





"""simple docstring"""





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    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def __init__(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=1_3       ,					__UpperCAmelCase=3_0       ,					__UpperCAmelCase=2       ,					__UpperCAmelCase=3       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=3_2       ,					__UpperCAmelCase=5       ,					__UpperCAmelCase=4       ,					__UpperCAmelCase=3_7       ,					__UpperCAmelCase="gelu"       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=1_0       ,					__UpperCAmelCase=0.02       ,					):



         '''simple docstring'''

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

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






       def 	snake_case		(       self ):



         '''simple docstring'''

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

         lowerCAmelCase__     :List[str]											=				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       ,					)

         return config, pixel_values






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				FlaxViTModel(config=__UpperCAmelCase )
         lowerCAmelCase__     :Dict											=				model(__UpperCAmelCase )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         lowerCAmelCase__     :Tuple											=				(self.image_size, self.image_size)
         lowerCAmelCase__     :Optional[Any]											=				(self.patch_size, self.patch_size)
         lowerCAmelCase__     :Tuple											=				(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 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				self.type_sequence_label_size
         lowerCAmelCase__     :str											=				FlaxViTForImageClassification(config=__UpperCAmelCase )
         lowerCAmelCase__     :str											=				model(__UpperCAmelCase )
         self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.type_sequence_label_size) )

         # test greyscale images
         lowerCAmelCase__     :Optional[int]											=				1
         lowerCAmelCase__     :Union[str, Any]											=				FlaxViTForImageClassification(__UpperCAmelCase )

         lowerCAmelCase__     :List[str]											=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
         lowerCAmelCase__     :Tuple											=				model(__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				self.prepare_config_and_inputs()
         (
             (
             lowerCAmelCase__
         )				,							(
             lowerCAmelCase__
         )				,							
         )     :str											=				config_and_inputs
         lowerCAmelCase__     :Union[str, Any]											=				{'pixel_values': pixel_values}
         return config, inputs_dict






@require_flax
class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




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






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				FlaxViTModelTester(self )
         lowerCAmelCase__     :Dict											=				ConfigTester(self       ,					config_class=__UpperCAmelCase       ,					has_text_modality=__UpperCAmelCase       ,					hidden_size=3_7 )






       def 	snake_case		(       self ):



         '''simple docstring'''

         self.config_tester.run_common_tests()






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :Union[str, Any]											=				self.model_tester.prepare_config_and_inputs_for_common()

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

           lowerCAmelCase__     :Any											=				['pixel_values']
           self.assertListEqual(arg_names[:1]       ,					__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :int											=				self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__ ):
             lowerCAmelCase__     :Union[str, Any]											=				self._prepare_for_class(__UpperCAmelCase       ,					__UpperCAmelCase )
             lowerCAmelCase__     :Optional[int]											=				model_class(__UpperCAmelCase )

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

             with self.subTest('JIT Enabled' ):
               lowerCAmelCase__     :List[Any]											=				model_jitted(**__UpperCAmelCase ).to_tuple()

             with self.subTest('JIT Disabled' ):
               with jax.disable_jit():
                 lowerCAmelCase__     :List[str]											=				model_jitted(**__UpperCAmelCase ).to_tuple()

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






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         for model_class_name in self.all_model_classes:
           lowerCAmelCase__     :Optional[int]											=				model_class_name.from_pretrained('google/vit-base-patch16-224' )
           lowerCAmelCase__     :List[Any]											=				model(np.ones((1, 3, 2_2_4, 2_2_4) ) )
           self.assertIsNotNone(__UpperCAmelCase )


"""simple docstring"""




from __future__ import annotations



def     __magic_name__		(					UpperCamelCase     :		str							,		UpperCamelCase     :		list[str] | None = None     )     ->						list[list[str]]:
    a__		=			word_bank or []
    # create a table
    a__		=			len(UpperCamelCase     ) + 1

    a__		=			[]
    for _ in range(UpperCamelCase     ):
        table.append([]     )
    # seed value
    a__		=			[[]]  # because empty string has empty combination

    # iterate through the indices
    for i in range(UpperCamelCase     ):
        # condition
        if table[i] != []:
            for word in word_bank:
                # slice condition
                if target[i : i + len(UpperCamelCase     )] == word:
                    a__		=			[
                        [word, *way] for way in table[i]
                    ]
                    # adds the word to every combination the current position holds
                    # now,push that combination to the table[i+len(word)]
                    table[i + len(UpperCamelCase     )] += new_combinations

    # combinations are in reverse order so reverse for better output
    for combination in table[len(UpperCamelCase     )]:
        combination.reverse()

    return table[len(UpperCamelCase     )]


if __name__ == "__main__":
    print(all_construct('jwajalapa', ['jwa', 'j', 'w', 'a', 'la', 'lapa']))
    print(all_construct('rajamati', ['s', 'raj', 'amat', 'raja', 'ma', 'i', 't']))
    print(
        all_construct(
            'hexagonosaurus',
            ['h', 'ex', 'hex', 'ag', 'ago', 'ru', 'auru', 'rus', 'go', 'no', 'o', 's'],
        )
    )


