


"""simple docstring"""


from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

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 TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor


class 			A__	:


     """simple docstring"""

     def __init__(	self:						Dict     ,		__a:						Union[str, Any]     ,		__a:						List[Any]=13     ,		__a:						Optional[Any]=30     ,		__a:						List[Any]=2     ,		__a:						Dict=3     ,		__a:						str=True     ,		__a:						Any=True     ,		__a:						List[str]=32     ,		__a:						Optional[Any]=2     ,		__a:						int=4     ,		__a:						Optional[Any]=37     ,		__a:						Union[str, Any]="gelu"     ,		__a:						str=0.1     ,		__a:						str=0.1     ,		__a:						Tuple=10     ,		__a:						Tuple=0.02     ,		__a:						Optional[int]=3     ,		__a:						Optional[Any]=0.6     ,		__a:						Dict=None     ,		)->    List[str]:
           lowerCamelCase					:					Any        =	parent
           lowerCamelCase					:					Optional[int]        =	batch_size
           lowerCamelCase					:					Dict        =	image_size
           lowerCamelCase					:					List[str]        =	patch_size
           lowerCamelCase					:					int        =	num_channels
           lowerCamelCase					:					str        =	is_training
           lowerCamelCase					:					Union[str, Any]        =	use_labels
           lowerCamelCase					:					Union[str, Any]        =	hidden_size
           lowerCamelCase					:					int        =	num_hidden_layers
           lowerCamelCase					:					List[str]        =	num_attention_heads
           lowerCamelCase					:					Optional[Any]        =	intermediate_size
           lowerCamelCase					:					Any        =	hidden_act
           lowerCamelCase					:					Tuple        =	hidden_dropout_prob
           lowerCamelCase					:					Optional[Any]        =	attention_probs_dropout_prob
           lowerCamelCase					:					List[Any]        =	type_sequence_label_size
           lowerCamelCase					:					Tuple        =	initializer_range
           lowerCamelCase					:					Dict        =	mask_ratio
           lowerCamelCase					:					int        =	scope

           # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
           # (we add 1 for the [CLS] token)
           lowerCamelCase					:					Union[str, Any]        =	(image_size // patch_size) ** 2
           lowerCamelCase					:					int        =	int(math.ceil((1 - mask_ratio) * (num_patches + 1)	)	)

     def 		a__						(	self:						int	)->    str:
           lowerCamelCase					:					Any        =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

           lowerCamelCase					:					Union[str, Any]        =	None
           if self.use_labels:
                 lowerCamelCase					:					List[Any]        =	ids_tensor([self.batch_size]     ,		self.type_sequence_label_size	)

           lowerCamelCase					:					Union[str, Any]        =	self.get_config()

           return config, pixel_values, labels

     def 		a__						(	self:						List[str]	)->    List[str]:
           return ViTMAEConfig(
               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     ,		decoder_hidden_size=self.hidden_size     ,		decoder_num_hidden_layers=self.num_hidden_layers     ,		decoder_num_attention_heads=self.num_attention_heads     ,		decoder_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     ,		mask_ratio=self.mask_ratio     ,		)

     def 		a__						(	self:						List[str]     ,		__a:						int     ,		__a:						str     ,		__a:						Union[str, Any]	)->    Tuple:
           lowerCamelCase					:					List[str]        =	TFViTMAEModel(config=A_	)
           lowerCamelCase					:					Any        =	model(A_     ,		training=A_	)
           self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, self.seq_length, self.hidden_size)	)

     def 		a__						(	self:						Optional[Any]     ,		__a:						Dict     ,		__a:						Union[str, Any]     ,		__a:						List[str]	)->    Optional[int]:
           lowerCamelCase					:					List[str]        =	TFViTMAEForPreTraining(A_	)
           lowerCamelCase					:					int        =	model(A_     ,		training=A_	)
           # expected sequence length = num_patches
           lowerCamelCase					:					str        =	(self.image_size // self.patch_size) ** 2
           lowerCamelCase					:					Tuple        =	self.patch_size**2 * self.num_channels
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, num_patches, expected_num_channels)	)

           # test greyscale images
           lowerCamelCase					:					List[str]        =	1
           lowerCamelCase					:					List[str]        =	TFViTMAEForPreTraining(A_	)

           lowerCamelCase					:					Any        =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
           lowerCamelCase					:					Union[str, Any]        =	model(A_     ,		training=A_	)
           lowerCamelCase					:					Union[str, Any]        =	self.patch_size**2
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, num_patches, expected_num_channels)	)







     def 		a__						(	self:						Dict	)->    int:
           lowerCamelCase					:					List[str]        =	self.prepare_config_and_inputs()
           ((lowerCamelCase)     ,			(lowerCamelCase)     ,			(lowerCamelCase))					:					List[Any]        =	config_and_inputs
           lowerCamelCase					:					Union[str, Any]        =	{"""pixel_values""": pixel_values}
           return config, inputs_dict


