




'''simple docstring'''

import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


a  :				Optional[Any]									=  'true'


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase=82,      __UpperCAmelCase=16	)		->					str:

					'''simple docstring'''

					set_seed(42	)
					snake_case_									=				RegressionModel()
					snake_case_									=				deepcopy(__UpperCAmelCase	)
					snake_case_									=				RegressionDataset(length=__UpperCAmelCase	)
					snake_case_									=				DataLoader(__UpperCAmelCase,      batch_size=__UpperCAmelCase	)
					model.to(accelerator.device	)
					snake_case_			,snake_case_									=				accelerator.prepare(__UpperCAmelCase,      __UpperCAmelCase	)
					return model, ddp_model, dataloader


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase=False	)		->					int:

					'''simple docstring'''

					snake_case_									=				AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''	)
					snake_case_									=				load_dataset('''glue''',      '''mrpc''',      split='''validation'''	)

					def tokenize_function(__UpperCAmelCase	):
										snake_case_									=				tokenizer(examples['''sentence1'''],      examples['''sentence2'''],      truncation=__UpperCAmelCase,      max_length=__UpperCAmelCase	)
										return outputs

					with accelerator.main_process_first():
										snake_case_									=				dataset.map(
										    __UpperCAmelCase,      batched=__UpperCAmelCase,      remove_columns=['''idx''', '''sentence1''', '''sentence2'''],      )

					snake_case_									=				tokenized_datasets.rename_column('''label''',      '''labels'''	)

					def collate_fn(__UpperCAmelCase	):
										if use_longest:
															return tokenizer.pad(__UpperCAmelCase,      padding='''longest''',      return_tensors='''pt'''	)
										return tokenizer.pad(__UpperCAmelCase,      padding='''max_length''',      max_length=128,      return_tensors='''pt'''	)

					return DataLoader(__UpperCAmelCase,      shuffle=__UpperCAmelCase,      collate_fn=__UpperCAmelCase,      batch_size=16	)


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					int:

					'''simple docstring'''

					snake_case_									=				Accelerator(dispatch_batches=__UpperCAmelCase,      split_batches=__UpperCAmelCase	)
					snake_case_									=				get_dataloader(__UpperCAmelCase,      not dispatch_batches	)
					snake_case_									=				AutoModelForSequenceClassification.from_pretrained(
					    '''hf-internal-testing/mrpc-bert-base-cased''',      return_dict=__UpperCAmelCase	)
					snake_case_			,snake_case_									=				accelerator.prepare(__UpperCAmelCase,      __UpperCAmelCase	)
					return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					Any:

					'''simple docstring'''

					snake_case_									=				[]
					for batch in dataloader:
										snake_case_			,snake_case_									=				batch.values()
										with torch.no_grad():
															snake_case_									=				model(__UpperCAmelCase	)
															snake_case_			,snake_case_									=				accelerator.gather_for_metrics((logit, target)	)
															logits_and_targets.append((logit, target)	)
					snake_case_			,snake_case_									=				[], []
					for logit, targ in logits_and_targets:
										logits.append(__UpperCAmelCase	)
										targs.append(__UpperCAmelCase	)
					snake_case_			,snake_case_									=				torch.cat(__UpperCAmelCase	), torch.cat(__UpperCAmelCase	)
					return logits, targs


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase=82,      __UpperCAmelCase=False,      __UpperCAmelCase=False,      __UpperCAmelCase=16	)		->					Dict:

					'''simple docstring'''

					snake_case_			,snake_case_			,snake_case_									=				get_basic_setup(__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)
					snake_case_			,snake_case_									=				generate_predictions(__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)
					assert (
					    len(__UpperCAmelCase	) == num_samples
					), F"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__UpperCAmelCase	)}"


def 		__magic_name__    (	__UpperCAmelCase = False,      __UpperCAmelCase = False	)		->					Union[str, Any]:

					'''simple docstring'''

					snake_case_									=				evaluate.load('''glue''',      '''mrpc'''	)
					snake_case_			,snake_case_									=				get_mrpc_setup(__UpperCAmelCase,      __UpperCAmelCase	)
					# First do baseline
					snake_case_			,snake_case_			,snake_case_									=				setup['''no''']
					model.to(__UpperCAmelCase	)
					model.eval()
					for batch in dataloader:
										batch.to(__UpperCAmelCase	)
										with torch.inference_mode():
															snake_case_									=				model(**__UpperCAmelCase	)
										snake_case_									=				outputs.logits.argmax(dim=-1	)
										metric.add_batch(predictions=__UpperCAmelCase,      references=batch['''labels''']	)
					snake_case_									=				metric.compute()

					# Then do distributed
					snake_case_			,snake_case_			,snake_case_									=				setup['''ddp''']
					model.eval()
					for batch in dataloader:
										with torch.inference_mode():
															snake_case_									=				model(**__UpperCAmelCase	)
										snake_case_									=				outputs.logits.argmax(dim=-1	)
										snake_case_									=				batch['''labels''']
										snake_case_			,snake_case_									=				accelerator.gather_for_metrics((preds, references)	)
										metric.add_batch(predictions=__UpperCAmelCase,      references=__UpperCAmelCase	)
					snake_case_									=				metric.compute()

					for key in "accuracy f1".split():
										assert math.isclose(
										    baseline[key],      distributed[key]	), F"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"


def 		__magic_name__    (	)		->					Union[str, Any]:

