




import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
		from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import MobileNetVaImageProcessor




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     str:
							a    =			self.config_class(**self.inputs_dict							)
							self.parent.assertTrue(hasattr(__lowerCamelCase					,      "tf_padding"							)							)
							self.parent.assertTrue(hasattr(__lowerCamelCase					,      "depth_multiplier"							)							)




class     snake_case__							:
				"""simple docstring"""



				def __init__(      self				:     List[str]					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Dict=13					,      __lowerCamelCase				:     Optional[Any]=3					,      __lowerCamelCase				:     List[Any]=32					,      __lowerCamelCase				:     str=0.25					,      __lowerCamelCase				:     Union[str, Any]=8					,      __lowerCamelCase				:     Dict=8					,      __lowerCamelCase				:     str=6					,      __lowerCamelCase				:     List[Any]=32					,      __lowerCamelCase				:     Union[str, Any]=True					,      __lowerCamelCase				:     Dict=True					,      __lowerCamelCase				:     Optional[Any]=True					,      __lowerCamelCase				:     List[Any]="relu6"					,      __lowerCamelCase				:     int=12_80					,      __lowerCamelCase				:     List[Any]=0.1					,      __lowerCamelCase				:     Optional[Any]=0.02					,      __lowerCamelCase				:     Union[str, Any]=True					,      __lowerCamelCase				:     List[Any]=True					,      __lowerCamelCase				:     Tuple=10					,      __lowerCamelCase				:     List[Any]=None					,      )					->     str:
							a    =			parent
							a    =			batch_size
							a    =			num_channels
							a    =			image_size
							a    =			depth_multiplier
							a    =			depth_divisible_by
							a    =			min_depth
							a    =			expand_ratio
							a    =			tf_padding
							a    =			output_stride
							a    =			first_layer_is_expansion
							a    =			finegrained_output
							a    =			hidden_act
							a    =			last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier							)
							a    =			classifier_dropout_prob
							a    =			use_labels
							a    =			is_training
							a    =			num_labels
							a    =			initializer_range
							a    =			scope





				def 							__UpperCAmelCase							(      self				:     str							)					->     Tuple:
							a    =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

							a    =			None
							a    =			None
							if self.use_labels:
										a    =			ids_tensor([self.batch_size]					,      self.num_labels							)
										a    =			ids_tensor([self.batch_size, self.image_size, self.image_size]					,      self.num_labels							)

							a    =			self.get_config()

							return config, pixel_values, labels, pixel_labels





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Union[str, Any]:
							return MobileNetVaConfig(
							    num_channels=self.num_channels					,      image_size=self.image_size					,      depth_multiplier=self.depth_multiplier					,      depth_divisible_by=self.depth_divisible_by					,      min_depth=self.min_depth					,      expand_ratio=self.expand_ratio					,      output_stride=self.output_stride					,      first_layer_is_expansion=self.first_layer_is_expansion					,      finegrained_output=self.finegrained_output					,      hidden_act=self.hidden_act					,      tf_padding=self.tf_padding					,      classifier_dropout_prob=self.classifier_dropout_prob					,      initializer_range=self.initializer_range					,      )





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     Dict					,      __lowerCamelCase				:     Optional[int]							)					->     int:
							a    =			MobileNetVaModel(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			model(__lowerCamelCase							)
							self.parent.assertEqual(
							    result.last_hidden_state.shape					,      (
							        self.batch_size,
							        self.last_hidden_size,
							        self.image_size // self.output_stride,
							        self.image_size // self.output_stride,
							    )					,      )
							self.parent.assertEqual(
							    result.pooler_output.shape					,      (self.batch_size, self.last_hidden_size)					,      )





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Dict					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Dict							)					->     int:
							a    =			self.num_labels
							a    =			MobileNetVaForImageClassification(__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			model(__lowerCamelCase					,      labels=__lowerCamelCase							)
							self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_labels)							)





				def 							__UpperCAmelCase							(      self				:     str					,      __lowerCamelCase				:     Optional[Any]					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     str							)					->     int:
							a    =			self.num_labels
							a    =			MobileNetVaForSemanticSegmentation(__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			model(__lowerCamelCase							)
							self.parent.assertEqual(
							    result.logits.shape					,      (
							        self.batch_size,
							        self.num_labels,
							        self.image_size // self.output_stride,
							        self.image_size // self.output_stride,
							    )					,      )
							a    =			model(__lowerCamelCase					,      labels=__lowerCamelCase							)
							self.parent.assertEqual(
							    result.logits.shape					,      (
							        self.batch_size,
							        self.num_labels,
							        self.image_size // self.output_stride,
							        self.image_size // self.output_stride,
							    )					,      )





