
'''simple docstring'''







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 PoolFormerImageProcessor


class 						__UpperCAmelCase      (					unittest.TestCase						):


							def __init__(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_=7    ,   lowerCAmelCase_=3    ,   lowerCAmelCase_=30    ,   lowerCAmelCase_=4_00    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_=0.9    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=[0.5, 0.5, 0.5]    ,   lowerCAmelCase_=[0.5, 0.5, 0.5]    ,   ):





								"""simple docstring"""






								_snake_case   						=		size if size is not None else {'shortest_edge': 30}
								_snake_case   						=		crop_size if crop_size is not None else {'height': 30, 'width': 30}
								_snake_case   						=		parent
								_snake_case   						=		batch_size
								_snake_case   						=		num_channels
								_snake_case   						=		min_resolution
								_snake_case   						=		max_resolution
								_snake_case   						=		do_resize_and_center_crop
								_snake_case   						=		size
								_snake_case   						=		crop_pct
								_snake_case   						=		crop_size
								_snake_case   						=		do_normalize
								_snake_case   						=		image_mean
								_snake_case   						=		image_std




							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return {
								    "size": self.size,
								    "do_resize_and_center_crop": self.do_resize_and_center_crop,
								    "crop_pct": self.crop_pct,
								    "crop_size": self.crop_size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								}


@require_torch
@require_vision
class 						__UpperCAmelCase      (					_lowerCamelCase						,   unittest.TestCase						):
							__lowercase      		=       PoolFormerImageProcessor if is_vision_available() else None


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		PoolFormerImageProcessingTester(self  )


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return self.image_processor_tester.prepare_image_processor_dict()


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.image_processing_class(**self.image_processor_dict  )
								self.assertTrue(hasattr(lowerCAmelCase_    ,   'do_resize_and_center_crop'  )  )
								self.assertTrue(hasattr(lowerCAmelCase_    ,   'size'  )  )
								self.assertTrue(hasattr(lowerCAmelCase_    ,   'crop_pct'  )  )
								self.assertTrue(hasattr(lowerCAmelCase_    ,   'do_normalize'  )  )
								self.assertTrue(hasattr(lowerCAmelCase_    ,   'image_mean'  )  )
								self.assertTrue(hasattr(lowerCAmelCase_    ,   'image_std'  )  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.image_processing_class.from_dict(self.image_processor_dict  )
								self.assertEqual(image_processor.size    ,   {'shortest_edge': 30}  )
								self.assertEqual(image_processor.crop_size    ,   {'height': 30, 'width': 30}  )

								_snake_case   						=		self.image_processing_class.from_dict(self.image_processor_dict    ,   size=42    ,   crop_size=84  )
								self.assertEqual(image_processor.size    ,   {'shortest_edge': 42}  )
								self.assertEqual(image_processor.crop_size    ,   {'height': 84, 'width': 84}  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								pass


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.image_processing_class(**self.image_processor_dict  )
								# create random PIL images
								_snake_case   						=		prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=lowerCAmelCase_  )
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_    ,   Image.Image  )

								# Test not batched input
								_snake_case   						=		image_processing(image_inputs[0]    ,   return_tensors='pt'  ).pixel_values

								self.assertEqual(
								    encoded_images.shape    ,   (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )    ,   )

								# Test batched
								_snake_case   						=		image_processing(lowerCAmelCase_    ,   return_tensors='pt'  ).pixel_values
								self.assertEqual(
								    encoded_images.shape    ,   (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )    ,   )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.image_processing_class(**self.image_processor_dict  )
								# create random numpy tensors
								_snake_case   						=		prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=lowerCAmelCase_    ,   numpify=lowerCAmelCase_  )
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_    ,   np.ndarray  )

								# Test not batched input
								_snake_case   						=		image_processing(image_inputs[0]    ,   return_tensors='pt'  ).pixel_values
								self.assertEqual(
								    encoded_images.shape    ,   (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )    ,   )

								# Test batched
								_snake_case   						=		image_processing(lowerCAmelCase_    ,   return_tensors='pt'  ).pixel_values
								self.assertEqual(
								    encoded_images.shape    ,   (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )    ,   )




							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.image_processing_class(**self.image_processor_dict  )
								# create random PyTorch tensors
								_snake_case   						=		prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=lowerCAmelCase_    ,   torchify=lowerCAmelCase_  )
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_    ,   torch.Tensor  )

								# Test not batched input
								_snake_case   						=		image_processing(image_inputs[0]    ,   return_tensors='pt'  ).pixel_values
								self.assertEqual(
								    encoded_images.shape    ,   (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )    ,   )

								# Test batched
								_snake_case   						=		image_processing(lowerCAmelCase_    ,   return_tensors='pt'  ).pixel_values
								self.assertEqual(
								    encoded_images.shape    ,   (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )    ,   )




from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor


class  A						:







    """simple docstring"""





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





        '''simple docstring'''







