
"""simple docstring"""






from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed






def 						_snake_case       (	lowercase__		:  Any      ,     lowercase__		:  Union[str, Any]      ,     lowercase__		:  Optional[Any]      ,     lowercase__		:  List[Any]		)  ->				int:


			'''simple docstring'''





			for param, grad_param in zip(model_a.parameters()      ,     model_b.parameters()		):
						if not param.requires_grad:
									continue
						if not did_step:
									# Grads should not be in sync
									assert (
									    torch.allclose(param.grad      ,     grad_param.grad		) is False
									), f"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
						else:
									# Grads should be in sync
									assert (
									    torch.allclose(param.grad      ,     grad_param.grad		) is True
									), f"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""






def 						_snake_case       (	lowercase__		:  str      ,     lowercase__		:  str      ,     lowercase__		:  List[str]      ,     lowercase__		:  str      ,     lowercase__		:  Optional[Any]=True		)  ->				str:


			'''simple docstring'''





			model.train()
			lowerCAmelCase_    :int			     =  model(_lowercase		)
			lowerCAmelCase_    :Tuple			     =  F.mse_loss(_lowercase      ,     target.to(output.device		)		)
			if not do_backward:
						loss /= accelerator.gradient_accumulation_steps
						loss.backward()
			else:
						accelerator.backward(_lowercase		)






def 						_snake_case       (	lowercase__		:  str      ,     lowercase__		:  Union[str, Any]=False		)  ->				Optional[Any]:


			'''simple docstring'''





			set_seed(4_2		)
			lowerCAmelCase_    :Optional[int]			     =  RegressionModel()
			lowerCAmelCase_    :Any			     =  deepcopy(_lowercase		)
			lowerCAmelCase_    :Optional[Any]			     =  RegressionDataset(length=8_0		)
			lowerCAmelCase_    :Optional[Any]			     =  DataLoader(_lowercase      ,     batch_size=1_6		)
			model.to(accelerator.device		)
			if sched:
						lowerCAmelCase_    :Dict			     =  AdamW(params=model.parameters()      ,     lr=1E-3		)
						lowerCAmelCase_    :List[str]			     =  AdamW(params=ddp_model.parameters()      ,     lr=1E-3		)
						lowerCAmelCase_    :int			     =  LambdaLR(_lowercase      ,     lr_lambda=lambda lowercase__		: epoch**0.65		)
						lowerCAmelCase_    :Dict			     =  LambdaLR(_lowercase      ,     lr_lambda=lambda lowercase__		: epoch**0.65		)
			# Make a copy of `model`
			if sched:
						lowerCAmelCase_    :Any			     =  accelerator.prepare(_lowercase      ,     _lowercase      ,     _lowercase      ,     _lowercase		)
			else:
						lowerCAmelCase_    :Any			     =  accelerator.prepare(_lowercase      ,     _lowercase		)
			if sched:
						return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
			return model, ddp_model, dataloader






def 						_snake_case       (	lowercase__		:  Optional[Any]		)  ->				str:


			'''simple docstring'''





			lowerCAmelCase_    :Optional[Any]			     =  get_training_setup(_lowercase		)
			# Use a single batch
			lowerCAmelCase_    :List[str]			     =  next(iter(_lowercase		)		).values()
			for iteration in range(3		):
						# Gather the distributed inputs and targs for the base model
						lowerCAmelCase_    :Optional[int]			     =  accelerator.gather((ddp_input, ddp_target)		)
						lowerCAmelCase_    :List[Any]			     =  input.to(accelerator.device		), target.to(accelerator.device		)
						# Perform our initial ground truth step in non "DDP"
						step_model(_lowercase      ,     _lowercase      ,     _lowercase      ,     _lowercase		)
						# Do "gradient accumulation" (noop)
						if iteration % 2 == 0:
									# Accumulate grads locally
									with accelerator.no_sync(_lowercase		):
												step_model(_lowercase      ,     _lowercase      ,     _lowercase      ,     _lowercase		)
						else:
									# Sync grads
									step_model(_lowercase      ,     _lowercase      ,     _lowercase      ,     _lowercase		)

						# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
						check_model_parameters(_lowercase      ,     _lowercase      ,     _lowercase      ,     _lowercase		)
						for param, ddp_param in zip(model.parameters()      ,     ddp_model.parameters()		):
									if not param.requires_grad:
												continue
									assert torch.allclose(
									    param.grad      ,     ddp_param.grad		), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

						# Shuffle ddp_input on each iteration
						torch.manual_seed(1_3_3_7 + iteration		)
						lowerCAmelCase_    :List[str]			     =  ddp_input[torch.randperm(len(_lowercase		)		)]






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


			'''simple docstring'''





			lowerCAmelCase_    :int			     =  get_training_setup(_lowercase		)
			# Use a single batch
			lowerCAmelCase_    :Union[str, Any]			     =  next(iter(_lowercase		)		).values()
			for iteration in range(3		):
						# Gather the distributed inputs and targs for the base model
						lowerCAmelCase_    :Any			     =  accelerator.gather((ddp_input, ddp_target)		)
						lowerCAmelCase_    :Optional[int]			     =  input.to(accelerator.device		), target.to(accelerator.device		)
						# Perform our initial ground truth step in non "DDP"
						step_model(_lowercase      ,     _lowercase      ,     _lowercase      ,     _lowercase		)
						# Do "gradient accumulation" (noop)
						if iteration % 2 == 0:
									# Accumulate grads locally
									with accelerator.no_sync(_lowercase		):
												step_model(_lowercase      ,     _lowercase      ,     _lowercase      ,     _lowercase		)
						else:
									# Sync grads
									step_model(_lowercase      ,     _lowercase      ,     _lowercase      ,     _lowercase		)

						# DDP model and model should only be in sync when not (iteration % 2 == 0)
						for param, ddp_param in zip(model.parameters()      ,     ddp_model.parameters()		):
									if not param.requires_grad:
												continue
									if iteration % 2 == 0:
												# Grads should not be in sync
												assert (
												    torch.allclose(param.grad      ,     ddp_param.grad		) is False
												), f"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
									else:
												# Grads should be in sync
												assert (
												    torch.allclose(param.grad      ,     ddp_param.grad		) is True
												), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
						torch.manual_seed(1_3_3_7 + iteration		)
						lowerCAmelCase_    :int			     =  ddp_input[torch.randperm(len(_lowercase		)		)]






def 						_snake_case       (	lowercase__		:  List[Any]=False      ,     lowercase__		:  Optional[Any]=False		)  ->				int:


			'''simple docstring'''





			lowerCAmelCase_    :List[str]			     =  Accelerator(
			    split_batches=_lowercase      ,     dispatch_batches=_lowercase      ,     gradient_accumulation_steps=2		)
			# Test that context manager behaves properly
			lowerCAmelCase_    :Optional[Any]			     =  get_training_setup(_lowercase		)
			for iteration, batch in enumerate(_lowercase		):
						lowerCAmelCase_    :List[Any]			     =  batch.values()
						# Gather the distributed inputs and targs for the base model
						lowerCAmelCase_    :Any			     =  accelerator.gather((ddp_input, ddp_target)		)
						lowerCAmelCase_    :Tuple			     =  input.to(accelerator.device		), target.to(accelerator.device		)
						# Perform our initial ground truth step in non "DDP"
						step_model(_lowercase      ,     _lowercase      ,     _lowercase      ,     _lowercase      ,     _lowercase		)
						# Do "gradient accumulation" (noop)
						with accelerator.accumulate(_lowercase		):
									step_model(_lowercase      ,     _lowercase      ,     _lowercase      ,     _lowercase		)