@require_tf
class 			A__	(						_lowercase ,     _lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					int													=(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
     snake_case__	:					str													={'''feature-extraction''': TFViTMAEModel} if is_tf_available() else {}

     snake_case__	:					Any													=False
     snake_case__	:					Any													=False
     snake_case__	:					str													=False
     snake_case__	:					str													=False

     def 		a__						(	self:						List[Any]	)->    Optional[int]:
           lowerCamelCase					:					List[str]        =	TFViTMAEModelTester(self	)
           lowerCamelCase					:					Optional[int]        =	ConfigTester(self     ,		config_class=A_     ,		has_text_modality=A_     ,		hidden_size=37	)

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

     @unittest.skip(reason="""ViTMAE does not use inputs_embeds"""	)
     def 		a__						(	self:						Any	)->    Any:
           pass

     def 		a__						(	self:						List[str]	)->    List[str]:
           lowerCamelCase     ,			lowerCamelCase					:					Tuple        =	self.model_tester.prepare_config_and_inputs_for_common()

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

     def 		a__						(	self:						Dict	)->    Optional[int]:
           lowerCamelCase     ,			lowerCamelCase					:					List[Any]        =	self.model_tester.prepare_config_and_inputs_for_common()

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

                 lowerCamelCase					:					Tuple        =	["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]     ,		A_	)

     def 		a__						(	self:						List[Any]	)->    List[str]:
           lowerCamelCase					:					Any        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A_	)

     def 		a__						(	self:						Optional[Any]	)->    int:
           lowerCamelCase					:					Union[str, Any]        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_pretraining(*A_	)

     def 		a__						(	self:						List[str]	)->    Optional[Any]:
           # make the mask reproducible
           np.random.seed(2	)

           lowerCamelCase     ,			lowerCamelCase					:					Optional[Any]        =	self.model_tester.prepare_config_and_inputs_for_common()

           lowerCamelCase					:					str        =	int((config.image_size // config.patch_size) ** 2	)
           lowerCamelCase					:					List[Any]        =	np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)

           for model_class in self.all_model_classes:
                 lowerCamelCase					:					str        =	model_class(A_	)
                 lowerCamelCase					:					Dict        =	self._prepare_for_class(A_     ,		A_	)

                 lowerCamelCase					:					Optional[int]        =	model(A_     ,		noise=A_	)

                 lowerCamelCase					:					int        =	copy.deepcopy(self._prepare_for_class(A_     ,		A_	)	)
                 lowerCamelCase					:					str        =	model(**A_     ,		noise=A_	)
                 lowerCamelCase					:					Optional[int]        =	outputs_dict[0].numpy()
                 lowerCamelCase					:					Any        =	outputs_keywords[0].numpy()

                 self.assertLess(np.sum(np.abs(output_dict - output_keywords	)	)     ,		1e-6	)

     def 		a__						(	self:						List[Any]	)->    Dict:
           # make the mask reproducible
           np.random.seed(2	)

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

           lowerCamelCase					:					Optional[Any]        =	int((config.image_size // config.patch_size) ** 2	)
           lowerCamelCase					:					str        =	np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)

           def prepare_numpy_arrays(__a:						Union[str, Any]	):
                 lowerCamelCase					:					Union[str, Any]        =	{}
                 for k, v in inputs_dict.items():
                       if tf.is_tensor(A_	):
                             lowerCamelCase					:					Tuple        =	v.numpy()
                       else:
                             lowerCamelCase					:					int        =	np.array(A_	)

                 return inputs_np_dict

           for model_class in self.all_model_classes:
                 lowerCamelCase					:					List[str]        =	model_class(A_	)

                 lowerCamelCase					:					Dict        =	self._prepare_for_class(A_     ,		A_	)
                 lowerCamelCase					:					Dict        =	prepare_numpy_arrays(A_	)

                 lowerCamelCase					:					Any        =	model(A_     ,		noise=A_	)
                 lowerCamelCase					:					int        =	model(**A_     ,		noise=A_	)
                 self.assert_outputs_same(A_     ,		A_	)

     def 		a__						(	self:						Tuple     ,		__a:						Dict     ,		__a:						Union[str, Any]     ,		__a:						Optional[Any]	)->    Dict:
           # make masks reproducible
           np.random.seed(2	)

           lowerCamelCase					:					Optional[Any]        =	int((tf_model.config.image_size // tf_model.config.patch_size) ** 2	)
           lowerCamelCase					:					Any        =	np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)
           lowerCamelCase					:					Dict        =	tf.constant(A_	)

