesseract(load_image(lowerCAmelCase__	)			,			lowerCAmelCase__			,			''''''	)	)	)

										# This model should also work if `image` is set to None
										__SCREAMING_SNAKE_CASE						:     str					=		dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question}			,			top_k=2	)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__			,			decimals=4	)			,			[
										        {'''score''': 0.4251, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
										        {'''score''': 0.0819, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
										    ]			,			)


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

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		dqa_pipeline(image=lowerCAmelCase__			,			question=lowerCAmelCase__			,			top_k=2	)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__			,			decimals=4	)			,			[
										        {'''score''': 0.9999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
										        {'''score''': 0.9998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
										    ]			,			)

										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		dqa_pipeline(
										    [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]			,			top_k=2	)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__			,			decimals=4	)			,			[
										        [
										            {'''score''': 0.9999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
										            {'''score''': 0.9998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
										        ]
										    ]
										    * 2			,			)

										__SCREAMING_SNAKE_CASE						:     List[str]					=		list(zip(*apply_tesseract(load_image(lowerCAmelCase__	)			,			lowerCAmelCase__			,			''''''	)	)	)

										# This model should also work if `image` is set to None
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question}			,			top_k=2	)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__			,			decimals=4	)			,			[
										        {'''score''': 0.9999, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
										        {'''score''': 0.9998, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
										    ]			,			)


				@slow
				@require_torch
				def 		__magic_name__(       self			:Union[str, Any]	)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     str					=		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'''			,			)

										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		INVOICE_URL
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		'''What is the invoice number?'''
										__SCREAMING_SNAKE_CASE						:     Tuple					=		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 		__magic_name__(       self			:Union[str, Any]	)		->			Tuple:
										pass




from importlib import import_module

from .logging import get_logger


__lowerCAmelCase	: str  				=get_logger(__name__)
class        _lowercase      :






				'''simple docstring'''



				def __init__(       self			:List[Any]			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:str=None	)		->			int:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		attrs or []
										if module is not None:
																for key in module.__dict__:
																						if key in attrs or not key.startswith('''__'''	):
																												setattr(self			,			lowerCAmelCase__			,			getattr(lowerCAmelCase__			,			lowerCAmelCase__	)	)
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		module._original_module if isinstance(lowerCAmelCase__			,			_PatchedModuleObj	) else module




class        _lowercase      :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Optional[int]						=		[]


				def __init__(       self			:Tuple			,			lowerCAmelCase__			:Optional[Any]			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:Any			,			lowerCAmelCase__			:Dict=None	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		obj
										__SCREAMING_SNAKE_CASE						:     str					=		target
										__SCREAMING_SNAKE_CASE						:     Dict					=		new
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		target.split('''.'''	)[0]
										__SCREAMING_SNAKE_CASE						:     List[str]					=		{}
										__SCREAMING_SNAKE_CASE						:     Tuple					=		attrs or []


				def __enter__(       self			:int	)		->			Dict:
										*__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.target.split('''.'''	)

										# Patch modules:
										# it's used to patch attributes of submodules like "os.path.join";
										# in this case we need to patch "os" and "os.path"

										for i in range(len(lowerCAmelCase__	)	):
																try:
																						__SCREAMING_SNAKE_CASE						:     Any					=		import_module('''.'''.join(submodules[: i + 1]	)	)
																except ModuleNotFoundError:
																						continue
																# We iterate over all the globals in self.obj in case we find "os" or "os.path"
																for attr in self.obj.__dir__():
																						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		getattr(self.obj			,			lowerCAmelCase__	)
																						# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
																						# This allows to patch renamed modules like "from os import path as ospath".
																						if obj_attr is submodule or (
																						    (isinstance(lowerCAmelCase__			,			_PatchedModuleObj	) and obj_attr._original_module is submodule)
																						):
																												__SCREAMING_SNAKE_CASE						:     int					=		obj_attr
																												# patch at top level
																												setattr(self.obj			,			lowerCAmelCase__			,			_PatchedModuleObj(lowerCAmelCase__			,			attrs=self.attrs	)	)
																												__SCREAMING_SNAKE_CASE						:     List[str]					=		getattr(self.obj			,			lowerCAmelCase__	)
																												# construct lower levels patches
																												for key in submodules[i + 1 :]:
																																		setattr(lowerCAmelCase__			,			lowerCAmelCase__			,			_PatchedModuleObj(getattr(lowerCAmelCase__			,			lowerCAmelCase__			,			lowerCAmelCase__	)			,			attrs=self.attrs	)	)
																																		__SCREAMING_SNAKE_CASE						:     Tuple					=		getattr(lowerCAmelCase__			,			lowerCAmelCase__	)
																												# finally set the target attribute
																												setattr(lowerCAmelCase__			,			lowerCAmelCase__			,			self.new	)

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

										if submodules:  # if it's an attribute of a submodule like "os.path.join"
																try:
																						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		getattr(import_module('''.'''.join(lowerCAmelCase__	)	)			,			lowerCAmelCase__	)
																except (AttributeError, ModuleNotFoundError):
																						return
																# We iterate over all the globals in self.obj in case we find "os.path.join"
																for attr in self.obj.__dir__():
																						# We don't check for the name of the global, but rather if its value *is* "os.path.join".
																						# This allows to patch renamed attributes like "from os.path import join as pjoin".
																						if getattr(self.obj			,			lowerCAmelCase__	) is attr_value:
																												__SCREAMING_SNAKE_CASE						:     Any					=		getattr(self.obj			,			lowerCAmelCase__	)
																												setattr(self.obj			,			lowerCAmelCase__			,			self.new	)
										elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
																__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		globals()['''__builtins__'''][target_attr]
																setattr(self.obj			,			lowerCAmelCase__			,			self.new	)
										else:
																raise RuntimeError(f'''Tried to patch attribute {target_attr} instead of a submodule.'''	)


				def __exit__(       self			:str			,			*lowerCAmelCase__			:Union[str, Any]	)		->			Optional[int]:
										for attr in list(self.original	):
																setattr(self.obj			,			lowerCAmelCase__			,			self.original.pop(lowerCAmelCase__	)	)


				def 		__magic_name__(       self			:List[Any]	)		->			List[Any]:
										self.__enter__()
										self._active_patches.append(self	)



				def 		__magic_name__(       self			:Optional[int]	)		->			int:
										try:
																self._active_patches.remove(self	)
										except ValueError:
																# If the patch hasn't been started this will fail
																return None

										return self.__exit__()