						# DDP model and model should only be in sync when not (iteration % 2 == 0)
						for param, ddp_param in zip(model.parameters()      ,     ddp_model.parameters()		):
									if not param.requires_grad:
												continue
									if ((iteration + 1) % 2 == 0) or (iteration == len(_lowercase		) - 1):
												# Grads should be in sync
												assert (
												    torch.allclose(param.grad      ,     ddp_param.grad		) is True
												), f"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
									else:
												# Grads should not be in sync
												assert (
												    torch.allclose(param.grad      ,     ddp_param.grad		) is False
												), f"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
						torch.manual_seed(1_3_3_7 + iteration		)
						lowerCAmelCase_    :Dict			     =  ddp_input[torch.randperm(len(_lowercase		)		)]
			GradientState._reset_state()






def 						_snake_case       (	lowercase__		:  Dict=False      ,     lowercase__		:  Optional[int]=False		)  ->				List[Any]:


			'''simple docstring'''





			lowerCAmelCase_    :int			     =  Accelerator(
			    split_batches=_lowercase      ,     dispatch_batches=_lowercase      ,     gradient_accumulation_steps=2		)
			# Test that context manager behaves properly
			lowerCAmelCase_    :Tuple			     =  get_training_setup(_lowercase      ,     _lowercase		)
			for iteration, batch in enumerate(_lowercase		):
						lowerCAmelCase_    :Union[str, Any]			     =  batch.values()
						# Gather the distributed inputs and targs for the base model
						lowerCAmelCase_    :Dict			     =  accelerator.gather((ddp_input, ddp_target)		)
						lowerCAmelCase_    :Any			     =  input.to(accelerator.device		), target.to(accelerator.device		)
						# Perform our initial ground truth step in non "DDP"
						model.train()
						ddp_model.train()
						step_model(_lowercase      ,     _lowercase      ,     _lowercase      ,     _lowercase      ,     _lowercase		)
						opt.step()

						if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(_lowercase		)):
									if split_batches:
												sched.step()
									else:
												for _ in range(accelerator.num_processes		):
															sched.step()
						opt.zero_grad()
						# Perform gradient accumulation under wrapper
						with accelerator.accumulate(_lowercase		):
									step_model(_lowercase      ,     _lowercase      ,     _lowercase      ,     _lowercase		)
									ddp_opt.step()
									ddp_sched.step()
									ddp_opt.zero_grad()

						# Learning rates should be the same
						assert (
						    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
						), f"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n"""
						lowerCAmelCase_    :Dict			     =  (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(_lowercase		))
						if accelerator.num_processes > 1:
									check_model_parameters(_lowercase      ,     _lowercase      ,     _lowercase      ,     _lowercase		)
						# Shuffle ddp_input on each iteration
						torch.manual_seed(1_3_3_7 + iteration		)
			GradientState._reset_state()






def 						_snake_case       (	)  ->				Dict:


			'''simple docstring'''





			lowerCAmelCase_    :List[str]			     =  Accelerator()

			lowerCAmelCase_    :Union[str, Any]			     =  RegressionDataset(length=8_0		)
			lowerCAmelCase_    :Tuple			     =  DataLoader(_lowercase      ,     batch_size=1_6		)
			lowerCAmelCase_    :Tuple			     =  RegressionDataset(length=9_6		)
			lowerCAmelCase_    :Dict			     =  DataLoader(_lowercase      ,     batch_size=1_6		)
			lowerCAmelCase_    :Tuple			     =  accelerator.prepare(_lowercase      ,     _lowercase		)
			assert accelerator.gradient_state.active_dataloader is None
			for iteration, _ in enumerate(_lowercase		):
						assert id(accelerator.gradient_state.active_dataloader		) == id(_lowercase		)
						if iteration < len(_lowercase		) - 1:
									assert not accelerator.gradient_state.end_of_dataloader
									if iteration == 1:
												for batch_num, _ in enumerate(_lowercase		):
															assert id(accelerator.gradient_state.active_dataloader		) == id(_lowercase		)
															if batch_num < len(_lowercase		) - 1:
																		assert not accelerator.gradient_state.end_of_dataloader
															else:
																		assert accelerator.gradient_state.end_of_dataloader
						else:
									assert accelerator.gradient_state.end_of_dataloader
			assert accelerator.gradient_state.active_dataloader is None






def 						_snake_case       (	)  ->				Optional[int]:


			'''simple docstring'''





			lowerCAmelCase_    :str			     =  Accelerator()
			lowerCAmelCase_    :List[Any]			     =  accelerator.state
			if state.local_process_index == 0:
						print("""**Test `accumulate` gradient accumulation with dataloader break**"""		)
			test_dataloader_break()
			if state.distributed_type == DistributedType.NO:
						if state.local_process_index == 0:
									print("""**Test NOOP `no_sync` context manager**"""		)
						test_noop_sync(_lowercase		)
			if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
						if state.local_process_index == 0:
									print("""**Test Distributed `no_sync` context manager**"""		)
						test_distributed_sync(_lowercase		)
			if state.distributed_type == DistributedType.MULTI_GPU:
						for split_batch in [True, False]:
									for dispatch_batches in [True, False]:
												if state.local_process_index == 0:
															print(
															    """**Test `accumulate` gradient accumulation, """      ,     f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""      ,     )
												test_gradient_accumulation(_lowercase      ,     _lowercase		)

    # Currently will break on torch 2.0 +, need to investigate why
			if is_torch_version("""<"""      ,     """2.0"""		) or state.distributed_type == DistributedType.NO:
						if state.local_process_index == 0:
									print(
									    """**Test `accumulate` gradient accumulation with optimizer and scheduler, """      ,     """`split_batches=False`, `dispatch_batches=False`**"""      ,     )
						test_gradient_accumulation_with_opt_and_scheduler()
						if state.distributed_type == DistributedType.MULTI_GPU:
									for split_batch in [True, False]:
												for dispatch_batches in [True, False]:
															if not split_batch and not dispatch_batches:
																		continue
															if state.local_process_index == 0:
																		print(
																		    """**Test `accumulate` gradient accumulation with optimizer and scheduler, """      ,     f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""      ,     )
															test_gradient_accumulation_with_opt_and_scheduler(_lowercase      ,     _lowercase		)






def 						_snake_case       (	lowercase__		:  Any		)  ->				Dict:


			'''simple docstring'''





			main()


if __name__ == "__main__":
				main()







import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
					from PIL import Image
else:





					class 					lowercase__						:


											@staticmethod
											def        __A       (       *UpperCamelCase__	:				Union[str, Any]    ,		**UpperCamelCase__	:				Optional[Any]						):





																	'''simple docstring'''





																	pass





@is_pipeline_test
@require_vision
@require_torch
class 					lowercase__						(   unittest.TestCase):
						UpperCamelCase_     =		MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING


						def        __A       (       self	:				Union[str, Any]    ,		UpperCamelCase__	:				Tuple    ,		UpperCamelCase__	:				List[Any]    ,		UpperCamelCase__	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[Any]							=							pipeline(
												    '''zero-shot-object-detection'''    ,		model='''hf-internal-testing/tiny-random-owlvit-object-detection'''						)

												SCREAMING_SNAKE_CASE							:    List[str]							=							[
												    {
												        '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
												        '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
												    }
												]
												return object_detector, examples


						def        __A       (       self	:				Union[str, Any]    ,		UpperCamelCase__	:				Any    ,		UpperCamelCase__	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							object_detector(examples[0]    ,		threshold=0.0						)

												SCREAMING_SNAKE_CASE							:    Tuple							=							len(UpperCamelCase__						)
												self.assertGreater(UpperCamelCase__    ,		0						)
												self.assertEqual(
												    UpperCamelCase__    ,		[
												        {
												            '''score''': ANY(UpperCamelCase__						),
												            '''label''': ANY(UpperCamelCase__						),
												            '''box''': {'''xmin''': ANY(UpperCamelCase__						), '''ymin''': ANY(UpperCamelCase__						), '''xmax''': ANY(UpperCamelCase__						), '''ymax''': ANY(UpperCamelCase__						)},
												        }
												        for i in range(UpperCamelCase__						)
												    ]    ,		)


						@require_tf
						@unittest.skip('''Zero Shot Object Detection not implemented in TF'''						)
						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												pass


