






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       snake_case__ :

       '''simple docstring'''



       def __init__(				self      ,    a__      ,    a__=13      ,    a__=30      ,    a__=2      ,    a__=3      ,    a__=True      ,    a__=True      ,    a__=32      ,    a__=2      ,    a__=4      ,    a__=37      ,    a__="gelu"      ,    a__=0.1      ,    a__=0.1      ,    a__=10      ,    a__=0.02      ,    a__=3      ,    a__=None      ,    )			->   Optional[Any]:






            '''simple docstring'''





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

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



       def        __lowercase    (				self     )			->   int:






            '''simple docstring'''





            __snake_case      :Optional[Any] 							=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

            __snake_case      :List[str] 							=    self.get_config()

            return config, pixel_values, labels



       def        __lowercase    (				self     )			->   str:






            '''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=a__      ,    initializer_range=self.initializer_range      ,    )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__     )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    TFViTModel(config=a__     )
            __snake_case      :str 							=    model(a__      ,    training=a__     )
            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.
            __snake_case      :Optional[Any] 							=    self.image_size // 2
            __snake_case      :int 							=    pixel_values[:, :, :image_size, :image_size]
            __snake_case      :int 							=    model(a__      ,    interpolate_pos_encoding=a__      ,    training=a__     )
            __snake_case      :Tuple 							=    (image_size // self.patch_size) ** 2 + 1
            self.parent.assertEqual(result.last_hidden_state.shape      ,    (self.batch_size, seq_length, self.hidden_size)     )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__     )			->   Dict:






            '''simple docstring'''





            __snake_case      :List[str] 							=    self.type_sequence_label_size
            __snake_case      :Optional[Any] 							=    TFViTForImageClassification(a__     )
            __snake_case      :List[str] 							=    model(a__      ,    labels=a__      ,    training=a__     )
            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.
            __snake_case      :Optional[Any] 							=    self.image_size // 2
            __snake_case      :Optional[Any] 							=    pixel_values[:, :, :image_size, :image_size]
            __snake_case      :Tuple 							=    model(a__      ,    interpolate_pos_encoding=a__      ,    training=a__     )
            self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.type_sequence_label_size)     )

            # test greyscale images
            __snake_case      :int 							=    1
            __snake_case      :Tuple 							=    TFViTForImageClassification(a__     )
            __snake_case      :str 							=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
            __snake_case      :Union[str, Any] 							=    model(a__     )
            self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.type_sequence_label_size)     )



       def        __lowercase    (				self     )			->   str:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    self.prepare_config_and_inputs()
            __snake_case							,			__snake_case							,			__snake_case      :Tuple 							=    config_and_inputs
            __snake_case      :Tuple 							=    {"""pixel_values""": pixel_values}
            return config, inputs_dict




@require_tf
class       snake_case__ (				lowercase_						,     lowercase_						,     unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase				:					str								=			(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
       lowerCamelCase				:					Union[str, Any]								=			(
           {"feature-extraction": TFViTModel, "image-classification": TFViTForImageClassification}
           if is_tf_available()
           else {}
       )

       lowerCamelCase				:					Optional[Any]								=			False
       lowerCamelCase				:					Dict								=			False
       lowerCamelCase				:					Any								=			False



       def        __lowercase    (				self     )			->   List[str]:






            '''simple docstring'''





            __snake_case      :Optional[Any] 							=    TFViTModelTester(self     )
            __snake_case      :Optional[int] 							=    ConfigTester(self      ,    config_class=a__      ,    has_text_modality=a__      ,    hidden_size=37     )



       def        __lowercase    (				self     )			->   Tuple:






            '''simple docstring'''





            self.config_tester.run_common_tests()



       @unittest.skip(reason="""ViT does not use inputs_embeds"""     )
       def        __lowercase    (				self     )			->   List[str]:






            '''simple docstring'''





            pass



       @unittest.skip(reason="""ViT does not use inputs_embeds"""     )
       def        __lowercase    (				self     )			->   Tuple:






            '''simple docstring'''





            pass



       def        __lowercase    (				self     )			->   int:






