
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     lowercase		:




							def __init__(    self       ,A__       ,A__=1_3       ,A__=3_0       ,A__=2       ,A__=3       ,A__=True       ,A__=True       ,A__=3_2       ,A__=5       ,A__=4       ,A__=3_7       ,A__="gelu"       ,A__=0.1       ,A__=0.1       ,A__=1_0       ,A__=0.02       ,A__=3       ,A__=0.6       ,A__=None       ,):
												lowercase						     =						parent
												lowercase						     =						batch_size
												lowercase						     =						image_size
												lowercase						     =						patch_size
												lowercase						     =						num_channels
												lowercase						     =						is_training
												lowercase						     =						use_labels
												lowercase						     =						hidden_size
												lowercase						     =						num_hidden_layers
												lowercase						     =						num_attention_heads
												lowercase						     =						intermediate_size
												lowercase						     =						hidden_act
												lowercase						     =						hidden_dropout_prob
												lowercase						     =						attention_probs_dropout_prob
												lowercase						     =						type_sequence_label_size
												lowercase						     =						initializer_range
												lowercase						     =						mask_ratio
												lowercase						     =						scope

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




							def 			A__				(    self):
												lowercase						     =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

												lowercase						     =						None
												if self.use_labels:
																	lowercase						     =						ids_tensor([self.batch_size]       ,self.type_sequence_label_size)

												lowercase						     =						self.get_config()

												return config, pixel_values, labels




							def 			A__				(    self):
												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=A__       ,initializer_range=self.initializer_range       ,mask_ratio=self.mask_ratio       ,)




							def 			A__				(    self       ,A__       ,A__       ,A__):
												lowercase						     =						ViTMAEModel(config=A__)
												model.to(A__)
												model.eval()
												lowercase						     =						model(A__)
												self.parent.assertEqual(result.last_hidden_state.shape       ,(self.batch_size, self.seq_length, self.hidden_size))




							def 			A__				(    self       ,A__       ,A__       ,A__):
												lowercase						     =						ViTMAEForPreTraining(A__)
												model.to(A__)
												model.eval()
												lowercase						     =						model(A__)
												lowercase						     =						(self.image_size // self.patch_size) ** 2
												lowercase						     =						self.patch_size**2 * self.num_channels
												self.parent.assertEqual(result.logits.shape       ,(self.batch_size, num_patches, expected_num_channels))

												# test greyscale images
												lowercase						     =						1
												lowercase						     =						ViTMAEForPreTraining(A__)
												model.to(A__)
												model.eval()
												lowercase						     =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
												lowercase						     =						model(A__)
												lowercase						     =						self.patch_size**2
												self.parent.assertEqual(result.logits.shape       ,(self.batch_size, num_patches, expected_num_channels))





							def 			A__				(    self):
												lowercase						     =						self.prepare_config_and_inputs()
												lowercase     ,						lowercase     ,						lowercase						     =						config_and_inputs
												lowercase						     =						{'''pixel_values''': pixel_values}
												return config, inputs_dict







@require_torch
class     lowercase		( SCREAMING_SNAKE_CASE__	,  SCREAMING_SNAKE_CASE__	,  unittest.TestCase		):
							lowercase_	:	Any	 =(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
							lowercase_	:	Optional[int]	 ={'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

							lowercase_	:	int	 =False
							lowercase_	:	List[Any]	 =False
							lowercase_	:	str	 =False
							lowercase_	:	int	 =False




							def 			A__				(    self):
												lowercase						     =						ViTMAEModelTester(self)
												lowercase						     =						ConfigTester(self       ,config_class=A__       ,has_text_modality=A__       ,hidden_size=3_7)




							def 			A__				(    self):
												self.config_tester.run_common_tests()




							@unittest.skip(reason='''ViTMAE does not use inputs_embeds''')
							def 			A__				(    self):
												pass




							def 			A__				(    self):
												lowercase     ,						lowercase						     =						self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	lowercase						     =						model_class(A__)
																	self.assertIsInstance(model.get_input_embeddings()       ,(nn.Module))
																	lowercase						     =						model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(A__       ,nn.Linear))




							def 			A__				(    self):
												lowercase     ,						lowercase						     =						self.model_tester.prepare_config_and_inputs_for_common()

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

																	lowercase						     =						['''pixel_values''']
																	self.assertListEqual(arg_names[:1]       ,A__)




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*A__)