           # Add `noise` argument.
           # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
           lowerCamelCase					:					List[str]        =	tf_noise

           super().check_pt_tf_models(A_     ,		A_     ,		A_	)

     def 		a__						(	self:						Optional[int]	)->    Any:
           # make mask reproducible
           np.random.seed(2	)

           lowerCamelCase     ,			lowerCamelCase					:					Dict        =	self.model_tester.prepare_config_and_inputs_for_common()

           lowerCamelCase					:					Tuple        =	{
               module_member
               for model_class in self.all_model_classes
               for module in (import_module(model_class.__module__	),)
               for module_member_name in dir(A_	)
               if module_member_name.endswith("""MainLayer"""	)
               # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
               and module_member_name[: -len("""MainLayer"""	)] == model_class.__name__[: -len("""Model"""	)]
               for module_member in (getattr(A_     ,		A_	),)
               if isinstance(A_     ,		A_	)
               and tf.keras.layers.Layer in module_member.__bases__
               and getattr(A_     ,		"""_keras_serializable"""     ,		A_	)
           }

           lowerCamelCase					:					Optional[Any]        =	int((config.image_size // config.patch_size) ** 2	)
           lowerCamelCase					:					str        =	np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)
           lowerCamelCase					:					Optional[Any]        =	tf.convert_to_tensor(A_	)
           inputs_dict.update({"""noise""": noise}	)

           for main_layer_class in tf_main_layer_classes:
                 lowerCamelCase					:					List[str]        =	main_layer_class(A_	)

                 lowerCamelCase					:					Union[str, Any]        =	{
                     name: tf.keras.Input(tensor.shape[1:]     ,		dtype=tensor.dtype	) for name, tensor in inputs_dict.items()
                 }

                 lowerCamelCase					:					List[str]        =	tf.keras.Model(A_     ,		outputs=main_layer(A_	)	)
                 lowerCamelCase					:					Optional[int]        =	model(A_	)

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       lowerCamelCase					:					Any        =	os.path.join(A_     ,		"""keras_model.h5"""	)
                       model.save(A_	)
                       lowerCamelCase					:					str        =	tf.keras.models.load_model(
                           A_     ,		custom_objects={main_layer_class.__name__: main_layer_class}	)
                       assert isinstance(A_     ,		tf.keras.Model	)
                       lowerCamelCase					:					Any        =	model(A_	)
                       self.assert_outputs_same(A_     ,		A_	)

     @slow
     def 		a__						(	self:						Any	)->    str:
           # make mask reproducible
           np.random.seed(2	)

           lowerCamelCase     ,			lowerCamelCase					:					Optional[int]        =	self.model_tester.prepare_config_and_inputs_for_common()

           lowerCamelCase					:					Tuple        =	int((config.image_size // config.patch_size) ** 2	)
           lowerCamelCase					:					Optional[Any]        =	np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)

           for model_class in self.all_model_classes:
                 lowerCamelCase					:					Optional[int]        =	model_class(A_	)
                 lowerCamelCase					:					List[str]        =	self._prepare_for_class(A_     ,		A_	)
                 lowerCamelCase					:					str        =	model(A_     ,		noise=A_	)

                 if model_class.__name__ == "TFViTMAEModel":
                       lowerCamelCase					:					Any        =	outputs.last_hidden_state.numpy()
                       lowerCamelCase					:					List[Any]        =	0
                 else:
                       lowerCamelCase					:					Any        =	outputs.logits.numpy()
                       lowerCamelCase					:					List[Any]        =	0

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       model.save_pretrained(A_     ,		saved_model=A_	)
                       lowerCamelCase					:					Optional[int]        =	model_class.from_pretrained(A_	)
                       lowerCamelCase					:					Tuple        =	model(A_     ,		noise=A_	)

                       if model_class.__name__ == "TFViTMAEModel":
                             lowerCamelCase					:					List[Any]        =	after_outputs["""last_hidden_state"""].numpy()
                             lowerCamelCase					:					Dict        =	0
                       else:
                             lowerCamelCase					:					Tuple        =	after_outputs["""logits"""].numpy()
                             lowerCamelCase					:					int        =	0

                       lowerCamelCase					:					Any        =	np.amax(np.abs(out_a - out_a	)	)
                       self.assertLessEqual(A_     ,		1e-5	)

     def 		a__						(	self:						str	)->    Any:
           # make mask reproducible
           np.random.seed(2	)

