





import inspect
import unittest

from transformers import ViTMSNConfig
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 ViTMSNForImageClassification, ViTMSNModel
							from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import ViTImageProcessor





class 				a			:






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase=13			, _lowerCAmelCase=30			, _lowerCAmelCase=2			, _lowerCAmelCase=3			, _lowerCAmelCase=True			, _lowerCAmelCase=True			, _lowerCAmelCase=32			, _lowerCAmelCase=5			, _lowerCAmelCase=4			, _lowerCAmelCase=37			, _lowerCAmelCase="gelu"			, _lowerCAmelCase=0.1			, _lowerCAmelCase=0.1			, _lowerCAmelCase=10			, _lowerCAmelCase=0.02			, _lowerCAmelCase=None			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Dict           =   parent
										__SCREAMING_SNAKE_CASE:				List[Any]           =   batch_size
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   image_size
										__SCREAMING_SNAKE_CASE:				Tuple           =   patch_size
										__SCREAMING_SNAKE_CASE:				str           =   num_channels
										__SCREAMING_SNAKE_CASE:				int           =   is_training
										__SCREAMING_SNAKE_CASE:				List[Any]           =   use_labels
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   hidden_size
										__SCREAMING_SNAKE_CASE:				str           =   num_hidden_layers
										__SCREAMING_SNAKE_CASE:				int           =   num_attention_heads
										__SCREAMING_SNAKE_CASE:				List[str]           =   intermediate_size
										__SCREAMING_SNAKE_CASE:				str           =   hidden_act
										__SCREAMING_SNAKE_CASE:				str           =   hidden_dropout_prob
										__SCREAMING_SNAKE_CASE:				List[str]           =   attention_probs_dropout_prob
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   type_sequence_label_size
										__SCREAMING_SNAKE_CASE:				List[str]           =   initializer_range
										__SCREAMING_SNAKE_CASE:				List[str]           =   scope

										# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   (image_size // patch_size) ** 2
										__SCREAMING_SNAKE_CASE:				Any           =   num_patches + 1






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[str]           =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

										__SCREAMING_SNAKE_CASE:				int           =   self.get_config()

										return config, pixel_values, labels






				def 			snake_case_    (  self					):

										"""simple docstring"""




										return ViTMSNConfig(
										    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			, initializer_range=self.initializer_range			, )






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Tuple           =   ViTMSNModel(config=_lowerCAmelCase					)
										model.to(_lowerCAmelCase					)
										model.eval()
										__SCREAMING_SNAKE_CASE:				Any           =   model(_lowerCAmelCase					)
										self.parent.assertEqual(result.last_hidden_state.shape			, (self.batch_size, self.seq_length, self.hidden_size)					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				int           =   self.type_sequence_label_size
										__SCREAMING_SNAKE_CASE:				List[Any]           =   ViTMSNForImageClassification(_lowerCAmelCase					)
										model.to(_lowerCAmelCase					)
										model.eval()
										__SCREAMING_SNAKE_CASE:				Dict           =   model(_lowerCAmelCase			, labels=_lowerCAmelCase					)
										print('''Pixel and labels shape: {pixel_values.shape}, {labels.shape}'''					)
										print('''Labels: {labels}'''					)
										self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.type_sequence_label_size)					)

										# test greyscale images
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   1
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   ViTMSNForImageClassification(_lowerCAmelCase					)
										model.to(_lowerCAmelCase					)
										model.eval()

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
										__SCREAMING_SNAKE_CASE:				int           =   model(_lowerCAmelCase					)
										self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.type_sequence_label_size)					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   self.prepare_config_and_inputs()
										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				List[Any]           =   config_and_inputs
										__SCREAMING_SNAKE_CASE:				int           =   {'''pixel_values''': pixel_values}
										return config, inputs_dict





@require_torch
class 				a			(      __lowercase   ,__lowercase   ,unittest.TestCase			):
				SCREAMING_SNAKE_CASE__     :   List[str]		      =     (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
				SCREAMING_SNAKE_CASE__     :   List[str]		      =     (
				    {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
				    if is_torch_available()
				    else {}
				)

				SCREAMING_SNAKE_CASE__     :   int		      =     False
				SCREAMING_SNAKE_CASE__     :   Tuple		      =     False
				SCREAMING_SNAKE_CASE__     :   Dict		      =     False
				SCREAMING_SNAKE_CASE__     :   Optional[Any]		      =     False






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   ViTMSNModelTester(self					)
										__SCREAMING_SNAKE_CASE:				str           =   ConfigTester(self			, config_class=_lowerCAmelCase			, has_text_modality=_lowerCAmelCase			, hidden_size=37					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										self.config_tester.run_common_tests()






