


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		:	str     ,  lowercase_		:	Optional[int]     ,  lowercase_		:	Any=13     ,  lowercase_		:	int=30     ,  lowercase_		:	Tuple=2     ,  lowercase_		:	Tuple=3     ,  lowercase_		:	Tuple=True     ,  lowercase_		:	List[str]=True     ,  lowercase_		:	List[str]=32     ,  lowercase_		:	List[Any]=5     ,  lowercase_		:	str=4     ,  lowercase_		:	int=37     ,  lowercase_		:	Union[str, Any]="gelu"     ,  lowercase_		:	Any=0.1     ,  lowercase_		:	Optional[Any]=0.1     ,  lowercase_		:	Any=10     ,  lowercase_		:	int=0.02     ,  lowercase_		:	Optional[int]=None     ,  )   ->		str:
             """simple docstring"""




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

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






      def 			lowerCamelCase			(   self		:	List[Any]					)   ->		List[str]:
             """simple docstring"""




             _lowerCamelCase			:						int            =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

             _lowerCamelCase			:						Optional[Any]            =self.get_config()

             return config, pixel_values, labels






      def 			lowerCamelCase			(   self		:	str					)   ->		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 			lowerCamelCase			(   self		:	str     ,  lowercase_		:	Union[str, Any]     ,  lowercase_		:	List[str]     ,  lowercase_		:	List[str]					)   ->		List[str]:
             """simple docstring"""




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






      def 			lowerCamelCase			(   self		:	Union[str, Any]     ,  lowercase_		:	str     ,  lowercase_		:	Optional[int]     ,  lowercase_		:	int					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						str            =self.type_sequence_label_size
             _lowerCamelCase			:						List[str]            =ViTMSNForImageClassification(lowercase_					)
             model.to(lowercase_					)
             model.eval()
             _lowerCamelCase			:						Union[str, Any]            =model(lowercase_     ,  labels=lowercase_					)
             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			:						Tuple            =1
             _lowerCamelCase			:						Optional[Any]            =ViTMSNForImageClassification(lowercase_					)
             model.to(lowercase_					)
             model.eval()

             _lowerCamelCase			:						Tuple            =floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
             _lowerCamelCase			:						Dict            =model(lowercase_					)
             self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.type_sequence_label_size)					)






      def 			lowerCamelCase			(   self		:	Dict					)   ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Any            =self.prepare_config_and_inputs()
             _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						List[Any]            =config_and_inputs
             _lowerCamelCase			:						List[str]            ={'pixel_values': pixel_values}
             return config, inputs_dict




@require_torch
class A    (  UpperCamelCase_       ,				UpperCamelCase_       ,				unittest.TestCase	):
      UpperCamelCase__				:    List[str] =(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
      UpperCamelCase__				:    Tuple =(
          {'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
          if is_torch_available()
          else {}
      )

      UpperCamelCase__				:    str =False
      UpperCamelCase__				:    List[str] =False
      UpperCamelCase__				:    List[Any] =False
      UpperCamelCase__				:    Union[str, Any] =False






      def 			lowerCamelCase			(   self		:	Dict					)   ->		str:
             """simple docstring"""




             _lowerCamelCase			:						Tuple            =ViTMSNModelTester(self					)
             _lowerCamelCase			:						Tuple            =ConfigTester(self     ,  config_class=lowercase_     ,  has_text_modality=lowercase_     ,  hidden_size=37					)






      def 			lowerCamelCase			(   self		:	str					)   ->		Optional[int]:
             """simple docstring"""




             self.config_tester.run_common_tests()






      @unittest.skip(reason='ViTMSN does not use inputs_embeds'					)
      def 			lowerCamelCase			(   self		:	Union[str, Any]					)   ->		Optional[Any]:
             """simple docstring"""




             pass






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase			:						List[Any]            =self.model_tester.prepare_config_and_inputs_for_common()

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






      def 			lowerCamelCase			(   self		:	Union[str, Any]					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase			:						str            =self.model_tester.prepare_config_and_inputs_for_common()

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

                    _lowerCamelCase			:						Optional[Any]            =['pixel_values']
                    self.assertListEqual(arg_names[:1]     ,  lowercase_					)






      def 			lowerCamelCase			(   self		:	Union[str, Any]					)   ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase			:						Optional[int]            =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*lowercase_					)






      def 			lowerCamelCase			(   self		:	Dict					)   ->		List[str]:
             """simple docstring"""




             _lowerCamelCase			:						Tuple            =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*lowercase_					)






