

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		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      Any       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=1_3       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=3_0       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=2       ,SCREAMING_SNAKE_CASE__		:      List[str]=3       ,SCREAMING_SNAKE_CASE__		:      int=True       ,SCREAMING_SNAKE_CASE__		:      str=True       ,SCREAMING_SNAKE_CASE__		:      List[Any]=3_2       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=5       ,SCREAMING_SNAKE_CASE__		:      List[Any]=4       ,SCREAMING_SNAKE_CASE__		:      Dict=3_7       ,SCREAMING_SNAKE_CASE__		:      Dict="gelu"       ,SCREAMING_SNAKE_CASE__		:      List[str]=0.1       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=0.1       ,SCREAMING_SNAKE_CASE__		:      int=1_0       ,SCREAMING_SNAKE_CASE__		:      List[Any]=0.02       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=None       ,):
								__lowerCamelCase    :    str 	=			parent
								__lowerCamelCase    :    Tuple 	=			batch_size
								__lowerCamelCase    :    List[str] 	=			image_size
								__lowerCamelCase    :    Tuple 	=			patch_size
								__lowerCamelCase    :    int 	=			num_channels
								__lowerCamelCase    :    List[Any] 	=			is_training
								__lowerCamelCase    :    Optional[int] 	=			use_labels
								__lowerCamelCase    :    int 	=			hidden_size
								__lowerCamelCase    :    List[Any] 	=			num_hidden_layers
								__lowerCamelCase    :    List[str] 	=			num_attention_heads
								__lowerCamelCase    :    Tuple 	=			intermediate_size
								__lowerCamelCase    :    Optional[int] 	=			hidden_act
								__lowerCamelCase    :    Any 	=			hidden_dropout_prob
								__lowerCamelCase    :    int 	=			attention_probs_dropout_prob
								__lowerCamelCase    :    Any 	=			type_sequence_label_size
								__lowerCamelCase    :    List[Any] 	=			initializer_range
								__lowerCamelCase    :    List[str] 	=			scope

								# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								__lowerCamelCase    :    Optional[Any] 	=			(image_size // patch_size) ** 2
								__lowerCamelCase    :    Any 	=			num_patches + 1
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    str 	=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

								__lowerCamelCase    :    Dict 	=			self.get_config()

								return config, pixel_values, labels
							def    lowerCAmelCase					(		self		:      List[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    lowerCAmelCase					(		self		:      str       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Optional[int]):
								__lowerCamelCase    :    str 	=			ViTMSNModel(config=SCREAMING_SNAKE_CASE__)
								model.to(SCREAMING_SNAKE_CASE__)
								model.eval()
								__lowerCamelCase    :    List[str] 	=			model(SCREAMING_SNAKE_CASE__)
								self.parent.assertEqual(result.last_hidden_state.shape       ,(self.batch_size, self.seq_length, self.hidden_size))
							def    lowerCAmelCase					(		self		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      Optional[int]):
								__lowerCamelCase    :    Optional[int] 	=			self.type_sequence_label_size
								__lowerCamelCase    :    Optional[Any] 	=			ViTMSNForImageClassification(SCREAMING_SNAKE_CASE__)
								model.to(SCREAMING_SNAKE_CASE__)
								model.eval()
								__lowerCamelCase    :    Optional[Any] 	=			model(SCREAMING_SNAKE_CASE__       ,labels=SCREAMING_SNAKE_CASE__)
								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
								__lowerCamelCase    :    int 	=			1
								__lowerCamelCase    :    Union[str, Any] 	=			ViTMSNForImageClassification(SCREAMING_SNAKE_CASE__)
								model.to(SCREAMING_SNAKE_CASE__)
								model.eval()

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


							def    lowerCAmelCase					(		self		:      List[str]):
								__lowerCamelCase    :    Tuple 	=			self.prepare_config_and_inputs()
								__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Optional[int] 	=			config_and_inputs
								__lowerCamelCase    :    int 	=			{'pixel_values': pixel_values}
								return config, inputs_dict






@require_torch
class 	A_		(	SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							unittest.TestCase    ):
							_UpperCAmelCase   :  Dict										=				(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
							_UpperCAmelCase   :  Any										=				(
							    {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
							    if is_torch_available()
							    else {}
							)

							_UpperCAmelCase   :  List[Any]										=				False
							_UpperCAmelCase   :  List[str]										=				False
							_UpperCAmelCase   :  str										=				False
							_UpperCAmelCase   :  Optional[int]										=				False
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								__lowerCamelCase    :    Optional[Any] 	=			ViTMSNModelTester(self)
								__lowerCamelCase    :    Optional[int] 	=			ConfigTester(self       ,config_class=SCREAMING_SNAKE_CASE__       ,has_text_modality=SCREAMING_SNAKE_CASE__       ,hidden_size=3_7)
							def    lowerCAmelCase					(		self		:      List[str]):
								self.config_tester.run_common_tests()
							@unittest.skip(reason='ViTMSN does not use inputs_embeds')
							def    lowerCAmelCase					(		self		:      int):
								pass
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase	,			__lowerCamelCase    :    List[Any] 	=			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									__lowerCamelCase    :    Union[str, Any] 	=			model_class(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(model.get_input_embeddings()       ,(nn.Module))
									__lowerCamelCase    :    Any 	=			model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__       ,nn.Linear))
							def    lowerCAmelCase					(		self		:      Dict):
								__lowerCamelCase	,			__lowerCamelCase    :    Dict 	=			self.model_tester.prepare_config_and_inputs_for_common()

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

									__lowerCamelCase    :    int 	=			['pixel_values']
									self.assertListEqual(arg_names[:1]       ,SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    List[Any] 	=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Optional[Any] 	=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__)


							@slow
							def    lowerCAmelCase					(		self		:      Tuple):
								for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__lowerCamelCase    :    List[Any] 	=			ViTMSNModel.from_pretrained(SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)






def        SCREAMING_SNAKE_CASE__	(  )  ->					Optional[Any]:
	__lowerCamelCase    :    Optional[int] 	=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
	return image






@require_torch
@require_vision
class 	A_		(	unittest.TestCase    ):
							@cached_property
							def    lowerCAmelCase					(		self		:      Dict):
								return ViTImageProcessor.from_pretrained('facebook/vit-msn-small') if is_vision_available() else None


							@slow
							def    lowerCAmelCase					(		self		:      Optional[int]):
								torch.manual_seed(2)
								__lowerCamelCase    :    Optional[int] 	=			ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small').to(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    List[str] 	=			self.default_image_processor
								__lowerCamelCase    :    str 	=			prepare_img()
								__lowerCamelCase    :    Any 	=			image_processor(images=SCREAMING_SNAKE_CASE__       ,return_tensors='pt').to(SCREAMING_SNAKE_CASE__)

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

								# verify the logits
								__lowerCamelCase    :    Any 	=			torch.Size((1, 1_0_0_0))
								self.assertEqual(outputs.logits.shape       ,SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    str 	=			torch.tensor([-0.0803, -0.4454, -0.2375]).to(SCREAMING_SNAKE_CASE__)

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







# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


a		=open  # noqa: we just need to have a builtin inside this module to test it properly