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     Tuple:
							a    =			self.prepare_config_and_inputs()
							a    ,		a    ,		a    ,		a    =			config_and_inputs
							a    =			{"pixel_values": pixel_values}
							return config, inputs_dict




@require_torch
class     snake_case__							(_UpperCamelCase    ,       _UpperCamelCase    ,       unittest.TestCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							List[str]							 =					(
				    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
				    if is_torch_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE_       :							Dict							 =					(
				    {
				        """feature-extraction""": MobileNetVaModel,
				        """image-classification""": MobileNetVaForImageClassification,
				        """image-segmentation""": MobileNetVaForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				SCREAMING_SNAKE_CASE_       :							int							 =					False
				SCREAMING_SNAKE_CASE_       :							Union[str, Any]							 =					False
				SCREAMING_SNAKE_CASE_       :							str							 =					False
				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					False





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Dict:
							a    =			MobileNetVaModelTester(self							)
							a    =			MobileNetVaConfigTester(self					,      config_class=__lowerCamelCase					,      has_text_modality=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     int:
							self.config_tester.run_common_tests()





				@unittest.skip(reason="MobileNetV2 does not use inputs_embeds"							)
				def 							__UpperCAmelCase							(      self				:     str							)					->     Dict:
							pass





				@unittest.skip(reason="MobileNetV2 does not support input and output embeddings"							)
				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     List[str]:
							pass





				@unittest.skip(reason="MobileNetV2 does not output attentions"							)
				def 							__UpperCAmelCase							(      self				:     int							)					->     Optional[Any]:
							pass





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     int:
							a    ,		a    =			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										a    =			model_class(__lowerCamelCase							)
										a    =			inspect.signature(model.forward							)
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										a    =			[*signature.parameters.keys()]

										a    =			["pixel_values"]
										self.assertListEqual(arg_names[:1]					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     Dict:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     Dict:
							def check_hidden_states_output(__lowerCamelCase				:     Optional[Any]					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Union[str, Any]							):
										a    =			model_class(__lowerCamelCase							)
										model.to(__lowerCamelCase							)
										model.eval()

										with torch.no_grad():
													a    =			model(**self._prepare_for_class(__lowerCamelCase					,      __lowerCamelCase							)							)

										a    =			outputs.hidden_states

										a    =			16
										self.assertEqual(len(__lowerCamelCase							)					,      __lowerCamelCase							)

							a    ,		a    =			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										a    =			True
										check_hidden_states_output(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase							)

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										a    =			True

										check_hidden_states_output(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Any							)					->     Tuple:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     str:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCamelCase							)





				@slow
				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     Optional[int]:
							for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										a    =			MobileNetVaModel.from_pretrained(__lowerCamelCase							)
										self.assertIsNotNone(__lowerCamelCase							)




def 			__magic_name__				( ):






			'''simple docstring'''







			a    =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
			return image






@require_torch
@require_vision
class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				@cached_property
				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     int:
							return (
							    MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v2_1.0_224"							) if is_vision_available() else None
							)





				@slow
				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Optional[int]:
							a    =			MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224"							).to(__lowerCamelCase							)

							a    =			self.default_image_processor
							a    =			prepare_img()
							a    =			image_processor(images=__lowerCamelCase					,      return_tensors="pt"							).to(__lowerCamelCase							)

							# forward pass
							with torch.no_grad():
										a    =			model(**__lowerCamelCase							)

							# verify the logits
							a    =			torch.Size((1, 10_01)							)
							self.assertEqual(outputs.logits.shape					,      __lowerCamelCase							)

							a    =			torch.tensor([0.2_445, -1.1_993, 0.1_905]							).to(__lowerCamelCase							)

							self.assertTrue(torch.allclose(outputs.logits[0, :3]					,      __lowerCamelCase					,      atol=1e-4							)							)





				@slow
				def 							__UpperCAmelCase							(      self				:     Any							)					->     Tuple:
							a    =			MobileNetVaForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"							)
							a    =			model.to(__lowerCamelCase							)

							a    =			MobileNetVaImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"							)

							a    =			prepare_img()
							a    =			image_processor(images=__lowerCamelCase					,      return_tensors="pt"							).to(__lowerCamelCase							)

							# forward pass
							with torch.no_grad():
										a    =			model(**__lowerCamelCase							)
							a    =			outputs.logits

							# verify the logits
							a    =			torch.Size((1, 21, 65, 65)							)
							self.assertEqual(logits.shape					,      __lowerCamelCase							)

							a    =			torch.tensor(
							    [
							        [[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
							        [[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
							        [[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
							    ]					,      device=__lowerCamelCase					,      )

							self.assertTrue(torch.allclose(logits[0, :3, :3, :3]					,      __lowerCamelCase					,      atol=1e-4							)							)










