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_     :






    '''simple docstring'''






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

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







    def 			UpperCamelCase__   (	self)       ->	List[str]:
        __UpperCamelCase     :int			  =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

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

        return config, pixel_values, labels







    def 			UpperCamelCase__   (	self)       ->	List[str]:
        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 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	Union[str, Any]:
        __UpperCamelCase     :int			  =		ViTMSNModel(config=_SCREAMING_SNAKE_CASE)
        model.to(_SCREAMING_SNAKE_CASE)
        model.eval()
        __UpperCamelCase     :Union[str, Any]			  =		model(_SCREAMING_SNAKE_CASE)
        self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	Dict:
        __UpperCamelCase     :List[Any]			  =		self.type_sequence_label_size
        __UpperCamelCase     :Any			  =		ViTMSNForImageClassification(_SCREAMING_SNAKE_CASE)
        model.to(_SCREAMING_SNAKE_CASE)
        model.eval()
        __UpperCamelCase     :Union[str, 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
        __UpperCamelCase     :List[str]			  =		1
        __UpperCamelCase     :int			  =		ViTMSNForImageClassification(_SCREAMING_SNAKE_CASE)
        model.to(_SCREAMING_SNAKE_CASE)
        model.eval()

        __UpperCamelCase     :Union[str, Any]			  =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
        __UpperCamelCase     :int			  =		model(_SCREAMING_SNAKE_CASE)
        self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size))







    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase     :Optional[int]			  =		self.prepare_config_and_inputs()
        __UpperCamelCase     :Any			  =		config_and_inputs
        __UpperCamelCase     :List[Any]			  =		{'''pixel_values''': pixel_values}
        return config, inputs_dict


@require_torch
class 	lowerCamelCase_     (   UpperCAmelCase_						,	UpperCAmelCase_						,	unittest.TestCase ):






    '''simple docstring'''






    a__	:						Optional[int]						=      (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
    a__	:						str						=      (
        {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
        if is_torch_available()
        else {}
    )

    a__	:						str						=      False
    a__	:						str						=      False
    a__	:						int						=      False
    a__	:						List[str]						=      False







    def 			UpperCamelCase__   (	self)       ->	int:
        __UpperCamelCase     :Optional[int]			  =		ViTMSNModelTester(self)
        __UpperCamelCase     :Any			  =		ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE , has_text_modality=_SCREAMING_SNAKE_CASE , hidden_size=37)







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







    @unittest.skip(reason='''ViTMSN does not use inputs_embeds''')
    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        pass







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase     :Union[str, Any]			  =		self.model_tester.prepare_config_and_inputs_for_common()

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







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        __UpperCamelCase     :str			  =		self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            __UpperCamelCase     :Optional[Any]			  =		model_class(_SCREAMING_SNAKE_CASE)
            __UpperCamelCase     :List[str]			  =		inspect.signature(model.forward)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            __UpperCamelCase     :Dict			  =		[*signature.parameters.keys()]

            __UpperCamelCase     :List[Any]			  =		['''pixel_values''']
            self.assertListEqual(arg_names[:1] , _SCREAMING_SNAKE_CASE)







    def 			UpperCamelCase__   (	self)       ->	int:
        __UpperCamelCase     :List[Any]			  =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE)







    def 			UpperCamelCase__   (	self)       ->	str:
        __UpperCamelCase     :int			  =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE)







    @slow
    def 			UpperCamelCase__   (	self)       ->	Dict:





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



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :Union[str, Any]			  =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
    return image


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






    '''simple docstring'''






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







    @slow
    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        torch.manual_seed(2)
        __UpperCamelCase     :List[str]			  =		ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small''').to(_SCREAMING_SNAKE_CASE)

        __UpperCamelCase     :Tuple			  =		self.default_image_processor
        __UpperCamelCase     :List[Any]			  =		prepare_img()
        __UpperCamelCase     :Optional[int]			  =		image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors='''pt''').to(_SCREAMING_SNAKE_CASE)

        # forward pass
        with torch.no_grad():
            __UpperCamelCase     :Tuple			  =		model(**_SCREAMING_SNAKE_CASE)

        # verify the logits
        __UpperCamelCase     :int			  =		torch.Size((1, 1_000))
        self.assertEqual(outputs.logits.shape , _SCREAMING_SNAKE_CASE)

        __UpperCamelCase     :Optional[int]			  =		torch.tensor([-0.08_03, -0.44_54, -0.23_75]).to(_SCREAMING_SNAKE_CASE)

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









import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version


def   _SCREAMING_SNAKE_CASE	( *SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE = None ,			SCREAMING_SNAKE_CASE=True ,			SCREAMING_SNAKE_CASE=2       ):
  from .. import __version__

  A_      :				Union[str, Any]		     =					take_from
  A_      :				Optional[Any]		     =					()
  if not isinstance(args[0] ,			SCREAMING_SNAKE_CASE       ):
    A_      :				List[str]		     =					(args,)

  for attribute, version_name, message in args:
    if version.parse(version.parse(SCREAMING_SNAKE_CASE       ).base_version       ) >= version.parse(SCREAMING_SNAKE_CASE       ):
      raise ValueError(
          f'''The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''''
          f''' version {__version__} is >= {version_name}'''       )

    A_      :				List[Any]		     =					None
    if isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) and attribute in deprecated_kwargs:
      values += (deprecated_kwargs.pop(SCREAMING_SNAKE_CASE       ),)
      A_      :				Optional[Any]		     =					f'''The `{attribute}` argument is deprecated and will be removed in version {version_name}.'''
    elif hasattr(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
      values += (getattr(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ),)
      A_      :				int		     =					f'''The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'''
    elif deprecated_kwargs is None:
      A_      :				List[Any]		     =					f'''`{attribute}` is deprecated and will be removed in version {version_name}.'''

    if warning is not None:
      A_      :				Union[str, Any]		     =					warning + ''' ''' if standard_warn else ''''''
      warnings.warn(warning + message ,			SCREAMING_SNAKE_CASE ,			stacklevel=SCREAMING_SNAKE_CASE       )

  if isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) and len(SCREAMING_SNAKE_CASE       ) > 0:
    A_      :				Dict		     =					inspect.getouterframes(inspect.currentframe()       )[1]
    A_      :				Optional[int]		     =					call_frame.filename
    A_      :				Optional[int]		     =					call_frame.lineno
    A_      :				str		     =					call_frame.function
    A_     ,       A_      :				List[str]		     =					next(iter(deprecated_kwargs.items()       )       )
    raise TypeError(f'''{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'''       )

  if len(SCREAMING_SNAKE_CASE       ) == 0:
    return
  elif len(SCREAMING_SNAKE_CASE       ) == 1:
    return values[0]
  return values