      @slow
      def 			lowerCamelCase			(   self		:	List[str]					)   ->		Optional[int]:
             """simple docstring"""




             for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _lowerCamelCase			:						List[Any]            =ViTMSNModel.from_pretrained(lowercase_					)
                    self.assertIsNotNone(lowercase_					)




def       a_ (       ):
       '''simple docstring'''





       _lowerCamelCase			:						str            =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
       return image




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






      @cached_property
      def 			lowerCamelCase			(   self		:	str					)   ->		Optional[Any]:
             """simple docstring"""




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






      @slow
      def 			lowerCamelCase			(   self		:	Dict					)   ->		List[str]:
             """simple docstring"""




             torch.manual_seed(2					)
             _lowerCamelCase			:						List[str]            =ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'					).to(lowercase_					)

             _lowerCamelCase			:						int            =self.default_image_processor
             _lowerCamelCase			:						Optional[Any]            =prepare_img()
             _lowerCamelCase			:						Union[str, Any]            =image_processor(images=lowercase_     ,  return_tensors='pt'					).to(lowercase_					)

             # forward pass
             with torch.no_grad():
                    _lowerCamelCase			:						List[str]            =model(**lowercase_					)

             # verify the logits
             _lowerCamelCase			:						Any            =torch.Size((1, 1000)					)
             self.assertEqual(outputs.logits.shape     ,  lowercase_					)

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

             self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,  lowercase_     ,  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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase		   =			logging.get_logger(__name__)


if is_vision_available():
 import PIL




class A    (  UpperCamelCase_	):
      UpperCamelCase__				:    List[str] =['pixel_values']






      def __init__(   self		:	Any     ,  lowercase_		:	bool = True     ,  lowercase_		:	Dict[str, int] = None     ,  lowercase_		:	PILImageResampling = PILImageResampling.BICUBIC     ,  lowercase_		:	bool = True     ,  lowercase_		:	Dict[str, int] = None     ,  lowercase_		:	bool = True     ,  lowercase_		:	Union[int, float] = 1 / 255     ,  lowercase_		:	bool = True     ,  lowercase_		:	Optional[Union[float, List[float]]] = None     ,  lowercase_		:	Optional[Union[float, List[float]]] = None     ,  lowercase_		:	bool = True     ,  **lowercase_		:	str     ,  )   ->		None:
             """simple docstring"""




             super().__init__(**lowercase_					)
             _lowerCamelCase			:						Optional[int]            =size if size is not None else {'shortest_edge': 224}
             _lowerCamelCase			:						List[Any]            =get_size_dict(lowercase_     ,  default_to_square=lowercase_					)
             _lowerCamelCase			:						str            =crop_size if crop_size is not None else {'height': 224, 'width': 224}
             _lowerCamelCase			:						str            =get_size_dict(lowercase_     ,  default_to_square=lowercase_     ,  param_name='crop_size'					)

             _lowerCamelCase			:						Dict            =do_resize
             _lowerCamelCase			:						int            =size
             _lowerCamelCase			:						Optional[Any]            =resample
             _lowerCamelCase			:						Optional[int]            =do_center_crop
             _lowerCamelCase			:						Tuple            =crop_size
             _lowerCamelCase			:						Optional[int]            =do_rescale
             _lowerCamelCase			:						Optional[Any]            =rescale_factor
             _lowerCamelCase			:						Union[str, Any]            =do_normalize
             _lowerCamelCase			:						Optional[int]            =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
             _lowerCamelCase			:						Any            =image_std if image_std is not None else OPENAI_CLIP_STD
             _lowerCamelCase			:						Any            =do_convert_rgb






      def 			lowerCamelCase			(   self		:	int     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Dict[str, int]     ,  lowercase_		:	PILImageResampling = PILImageResampling.BICUBIC     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	Any     ,  )   ->		np.ndarray:
             """simple docstring"""




             _lowerCamelCase			:						int            =get_size_dict(lowercase_     ,  default_to_square=lowercase_					)
             if "shortest_edge" not in size:
                    raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''					)
             _lowerCamelCase			:						Union[str, Any]            =get_resize_output_image_size(lowercase_     ,  size=size['shortest_edge']     ,  default_to_square=lowercase_					)
             return resize(lowercase_     ,  size=lowercase_     ,  resample=lowercase_     ,  data_format=lowercase_     ,  **lowercase_					)






      def 			lowerCamelCase			(   self		:	Union[str, Any]     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Dict[str, int]     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	Tuple     ,  )   ->		np.ndarray:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =get_size_dict(lowercase_					)
             if "height" not in size or "width" not in size:
                    raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''					)
             return center_crop(lowercase_     ,  size=(size['height'], size['width'])     ,  data_format=lowercase_     ,  **lowercase_					)






      def 			lowerCamelCase			(   self		:	str     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Union[int, float]     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	int     ,  )   ->		str:
             """simple docstring"""




