





"""simple docstring"""





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        _lowerCamelCase      :


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

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


						def     snake_case_   (self		) ->   List[str]:
								UpperCamelCase   =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

								UpperCamelCase   =	self.get_config()

								return config, pixel_values, labels


						def     snake_case_   (self		) ->   Union[str, Any]:
								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   ,							__a   ,							__a   ,							__a		) ->   Union[str, Any]:
								UpperCamelCase   =	ViTMSNModel(config=__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase   =	model(__a		)
								self.parent.assertEqual(result.last_hidden_state.shape   ,							(self.batch_size, self.seq_length, self.hidden_size)		)


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a		) ->   List[Any]:
								UpperCamelCase   =	self.type_sequence_label_size
								UpperCamelCase   =	ViTMSNForImageClassification(__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase   =	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
								UpperCamelCase   =	1
								UpperCamelCase   =	ViTMSNForImageClassification(__a		)
								model.to(__a		)
								model.eval()

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




						def     snake_case_   (self		) ->   Dict:
								UpperCamelCase   =	self.prepare_config_and_inputs()
								UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	config_and_inputs
								UpperCamelCase   =	{"pixel_values": pixel_values}
								return config, inputs_dict





@require_torch
class        _lowerCamelCase      (       _lowercase  ,			_lowercase  ,			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     snake_case_   (self		) ->   int:
								UpperCamelCase   =	ViTMSNModelTester(self		)
								UpperCamelCase   =	ConfigTester(self   ,							config_class=__a   ,							has_text_modality=__a   ,							hidden_size=37		)


						def     snake_case_   (self		) ->   Optional[int]:
								self.config_tester.run_common_tests()


						@unittest.skip(reason="ViTMSN does not use inputs_embeds"		)
						def     snake_case_   (self		) ->   List[Any]:
								pass


						def     snake_case_   (self		) ->   str:
								UpperCamelCase   ,				UpperCamelCase   =	self.model_tester.prepare_config_and_inputs_for_common()

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


						def     snake_case_   (self		) ->   Tuple:
								UpperCamelCase   ,				UpperCamelCase   =	self.model_tester.prepare_config_and_inputs_for_common()

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

										UpperCamelCase   =	["pixel_values"]
										self.assertListEqual(arg_names[:1]   ,							__a		)


						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__a		)


						def     snake_case_   (self		) ->   Optional[Any]:
								UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*__a		)




						@slow
						def     snake_case_   (self		) ->   Optional[int]:
								for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										UpperCamelCase   =	ViTMSNModel.from_pretrained(__a		)
										self.assertIsNotNone(__a		)



def       a__					(					):




		"""simple docstring"""


		UpperCamelCase   =	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
		return image






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


						@cached_property
						def     snake_case_   (self		) ->   Any:
								return ViTImageProcessor.from_pretrained("facebook/vit-msn-small"		) if is_vision_available() else None




						@slow
						def     snake_case_   (self		) ->   int:
								torch.manual_seed(2		)
								UpperCamelCase   =	ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"		).to(__a		)

								UpperCamelCase   =	self.default_image_processor
								UpperCamelCase   =	prepare_img()
								UpperCamelCase   =	image_processor(images=__a   ,							return_tensors="pt"		).to(__a		)

								# forward pass
								with torch.no_grad():
										UpperCamelCase   =	model(**__a		)

								# verify the logits
								UpperCamelCase   =	torch.Size((1, 10_00)		)
								self.assertEqual(outputs.logits.shape   ,							__a		)

								UpperCamelCase   =	torch.tensor([-0.0803, -0.4454, -0.2375]		).to(__a		)

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













"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCAmelCase__     			=			{'''tokenization_bertweet''': ['''BertweetTokenizer''']}


if TYPE_CHECKING:
						from .tokenization_bertweet import BertweetTokenizer

else:
						import sys

						lowerCAmelCase__     			=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






