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       lowercase__		:


						def __init__( self			:       Any					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       List[Any]=13					,	UpperCamelCase__			:       Any=30					,	UpperCamelCase__			:       Optional[int]=2					,	UpperCamelCase__			:       Optional[int]=3					,	UpperCamelCase__			:       List[str]=True					,	UpperCamelCase__			:       str=True					,	UpperCamelCase__			:       Tuple=32					,	UpperCamelCase__			:       List[Any]=5					,	UpperCamelCase__			:       str=4					,	UpperCamelCase__			:       Any=37					,	UpperCamelCase__			:       str="gelu"					,	UpperCamelCase__			:       Any=0.1					,	UpperCamelCase__			:       int=0.1					,	UpperCamelCase__			:       Optional[int]=10					,	UpperCamelCase__			:       Optional[int]=0.02					,	UpperCamelCase__			:       Dict=None					,	):





												'''simple docstring'''





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

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


						def  __A ( self			:       Tuple		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

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

												return config, pixel_values, labels


						def  __A ( self			:       Optional[Any]		):





												'''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  __A ( self			:       List[str]					,	UpperCamelCase__			:       Any					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							ViTMSNModel(config=UpperCamelCase__		)
												model.to(UpperCamelCase__		)
												model.eval()
												SCREAMING_SNAKE_CASE							:					List[Any]           =							model(UpperCamelCase__		)
												self.parent.assertEqual(result.last_hidden_state.shape					,	(self.batch_size, self.seq_length, self.hidden_size)		)


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							self.type_sequence_label_size
												SCREAMING_SNAKE_CASE							:					List[Any]           =							ViTMSNForImageClassification(UpperCamelCase__		)
												model.to(UpperCamelCase__		)
												model.eval()
												SCREAMING_SNAKE_CASE							:					str           =							model(UpperCamelCase__					,	labels=UpperCamelCase__		)
												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							:					List[Any]           =							1
												SCREAMING_SNAKE_CASE							:					int           =							ViTMSNForImageClassification(UpperCamelCase__		)
												model.to(UpperCamelCase__		)
												model.eval()

												SCREAMING_SNAKE_CASE							:					Dict           =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							model(UpperCamelCase__		)
												self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.type_sequence_label_size)		)



						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





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






@require_torch
class       lowercase__		(     UpperCamelCase_			,							UpperCamelCase_			,							unittest.TestCase):
						UpperCamelCase_  =	(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
						UpperCamelCase_  =	(
						    {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
						    if is_torch_available()
						    else {}
						)

						UpperCamelCase_  =	False
						UpperCamelCase_  =	False
						UpperCamelCase_  =	False
						UpperCamelCase_  =	False


						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							ViTMSNModelTester(self		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							ConfigTester(self					,	config_class=UpperCamelCase__					,	has_text_modality=UpperCamelCase__					,	hidden_size=37		)


						def  __A ( self			:       Any		):





												'''simple docstring'''





												self.config_tester.run_common_tests()


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





												'''simple docstring'''





												pass


						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Any           =							self.model_tester.prepare_config_and_inputs_for_common()

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


						def  __A ( self			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					str           =							self.model_tester.prepare_config_and_inputs_for_common()

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

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


						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*UpperCamelCase__		)


						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__		)



						@slow
						def  __A ( self			:       Tuple		):





												'''simple docstring'''





												for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							ViTMSNModel.from_pretrained(UpperCamelCase__		)
																		self.assertIsNotNone(UpperCamelCase__		)






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





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


						@cached_property
						def  __A ( self			:       Tuple		):





												'''simple docstring'''





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



						@slow
						def  __A ( self			:       Dict		):





												'''simple docstring'''





												torch.manual_seed(2		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''		).to(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					int           =							self.default_image_processor
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							prepare_img()
												SCREAMING_SNAKE_CASE							:					Tuple           =							image_processor(images=UpperCamelCase__					,	return_tensors='''pt'''		).to(UpperCamelCase__		)

												# forward pass
												with torch.no_grad():
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							model(**UpperCamelCase__		)

												# verify the logits
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							torch.Size((1, 1000)		)
												self.assertEqual(outputs.logits.shape					,	UpperCamelCase__		)

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

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






import os

try:
					from .build_directory_md import good_file_paths
except ImportError:
					from build_directory_md import good_file_paths  # type: ignore

__UpperCamelCase	: Dict    		=  list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

__UpperCamelCase	: Tuple    		=  [file for file in filepaths if file != file.lower()]
if upper_files:
					print(f"""{len(upper_files)} files contain uppercase characters:""")
					print('\n'.join(upper_files) + '\n')

__UpperCamelCase	: List[Any]    		=  [file for file in filepaths if ' ' in file]
if space_files:
					print(f"""{len(space_files)} files contain space characters:""")
					print('\n'.join(space_files) + '\n')

__UpperCamelCase	: List[Any]    		=  [file for file in filepaths if '-' in file]
if hyphen_files:
					print(f"""{len(hyphen_files)} files contain hyphen characters:""")
					print('\n'.join(hyphen_files) + '\n')

__UpperCamelCase	: List[Any]    		=  [file for file in filepaths if os.sep not in file]
if nodir_files:
					print(f"""{len(nodir_files)} files are not in a directory:""")
					print('\n'.join(nodir_files) + '\n')

__UpperCamelCase	: Optional[Any]    		=  len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
					import sys

					sys.exit(bad_files)