            '''simple docstring'''





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

            for model_class in self.all_model_classes:
                 __snake_case      :Dict 							=    model_class(a__     )
                 self.assertIsInstance(model.get_input_embeddings()      ,    (tf.keras.layers.Layer)     )
                 __snake_case      :List[str] 							=    model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(a__      ,    tf.keras.layers.Layer     )     )



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






            '''simple docstring'''





            __snake_case							,			__snake_case      :Tuple 							=    self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 __snake_case      :List[Any] 							=    model_class(a__     )
                 __snake_case      :List[str] 							=    inspect.signature(model.call     )
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 __snake_case      :Optional[int] 							=    [*signature.parameters.keys()]

                 __snake_case      :Tuple 							=    ["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]      ,    a__     )



       def        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*a__     )



       def        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*a__     )



       @slow
       def        __lowercase    (				self     )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :Any 							=    TFViTModel.from_pretrained("""google/vit-base-patch16-224"""     )
            self.assertIsNotNone(a__     )






def 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :Optional[int] 							=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
     return image




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

       '''simple docstring'''



       @cached_property
       def        __lowercase    (				self     )			->   str:






            '''simple docstring'''





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



       @slow
       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            __snake_case      :Any 							=    TFViTForImageClassification.from_pretrained("""google/vit-base-patch16-224"""     )

            __snake_case      :Tuple 							=    self.default_image_processor
            __snake_case      :List[str] 							=    prepare_img()
            __snake_case      :List[str] 							=    image_processor(images=a__      ,    return_tensors="""tf"""     )

            # forward pass
            __snake_case      :Any 							=    model(**a__     )

            # verify the logits
            __snake_case      :Any 							=    tf.TensorShape((1, 10_00)     )
            self.assertEqual(outputs.logits.shape      ,    a__     )

            __snake_case      :Optional[int] 							=    tf.constant([-0.27_44, 0.82_15, -0.08_36]     )

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










from __future__ import annotations




class       snake_case__ :

       '''simple docstring'''



       def __init__(				self      ,    a__     )			->   None:






            '''simple docstring'''





            __snake_case      :str 							=    order

            # a_{0} ... a_{k}
            __snake_case      :int 							=    [1.0] + [0.0] * order
            # b_{0} ... b_{k}
            __snake_case      :Any 							=    [1.0] + [0.0] * order

            # x[n-1] ... x[n-k]
            __snake_case      :List[Any] 							=    [0.0] * self.order
            # y[n-1] ... y[n-k]
            __snake_case      :int 							=    [0.0] * self.order



       def        __lowercase    (				self      ,    a__      ,    a__     )			->   None:






            '''simple docstring'''





            if len(a__     ) < self.order:
                 __snake_case      :Optional[int] 							=    [1.0, *a_coeffs]

            if len(a__     ) != self.order + 1:
                 __snake_case      :Dict 							=    (
                     F'''Expected a_coeffs to have {self.order + 1} elements '''
                     F'''for {self.order}-order filter, got {len(a__     )}'''
                 )
                 raise ValueError(a__     )

            if len(a__     ) != self.order + 1:
                 __snake_case      :Optional[Any] 							=    (
                     F'''Expected b_coeffs to have {self.order + 1} elements '''
                     F'''for {self.order}-order filter, got {len(a__     )}'''
                 )
                 raise ValueError(a__     )

            __snake_case      :str 							=    a_coeffs
            __snake_case      :str 							=    b_coeffs



       def        __lowercase    (				self      ,    a__     )			->   float:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    0.0

            # Start at index 1 and do index 0 at the end.
            for i in range(1      ,    self.order + 1     ):
                 result += (
                     self.b_coeffs[i] * self.input_history[i - 1]
                     - self.a_coeffs[i] * self.output_history[i - 1]
                 )

            __snake_case      :Dict 							=    (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

            __snake_case      :Optional[int] 							=    self.input_history[:-1]
            __snake_case      :Optional[Any] 							=    self.output_history[:-1]

            __snake_case      :Union[str, Any] 							=    sample
            __snake_case      :Optional[Any] 							=    result

            return result