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_pretraining(*A__)




							def 			A__				(    self       ,A__       ,A__       ,A__):
												# make masks reproducible
												np.random.seed(2)

												lowercase						     =						int((pt_model.config.image_size // pt_model.config.patch_size) ** 2)
												lowercase						     =						np.random.uniform(size=(self.model_tester.batch_size, num_patches))
												lowercase						     =						torch.from_numpy(A__)

												# Add `noise` argument.
												# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
												lowercase						     =						pt_noise

												super().check_pt_tf_models(A__       ,A__       ,A__)




							def 			A__				(    self):
												lowercase     ,						lowercase						     =						self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	lowercase						     =						model_class(A__)
																	model.to(A__)
																	model.eval()
																	# make random mask reproducible
																	torch.manual_seed(2)
																	with torch.no_grad():
																						lowercase						     =						model(**self._prepare_for_class(A__       ,A__))

																	lowercase						     =						outputs[0].cpu().numpy()
																	lowercase						     =						0

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						model.save_pretrained(A__)
																						lowercase						     =						model_class.from_pretrained(A__)
																						model.to(A__)
																						# make random mask reproducible
																						torch.manual_seed(2)
																						with torch.no_grad():
																											lowercase						     =						model(**self._prepare_for_class(A__       ,A__))

																						# Make sure we don't have nans
																						lowercase						     =						after_outputs[0].cpu().numpy()
																						lowercase						     =						0
																						lowercase						     =						np.amax(np.abs(out_a - out_a))
																						self.assertLessEqual(A__       ,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 			A__				(    self):
												pass




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




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




							@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load''')
							def 			A__				(    self):
												pass




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





							@slow
							def 			A__				(    self):
												for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase						     =						ViTMAEModel.from_pretrained(A__)
																	self.assertIsNotNone(A__)



def 	UpperCamelCase   (							):
					'''simple docstring'''
					lowercase						     =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
					return image







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




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





							@slow
							def 			A__				(    self):
												# make random mask reproducible across the PT and TF model
												np.random.seed(2)

												lowercase						     =						ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base''').to(A__)

												lowercase						     =						self.default_image_processor
												lowercase						     =						prepare_img()
												lowercase						     =						image_processor(images=A__       ,return_tensors='''pt''').to(A__)

												# 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)
												lowercase						     =						ViTMAEConfig()
												lowercase						     =						int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2)
												lowercase						     =						np.random.uniform(size=(1, num_patches))

												# forward pass
												with torch.no_grad():
																	lowercase						     =						model(**A__       ,noise=torch.from_numpy(A__).to(device=A__))

												# verify the logits
												lowercase						     =						torch.Size((1, 1_9_6, 7_6_8))
												self.assertEqual(outputs.logits.shape       ,A__)

												lowercase						     =						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(A__)       ,atol=1E-4))






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		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	int	 =ComputeEnvironment.AMAZON_SAGEMAKER
							lowercase_	:	Optional[int]	 =True
							lowercase_	:	Any	 ='''ml.p3.2xlarge'''
							lowercase_	:	Any	 ='''accelerate_sagemaker_execution_role'''
							lowercase_	:	Union[str, Any]	 ='''hf-sm'''
							lowercase_	:	Any	 ='''us-east-1'''
							lowercase_	:	List[str]	 =1
							lowercase_	:	Any	 ='''accelerate-sagemaker-1'''
							lowercase_	:	Union[str, Any]	 ='''1.6'''
							lowercase_	:	Any	 ='''4.4'''
							lowercase_	:	Any	 ='''train.py'''
							lowercase_	:	int	 =[
							    '''--model_name_or_path''',
							    '''bert''',
							    '''--do_train''',
							    '''False''',
							    '''--epochs''',
							    '''3''',
							    '''--learning_rate''',
							    '''5e-5''',
							    '''--max_steps''',
							    '''50.5''',
							]
							lowercase_	:	List[Any]	 =[
							    '''--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		):




							def 			A__				(    self):
												# If no defaults are changed, `to_kwargs` returns an empty dict.
												lowercase						     =						_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args)
												assert isinstance(converted_args['''model_name_or_path''']       ,A__)
												assert isinstance(converted_args['''do_train''']       ,A__)
												assert isinstance(converted_args['''epochs''']       ,A__)
												assert isinstance(converted_args['''learning_rate''']       ,A__)
												assert isinstance(converted_args['''max_steps''']       ,A__)

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