           lowerCamelCase     ,			lowerCamelCase					:					Any        =	self.model_tester.prepare_config_and_inputs_for_common()

           lowerCamelCase					:					Tuple        =	int((config.image_size // config.patch_size) ** 2	)
           lowerCamelCase					:					List[Any]        =	np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)

           for model_class in self.all_model_classes:
                 lowerCamelCase					:					List[Any]        =	model_class(A_	)
                 lowerCamelCase					:					Any        =	self._prepare_for_class(A_     ,		A_	)

                 lowerCamelCase					:					Dict        =	model(A_     ,		noise=A_	)
                 lowerCamelCase					:					Any        =	model.get_config()
                 # make sure that returned config is jsonifiable, which is required by keras
                 json.dumps(A_	)
                 lowerCamelCase					:					Optional[int]        =	model_class.from_config(model.get_config()	)
                 # make sure it also accepts a normal config
                 lowerCamelCase					:					List[Any]        =	model_class.from_config(model.config	)
                 lowerCamelCase					:					Optional[int]        =	new_model(A_	)  # Build model
                 new_model.set_weights(model.get_weights()	)
                 lowerCamelCase					:					Any        =	new_model(A_     ,		noise=A_	)

                 self.assert_outputs_same(A_     ,		A_	)

     @unittest.skip(
         reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."""	)
     def 		a__						(	self:						Optional[int]	)->    Optional[Any]:
           pass

     @unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""	)
     def 		a__						(	self:						List[Any]	)->    Dict:
           pass







     @slow
     def 		a__						(	self:						Union[str, Any]	)->    List[str]:
           lowerCamelCase					:					Dict        =	TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224"""	)
           self.assertIsNotNone(A_	)




def   snake_case		(     )   ->      str:
      lowerCamelCase					:					Tuple        =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
      return image






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


     """simple docstring"""

     @cached_property
     def 		a__						(	self:						Dict	)->    Union[str, Any]:
           return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""	) if is_vision_available() else None







     @slow
     def 		a__						(	self:						Union[str, Any]	)->    Optional[int]:
           # make random mask reproducible across the PT and TF model
           np.random.seed(2	)

           lowerCamelCase					:					int        =	TFViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""	)

           lowerCamelCase					:					int        =	self.default_image_processor
           lowerCamelCase					:					Optional[Any]        =	prepare_img()
           lowerCamelCase					:					Any        =	image_processor(images=A_     ,		return_tensors="""tf"""	)

           # prepare a noise vector that will be also used for testing the TF model
           # (this way we can ensure that the PT and TF models operate on the same inputs)
           lowerCamelCase					:					Optional[Any]        =	ViTMAEConfig()
           lowerCamelCase					:					int        =	int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2	)
           lowerCamelCase					:					Union[str, Any]        =	np.random.uniform(size=(1, num_patches)	)

           # forward pass
           lowerCamelCase					:					int        =	model(**A_     ,		noise=A_	)

           # verify the logits
           lowerCamelCase					:					Optional[Any]        =	tf.convert_to_tensor([1, 196, 768]	)
           self.assertEqual(outputs.logits.shape     ,		A_	)

           lowerCamelCase					:					Dict        =	tf.convert_to_tensor(
               [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]]	)

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








"""simple docstring"""


import unittest

from knapsack import greedy_knapsack as kp


class 			A__	(						unittest.TestCase):


     """simple docstring"""

     def 		a__						(	self:						Optional[int]	)->    Union[str, Any]:
           lowerCamelCase					:					Tuple        =	[10, 20, 30, 40, 50, 60]
           lowerCamelCase					:					Union[str, Any]        =	[2, 4, 6, 8, 10, 12]
           lowerCamelCase					:					Union[str, Any]        =	100
           self.assertEqual(kp.calc_profit(__a     ,		__a     ,		__a	)     ,		210	)

     def 		a__						(	self:						str	)->    str:
           self.assertRaisesRegex(__a     ,		"""max_weight must greater than zero."""	)

     def 		a__						(	self:						str	)->    List[Any]:
           self.assertRaisesRegex(__a     ,		"""Weight can not be negative."""	)

     def 		a__						(	self:						Any	)->    Dict:
           self.assertRaisesRegex(__a     ,		"""Profit can not be negative."""	)

     def 		a__						(	self:						Optional[Any]	)->    List[Any]:
           self.assertRaisesRegex(__a     ,		"""max_weight must greater than zero."""	)







     def 		a__						(	self:						Optional[Any]	)->    Tuple:
           self.assertRaisesRegex(
               __a     ,		"""The length of profit and weight must be same."""	)


if __name__ == "__main__":
    unittest.main()




