



import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
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 torch import nn

						from transformers import ViTMAEForPreTraining, ViTMAEModel
						from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor



class 				__snake_case						:




							def __init__(       self     :       int				,				A_     :       str				,				A_     :       Any=1_3				,				A_     :       Dict=3_0				,				A_     :       Tuple=2				,				A_     :       Tuple=3				,				A_     :       str=True				,				A_     :       int=True				,				A_     :       str=3_2				,				A_     :       Tuple=5				,				A_     :       Dict=4				,				A_     :       Tuple=3_7				,				A_     :       Any="gelu"				,				A_     :       str=0.1				,				A_     :       Tuple=0.1				,				A_     :       Optional[int]=1_0				,				A_     :       List[Any]=0.02				,				A_     :       Optional[int]=3				,				A_     :       Optional[int]=0.6				,				A_     :       int=None				,				):
									lowerCAmelCase_			:     int									= parent
									lowerCAmelCase_			:     List[str]									= batch_size
									lowerCAmelCase_			:     Optional[int]									= image_size
									lowerCAmelCase_			:     List[str]									= patch_size
									lowerCAmelCase_			:     int									= num_channels
									lowerCAmelCase_			:     Tuple									= is_training
									lowerCAmelCase_			:     Optional[int]									= use_labels
									lowerCAmelCase_			:     Optional[Any]									= hidden_size
									lowerCAmelCase_			:     List[str]									= num_hidden_layers
									lowerCAmelCase_			:     Optional[int]									= num_attention_heads
									lowerCAmelCase_			:     Optional[Any]									= intermediate_size
									lowerCAmelCase_			:     Tuple									= hidden_act
									lowerCAmelCase_			:     str									= hidden_dropout_prob
									lowerCAmelCase_			:     List[str]									= attention_probs_dropout_prob
									lowerCAmelCase_			:     Optional[Any]									= type_sequence_label_size
									lowerCAmelCase_			:     Tuple									= initializer_range
									lowerCAmelCase_			:     int									= mask_ratio
									lowerCAmelCase_			:     Optional[int]									= scope

									# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
									# (we add 1 for the [CLS] token)
									lowerCAmelCase_			:     Union[str, Any]									= (image_size // patch_size) ** 2
									lowerCAmelCase_			:     List[str]									= int(math.ceil((1 - mask_ratio) * (num_patches + 1)))




							def    UpperCAmelCase__			(       self     :       List[Any]):
									lowerCAmelCase_			:     int									= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

									lowerCAmelCase_			:     Optional[Any]									= None
									if self.use_labels:
											lowerCAmelCase_			:     Optional[Any]									= ids_tensor([self.batch_size]				,				self.type_sequence_label_size)

									lowerCAmelCase_			:     Optional[int]									= self.get_config()

									return config, pixel_values, labels




							def    UpperCAmelCase__			(       self     :       List[str]):
									return ViTMAEConfig(
									    image_size=self.image_size				,				patch_size=self.patch_size				,				num_channels=self.num_channels				,				hidden_size=self.hidden_size				,				num_hidden_layers=self.num_hidden_layers				,				num_attention_heads=self.num_attention_heads				,				intermediate_size=self.intermediate_size				,				hidden_act=self.hidden_act				,				hidden_dropout_prob=self.hidden_dropout_prob				,				attention_probs_dropout_prob=self.attention_probs_dropout_prob				,				is_decoder=UpperCamelCase_				,				initializer_range=self.initializer_range				,				mask_ratio=self.mask_ratio				,				)




							def    UpperCAmelCase__			(       self     :       int				,				A_     :       Any				,				A_     :       List[Any]				,				A_     :       List[Any]):
									lowerCAmelCase_			:     List[Any]									= ViTMAEModel(config=UpperCamelCase_)
									model.to(UpperCamelCase_)
									model.eval()
									lowerCAmelCase_			:     Tuple									= model(UpperCamelCase_)
									self.parent.assertEqual(result.last_hidden_state.shape				,				(self.batch_size, self.seq_length, self.hidden_size))




							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       Tuple				,				A_     :       Tuple				,				A_     :       int):
									lowerCAmelCase_			:     Optional[Any]									= ViTMAEForPreTraining(UpperCamelCase_)
									model.to(UpperCamelCase_)
									model.eval()
									lowerCAmelCase_			:     Optional[Any]									= model(UpperCamelCase_)
									lowerCAmelCase_			:     str									= (self.image_size // self.patch_size) ** 2
									lowerCAmelCase_			:     List[Any]									= self.patch_size**2 * self.num_channels
									self.parent.assertEqual(result.logits.shape				,				(self.batch_size, num_patches, expected_num_channels))

