
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import GLPNImageProcessor





class       _lowerCAmelCase   (      unittest.TestCase			):




				def __init__(   self							,							_UpperCamelCase							,							_UpperCamelCase=7							,							_UpperCamelCase=3							,							_UpperCamelCase=18							,							_UpperCamelCase=30							,							_UpperCamelCase=400							,							_UpperCamelCase=True							,							_UpperCamelCase=32							,							_UpperCamelCase=True							,							)							-> Tuple:
							lowerCAmelCase_         =      parent
							lowerCAmelCase_         =      batch_size
							lowerCAmelCase_         =      num_channels
							lowerCAmelCase_         =      image_size
							lowerCAmelCase_         =      min_resolution
							lowerCAmelCase_         =      max_resolution
							lowerCAmelCase_         =      do_resize
							lowerCAmelCase_         =      size_divisor
							lowerCAmelCase_         =      do_rescale




				def 	__a		(   self	)							-> Tuple:
							return {
							    "do_resize": self.do_resize,
							    "size_divisor": self.size_divisor,
							    "do_rescale": self.do_rescale,
							}





@require_torch
@require_vision
class       _lowerCAmelCase   (      __a   , unittest.TestCase			):
				_lowercase     						=GLPNImageProcessor if is_vision_available() else None




				def 	__a		(   self	)							-> List[str]:
							lowerCAmelCase_         =      GLPNImageProcessingTester(self	)




				@property
				def 	__a		(   self	)							-> Union[str, Any]:
							return self.image_processor_tester.prepare_image_processor_dict()




				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      self.image_processing_class(**self.image_processor_dict	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"do_resize"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"size_divisor"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"resample"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"do_rescale"	)	)




				def 	__a		(   self	)							-> Tuple:
							pass




				def 	__a		(   self	)							-> List[Any]:
							# Initialize image_processing
							lowerCAmelCase_         =      self.image_processing_class(**self.image_processor_dict	)
							# create random PIL images
							lowerCAmelCase_         =      prepare_image_inputs(self.image_processor_tester							,							equal_resolution=_UpperCamelCase	)
							for image in image_inputs:
										self.assertIsInstance(_UpperCamelCase							,							Image.Image	)

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							lowerCAmelCase_         =      image_processing(image_inputs[0]							,							return_tensors="pt"	).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0	)
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0	)




				def 	__a		(   self	)							-> Optional[Any]:
							# Initialize image_processing
							lowerCAmelCase_         =      self.image_processing_class(**self.image_processor_dict	)
							# create random numpy tensors
							lowerCAmelCase_         =      prepare_image_inputs(self.image_processor_tester							,							equal_resolution=_UpperCamelCase							,							numpify=_UpperCamelCase	)
							for image in image_inputs:
										self.assertIsInstance(_UpperCamelCase							,							np.ndarray	)

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							lowerCAmelCase_         =      image_processing(image_inputs[0]							,							return_tensors="pt"	).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0	)
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0	)




				def 	__a		(   self	)							-> Union[str, Any]:
							# Initialize image_processing
							lowerCAmelCase_         =      self.image_processing_class(**self.image_processor_dict	)
							# create random PyTorch tensors
							lowerCAmelCase_         =      prepare_image_inputs(self.image_processor_tester							,							equal_resolution=_UpperCamelCase							,							torchify=_UpperCamelCase	)
							for image in image_inputs:
										self.assertIsInstance(_UpperCamelCase							,							torch.Tensor	)

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							lowerCAmelCase_         =      image_processing(image_inputs[0]							,							return_tensors="pt"	).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0	)
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0	)





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

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




				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

							lowerCAmelCase_         =      self.get_config()

							return config, pixel_values, labels




				def 	__a		(   self	)							-> Optional[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 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> str:
							lowerCAmelCase_         =      ViTMSNModel(config=_UpperCamelCase	)
							model.to(_UpperCamelCase	)
							model.eval()
							lowerCAmelCase_         =      model(_UpperCamelCase	)
							self.parent.assertEqual(result.last_hidden_state.shape							,							(self.batch_size, self.seq_length, self.hidden_size)	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> str:
							lowerCAmelCase_         =      self.type_sequence_label_size
							lowerCAmelCase_         =      ViTMSNForImageClassification(_UpperCamelCase	)
							model.to(_UpperCamelCase	)
							model.eval()
							lowerCAmelCase_         =      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_         =      1
							lowerCAmelCase_         =      ViTMSNForImageClassification(_UpperCamelCase	)
							model.to(_UpperCamelCase	)
							model.eval()

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




				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      self.prepare_config_and_inputs()
							lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      config_and_inputs
							lowerCAmelCase_         =      {"pixel_values": pixel_values}
							return config, inputs_dict





@require_torch
class       _lowerCAmelCase   (      __a   , __a   , unittest.TestCase			):
				_lowercase     						=(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
				_lowercase     						=(
				    {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
				    if is_torch_available()
				    else {}
				)

				_lowercase     						=False
				_lowercase     						=False
				_lowercase     						=False
				_lowercase     						=False




				def 	__a		(   self	)							-> Optional[int]:
							lowerCAmelCase_         =      ViTMSNModelTester(self	)
							lowerCAmelCase_         =      ConfigTester(self							,							config_class=_UpperCamelCase							,							has_text_modality=_UpperCamelCase							,							hidden_size=37	)




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




				@unittest.skip(reason="ViTMSN does not use inputs_embeds"	)
				def 	__a		(   self	)							-> str:
							pass




				def 	__a		(   self	)							-> Any:
							lowerCAmelCase_      ,    lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs_for_common()

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




				def 	__a		(   self	)							-> Optional[Any]:
							lowerCAmelCase_      ,    lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs_for_common()

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

										lowerCAmelCase_         =      ["pixel_values"]
										self.assertListEqual(arg_names[:1]							,							_UpperCamelCase	)




				def 	__a		(   self	)							-> str:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCamelCase	)




				def 	__a		(   self	)							-> Any:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase	)




				@slow
				def 	__a		(   self	)							-> Union[str, Any]:
							for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase_         =      ViTMSNModel.from_pretrained(_UpperCamelCase	)
										self.assertIsNotNone(_UpperCamelCase	)






def   lowerCamelCase__							(							):




			"""simple docstring"""


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





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




				@cached_property
				def 	__a		(   self	)							-> List[str]:
							return ViTImageProcessor.from_pretrained("facebook/vit-msn-small"	) if is_vision_available() else None




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

							lowerCAmelCase_         =      self.default_image_processor
							lowerCAmelCase_         =      prepare_img()
							lowerCAmelCase_         =      image_processor(images=_UpperCamelCase							,							return_tensors="pt"	).to(_UpperCamelCase	)

							# forward pass
							with torch.no_grad():
										lowerCAmelCase_         =      model(**_UpperCamelCase	)

							# verify the logits
							lowerCAmelCase_         =      torch.Size((1, 1_000)	)
							self.assertEqual(outputs.logits.shape							,							_UpperCamelCase	)

							lowerCAmelCase_         =      torch.tensor([-0.0803, -0.4454, -0.2375]	).to(_UpperCamelCase	)

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