             return rescale(lowercase_     ,  scale=lowercase_     ,  data_format=lowercase_     ,  **lowercase_					)






      def 			lowerCamelCase			(   self		:	Optional[int]     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Union[float, List[float]]     ,  lowercase_		:	Union[float, List[float]]     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	Tuple     ,  )   ->		np.ndarray:
             """simple docstring"""




             return normalize(lowercase_     ,  mean=lowercase_     ,  std=lowercase_     ,  data_format=lowercase_     ,  **lowercase_					)






      def 			lowerCamelCase			(   self		:	List[str]     ,  lowercase_		:	ImageInput     ,  lowercase_		:	bool = None     ,  lowercase_		:	Dict[str, int] = None     ,  lowercase_		:	PILImageResampling = None     ,  lowercase_		:	bool = None     ,  lowercase_		:	int = None     ,  lowercase_		:	bool = None     ,  lowercase_		:	float = None     ,  lowercase_		:	bool = None     ,  lowercase_		:	Optional[Union[float, List[float]]] = None     ,  lowercase_		:	Optional[Union[float, List[float]]] = None     ,  lowercase_		:	bool = None     ,  lowercase_		:	Optional[Union[str, TensorType]] = None     ,  lowercase_		:	Optional[ChannelDimension] = ChannelDimension.FIRST     ,  **lowercase_		:	Union[str, Any]     ,  )   ->		PIL.Image.Image:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =do_resize if do_resize is not None else self.do_resize
             _lowerCamelCase			:						List[str]            =size if size is not None else self.size
             _lowerCamelCase			:						Any            =get_size_dict(lowercase_     ,  param_name='size'     ,  default_to_square=lowercase_					)
             _lowerCamelCase			:						str            =resample if resample is not None else self.resample
             _lowerCamelCase			:						List[Any]            =do_center_crop if do_center_crop is not None else self.do_center_crop
             _lowerCamelCase			:						Optional[Any]            =crop_size if crop_size is not None else self.crop_size
             _lowerCamelCase			:						Dict            =get_size_dict(lowercase_     ,  param_name='crop_size'     ,  default_to_square=lowercase_					)
             _lowerCamelCase			:						str            =do_rescale if do_rescale is not None else self.do_rescale
             _lowerCamelCase			:						Tuple            =rescale_factor if rescale_factor is not None else self.rescale_factor
             _lowerCamelCase			:						List[str]            =do_normalize if do_normalize is not None else self.do_normalize
             _lowerCamelCase			:						Tuple            =image_mean if image_mean is not None else self.image_mean
             _lowerCamelCase			:						int            =image_std if image_std is not None else self.image_std
             _lowerCamelCase			:						Tuple            =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

             _lowerCamelCase			:						Any            =make_list_of_images(lowercase_					)

             if not valid_images(lowercase_					):
                    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:
                    raise ValueError('Size must be specified if do_resize is True.'					)

             if do_center_crop and crop_size is None:
                    raise ValueError('Crop size must be specified if do_center_crop is True.'					)

             if do_rescale and rescale_factor is None:
                    raise ValueError('Rescale factor must be specified if do_rescale is True.'					)

             if do_normalize and (image_mean is None or image_std is None):
                    raise ValueError('Image mean and std must be specified if do_normalize is True.'					)

             # PIL RGBA images are converted to RGB
             if do_convert_rgb:
                    _lowerCamelCase			:						Tuple            =[convert_to_rgb(lowercase_					) for image in images]

             # All transformations expect numpy arrays.
             _lowerCamelCase			:						Optional[Any]            =[to_numpy_array(lowercase_					) for image in images]

             if do_resize:
                    _lowerCamelCase			:						Optional[Any]            =[self.resize(image=lowercase_     ,  size=lowercase_     ,  resample=lowercase_					) for image in images]

             if do_center_crop:
                    _lowerCamelCase			:						Optional[int]            =[self.center_crop(image=lowercase_     ,  size=lowercase_					) for image in images]

             if do_rescale:
                    _lowerCamelCase			:						Optional[Any]            =[self.rescale(image=lowercase_     ,  scale=lowercase_					) for image in images]

             if do_normalize:
                    _lowerCamelCase			:						List[Any]            =[self.normalize(image=lowercase_     ,  mean=lowercase_     ,  std=lowercase_					) for image in images]

             _lowerCamelCase			:						List[str]            =[to_channel_dimension_format(lowercase_     ,  lowercase_					) for image in images]

             _lowerCamelCase			:						Tuple            ={'pixel_values': images}
             return BatchFeature(data=lowercase_     ,  tensor_type=lowercase_					)