									# test greyscale images
									lowerCAmelCase_			:     str									= 1
									lowerCAmelCase_			:     Dict									= ViTMAEForPreTraining(UpperCamelCase_)
									model.to(UpperCamelCase_)
									model.eval()
									lowerCAmelCase_			:     Optional[int]									= floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
									lowerCAmelCase_			:     str									= model(UpperCamelCase_)
									lowerCAmelCase_			:     List[str]									= self.patch_size**2
									self.parent.assertEqual(result.logits.shape				,				(self.batch_size, num_patches, expected_num_channels))







							def    UpperCAmelCase__			(       self     :       Optional[Any]):
									lowerCAmelCase_			:     Tuple									= self.prepare_config_and_inputs()
									lowerCAmelCase_			:     int									= config_and_inputs
									lowerCAmelCase_			:     str									= {'''pixel_values''': pixel_values}
									return config, inputs_dict





@require_torch
class 				__snake_case						(     A__						,A__						,unittest.TestCase						):
							_a			      = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
							_a			      = {'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

							_a			      = False
							_a			      = False
							_a			      = False
							_a			      = False




							def    UpperCAmelCase__			(       self     :       List[Any]):
									lowerCAmelCase_			:     Dict									= ViTMAEModelTester(self)
									lowerCAmelCase_			:     Optional[int]									= ConfigTester(self				,				config_class=UpperCamelCase_				,				has_text_modality=UpperCamelCase_				,				hidden_size=3_7)




							def    UpperCAmelCase__			(       self     :       Optional[Any]):
									self.config_tester.run_common_tests()




							@unittest.skip(reason='''ViTMAE does not use inputs_embeds''')
							def    UpperCAmelCase__			(       self     :       Optional[Any]):
									pass




							def    UpperCAmelCase__			(       self     :       List[str]):
									lowerCAmelCase_			:     Any									= self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											lowerCAmelCase_			:     Tuple									= model_class(UpperCamelCase_)
											self.assertIsInstance(model.get_input_embeddings()				,				(nn.Module))
											lowerCAmelCase_			:     Optional[int]									= model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(UpperCamelCase_				,				nn.Linear))




							def    UpperCAmelCase__			(       self     :       str):
									lowerCAmelCase_			:     Union[str, Any]									= self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											lowerCAmelCase_			:     Optional[int]									= model_class(UpperCamelCase_)
											lowerCAmelCase_			:     List[str]									= inspect.signature(model.forward)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											lowerCAmelCase_			:     Tuple									= [*signature.parameters.keys()]

											lowerCAmelCase_			:     Dict									= ['''pixel_values''']
											self.assertListEqual(arg_names[:1]				,				UpperCamelCase_)




							def    UpperCAmelCase__			(       self     :       Dict):
									lowerCAmelCase_			:     List[Any]									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*UpperCamelCase_)




							def    UpperCAmelCase__			(       self     :       Union[str, Any]):
									lowerCAmelCase_			:     Dict									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_pretraining(*UpperCamelCase_)




							def    UpperCAmelCase__			(       self     :       Optional[Any]				,				A_     :       Dict				,				A_     :       Optional[int]				,				A_     :       List[str]):
									np.random.seed(2)

									lowerCAmelCase_			:     Optional[int]									= int((pt_model.config.image_size // pt_model.config.patch_size) ** 2)
									lowerCAmelCase_			:     List[Any]									= np.random.uniform(size=(self.model_tester.batch_size, num_patches))
									lowerCAmelCase_			:     int									= torch.from_numpy(UpperCamelCase_)

									# Add `noise` argument.
									# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
									lowerCAmelCase_			:     List[Any]									= pt_noise

									super().check_pt_tf_models(UpperCamelCase_				,				UpperCamelCase_				,				UpperCamelCase_)




							def    UpperCAmelCase__			(       self     :       List[str]):
									lowerCAmelCase_			:     List[str]									= self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											lowerCAmelCase_			:     List[str]									= model_class(UpperCamelCase_)
											model.to(UpperCamelCase_)
											model.eval()
											# make random mask reproducible
											torch.manual_seed(2)
											with torch.no_grad():
													lowerCAmelCase_			:     List[str]									= model(**self._prepare_for_class(UpperCamelCase_				,				UpperCamelCase_))

											lowerCAmelCase_			:     Optional[Any]									= outputs[0].cpu().numpy()
											lowerCAmelCase_			:     Union[str, Any]									= 0

											with tempfile.TemporaryDirectory() as tmpdirname:
													model.save_pretrained(UpperCamelCase_)
													lowerCAmelCase_			:     List[str]									= model_class.from_pretrained(UpperCamelCase_)
													model.to(UpperCamelCase_)
													# make random mask reproducible
													torch.manual_seed(2)
													with torch.no_grad():
															lowerCAmelCase_			:     Any									= model(**self._prepare_for_class(UpperCamelCase_				,				UpperCamelCase_))