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


__lowerCAmelCase	: List[str]  				='true'


def        _UpperCamelCase							(      lowercase__    ,       lowercase__=82    ,       lowercase__=16      ):
						set_seed(42      )
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		RegressionModel()
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		deepcopy(lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Any					=		RegressionDataset(length=lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		DataLoader(lowercase__    ,       batch_size=lowercase__      )
						model.to(accelerator.device      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		accelerator.prepare(lowercase__    ,       lowercase__      )
						return model, ddp_model, dataloader


def        _UpperCamelCase							(      lowercase__    ,       lowercase__=False      ):
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''      )
						__SCREAMING_SNAKE_CASE						:     str					=		load_dataset('''glue'''    ,       '''mrpc'''    ,       split='''validation'''      )

						def tokenize_function(lowercase__      ):
												__SCREAMING_SNAKE_CASE						:     Dict					=		tokenizer(examples['''sentence1''']    ,       examples['''sentence2''']    ,       truncation=lowercase__    ,       max_length=lowercase__      )
												return outputs

						with accelerator.main_process_first():
												__SCREAMING_SNAKE_CASE						:     Tuple					=		dataset.map(
												    lowercase__    ,       batched=lowercase__    ,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']    ,       )

						__SCREAMING_SNAKE_CASE						:     List[Any]					=		tokenized_datasets.rename_column('''label'''    ,       '''labels'''      )

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

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


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


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     List[str]					=		[]
						for batch in dataloader:
												__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		batch.values()
												with torch.no_grad():
																		__SCREAMING_SNAKE_CASE						:     Dict					=		model(lowercase__      )
																		__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     str					=		accelerator.gather_for_metrics((logit, target)      )
																		logits_and_targets.append((logit, target)      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     str					=		[], []
						for logit, targ in logits_and_targets:
												logits.append(lowercase__      )
												targs.append(lowercase__      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Any					=		torch.cat(lowercase__      ), torch.cat(lowercase__      )
						return logits, targs


def        _UpperCamelCase							(      lowercase__    ,       lowercase__=82    ,       lowercase__=False    ,       lowercase__=False    ,       lowercase__=16      ):
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Tuple					=		get_basic_setup(lowercase__    ,       lowercase__    ,       lowercase__      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     List[str]					=		generate_predictions(lowercase__    ,       lowercase__    ,       lowercase__      )
						assert (
						    len(lowercase__      ) == num_samples
						), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowercase__      )}'''


def        _UpperCamelCase							(      lowercase__ = False    ,       lowercase__ = False      ):
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		evaluate.load('''glue'''    ,       '''mrpc'''      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     List[Any]					=		get_mrpc_setup(lowercase__    ,       lowercase__      )
						# First do baseline
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		setup['''no''']
						model.to(lowercase__      )
						model.eval()
						for batch in dataloader:
												batch.to(lowercase__      )
												with torch.inference_mode():
																		__SCREAMING_SNAKE_CASE						:     Dict					=		model(**lowercase__      )
												__SCREAMING_SNAKE_CASE						:     Dict					=		outputs.logits.argmax(dim=-1      )
												metric.add_batch(predictions=lowercase__    ,       references=batch['''labels''']      )
						__SCREAMING_SNAKE_CASE						:     int					=		metric.compute()

						# Then do distributed
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Dict					=		setup['''ddp''']
						model.eval()
						for batch in dataloader:
												with torch.inference_mode():
																		__SCREAMING_SNAKE_CASE						:     int					=		model(**lowercase__      )
												__SCREAMING_SNAKE_CASE						:     str					=		outputs.logits.argmax(dim=-1      )
												__SCREAMING_SNAKE_CASE						:     Any					=		batch['''labels''']
												__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Dict					=		accelerator.gather_for_metrics((preds, references)      )
												metric.add_batch(predictions=lowercase__    ,       references=lowercase__      )
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		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        _UpperCamelCase							(      ):
						__SCREAMING_SNAKE_CASE						:     Dict					=		Accelerator(split_batches=lowercase__    ,       dispatch_batches=lowercase__      )
						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(lowercase__    ,       lowercase__      )
																								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]:
																		__SCREAMING_SNAKE_CASE						:     List[Any]					=		Accelerator(split_batches=lowercase__    ,       dispatch_batches=lowercase__      )
																		if accelerator.is_local_main_process:
																								print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''      )
																		test_torch_metrics(lowercase__    ,       99      )
																		accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print('''**Test last batch is not dropped when perfectly divisible**'''      )
						__SCREAMING_SNAKE_CASE						:     Tuple					=		Accelerator()
						test_torch_metrics(lowercase__    ,       512      )
						accelerator.state._reset_state()


def        _UpperCamelCase							(      lowercase__      ):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
						main()
