

"""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     snake_case	:

				def __init__(	self							:				Tuple     ,  UpperCamelCase__							:				Tuple     ,  UpperCamelCase__							:				int=1_3     ,  UpperCamelCase__							:				Union[str, Any]=3_0     ,  UpperCamelCase__							:				int=2     ,  UpperCamelCase__							:				Dict=3     ,  UpperCamelCase__							:				Optional[Any]=True     ,  UpperCamelCase__							:				int=True     ,  UpperCamelCase__							:				Optional[Any]=3_2     ,  UpperCamelCase__							:				List[str]=5     ,  UpperCamelCase__							:				int=4     ,  UpperCamelCase__							:				Optional[int]=3_7     ,  UpperCamelCase__							:				int="gelu"     ,  UpperCamelCase__							:				Optional[Any]=0.1     ,  UpperCamelCase__							:				List[str]=0.1     ,  UpperCamelCase__							:				Optional[Any]=1_0     ,  UpperCamelCase__							:				Optional[int]=0.02     ,  UpperCamelCase__							:				Optional[int]=None     ,  )->   Tuple:



						'''simple docstring'''


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

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

				def 							lowercase_     (	self							:				str)->   Optional[Any]:



						'''simple docstring'''


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

						__lowerCAmelCase:       str          =			None
						if self.use_labels:
								__lowerCAmelCase:       int          =			ids_tensor([self.batch_size]     ,  self.type_sequence_label_size)

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

						return config, pixel_values, labels

				def 							lowercase_     (	self							:				List[str])->   List[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 							lowercase_     (	self							:				int     ,  UpperCamelCase__							:				Optional[Any]     ,  UpperCamelCase__							:				Tuple     ,  UpperCamelCase__							:				List[str])->   List[Any]:



						'''simple docstring'''


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

				def 							lowercase_     (	self							:				int     ,  UpperCamelCase__							:				List[Any]     ,  UpperCamelCase__							:				Tuple     ,  UpperCamelCase__							:				List[str])->   int:



						'''simple docstring'''


						__lowerCAmelCase:       Tuple          =			self.type_sequence_label_size
						__lowerCAmelCase:       Union[str, Any]          =			ViTMSNForImageClassification(UpperCamelCase__)
						model.to(UpperCamelCase__)
						model.eval()
						__lowerCAmelCase:       Dict          =			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
						__lowerCAmelCase:       str          =			1
						__lowerCAmelCase:       int          =			ViTMSNForImageClassification(UpperCamelCase__)
						model.to(UpperCamelCase__)
						model.eval()

						__lowerCAmelCase:       Optional[int]          =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
						__lowerCAmelCase:       str          =			model(UpperCamelCase__)
						self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.type_sequence_label_size))




				def 							lowercase_     (	self							:				List[Any])->   List[str]:



						'''simple docstring'''


						__lowerCAmelCase:       Tuple          =			self.prepare_config_and_inputs()
						__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase:       Optional[Any]          =			config_and_inputs
						__lowerCAmelCase:       Optional[Any]          =			{"pixel_values": pixel_values}
						return config, inputs_dict





@require_torch
class     snake_case	(				__snake_case,							__snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_   :      List[Any]         =  (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
				SCREAMING_SNAKE_CASE_   :      Optional[Any]         =  (
				    {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
				    if is_torch_available()
				    else {}
				)

				SCREAMING_SNAKE_CASE_   :      Optional[int]         =  False
				SCREAMING_SNAKE_CASE_   :      List[str]         =  False
				SCREAMING_SNAKE_CASE_   :      Optional[Any]         =  False
				SCREAMING_SNAKE_CASE_   :      Any         =  False

				def 							lowercase_     (	self							:				List[Any])->   Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			ViTMSNModelTester(self)
						__lowerCAmelCase:       int          =			ConfigTester(self     ,  config_class=UpperCamelCase__     ,  has_text_modality=UpperCamelCase__     ,  hidden_size=3_7)

				def 							lowercase_     (	self							:				List[Any])->   List[Any]:



						'''simple docstring'''


						self.config_tester.run_common_tests()

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



						'''simple docstring'''


						pass

				def 							lowercase_     (	self							:				Optional[int])->   List[str]:



						'''simple docstring'''


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

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

				def 							lowercase_     (	self							:				int)->   Union[str, Any]:



						'''simple docstring'''


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

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

								__lowerCAmelCase:       str          =			["pixel_values"]
								self.assertListEqual(arg_names[:1]     ,  UpperCamelCase__)

				def 							lowercase_     (	self							:				Union[str, Any])->   str:



						'''simple docstring'''


						__lowerCAmelCase:       List[str]          =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*UpperCamelCase__)

				def 							lowercase_     (	self							:				Dict)->   Dict:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__)




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



						'''simple docstring'''


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






def     a__    (    )	->	List[Any]:
		__lowerCAmelCase:       Dict          =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
		return image





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

				@cached_property
				def 							lowercase_     (	self							:				str)->   Any:



						'''simple docstring'''


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




				@slow
				def 							lowercase_     (	self							:				Union[str, Any])->   Any:



						'''simple docstring'''


						torch.manual_seed(2)
						__lowerCAmelCase:       Optional[Any]          =			ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small").to(UpperCamelCase__)

						__lowerCAmelCase:       Tuple          =			self.default_image_processor
						__lowerCAmelCase:       Optional[Any]          =			prepare_img()
						__lowerCAmelCase:       Tuple          =			image_processor(images=UpperCamelCase__     ,  return_tensors="pt").to(UpperCamelCase__)

						# forward pass
						with torch.no_grad():
								__lowerCAmelCase:       Any          =			model(**UpperCamelCase__)

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

						__lowerCAmelCase:       Tuple          =			torch.tensor([-0.0803, -0.4454, -0.2375]).to(UpperCamelCase__)

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





"""simple docstring"""


import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union





@dataclass
class     snake_case	:
				SCREAMING_SNAKE_CASE_   :      Optional[Union[str, Path]]         =  None
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      Optional[Dict]         =  None
				SCREAMING_SNAKE_CASE_   :      Optional[str]         =  None
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      bool         =  True
				SCREAMING_SNAKE_CASE_   :      Optional[int]         =  None
				SCREAMING_SNAKE_CASE_   :      int         =  1
				SCREAMING_SNAKE_CASE_   :      Optional[Union[str, bool]]         =  None
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      Optional[Dict]         =  None
				SCREAMING_SNAKE_CASE_   :      Optional[str]         =  None

				def 							lowercase_     (	self							:				str)->   "DownloadConfig":



						'''simple docstring'''


						return self.__class__(**{k: copy.deepcopy(UpperCamelCase__) for k, v in self.__dict__.items()})