													# Make sure we don't have nans
													lowerCAmelCase_			:     int									= after_outputs[0].cpu().numpy()
													lowerCAmelCase_			:     Optional[int]									= 0
													lowerCAmelCase_			:     List[Any]									= np.amax(np.abs(out_a - out_a))
													self.assertLessEqual(UpperCamelCase_				,				1e-5)




							@unittest.skip(
							    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.''')
							def    UpperCAmelCase__			(       self     :       Any):
									pass




							@unittest.skip(
							    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.''')
							def    UpperCAmelCase__			(       self     :       Optional[Any]):
									pass




							@unittest.skip(
							    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.''')
							def    UpperCAmelCase__			(       self     :       Dict):
									pass




							@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load''')
							def    UpperCAmelCase__			(       self     :       Union[str, Any]):
									pass




							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
							def    UpperCAmelCase__			(       self     :       int):
									pass







							@slow
							def    UpperCAmelCase__			(       self     :       str):


									for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											lowerCAmelCase_			:     Any									= ViTMAEModel.from_pretrained(UpperCamelCase_)
											self.assertIsNotNone(UpperCamelCase_)





def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     int									= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
		return image





@require_torch
@require_vision
class 				__snake_case						(     unittest.TestCase						):




							@cached_property
							def    UpperCAmelCase__			(       self     :       Dict):
									return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base''') if is_vision_available() else None







							@slow
							def    UpperCAmelCase__			(       self     :       Dict):
									np.random.seed(2)

									lowerCAmelCase_			:     Tuple									= ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base''').to(UpperCamelCase_)

									lowerCAmelCase_			:     Tuple									= self.default_image_processor
									lowerCAmelCase_			:     Dict									= prepare_img()
									lowerCAmelCase_			:     Tuple									= image_processor(images=UpperCamelCase_				,				return_tensors='''pt''').to(UpperCamelCase_)

									# prepare a noise vector that will be also used for testing the TF model
									# (this way we can ensure that the PT and TF models operate on the same inputs)
									lowerCAmelCase_			:     Optional[int]									= ViTMAEConfig()
									lowerCAmelCase_			:     List[str]									= int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2)
									lowerCAmelCase_			:     Optional[Any]									= np.random.uniform(size=(1, num_patches))

									# forward pass
									with torch.no_grad():
											lowerCAmelCase_			:     Dict									= model(**UpperCamelCase_				,				noise=torch.from_numpy(UpperCamelCase_).to(device=UpperCamelCase_))

									# verify the logits
									lowerCAmelCase_			:     str									= torch.Size((1, 1_9_6, 7_6_8))
									self.assertEqual(outputs.logits.shape				,				UpperCamelCase_)

									lowerCAmelCase_			:     Dict									= torch.tensor(
									    [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]])

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









'''simple docstring'''
import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				ComputeEnvironment.AMAZON_SAGEMAKER
		_a     =				True
		_a     =				'ml.p3.2xlarge'
		_a     =				'accelerate_sagemaker_execution_role'
		_a     =				'hf-sm'
		_a     =				'us-east-1'
		_a     =				1
		_a     =				'accelerate-sagemaker-1'
		_a     =				'1.6'
		_a     =				'4.4'
		_a     =				'train.py'
		_a     =				[
		    '--model_name_or_path',
		    'bert',
		    '--do_train',
		    'False',
		    '--epochs',
		    '3',
		    '--learning_rate',
		    '5e-5',
		    '--max_steps',
		    '50.5',
		]
		_a     =				[
		    '--model_name_or_path',
		    'bert',
		    '--do_train',
		    '--do_test',
		    'False',
		    '--do_predict',
		    '--epochs',
		    '3',
		    '--learning_rate',
		    '5e-5',
		    '--max_steps',
		    '50.5',
		]



class    lowercase	(							unittest.TestCase					):

		"""simple docstring"""

		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Union[str, Any]												= _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args							)
							assert isinstance(converted_args['''model_name_or_path''']    ,   UpperCamelCase_							)
							assert isinstance(converted_args['''do_train''']    ,   UpperCamelCase_							)
							assert isinstance(converted_args['''epochs''']    ,   UpperCamelCase_							)
							assert isinstance(converted_args['''learning_rate''']    ,   UpperCamelCase_							)
							assert isinstance(converted_args['''max_steps''']    ,   UpperCamelCase_							)

							with pytest.raises(UpperCamelCase_							):
												_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args							)