



import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
     import jax

     from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel






class SCREAMING_SNAKE_CASE__     (		unittest.TestCase    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__=13,		lowerCamelCase__=30,		lowerCamelCase__=2,		lowerCamelCase__=3,		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=32,		lowerCamelCase__=5,		lowerCamelCase__=4,		lowerCamelCase__=37,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=10,		lowerCamelCase__=0.02,		):
        A :       List[str]          =   parent
        A :       Optional[int]          =   batch_size
        A :       Any          =   image_size
        A :       Any          =   patch_size
        A :       List[str]          =   num_channels
        A :       int          =   is_training
        A :       int          =   use_labels
        A :       Union[str, Any]          =   hidden_size
        A :       Any          =   num_hidden_layers
        A :       Any          =   num_attention_heads
        A :       Any          =   intermediate_size
        A :       List[str]          =   hidden_act
        A :       Dict          =   hidden_dropout_prob
        A :       List[str]          =   attention_probs_dropout_prob
        A :       Optional[int]          =   type_sequence_label_size
        A :       str          =   initializer_range

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






    def 			_lowerCAmelCase					(       self		):
        A :       Optional[Any]          =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

        A :       Optional[Any]          =   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=lowerCamelCase__,		initializer_range=self.initializer_range,		)

        return config, pixel_values






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__		):
        A :       int          =   FlaxViTModel(config=lowerCamelCase__		)
        A :       List[Any]          =   model(lowerCamelCase__		)
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
        A :       List[Any]          =   (self.image_size, self.image_size)
        A :       List[Any]          =   (self.patch_size, self.patch_size)
        A :       str          =   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        self.parent.assertEqual(result.last_hidden_state.shape,		(self.batch_size, num_patches + 1, self.hidden_size)		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__		):
        A :       Optional[Any]          =   self.type_sequence_label_size
        A :       Any          =   FlaxViTForImageClassification(config=lowerCamelCase__		)
        A :       Optional[int]          =   model(lowerCamelCase__		)
        self.parent.assertEqual(result.logits.shape,		(self.batch_size, self.type_sequence_label_size)		)

        # test greyscale images
        A :       Optional[int]          =   1
        A :       List[Any]          =   FlaxViTForImageClassification(lowerCamelCase__		)

        A :       Optional[Any]          =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
        A :       Optional[Any]          =   model(lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   self.prepare_config_and_inputs()
        (
            (
            A
        )     ,							(
            A
        )     ,							
        ) :       Tuple          =   config_and_inputs
        A :       List[str]          =   {"""pixel_values""": pixel_values}
        return config, inputs_dict






@require_flax
class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__ ,						unittest.TestCase    ):




    '''simple docstring'''






    __lowerCamelCase			: List[Any]					=      (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()






    def 			_lowerCAmelCase					(       self		):
        A :       Union[str, Any]          =   FlaxViTModelTester(self		)
        A :       Optional[Any]          =   ConfigTester(self,		config_class=lowerCamelCase__,		has_text_modality=lowerCamelCase__,		hidden_size=37		)






    def 			_lowerCAmelCase					(       self		):
        self.config_tester.run_common_tests()






    def 			_lowerCAmelCase					(       self		):
        A :       Any          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A :       str          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A     ,							A :       List[str]          =   self.model_tester.prepare_config_and_inputs_for_common()

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

            A :       Optional[Any]          =   ["""pixel_values"""]
            self.assertListEqual(arg_names[:1],		lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A     ,							A :       Dict          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__		):
                A :       Tuple          =   self._prepare_for_class(lowerCamelCase__,		lowerCamelCase__		)
                A :       List[str]          =   model_class(lowerCamelCase__		)

                @jax.jit
                def model_jitted(lowerCamelCase__,		**lowerCamelCase__		):
                    return model(pixel_values=lowerCamelCase__,		**lowerCamelCase__		)

                with self.subTest("""JIT Enabled"""		):
                    A :       List[Any]          =   model_jitted(**lowerCamelCase__		).to_tuple()

                with self.subTest("""JIT Disabled"""		):
                    with jax.disable_jit():
                        A :       List[Any]          =   model_jitted(**lowerCamelCase__		).to_tuple()

                self.assertEqual(len(lowerCamelCase__		),		len(lowerCamelCase__		)		)
                for jitted_output, output in zip(lowerCamelCase__,		lowerCamelCase__		):
                    self.assertEqual(jitted_output.shape,		output.shape		)






    @slow
    def 			_lowerCAmelCase					(       self		):
        for model_class_name in self.all_model_classes:
            A :       Any          =   model_class_name.from_pretrained("""google/vit-base-patch16-224"""		)
            A :       int          =   model(np.ones((1, 3, 224, 224)		)		)
            self.assertIsNotNone(lowerCamelCase__		)







def 	__UpperCamelCase	(  _lowerCAmelCase		)       ->  list:



    """simple docstring"""






    return [
        txt[:a] + txt[a].upper() + txt[a + 1 :]
        for a in range(len(_lowerCAmelCase		)		)
        if txt[a].isalpha()
    ]


if __name__ == "__main__":
     __import__("""doctest""").testmod()


