


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      :






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

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






		def _snake_case		(						self				:     Dict):
				SCREAMING_SNAKE_CASE_				:int				     =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

				SCREAMING_SNAKE_CASE_				:Tuple				     =						None
				if self.use_labels:
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						ids_tensor([self.batch_size]   ,  self.type_sequence_label_size)

				SCREAMING_SNAKE_CASE_				:str				     =						self.get_config()

				return config, pixel_values, labels






		def _snake_case		(						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 _snake_case		(						self				:     Dict   ,  UpperCAmelCase				:     List[str]   ,  UpperCAmelCase				:     Any   ,  UpperCAmelCase				:     Any):
				SCREAMING_SNAKE_CASE_				:Any				     =						ViTMSNModel(config=UpperCAmelCase)
				model.to(UpperCAmelCase)
				model.eval()
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						model(UpperCAmelCase)
				self.parent.assertEqual(result.last_hidden_state.shape   ,  (self.batch_size, self.seq_length, self.hidden_size))






		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     Optional[Any]   ,  UpperCAmelCase				:     Dict   ,  UpperCAmelCase				:     str):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						self.type_sequence_label_size
				SCREAMING_SNAKE_CASE_				:List[str]				     =						ViTMSNForImageClassification(UpperCAmelCase)
				model.to(UpperCAmelCase)
				model.eval()
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						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
				SCREAMING_SNAKE_CASE_				:int				     =						1
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						ViTMSNForImageClassification(UpperCAmelCase)
				model.to(UpperCAmelCase)
				model.eval()

				SCREAMING_SNAKE_CASE_				:Dict				     =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						model(UpperCAmelCase)
				self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.type_sequence_label_size))







		def _snake_case		(						self				:     Optional[int]):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:List[Any]				     =						config_and_inputs
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						{"pixel_values": pixel_values}
				return config, inputs_dict




@require_torch
class 						_UpperCAmelCase      (     lowercase		,		lowercase		,		unittest.TestCase							):
		lowerCamelCase_	:			str       			=  (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
		lowerCamelCase_	:			List[Any]       			=  (
		    {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
		    if is_torch_available()
		    else {}
		)

		lowerCamelCase_	:			int       			=  False
		lowerCamelCase_	:			List[str]       			=  False
		lowerCamelCase_	:			Optional[int]       			=  False
		lowerCamelCase_	:			List[str]       			=  False






		def _snake_case		(						self				:     str):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						ViTMSNModelTester(self)
				SCREAMING_SNAKE_CASE_				:Any				     =						ConfigTester(self   ,  config_class=UpperCAmelCase   ,  has_text_modality=UpperCAmelCase   ,  hidden_size=37)






		def _snake_case		(						self				:     Dict):
				self.config_tester.run_common_tests()






		@unittest.skip(reason="ViTMSN does not use inputs_embeds")
		def _snake_case		(						self				:     Optional[int]):
				pass






		def _snake_case		(						self				:     int):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Tuple				     =						self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						SCREAMING_SNAKE_CASE_				:Dict				     =						model_class(UpperCAmelCase)
						self.assertIsInstance(model.get_input_embeddings()   ,  (nn.Module))
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						model.get_output_embeddings()
						self.assertTrue(x is None or isinstance(UpperCAmelCase   ,  nn.Linear))






		def _snake_case		(						self				:     Any):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						model_class(UpperCAmelCase)
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						inspect.signature(model.forward)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						SCREAMING_SNAKE_CASE_				:int				     =						[*signature.parameters.keys()]

						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						["pixel_values"]
						self.assertListEqual(arg_names[:1]   ,  UpperCAmelCase)






		def _snake_case		(						self				:     Tuple):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*UpperCAmelCase)






		def _snake_case		(						self				:     Optional[Any]):
				SCREAMING_SNAKE_CASE_				:str				     =						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase)







		@slow
		def _snake_case		(						self				:     List[Any]):
				for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						SCREAMING_SNAKE_CASE_				:Any				     =						ViTMSNModel.from_pretrained(UpperCAmelCase)
						self.assertIsNotNone(UpperCAmelCase)




def     lowercase	(    ):



		'''simple docstring'''




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



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






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







		@slow
		def _snake_case		(						self				:     str):
				torch.manual_seed(2)
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small").to(UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:str				     =						self.default_image_processor
				SCREAMING_SNAKE_CASE_				:List[str]				     =						prepare_img()
				SCREAMING_SNAKE_CASE_				:List[str]				     =						image_processor(images=UpperCAmelCase   ,  return_tensors="pt").to(UpperCAmelCase)

				# forward pass
				with torch.no_grad():
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						model(**UpperCAmelCase)

				# verify the logits
				SCREAMING_SNAKE_CASE_				:Any				     =						torch.Size((1, 10_00))
				self.assertEqual(outputs.logits.shape   ,  UpperCAmelCase)

