

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 	UpperCAmelCase__		:






		"""simple docstring"""


		def __init__(							self						:		int     ,       __lowerCamelCase						:		List[str]     ,       __lowerCamelCase						:		Optional[Any]=13     ,       __lowerCamelCase						:		str=30     ,       __lowerCamelCase						:		str=2     ,       __lowerCamelCase						:		Union[str, Any]=3     ,       __lowerCamelCase						:		Optional[Any]=True     ,       __lowerCamelCase						:		int=True     ,       __lowerCamelCase						:		Union[str, Any]=32     ,       __lowerCamelCase						:		List[Any]=5     ,       __lowerCamelCase						:		Union[str, Any]=4     ,       __lowerCamelCase						:		int=37     ,       __lowerCamelCase						:		Any="gelu"     ,       __lowerCamelCase						:		Union[str, Any]=0.1     ,       __lowerCamelCase						:		str=0.1     ,       __lowerCamelCase						:		List[str]=10     ,       __lowerCamelCase						:		Dict=0.02     ,       __lowerCamelCase						:		Tuple=None     ,       )		->							int:
							SCREAMING_SNAKE_CASE__   =   parent
							SCREAMING_SNAKE_CASE__   =   batch_size
							SCREAMING_SNAKE_CASE__   =   image_size
							SCREAMING_SNAKE_CASE__   =   patch_size
							SCREAMING_SNAKE_CASE__   =   num_channels
							SCREAMING_SNAKE_CASE__   =   is_training
							SCREAMING_SNAKE_CASE__   =   use_labels
							SCREAMING_SNAKE_CASE__   =   hidden_size
							SCREAMING_SNAKE_CASE__   =   num_hidden_layers
							SCREAMING_SNAKE_CASE__   =   num_attention_heads
							SCREAMING_SNAKE_CASE__   =   intermediate_size
							SCREAMING_SNAKE_CASE__   =   hidden_act
							SCREAMING_SNAKE_CASE__   =   hidden_dropout_prob
							SCREAMING_SNAKE_CASE__   =   attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE__   =   type_sequence_label_size
							SCREAMING_SNAKE_CASE__   =   initializer_range
							SCREAMING_SNAKE_CASE__   =   scope

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






		def 				lowercase_		(							self						:		Any )		->							Optional[Any]:
							SCREAMING_SNAKE_CASE__   =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

							SCREAMING_SNAKE_CASE__   =   self.get_config()

							return config, pixel_values, labels






		def 				lowercase_		(							self						:		Optional[int] )		->							int:
							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 				lowercase_		(							self						:		Tuple     ,       __lowerCamelCase						:		Any     ,       __lowerCamelCase						:		List[Any]     ,       __lowerCamelCase						:		int )		->							Union[str, Any]:
							SCREAMING_SNAKE_CASE__   =   ViTMSNModel(config=_a )
							model.to(_a )
							model.eval()
							SCREAMING_SNAKE_CASE__   =   model(_a )
							self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, self.seq_length, self.hidden_size) )






		def 				lowercase_		(							self						:		List[Any]     ,       __lowerCamelCase						:		str     ,       __lowerCamelCase						:		Tuple     ,       __lowerCamelCase						:		Dict )		->							Optional[int]:
							SCREAMING_SNAKE_CASE__   =   self.type_sequence_label_size
							SCREAMING_SNAKE_CASE__   =   ViTMSNForImageClassification(_a )
							model.to(_a )
							model.eval()
							SCREAMING_SNAKE_CASE__   =   model(_a     ,       labels=_a )
							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__   =   1
							SCREAMING_SNAKE_CASE__   =   ViTMSNForImageClassification(_a )
							model.to(_a )
							model.eval()

