





'''simple docstring'''






import json
import multiprocessing
import os
import re
from collections import defaultdict

import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList


__UpperCamelCase					    =	["\nclass", "\ndef", "\n#", "\n@", "\nprint", "\nif"]




class  _A (					__lowercase		):

		def __init__(   self					:   Tuple  ,      __magic_name__					:   Dict  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   Tuple=None  ,      __magic_name__					:   Optional[Any]=1			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Any							      =     tokenizer
					__snake_case					:      Dict							      =     dataset
					__snake_case					:      Optional[Any]							      =     len(__magic_name__			) if n_tasks is None else n_tasks
					__snake_case					:      List[Any]							      =     n_copies

		def __iter__(   self					:   int			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     []
					for task in range(self.n_tasks			):
								# without strip, the model generate commented codes ...
								prompts.append(self.tokenizer.eos_token + self.dataset[task]["""prompt"""].strip()			)
					__snake_case					:      Optional[int]							      =     self.tokenizer(__magic_name__  ,      padding=__magic_name__  ,      return_tensors="""pt"""			)
					for task in range(self.n_tasks			):
								for _ in range(self.n_copies			):
											yield {
											    "ids": outputs.input_ids[task],
											    "task_id": task,
											    "input_len": outputs.attention_mask[task].sum(),
											}





class  _A (					__lowercase		):

		def __init__(   self					:   Any  ,      __magic_name__					:   Optional[Any]  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   str			)					->    Tuple:


					"""simple docstring"""

					__snake_case					:      int							      =     start_length
					__snake_case					:      Union[str, Any]							      =     eof_strings
					__snake_case					:      Dict							      =     tokenizer

		def __call__(   self					:   str  ,      __magic_name__					:   str  ,      __magic_name__					:   Union[str, Any]  ,      **__magic_name__					:   List[Any]			)					->    List[Any]:


					"""simple docstring"""

					__snake_case					:      Optional[Any]							      =     self.tokenizer.batch_decode(input_ids[:, self.start_length :]			)
					__snake_case					:      int							      =     []
					for decoded_generation in decoded_generations:
								done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings			)			)
					return all(__magic_name__			)







def 					_a						(       _lowerCamelCase ) ->				Any:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     re.split("""(%s)""" % """|""".join(_lowerCamelCase )    ,	_lowerCamelCase )
			# last string should be ""
			return "".join(string_list[:-2] )







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase=20    ,	**_lowerCamelCase ) ->				int:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     defaultdict(_lowerCamelCase )  # dict of list of generated tokens
			for step, batch in tqdm(enumerate(_lowerCamelCase ) ):
						with torch.no_grad():
									__snake_case					:      int							      =     batch["""ids"""].shape[-1]
									__snake_case					:      Dict							      =     accelerator.unwrap_model(_lowerCamelCase ).generate(
									    input_ids=batch["""ids"""][:, : batch["""input_len"""]]    ,	num_return_sequences=_lowerCamelCase    ,	**_lowerCamelCase )
									# each task is generated batch_size times
									__snake_case					:      int							      =     batch["""task_id"""].repeat(_lowerCamelCase )
									__snake_case					:      Tuple							      =     accelerator.pad_across_processes(
									    _lowerCamelCase    ,	dim=1    ,	pad_index=tokenizer.pad_token_id )

									__snake_case      ,     __snake_case					:      Any							      =     accelerator.gather((generated_tokens, generated_tasks) )
									__snake_case					:      List[str]							      =     generated_tokens.cpu().numpy()
									__snake_case					:      Any							      =     generated_tasks.cpu().numpy()

									for task, generated_tokens in zip(_lowerCamelCase    ,	_lowerCamelCase ):
												gen_token_dict[task].append(_lowerCamelCase )

			__snake_case					:      List[str]							      =     [[] for _ in range(_lowerCamelCase )]
			for task, generated_tokens in gen_token_dict.items():
						for s in generated_tokens:
									__snake_case					:      int							      =     tokenizer.decode(_lowerCamelCase    ,	skip_special_tokens=_lowerCamelCase    ,	clean_up_tokenization_spaces=_lowerCamelCase )
									code_gens[task].append(remove_last_block(_lowerCamelCase ) )
			return code_gens







def 					_a						(       ) ->				List[Any]:



			"""simple docstring"""


			__snake_case					:      int							      =     HfArgumentParser(_lowerCamelCase )
			__snake_case					:      Dict							      =     parser.parse_args()

			transformers.logging.set_verbosity_error()
			# enables code execution in code_eval metric
			__snake_case					:      Optional[int]							      =     args.HF_ALLOW_CODE_EVAL
			# make sure tokenizer plays nice with multiprocessing
			__snake_case					:      int							      =     """false"""

			if args.num_workers is None:
						__snake_case					:      Dict							      =     multiprocessing.cpu_count()

			# Use dataset load to feed to accelerate
			__snake_case					:      str							      =     Accelerator()
			set_seed(args.seed    ,	device_specific=_lowerCamelCase )

			# Load model and tokenizer
			__snake_case					:      Any							      =     AutoTokenizer.from_pretrained(args.model_ckpt )
			__snake_case					:      Dict							      =     tokenizer.eos_token
			__snake_case					:      str							      =     AutoModelForCausalLM.from_pretrained(args.model_ckpt )

			# Generation settings
			__snake_case					:      Optional[int]							      =     {
			    """do_sample""": args.do_sample,
			    """temperature""": args.temperature,
			    """max_new_tokens""": args.max_new_tokens,
			    """top_p""": args.top_p,
			    """top_k""": args.top_k,
			    """stopping_criteria""": StoppingCriteriaList([EndOfFunctionCriteria(0    ,	_lowerCamelCase    ,	_lowerCamelCase )] ),
			}

			# Load evaluation dataset and metric
			__snake_case					:      Dict							      =     load_dataset("""openai_humaneval""" )
			__snake_case					:      int							      =     load_metric("""code_eval""" )

			__snake_case					:      int							      =     args.num_tasks if args.num_tasks is not None else len(human_eval["""test"""] )
			__snake_case					:      str							      =     args.n_samples // args.batch_size

			__snake_case					:      List[str]							      =     TokenizedDataset(_lowerCamelCase    ,	human_eval["""test"""]    ,	n_copies=_lowerCamelCase    ,	n_tasks=_lowerCamelCase )
			# do not confuse args.batch_size, which is actually the num_return_sequences
			__snake_case					:      Dict							      =     DataLoader(_lowerCamelCase    ,	batch_size=1 )

			# Run a quick test to see if code evaluation is enabled
			try:
						__snake_case					:      int							      =     code_eval_metric.compute(references=[""""""]    ,	predictions=[[""""""]] )
			except ValueError as exception:
						print(
						    """Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL=\"1\"`"""
						    """ flag to enable code evaluation.""" )
						raise exception

			__snake_case      ,     __snake_case					:      List[str]							      =     accelerator.prepare(_lowerCamelCase    ,	_lowerCamelCase )

			__snake_case					:      Optional[int]							      =     complete_code(
			    _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	n_tasks=_lowerCamelCase    ,	batch_size=args.batch_size    ,	**_lowerCamelCase    ,	)

			if accelerator.is_main_process:
						__snake_case					:      str							      =     []

						for task in tqdm(range(_lowerCamelCase ) ):
									__snake_case					:      Union[str, Any]							      =     human_eval["""test"""][task]["""test"""]
									__snake_case					:      List[str]							      =     F'''check({human_eval["test"][task]["entry_point"]})'''
									references.append("""\n""" + test_func + """\n""" + entry_point )

						# Evaluate completions with "code_eval" metric
						__snake_case      ,     __snake_case					:      Optional[int]							      =     code_eval_metric.compute(
						    references=_lowerCamelCase    ,	predictions=_lowerCamelCase    ,	num_workers=args.num_workers )
						print(F'''Results: {pass_at_k}''' )

						# Save results to json file
						with open(args.output_file    ,	"""w""" ) as fp:
									json.dump(_lowerCamelCase    ,	_lowerCamelCase )


# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
	main()





"""simple docstring"""





import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
							from PIL import Image
else:







							class 							SCREAMING_SNAKE_CASE   :






												"""simple docstring"""



												@staticmethod
												def 			__lowerCAmelCase					(			*lowercase_     :							Dict  ,**lowercase_     :							str						):
													pass







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 							SCREAMING_SNAKE_CASE   (						unittest.TestCase			):






					"""simple docstring"""



					lowercase__												=			MODEL_FOR_OBJECT_DETECTION_MAPPING







					def 			__lowerCAmelCase					(			self     :							str  ,lowercase_     :							int  ,lowercase_     :							str  ,lowercase_     :							List[str]						):
						lowerCAmelCase__      :		int    							=      ObjectDetectionPipeline(model=lowercase_  ,image_processor=lowercase_						)
						return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]







					def 			__lowerCAmelCase					(			self     :							int  ,lowercase_     :							str  ,lowercase_     :							str						):
						lowerCAmelCase__      :		List[str]    							=      object_detector('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  ,threshold=0.0						)

						self.assertGreater(len(lowercase_						)  ,0						)
						for detected_object in outputs:
							self.assertEqual(
							    lowercase_  ,{
							        '''score''': ANY(lowercase_						),
							        '''label''': ANY(lowercase_						),
							        '''box''': {'''xmin''': ANY(lowercase_						), '''ymin''': ANY(lowercase_						), '''xmax''': ANY(lowercase_						), '''ymax''': ANY(lowercase_						)},
							    }  ,)

						import datasets

						lowerCAmelCase__      :		str    							=      datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''  ,'''image'''  ,split='''test'''						)

						lowerCAmelCase__      :		Dict    							=      [
						    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						),
						    '''http://images.cocodataset.org/val2017/000000039769.jpg''',
						    # RGBA
						    dataset[0]['''file'''],
						    # LA
						    dataset[1]['''file'''],
						    # L
						    dataset[2]['''file'''],
						]
						lowerCAmelCase__      :		Optional[Any]    							=      object_detector(lowercase_  ,threshold=0.0						)

						self.assertEqual(len(lowercase_						)  ,len(lowercase_						)						)
						for outputs in batch_outputs:
							self.assertGreater(len(lowercase_						)  ,0						)
							for detected_object in outputs:
								self.assertEqual(
								    lowercase_  ,{
								        '''score''': ANY(lowercase_						),
								        '''label''': ANY(lowercase_						),
								        '''box''': {'''xmin''': ANY(lowercase_						), '''ymin''': ANY(lowercase_						), '''xmax''': ANY(lowercase_						), '''ymax''': ANY(lowercase_						)},
								    }  ,)







					@require_tf
					@unittest.skip('''Object detection not implemented in TF'''						)
					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						pass







					@require_torch
					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						lowerCAmelCase__      :		Optional[Any]    							=      '''hf-internal-testing/tiny-detr-mobilenetsv3'''

						lowerCAmelCase__      :		Union[str, Any]    							=      AutoModelForObjectDetection.from_pretrained(lowercase_						)
						lowerCAmelCase__      :		Any    							=      AutoFeatureExtractor.from_pretrained(lowercase_						)
						lowerCAmelCase__      :		Optional[int]    							=      ObjectDetectionPipeline(model=lowercase_  ,feature_extractor=lowercase_						)

						lowerCAmelCase__      :		Any    							=      object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''  ,threshold=0.0						)

						self.assertEqual(
						    nested_simplify(lowercase_  ,decimals=4						)  ,[
						        {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
						        {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
						    ]  ,)

						lowerCAmelCase__      :		List[Any]    							=      object_detector(
						    [
						        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
						        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
						    ]  ,threshold=0.0  ,)

						self.assertEqual(
						    nested_simplify(lowercase_  ,decimals=4						)  ,[
						        [
						            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
						            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
						        ],
						        [
						            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
						            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_5_9, '''ymin''': 1_2_0, '''xmax''': 4_8_0, '''ymax''': 3_5_9}},
						        ],
						    ]  ,)