						@require_torch
						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							pipeline(
												    '''zero-shot-object-detection'''    ,		model='''hf-internal-testing/tiny-random-owlvit-object-detection'''						)

												SCREAMING_SNAKE_CASE							:    str							=							object_detector(
												    '''./tests/fixtures/tests_samples/COCO/000000039769.png'''    ,		candidate_labels=['''cat''', '''remote''', '''couch''']    ,		threshold=0.64    ,		)

												self.assertEqual(
												    nested_simplify(UpperCamelCase__    ,		decimals=4						)    ,		[
												        {'''score''': 0.7235, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
												        {'''score''': 0.7218, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
												        {'''score''': 0.7184, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
												        {'''score''': 0.6748, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
												        {'''score''': 0.6656, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
												        {'''score''': 0.6614, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
												        {'''score''': 0.6456, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
												        {'''score''': 0.642, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
												        {'''score''': 0.6419, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
												    ]    ,		)

												SCREAMING_SNAKE_CASE							:    str							=							object_detector(
												    [
												        {
												            '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
												            '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
												        }
												    ]    ,		threshold=0.64    ,		)

												self.assertEqual(
												    nested_simplify(UpperCamelCase__    ,		decimals=4						)    ,		[
												        [
												            {'''score''': 0.7235, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
												            {'''score''': 0.7218, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
												            {'''score''': 0.7184, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
												            {'''score''': 0.6748, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
												            {'''score''': 0.6656, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
												            {'''score''': 0.6614, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
												            {'''score''': 0.6456, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
												            {'''score''': 0.642, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
												            {'''score''': 0.6419, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
												        ]
												    ]    ,		)


						@require_torch
						@slow
						def        __A       (       self	:				Tuple						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							pipeline('''zero-shot-object-detection'''						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							object_detector(
												    '''http://images.cocodataset.org/val2017/000000039769.jpg'''    ,		candidate_labels=['''cat''', '''remote''', '''couch''']    ,		)
												self.assertEqual(
												    nested_simplify(UpperCamelCase__    ,		decimals=4						)    ,		[
												        {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
												        {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
												        {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
												        {'''score''': 0.1474, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
												        {'''score''': 0.1208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
												    ]    ,		)

												SCREAMING_SNAKE_CASE							:    int							=							object_detector(
												    [
												        {
												            '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
												            '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
												        },
												        {
												            '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
												            '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
												        },
												    ]    ,		)
												self.assertEqual(
												    nested_simplify(UpperCamelCase__    ,		decimals=4						)    ,		[
												        [
												            {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
												            {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
												            {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
												            {'''score''': 0.1474, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
												            {'''score''': 0.1208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
												        ],
												        [
												            {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
												            {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
												            {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
												            {'''score''': 0.1474, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
												            {'''score''': 0.1208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
												        ],
												    ]    ,		)


						@require_tf
						@unittest.skip('''Zero Shot Object Detection not implemented in TF'''						)
						def        __A       (       self	:				str						):





												'''simple docstring'''





												pass


						@require_torch
						@slow
						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							0.2
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							pipeline('''zero-shot-object-detection'''						)

												SCREAMING_SNAKE_CASE							:    Dict							=							object_detector(
												    '''http://images.cocodataset.org/val2017/000000039769.jpg'''    ,		candidate_labels=['''cat''', '''remote''', '''couch''']    ,		threshold=UpperCamelCase__    ,		)
												self.assertEqual(
												    nested_simplify(UpperCamelCase__    ,		decimals=4						)    ,		[
												        {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
												        {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
												        {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
												    ]    ,		)



						@require_torch
						@slow
						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							2
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							pipeline('''zero-shot-object-detection'''						)

												SCREAMING_SNAKE_CASE							:    List[str]							=							object_detector(
												    '''http://images.cocodataset.org/val2017/000000039769.jpg'''    ,		candidate_labels=['''cat''', '''remote''', '''couch''']    ,		top_k=UpperCamelCase__    ,		)
												self.assertEqual(
												    nested_simplify(UpperCamelCase__    ,		decimals=4						)    ,		[
												        {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
												        {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
												    ]    ,		)