					'''simple docstring'''

					snake_case_									=				Accelerator(split_batches=__UpperCAmelCase,      dispatch_batches=__UpperCAmelCase	)
					if accelerator.is_local_main_process:
										datasets.utils.logging.set_verbosity_warning()
										transformers.utils.logging.set_verbosity_warning()
					else:
										datasets.utils.logging.set_verbosity_error()
										transformers.utils.logging.set_verbosity_error()
					# These are a bit slower so they should only be ran on the GPU or TPU
					if torch.cuda.is_available() or is_tpu_available():
										if accelerator.is_local_main_process:
															print('''**Testing gather_for_metrics**'''	)
										for split_batches in [True, False]:
															for dispatch_batches in [True, False]:
																				if accelerator.is_local_main_process:
																									print(F"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"	)
																				test_mrpc(__UpperCAmelCase,      __UpperCAmelCase	)
																				accelerator.state._reset_state()
					if accelerator.is_local_main_process:
										print('''**Test torch metrics**'''	)
					for split_batches in [True, False]:
										for dispatch_batches in [True, False]:
															snake_case_									=				Accelerator(split_batches=__UpperCAmelCase,      dispatch_batches=__UpperCAmelCase	)
															if accelerator.is_local_main_process:
																				print(F"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"	)
															test_torch_metrics(__UpperCAmelCase,      99	)
															accelerator.state._reset_state()
					if accelerator.is_local_main_process:
										print('''**Test last batch is not dropped when perfectly divisible**'''	)
					snake_case_									=				Accelerator()
					test_torch_metrics(__UpperCAmelCase,      512	)
					accelerator.state._reset_state()


def 		__magic_name__    (	__UpperCAmelCase	)		->					str:

					'''simple docstring'''

					main()


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 						a				:


											@staticmethod
											def   A_ (		*lowercase_    :					int       ,				**lowercase_    :					str					):
																pass






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 						a				(   unittest.TestCase     ):
				snake_case_      =		MODEL_FOR_OBJECT_DETECTION_MAPPING


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


				def   A_ (		self    :					Dict       ,				lowercase_    :					List[Any]       ,				lowercase_    :					int					):
									snake_case_									=				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

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

									snake_case_									=				[
									    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'''],
									]
									snake_case_									=				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   A_ (		self    :					int					):
									pass


				@require_torch
				def   A_ (		self    :					Tuple					):
									snake_case_									=				'''hf-internal-testing/tiny-detr-mobilenetsv3'''

									snake_case_									=				AutoModelForObjectDetection.from_pretrained(lowercase_					)
									snake_case_									=				AutoFeatureExtractor.from_pretrained(lowercase_					)
									snake_case_									=				ObjectDetectionPipeline(model=lowercase_       ,				feature_extractor=lowercase_					)

									snake_case_									=				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''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
									        {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
									    ]       ,				)

									snake_case_									=				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''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
									            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
									        ],
									        [
									            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
									            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
									        ],
									    ]       ,				)


				@require_torch
				@slow
				def   A_ (		self    :					Optional[int]					):
									snake_case_									=				'''facebook/detr-resnet-50'''

									snake_case_									=				AutoModelForObjectDetection.from_pretrained(lowercase_					)
									snake_case_									=				AutoFeatureExtractor.from_pretrained(lowercase_					)
									snake_case_									=				ObjectDetectionPipeline(model=lowercase_       ,				feature_extractor=lowercase_					)

									snake_case_									=				object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
									self.assertEqual(
									    nested_simplify(lowercase_       ,				decimals=4					)       ,				[
									        {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
									        {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
									        {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
									        {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
									        {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
									    ]       ,				)

									snake_case_									=				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''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
									            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
									            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
									            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
									            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
									        ],
									        [
									            {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
									            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
									            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
									            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
									            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
									        ],
									    ]       ,				)


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

									snake_case_									=				pipeline('''object-detection'''       ,				model=lowercase_					)

									snake_case_									=				object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
									self.assertEqual(
									    nested_simplify(lowercase_       ,				decimals=4					)       ,				[
									        {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
									        {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
									        {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
									        {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
									        {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
									    ]       ,				)

									snake_case_									=				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''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
									            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
									            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
									            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
									            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
									        ],
									        [
									            {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
									            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
									            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
									            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
									            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
									        ],
									    ]       ,				)


				@require_torch
				@slow
				def   A_ (		self    :					str					):
									snake_case_									=				0.9985
									snake_case_									=				'''facebook/detr-resnet-50'''

									snake_case_									=				pipeline('''object-detection'''       ,				model=lowercase_					)

									snake_case_									=				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''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
									        {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
									    ]       ,				)


				@require_torch
				@require_pytesseract
				@slow
				def   A_ (		self    :					Dict					):
									snake_case_									=				'''Narsil/layoutlmv3-finetuned-funsd'''
									snake_case_									=				0.9993

									snake_case_									=				pipeline('''object-detection'''       ,				model=lowercase_       ,				threshold=lowercase_					)

									snake_case_									=				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''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
									        {'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
									    ]       ,				)