					@require_torch
					@slow
					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		List[Any]    							=      '''facebook/detr-resnet-50'''

						lowerCAmelCase__      :		Any    							=      AutoModelForObjectDetection.from_pretrained(lowercase_						)
						lowerCAmelCase__      :		Dict    							=      AutoFeatureExtractor.from_pretrained(lowercase_						)
						lowerCAmelCase__      :		Any    							=      ObjectDetectionPipeline(model=lowercase_  ,feature_extractor=lowercase_						)

						lowerCAmelCase__      :		Tuple    							=      object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''						)
						self.assertEqual(
						    nested_simplify(lowercase_  ,decimals=4						)  ,[
						        {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
						        {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
						        {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
						        {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						        {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
						    ]  ,)

						lowerCAmelCase__      :		List[Any]    							=      object_detector(
						    [
						        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
						        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
						    ]						)
						self.assertEqual(
						    nested_simplify(lowercase_  ,decimals=4						)  ,[
						        [
						            {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
						            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
						            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
						            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
						        ],
						        [
						            {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
						            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
						            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
						            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
						        ],
						    ]  ,)







					@require_torch
					@slow
					def 			__lowerCAmelCase					(			self     :							Tuple						):
						lowerCAmelCase__      :		Tuple    							=      '''facebook/detr-resnet-50'''

						lowerCAmelCase__      :		Tuple    							=      pipeline('''object-detection'''  ,model=lowercase_						)

						lowerCAmelCase__      :		Optional[int]    							=      object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''						)
						self.assertEqual(
						    nested_simplify(lowercase_  ,decimals=4						)  ,[
						        {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
						        {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
						        {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
						        {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						        {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
						    ]  ,)

						lowerCAmelCase__      :		Any    							=      object_detector(
						    [
						        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
						        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
						    ]						)
						self.assertEqual(
						    nested_simplify(lowercase_  ,decimals=4						)  ,[
						        [
						            {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
						            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
						            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
						            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
						        ],
						        [
						            {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_0, '''xmax''': 1_7_5, '''ymax''': 1_1_7}},
						            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_3, '''ymin''': 7_2, '''xmax''': 3_6_8, '''ymax''': 1_8_7}},
						            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_3_9, '''ymax''': 4_7_3}},
						            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
						        ],
						    ]  ,)







					@require_torch
					@slow
					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		List[Any]    							=      0.9985
						lowerCAmelCase__      :		Dict    							=      '''facebook/detr-resnet-50'''

						lowerCAmelCase__      :		List[Any]    							=      pipeline('''object-detection'''  ,model=lowercase_						)

						lowerCAmelCase__      :		int    							=      object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''  ,threshold=lowercase_						)
						self.assertEqual(
						    nested_simplify(lowercase_  ,decimals=4						)  ,[
						        {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 1_3, '''ymin''': 5_2, '''xmax''': 3_1_4, '''ymax''': 4_7_0}},
						        {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_4_5, '''ymin''': 2_3, '''xmax''': 6_4_0, '''ymax''': 3_6_8}},
						    ]  ,)







					@require_torch
					@require_pytesseract
					@slow
					def 			__lowerCAmelCase					(			self     :							str						):
						lowerCAmelCase__      :		Optional[Any]    							=      '''Narsil/layoutlmv3-finetuned-funsd'''
						lowerCAmelCase__      :		List[Any]    							=      0.9993

						lowerCAmelCase__      :		Any    							=      pipeline('''object-detection'''  ,model=lowercase_  ,threshold=lowercase_						)

						lowerCAmelCase__      :		List[str]    							=      object_detector(
						    '''https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'''						)
						self.assertEqual(
						    nested_simplify(lowercase_  ,decimals=4						)  ,[
						        {'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 2_9_4, '''ymin''': 2_5_4, '''xmax''': 3_4_3, '''ymax''': 2_6_4}},
						        {'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 2_9_4, '''ymin''': 2_5_4, '''xmax''': 3_4_3, '''ymax''': 2_6_4}},
						    ]  ,)