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

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










from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


SCREAMING_SNAKE_CASE__     					=			logging.get_logger(__name__)


def     lowercase	(    a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Dict				     =						b.T
		SCREAMING_SNAKE_CASE_				:str				     =						np.sum(np.square(a )     , axis=1 )
		SCREAMING_SNAKE_CASE_				:List[Any]				     =						np.sum(np.square(a )     , axis=0 )
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						np.matmul(a     , a )
		SCREAMING_SNAKE_CASE_				:Any				     =						aa[:, None] - 2 * ab + ba[None, :]
		return d


def     lowercase	(    a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:str				     =						x.reshape(-1     , 3 )
		SCREAMING_SNAKE_CASE_				:str				     =						squared_euclidean_distance(a     , a )
		return np.argmin(a     , axis=1 )



class 						_UpperCAmelCase      (     lowercase							):
		lowerCamelCase_	:			List[str]       			=  ["""pixel_values"""]






		def __init__(						self				:     Dict   ,  UpperCAmelCase				:     Optional[Union[List[List[int]], np.ndarray]] = None   ,  UpperCAmelCase				:     bool = True   ,  UpperCAmelCase				:     Dict[str, int] = None   ,  UpperCAmelCase				:     PILImageResampling = PILImageResampling.BILINEAR   ,  UpperCAmelCase				:     bool = True   ,  UpperCAmelCase				:     bool = True   ,  **UpperCAmelCase				:     Union[str, Any]   ,  ):
				super().__init__(**UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						size if size is not None else {"height": 2_56, "width": 2_56}
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						get_size_dict(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:int				     =						np.array(UpperCAmelCase) if clusters is not None else None
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						do_resize
				SCREAMING_SNAKE_CASE_				:Dict				     =						size
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						resample
				SCREAMING_SNAKE_CASE_				:Dict				     =						do_normalize
				SCREAMING_SNAKE_CASE_				:Tuple				     =						do_color_quantize






		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     np.ndarray   ,  UpperCAmelCase				:     Dict[str, int]   ,  UpperCAmelCase				:     PILImageResampling = PILImageResampling.BILINEAR   ,  UpperCAmelCase				:     Optional[Union[str, ChannelDimension]] = None   ,  **UpperCAmelCase				:     List[Any]   ,  ):
				SCREAMING_SNAKE_CASE_				:str				     =						get_size_dict(UpperCAmelCase)
				if "height" not in size or "width" not in size:
						raise ValueError(F"Size dictionary must contain both height and width keys. Got {size.keys()}")
				return resize(
				    UpperCAmelCase   ,  size=(size["height"], size["width"])   ,  resample=UpperCAmelCase   ,  data_format=UpperCAmelCase   ,  **UpperCAmelCase)






		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     np.ndarray   ,  UpperCAmelCase				:     Optional[Union[str, ChannelDimension]] = None   ,  ):
				SCREAMING_SNAKE_CASE_				:int				     =						rescale(image=UpperCAmelCase   ,  scale=1 / 127.5   ,  data_format=UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:List[str]				     =						image - 1
				return image







		def _snake_case		(						self				:     List[str]   ,  UpperCAmelCase				:     ImageInput   ,  UpperCAmelCase				:     bool = None   ,  UpperCAmelCase				:     Dict[str, int] = None   ,  UpperCAmelCase				:     PILImageResampling = None   ,  UpperCAmelCase				:     bool = None   ,  UpperCAmelCase				:     Optional[bool] = None   ,  UpperCAmelCase				:     Optional[Union[List[List[int]], np.ndarray]] = None   ,  UpperCAmelCase				:     Optional[Union[str, TensorType]] = None   ,  UpperCAmelCase				:     Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST   ,  **UpperCAmelCase				:     Optional[Any]   ,  ):
				SCREAMING_SNAKE_CASE_				:Any				     =						do_resize if do_resize is not None else self.do_resize
				SCREAMING_SNAKE_CASE_				:Dict				     =						size if size is not None else self.size
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						get_size_dict(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						resample if resample is not None else self.resample
				SCREAMING_SNAKE_CASE_				:Dict				     =						do_normalize if do_normalize is not None else self.do_normalize
				SCREAMING_SNAKE_CASE_				:List[str]				     =						do_color_quantize if do_color_quantize is not None else self.do_color_quantize
				SCREAMING_SNAKE_CASE_				:Tuple				     =						clusters if clusters is not None else self.clusters
				SCREAMING_SNAKE_CASE_				:Tuple				     =						np.array(UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:Dict				     =						make_list_of_images(UpperCAmelCase)

				if not valid_images(UpperCAmelCase):
						raise ValueError(
						    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
						    "torch.Tensor, tf.Tensor or jax.ndarray.")

				if do_resize and size is None or resample is None:
						raise ValueError("Size and resample must be specified if do_resize is True.")

				if do_color_quantize and clusters is None:
						raise ValueError("Clusters must be specified if do_color_quantize is True.")

				# All transformations expect numpy arrays.
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[to_numpy_array(UpperCAmelCase) for image in images]

				if do_resize:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						[self.resize(image=UpperCAmelCase   ,  size=UpperCAmelCase   ,  resample=UpperCAmelCase) for image in images]

				if do_normalize:
						SCREAMING_SNAKE_CASE_				:int				     =						[self.normalize(image=UpperCAmelCase) for image in images]

				if do_color_quantize:
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						[to_channel_dimension_format(UpperCAmelCase   ,  ChannelDimension.LAST) for image in images]
						# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						np.array(UpperCAmelCase)
						SCREAMING_SNAKE_CASE_				:Tuple				     =						color_quantize(UpperCAmelCase   ,  UpperCAmelCase).reshape(images.shape[:-1])

						# flatten to (batch_size, height*width)
						SCREAMING_SNAKE_CASE_				:Any				     =						images.shape[0]
						SCREAMING_SNAKE_CASE_				:Dict				     =						images.reshape(UpperCAmelCase   ,  -1)

						# We need to convert back to a list of images to keep consistent behaviour across processors.
						SCREAMING_SNAKE_CASE_				:Any				     =						list(UpperCAmelCase)
				else:
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						[to_channel_dimension_format(UpperCAmelCase   ,  UpperCAmelCase) for image in images]

				SCREAMING_SNAKE_CASE_				:str				     =						{"input_ids": images}
				return BatchFeature(data=UpperCAmelCase   ,  tensor_type=UpperCAmelCase)