				@unittest.skip(reason='''ViTMSN does not use inputs_embeds'''					)
				def 			snake_case_    (  self					):

										"""simple docstring"""




										pass






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																__SCREAMING_SNAKE_CASE:				List[Any]           =   model_class(_lowerCAmelCase					)
																self.assertIsInstance(model.get_input_embeddings()			, (nn.Module)					)
																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(_lowerCAmelCase			, nn.Linear					)					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				List[str]           =   self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																__SCREAMING_SNAKE_CASE:				int           =   model_class(_lowerCAmelCase					)
																__SCREAMING_SNAKE_CASE:				Dict           =   inspect.signature(model.forward					)
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   [*signature.parameters.keys()]

																__SCREAMING_SNAKE_CASE:				Optional[int]           =   ['''pixel_values''']
																self.assertListEqual(arg_names[:1]			, _lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Tuple           =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase					)






				@slow
				def 			snake_case_    (  self					):

										"""simple docstring"""




										for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__SCREAMING_SNAKE_CASE:				Tuple           =   ViTMSNModel.from_pretrained(_lowerCAmelCase					)
																self.assertIsNotNone(_lowerCAmelCase					)






def 		lowerCAmelCase  (	)      ->    Any:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Any           =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
						return image





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






				@cached_property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small'''					) if is_vision_available() else None






				@slow
				def 			snake_case_    (  self					):

										"""simple docstring"""




										torch.manual_seed(2					)
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''					).to(_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				str           =   self.default_image_processor
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   prepare_img()
										__SCREAMING_SNAKE_CASE:				int           =   image_processor(images=_lowerCAmelCase			, return_tensors='''pt'''					).to(_lowerCAmelCase					)

										# forward pass
										with torch.no_grad():
																__SCREAMING_SNAKE_CASE:				Any           =   model(**_lowerCAmelCase					)

										# verify the logits
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   torch.Size((1, 1000)					)
										self.assertEqual(outputs.logits.shape			, _lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				List[Any]           =   torch.tensor([-0.0803, -0.4454, -0.2375]					).to(_lowerCAmelCase					)

										self.assertTrue(torch.allclose(outputs.logits[0, :3]			, _lowerCAmelCase			, 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 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   Any		      =     ComputeEnvironment.AMAZON_SAGEMAKER
				SCREAMING_SNAKE_CASE__     :   Optional[int]		      =     True
				SCREAMING_SNAKE_CASE__     :   int		      =     '''ml.p3.2xlarge'''
				SCREAMING_SNAKE_CASE__     :   Union[str, Any]		      =     '''accelerate_sagemaker_execution_role'''
				SCREAMING_SNAKE_CASE__     :   List[Any]		      =     '''hf-sm'''
				SCREAMING_SNAKE_CASE__     :   Any		      =     '''us-east-1'''
				SCREAMING_SNAKE_CASE__     :   Any		      =     1
				SCREAMING_SNAKE_CASE__     :   Dict		      =     '''accelerate-sagemaker-1'''
				SCREAMING_SNAKE_CASE__     :   Dict		      =     '''1.6'''
				SCREAMING_SNAKE_CASE__     :   int		      =     '''4.4'''
				SCREAMING_SNAKE_CASE__     :   int		      =     '''train.py'''
				SCREAMING_SNAKE_CASE__     :   Tuple		      =     [
				    '''--model_name_or_path''',
				    '''bert''',
				    '''--do_train''',
				    '''False''',
				    '''--epochs''',
				    '''3''',
				    '''--learning_rate''',
				    '''5e-5''',
				    '''--max_steps''',
				    '''50.5''',
				]
				SCREAMING_SNAKE_CASE__     :   List[str]		      =     [
				    '''--model_name_or_path''',
				    '''bert''',
				    '''--do_train''',
				    '''--do_test''',
				    '''False''',
				    '''--do_predict''',
				    '''--epochs''',
				    '''3''',
				    '''--learning_rate''',
				    '''5e-5''',
				    '''--max_steps''',
				    '''50.5''',
				]





class 				a			(      unittest.TestCase			):






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args					)
										assert isinstance(converted_args['''model_name_or_path''']			, _lowerCAmelCase					)
										assert isinstance(converted_args['''do_train''']			, _lowerCAmelCase					)
										assert isinstance(converted_args['''epochs''']			, _lowerCAmelCase					)
										assert isinstance(converted_args['''learning_rate''']			, _lowerCAmelCase					)
										assert isinstance(converted_args['''max_steps''']			, _lowerCAmelCase					)

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