							SCREAMING_SNAKE_CASE__   =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
							SCREAMING_SNAKE_CASE__   =   model(_a )
							self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.type_sequence_label_size) )







		def 				lowercase_		(							self						:		Any )		->							List[Any]:
							SCREAMING_SNAKE_CASE__   =   self.prepare_config_and_inputs()
							SCREAMING_SNAKE_CASE__   =   config_and_inputs
							SCREAMING_SNAKE_CASE__   =   {'pixel_values': pixel_values}
							return config, inputs_dict






@require_torch
class 	UpperCAmelCase__		(	lowercase__						,					lowercase__						,					unittest.TestCase	):






		"""simple docstring"""


		a									= (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
		a									= (
		    {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
		    if is_torch_available()
		    else {}
		)

		a									= False
		a									= False
		a									= False
		a									= False






		def 				lowercase_		(							self						:		Optional[int] )		->							Optional[Any]:
							SCREAMING_SNAKE_CASE__   =   ViTMSNModelTester(self )
							SCREAMING_SNAKE_CASE__   =   ConfigTester(self     ,       config_class=_a     ,       has_text_modality=_a     ,       hidden_size=37 )






		def 				lowercase_		(							self						:		str )		->							int:
							self.config_tester.run_common_tests()






		@unittest.skip(reason='''ViTMSN does not use inputs_embeds''' )
		def 				lowercase_		(							self						:		List[str] )		->							Dict:
							pass






		def 				lowercase_		(							self						:		Union[str, Any] )		->							str:
							SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs_for_common()

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






		def 				lowercase_		(							self						:		Any )		->							int:
							SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs_for_common()

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

												SCREAMING_SNAKE_CASE__   =   ['pixel_values']
												self.assertListEqual(arg_names[:1]     ,       _a )






		def 				lowercase_		(							self						:		List[str] )		->							Dict:
							SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_a )






		def 				lowercase_		(							self						:		Optional[Any] )		->							Tuple:
							SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_a )







		@slow
		def 				lowercase_		(							self						:		int )		->							str:






							for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												SCREAMING_SNAKE_CASE__   =   ViTMSNModel.from_pretrained(_a )
												self.assertIsNotNone(_a )



def     UpperCAmelCase_       (						):





					'''simple docstring'''




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







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






		"""simple docstring"""


		@cached_property
		def 				lowercase_		(							self						:		Union[str, Any] )		->							Any:
							return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small''' ) if is_vision_available() else None







		@slow
		def 				lowercase_		(							self						:		Union[str, Any] )		->							int:
							torch.manual_seed(2 )
							SCREAMING_SNAKE_CASE__   =   ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small''' ).to(_a )

							SCREAMING_SNAKE_CASE__   =   self.default_image_processor
							SCREAMING_SNAKE_CASE__   =   prepare_img()
							SCREAMING_SNAKE_CASE__   =   image_processor(images=_a     ,       return_tensors='''pt''' ).to(_a )

							# forward pass
							with torch.no_grad():
												SCREAMING_SNAKE_CASE__   =   model(**_a )

							# verify the logits
							SCREAMING_SNAKE_CASE__   =   torch.Size((1, 1000) )
							self.assertEqual(outputs.logits.shape     ,       _a )

							SCREAMING_SNAKE_CASE__   =   torch.tensor([-0.0803, -0.4454, -0.2375] ).to(_a )

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





from argparse import ArgumentParser

from .env import EnvironmentCommand
def     UpperCAmelCase_       (						):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   ArgumentParser('''Diffusers CLI tool'''				,		usage='''diffusers-cli <command> [<args>]'''    )
     SCREAMING_SNAKE_CASE__   =   parser.add_subparsers(help='''diffusers-cli command helpers'''    )

     # Register commands
     EnvironmentCommand.register_subcommand(_A    )

     # Let's go
     SCREAMING_SNAKE_CASE__   =   parser.parse_args()

     if not hasattr(_A				,		'''func'''    ):
          parser.print_help()
          exit(1    )

     # Run
     SCREAMING_SNAKE_CASE__   =   args.func(_A    )
     service.run()


if __name__ == "__main__":
       main()