        A__          =   parent
        A__          =   batch_size
        A__          =   image_size
        A__          =   patch_size
        A__          =   num_channels
        A__          =   is_training
        A__          =   use_labels
        A__          =   hidden_size
        A__          =   num_hidden_layers
        A__          =   num_attention_heads
        A__          =   intermediate_size
        A__          =   hidden_act
        A__          =   hidden_dropout_prob
        A__          =   attention_probs_dropout_prob
        A__          =   type_sequence_label_size
        A__          =   initializer_range
        A__          =   scope

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


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





        '''simple docstring'''







        A__          =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

        A__          =   self.get_config()

        return config, pixel_values, labels


    def 						snake_case__     (      self :       Tuple		)->    List[Any]:





        '''simple docstring'''







        return ViTConfig(
            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,is_decoder=lowercase_,initializer_range=self.initializer_range,)


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





        '''simple docstring'''







        A__          =   TFViTModel(config=lowercase_		)
        A__          =   model(lowercase_,training=lowercase_		)
        self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)		)

        # Test with an image with different size than the one specified in config.
        A__          =   self.image_size // 2
        A__          =   pixel_values[:, :, :image_size, :image_size]
        A__          =   model(lowercase_,interpolate_pos_encoding=lowercase_,training=lowercase_		)
        A__          =   (image_size // self.patch_size) ** 2 + 1
        self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, seq_length, self.hidden_size)		)


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





        '''simple docstring'''







        A__          =   self.type_sequence_label_size
        A__          =   TFViTForImageClassification(lowercase_		)
        A__          =   model(lowercase_,labels=lowercase_,training=lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size)		)

        # Test with an image with different size than the one specified in config.
        A__          =   self.image_size // 2
        A__          =   pixel_values[:, :, :image_size, :image_size]
        A__          =   model(lowercase_,interpolate_pos_encoding=lowercase_,training=lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size)		)

        # test greyscale images
        A__          =   1
        A__          =   TFViTForImageClassification(lowercase_		)
        A__          =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
        A__          =   model(lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size)		)


    def 						snake_case__     (      self :       Any		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.prepare_config_and_inputs()
        A__      ,						A__      ,						A__          =   config_and_inputs
        A__          =   {'pixel_values': pixel_values}
        return config, inputs_dict




@require_tf
class  A						(							_UpperCAmelCase    ,							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
    lowerCamelCase			=							(
        {'feature-extraction': TFViTModel, 'image-classification': TFViTForImageClassification}
        if is_tf_available()
        else {}
    )

    lowerCamelCase			=							False
    lowerCamelCase			=							False
    lowerCamelCase			=							False


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





        '''simple docstring'''







        A__          =   TFViTModelTester(self		)
        A__          =   ConfigTester(self,config_class=lowercase_,has_text_modality=lowercase_,hidden_size=3_7		)


    def 						snake_case__     (      self :       Any		)->    Optional[Any]:





        '''simple docstring'''







        self.config_tester.run_common_tests()


    @unittest.skip(reason='ViT does not use inputs_embeds'		)
    def 						snake_case__     (      self :       Optional[Any]		)->    str:





        '''simple docstring'''







        pass


    @unittest.skip(reason='ViT does not use inputs_embeds'		)
    def 						snake_case__     (      self :       Any		)->    int:





        '''simple docstring'''







        pass


    def 						snake_case__     (      self :       str		)->    Dict:





        '''simple docstring'''







        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            A__          =   model_class(lowercase_		)
            self.assertIsInstance(model.get_input_embeddings(),(tf.keras.layers.Layer)		)
            A__          =   model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(lowercase_,tf.keras.layers.Layer		)		)


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





        '''simple docstring'''







        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()

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

            A__          =   ['pixel_values']
            self.assertListEqual(arg_names[:1],lowercase_		)


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





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase_		)


    def 						snake_case__     (      self :       Optional[Any]		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowercase_		)


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





        '''simple docstring'''







        A__          =   TFViTModel.from_pretrained('google/vit-base-patch16-224'		)
        self.assertIsNotNone(lowercase_		)




def 						_snake_case(    )							->      str:





    '''simple docstring'''






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


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







    """simple docstring"""





    @cached_property
    def 						snake_case__     (      self :       List[Any]		)->    str:





        '''simple docstring'''







        return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'		) if is_vision_available() else None


    @slow
    def 						snake_case__     (      self :       Any		)->    Dict:





        '''simple docstring'''







        A__          =   TFViTForImageClassification.from_pretrained('google/vit-base-patch16-224'		)

        A__          =   self.default_image_processor
        A__          =   prepare_img()
        A__          =   image_processor(images=lowercase_,return_tensors='tf'		)

        # forward pass
        A__          =   model(**lowercase_		)

        # verify the logits
        A__          =   tf.TensorShape((1, 1_0_0_0)		)
        self.assertEqual(outputs.logits.shape,lowercase_		)

        A__          =   tf.constant([-0.2_744, 0.8_215, -0.0_836]		)

        tf.debugging.assert_near(outputs.logits[0, :3],lowercase_,atol=1E-4		)

