
'''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 						a   :




										@staticmethod
										def 	__snake_case							(							*SCREAMING_SNAKE_CASE_	:				Any,    **SCREAMING_SNAKE_CASE_	:				List[str]      ):
																pass




						def      A (       A_							:					int       )     ->   str:
												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.
UpperCAmelCase    					=   (
    "https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"
)

@is_pipeline_test
@require_torch
@require_vision
class 						a   (				unittest.TestCase    ):
				_snake_case		    = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING




				@require_pytesseract
				@require_vision
				def 	__snake_case							(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				List[str]      ):
										snake_case       :     int =    pipeline(
										    '''document-question-answering''',    model=__UpperCamelCase,    tokenizer=__UpperCamelCase,    image_processor=__UpperCamelCase      )

										snake_case       :     Tuple =    INVOICE_URL
										snake_case       :     List[Any] =    list(zip(*apply_tesseract(load_image(__UpperCamelCase      ),    __UpperCamelCase,    ''''''      )      )      )
										snake_case       :     Any =    '''What is the placebo?'''
										snake_case       :     str =    [
										    {
										        '''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	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Any,    SCREAMING_SNAKE_CASE_	:				Any      ):
										snake_case       :     str =    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	:				int      ):
										snake_case       :     List[str] =    pipeline('''document-question-answering''',    model='''hf-internal-testing/tiny-random-layoutlmv2'''      )
										snake_case       :     List[str] =    INVOICE_URL
										snake_case       :     str =    '''How many cats are there?'''

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

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

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

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

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

										snake_case       :     str =    dqa_pipeline(
										    [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}],    top_k=2      )
										self.assertEqual(
										    nested_simplify(__UpperCamelCase,    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 	__snake_case							(							self	:				str      ):
										snake_case       :     Optional[Any] =    pipeline(
										    '''document-question-answering''',    model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa''',    revision='''9977165''',    max_seq_len=50,    )
										snake_case       :     Dict =    INVOICE_URL
										snake_case       :     Tuple =    '''What is the invoice number?'''

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

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

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

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

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

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

										snake_case       :     Dict =    list(zip(*apply_tesseract(load_image(__UpperCamelCase      ),    __UpperCamelCase,    ''''''      )      )      )

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

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

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

										snake_case       :     Dict =    list(zip(*apply_tesseract(load_image(__UpperCamelCase      ),    __UpperCamelCase,    ''''''      )      )      )

										# This model should also work if `image` is set to None
										snake_case       :     int =    dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question},    top_k=2      )
										self.assertEqual(
										    nested_simplify(__UpperCamelCase,    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 	__snake_case							(							self	:				List[str]      ):
										snake_case       :     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''',    )

										snake_case       :     Optional[Any] =    INVOICE_URL
										snake_case       :     List[str] =    '''What is the invoice number?'''
										snake_case       :     Any =    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	:				int      ):
										pass



'''simple docstring'''





import numpy as np

class 						a   :




				def __init__(							self	:				Dict      ):
										snake_case       :     List[str] =    (0, 0)
										snake_case       :     Tuple =    None
										snake_case       :     Union[str, Any] =    0
										snake_case       :     Dict =    0
										snake_case       :     List[str] =    0




				def __eq__(							self	:				str,    SCREAMING_SNAKE_CASE_	:				List[Any]      ):
										return self.position == cell.position




				def 	__snake_case							(							self	:				Optional[int]      ):
										print(self.position      )




class 						a   :




				def __init__(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=(5, 5)      ):
										snake_case       :     int =    np.zeros(SCREAMING_SNAKE_CASE_      )
										snake_case       :     Optional[int] =    world_size[0]
										snake_case       :     Optional[Any] =    world_size[1]




				def 	__snake_case							(							self	:				Any      ):
										print(self.w      )




				def 	__snake_case							(							self	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[int]      ):
										snake_case       :     Union[str, Any] =    [
										    (-1, -1),
										    (-1, 0),
										    (-1, 1),
										    (0, -1),
										    (0, 1),
										    (1, -1),
										    (1, 0),
										    (1, 1),
										]
										snake_case       :     str =    cell.position[0]
										snake_case       :     Optional[Any] =    cell.position[1]
										snake_case       :     Any =    []
										for n in neughbour_cord:
																snake_case       :     List[str] =    current_x + n[0]
																snake_case       :     Tuple =    current_y + n[1]
																if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
																						snake_case       :     int =    Cell()
																						snake_case       :     int =    (x, y)
																						snake_case       :     Any =    cell
																						neighbours.append(SCREAMING_SNAKE_CASE_      )
										return neighbours




def      A (       A_							:					Tuple    ,							A_							:					Tuple    ,							A_							:					Union[str, Any]       ):
						snake_case       :     Union[str, Any] =    []
						snake_case       :     Union[str, Any] =    []
						_open.append(A_       )

						while _open:
												snake_case       :     List[Any] =    np.argmin([n.f for n in _open]       )
												snake_case       :     List[Any] =    _open[min_f]
												_closed.append(_open.pop(A_       )       )
												if current == goal:
																		break
												for n in world.get_neigbours(A_       ):
																		for c in _closed:
																								if c == n:
																														continue
																		snake_case       :     Dict =    current.g + 1
																		snake_case,					snake_case       :     Optional[Any] =    n.position
																		snake_case,					snake_case       :     Union[str, Any] =    goal.position
																		snake_case       :     Tuple =    (ya - ya) ** 2 + (xa - xa) ** 2
																		snake_case       :     Union[str, Any] =    n.h + n.g

																		for c in _open:
																								if c == n and c.f < n.f:
																														continue
																		_open.append(A_       )
						snake_case       :     Dict =    []
						while current.parent is not None:
												path.append(current.position       )
												snake_case       :     Tuple =    current.parent
						path.append(current.position       )
						return path[::-1]


if __name__ == "__main__":
						UpperCAmelCase    					=   Gridworld()
						# Start position and goal
						UpperCAmelCase    					=   Cell()
						UpperCAmelCase    					=   (0, 0)
						UpperCAmelCase    					=   Cell()
						UpperCAmelCase    					=   (4, 4)
						print(f'''path from {start.position} to {goal.position}''')
						UpperCAmelCase    					=   astar(world, start, goal)
						#   Just for visual reasons.
						for i in s:
												UpperCAmelCase    					=   1
						print(world.w)

