






'''simple docstring'''







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 _lowercase	:




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

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




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__					:	List[str]         =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

														lowerCamelCase__					:	int         =       None
														if self.use_labels:
																					lowerCamelCase__					:	Dict         =       ids_tensor([self.batch_size]			,							self.type_sequence_label_size     )

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

														return config, pixel_values, labels




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														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=UpperCamelCase__			,							initializer_range=self.initializer_range			,							)




							def 		lowerCamelCase_				(							self:				Optional[Any]			,							UpperCamelCase__:				Union[str, Any]			,							UpperCamelCase__:				Any			,							UpperCamelCase__:				Dict     ):
														lowerCamelCase__					:	str         =       TFViTModel(config=UpperCamelCase__     )
														lowerCamelCase__					:	Dict         =       model(UpperCamelCase__			,							training=UpperCamelCase__     )
														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.
														lowerCamelCase__					:	Tuple         =       self.image_size // 2
														lowerCamelCase__					:	Dict         =       pixel_values[:, :, :image_size, :image_size]
														lowerCamelCase__					:	Dict         =       model(UpperCamelCase__			,							interpolate_pos_encoding=UpperCamelCase__			,							training=UpperCamelCase__     )
														lowerCamelCase__					:	int         =       (image_size // self.patch_size) ** 2 + 1
														self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, seq_length, self.hidden_size)     )




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

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




							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	str         =       self.prepare_config_and_inputs()
														lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__					:	Optional[int]         =       config_and_inputs
														lowerCamelCase__					:	List[str]         =       {"""pixel_values""": pixel_values}
														return config, inputs_dict



@require_tf
class _lowercase	(				_lowercase				,      _lowercase				,      unittest.TestCase		):
							a				      =       (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
							a				      =       (
							    {"""feature-extraction""": TFViTModel, """image-classification""": TFViTForImageClassification}
							    if is_tf_available()
							    else {}
							)

							a				      =       False
							a				      =       False
							a				      =       False




							def 		lowerCamelCase_				(							self:				List[Any]     ):
														lowerCamelCase__					:	List[str]         =       TFViTModelTester(self     )
														lowerCamelCase__					:	Any         =       ConfigTester(self			,							config_class=UpperCamelCase__			,							has_text_modality=UpperCamelCase__			,							hidden_size=37     )




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														self.config_tester.run_common_tests()




							@unittest.skip(reason="""ViT does not use inputs_embeds"""     )
							def 		lowerCamelCase_				(							self:				int     ):
														pass




							@unittest.skip(reason="""ViT does not use inputs_embeds"""     )
							def 		lowerCamelCase_				(							self:				Any     ):
														pass




							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	Optional[Any]         =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Optional[Any]         =       model_class(UpperCamelCase__     )
																					self.assertIsInstance(model.get_input_embeddings()			,							(tf.keras.layers.Layer)     )
																					lowerCamelCase__					:	Any         =       model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(UpperCamelCase__			,							tf.keras.layers.Layer     )     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	Any         =       self.model_tester.prepare_config_and_inputs_for_common()

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

																					lowerCamelCase__					:	List[str]         =       ["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__					:	Dict         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														lowerCamelCase__					:	Optional[int]         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__     )




							@slow
							def 		lowerCamelCase_				(							self:				str     ):
														lowerCamelCase__					:	List[str]         =       TFViTModel.from_pretrained("""google/vit-base-patch16-224"""     )
														self.assertIsNotNone(UpperCamelCase__     )





def  SCREAMING_SNAKE_CASE_       ()      ->       Optional[Any]:
							lowerCamelCase__					:	List[str]         =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
							return image



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




							@cached_property
							def 		lowerCamelCase_				(							self:				Any     ):
														return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224"""     ) if is_vision_available() else None




							@slow
							def 		lowerCamelCase_				(							self:				Tuple     ):
														lowerCamelCase__					:	str         =       TFViTForImageClassification.from_pretrained("""google/vit-base-patch16-224"""     )

														lowerCamelCase__					:	str         =       self.default_image_processor
														lowerCamelCase__					:	Optional[Any]         =       prepare_img()
														lowerCamelCase__					:	Optional[int]         =       image_processor(images=UpperCamelCase__			,							return_tensors="""tf"""     )

														# forward pass
														lowerCamelCase__					:	Union[str, Any]         =       model(**UpperCamelCase__     )

														# verify the logits
														lowerCamelCase__					:	int         =       tf.TensorShape((1, 1_000)     )
														self.assertEqual(outputs.logits.shape			,							UpperCamelCase__     )

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

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








"""simple docstring"""



def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  list						)   ->Any:



    '''simple docstring'''







    _enforce_args(snake_case_				,snake_case_						)
    if n == 0:
        return 0
    __A    :						int						 =     float('''-inf'''						)
    for i in range(1				,n + 1						):
        __A    :						Union[str, Any]						 =     max(
            snake_case_				,prices[i - 1] + naive_cut_rod_recursive(n - i				,snake_case_						)						)

    return max_revue







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  list						)   ->int:



    '''simple docstring'''







    _enforce_args(snake_case_				,snake_case_						)
    __A    :						Dict						 =     [float('''-inf'''						) for _ in range(n + 1						)]
    return _top_down_cut_rod_recursive(snake_case_				,snake_case_				,snake_case_						)







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  list				,snake_case_  :  list						)   ->Any:



    '''simple docstring'''







    if max_rev[n] >= 0:
        return max_rev[n]
    elif n == 0:
        return 0
    else:
        __A    :						Any						 =     float('''-inf'''						)
        for i in range(1				,n + 1						):
            __A    :						Union[str, Any]						 =     max(
                snake_case_				,prices[i - 1] + _top_down_cut_rod_recursive(n - i				,snake_case_				,snake_case_						)				,)

        __A    :						Any						 =     max_revenue

    return max_rev[n]







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  list						)   ->Any:



    '''simple docstring'''







    _enforce_args(snake_case_				,snake_case_						)

    # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
    # length 0.
    __A    :						Union[str, Any]						 =     [float('''-inf'''						) for _ in range(n + 1						)]
    __A    :						List[Any]						 =     0

    for i in range(1				,n + 1						):
        __A    :						Union[str, Any]						 =     max_rev[i]
        for j in range(1				,i + 1						):
            __A    :						str						 =     max(snake_case_				,prices[j - 1] + max_rev[i - j]						)

        __A    :						List[str]						 =     max_revenue_i

    return max_rev[n]







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  list						)   ->Union[str, Any]:



    '''simple docstring'''







    if n < 0:
        __A    :						Union[str, Any]						 =     F"""n must be greater than or equal to 0. Got n = {n}"""
        raise ValueError(snake_case_						)

    if n > len(snake_case_						):
        __A    :						List[Any]						 =     (
            '''Each integral piece of rod must have a corresponding price. '''
            F"""Got n = {n} but length of prices = {len(snake_case_						)}"""
        )
        raise ValueError(snake_case_						)







def 		__lowercase     (   )   ->str:



    '''simple docstring'''







    __A    :						Any						 =     [6, 10, 12, 15, 20, 23]
    __A    :						Union[str, Any]						 =     len(snake_case_						)

    # the best revenue comes from cutting the rod into 6 pieces, each
    # of length 1 resulting in a revenue of 6 * 6 = 36.
    __A    :						str						 =     36

    __A    :						Any						 =     top_down_cut_rod(snake_case_				,snake_case_						)
    __A    :						Any						 =     bottom_up_cut_rod(snake_case_				,snake_case_						)
    __A    :						Optional[Any]						 =     naive_cut_rod_recursive(snake_case_				,snake_case_						)

    assert expected_max_revenue == max_rev_top_down
    assert max_rev_top_down == max_rev_bottom_up
    assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
       main()




