




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 						_A (  unittest.TestCase				):






  def __init__(  self		:     List[Any]				,						_A		:     List[Any]				,						_A		:     List[Any]=13				,						_A		:     Optional[Any]=30				,						_A		:     List[str]=2				,						_A		:     Optional[Any]=3				,						_A		:     Optional[int]=True				,						_A		:     List[Any]=True				,						_A		:     int=32				,						_A		:     Any=5				,						_A		:     Optional[int]=4				,						_A		:     List[str]=37				,						_A		:     Tuple="gelu"				,						_A		:     Union[str, Any]=0.1				,						_A		:     Optional[int]=0.1				,						_A		:     Optional[int]=10				,						_A		:     str=0.02				,						)		->   str:






       """simple docstring"""







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

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






  def  __a      (  self		:     List[Any]				)		->   Optional[Any]:






       """simple docstring"""







       lowercase :       List[Any]       =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

       lowercase :       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=_A				,						initializer_range=self.initializer_range				,						)

       return config, pixel_values






  def  __a      (  self		:     Tuple				,						_A		:     Any				,						_A		:     List[Any]				)		->   str:






       """simple docstring"""







       lowercase :       Any       =			FlaxViTModel(config=_A				)
       lowercase :       Tuple       =			model(_A				)
       # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
       lowercase :       List[str]       =			(self.image_size, self.image_size)
       lowercase :       Dict       =			(self.patch_size, self.patch_size)
       lowercase :       List[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  __a      (  self		:     str				,						_A		:     str				,						_A		:     List[Any]				)		->   List[Any]:






       """simple docstring"""







       lowercase :       Dict       =			self.type_sequence_label_size
       lowercase :       List[Any]       =			FlaxViTForImageClassification(config=_A				)
       lowercase :       List[Any]       =			model(_A				)
       self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.type_sequence_label_size)				)

       # test greyscale images
       lowercase :       Dict       =			1
       lowercase :       Optional[Any]       =			FlaxViTForImageClassification(_A				)

       lowercase :       List[Any]       =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
       lowercase :       Optional[int]       =			model(_A				)






  def  __a      (  self		:     Optional[Any]				)		->   List[Any]:






       """simple docstring"""







       lowercase :       str       =			self.prepare_config_and_inputs()
       (
           lowercase	
       ) :       Tuple       =			config_and_inputs
       lowercase :       Any       =			{'''pixel_values''': pixel_values}
       return config, inputs_dict


@require_flax
class 						_A (  _lowerCamelCase      ,       unittest.TestCase				):
  _UpperCamelCase       :      Any			=      (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()






  def  __a      (  self		:     Tuple				)		->   None:






       """simple docstring"""







       lowercase :       Optional[Any]       =			FlaxViTModelTester(self				)
       lowercase :       List[Any]       =			ConfigTester(self				,						config_class=_A				,						has_text_modality=_A				,						hidden_size=37				)






  def  __a      (  self		:     Union[str, Any]				)		->   Union[str, Any]:






       """simple docstring"""







       self.config_tester.run_common_tests()






  def  __a      (  self		:     Optional[Any]				)		->   int:






       """simple docstring"""







       lowercase :       int       =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_A				)






  def  __a      (  self		:     int				)		->   List[str]:






       """simple docstring"""







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






  def  __a      (  self		:     Optional[Any]				)		->   str:






       """simple docstring"""







       lowercase :       int       =			self.model_tester.prepare_config_and_inputs_for_common()

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

            lowercase :       Optional[Any]       =			['''pixel_values''']
            self.assertListEqual(arg_names[:1]				,						_A				)






  def  __a      (  self		:     Union[str, Any]				)		->   Dict:






       """simple docstring"""







       lowercase :       List[str]       =			self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__				):
                 lowercase :       List[str]       =			self._prepare_for_class(_A				,						_A				)
                 lowercase :       Optional[int]       =			model_class(_A				)

                 @jax.jit
                 def model_jitted(_A		:     Dict				,						**_A		:     List[str]				):
                      return model(pixel_values=_A				,						**_A				)

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

                 with self.subTest('''JIT Disabled'''				):
                      with jax.disable_jit():
                           lowercase :       Dict       =			model_jitted(**_A				).to_tuple()

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






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






       """simple docstring"""







       for model_class_name in self.all_model_classes:
            lowercase :       List[str]       =			model_class_name.from_pretrained('''google/vit-base-patch16-224'''				)
            lowercase :       Optional[int]       =			model(np.ones((1, 3, 224, 224)				)				)
            self.assertIsNotNone(_A				)









def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__   )			->							int:





     '''simple docstring'''


     if len(__magic_name__   ) != len(__magic_name__   ):
          raise ValueError('''The length of profit and weight must be same.'''   )
     if max_weight <= 0:
          raise ValueError('''max_weight must greater than zero.'''   )
     if any(p < 0 for p in profit   ):
          raise ValueError('''Profit can not be negative.'''   )
     if any(w < 0 for w in weight   ):
          raise ValueError('''Weight can not be negative.'''   )

     # List created to store profit gained for the 1kg in case of each weight
     # respectively.  Calculate and append profit/weight for each element.
     lowercase :       str       =			[p / w for p, w in zip(__magic_name__			,		__magic_name__   )]

     # Creating a copy of the list and sorting profit/weight in ascending order
     lowercase :       str       =			sorted(__magic_name__   )

     # declaring useful variables
     lowercase :       Union[str, Any]       =			len(__magic_name__   )
     lowercase :       Optional[int]       =			0
     lowercase :       Optional[int]       =			0
     lowercase :       int       =			0

     # loop till the total weight do not reach max limit e.g. 15 kg and till i<length
     while limit <= max_weight and i < length:
          # flag value for encountered greatest element in sorted_profit_by_weight
          lowercase :       Optional[int]       =			sorted_profit_by_weight[length - i - 1]
          lowercase :       Union[str, Any]       =			profit_by_weight.index(__magic_name__   )
          lowercase :       Any       =			-1

          # check if the weight encountered is less than the total weight
          # encountered before.
          if max_weight - limit >= weight[index]:
               limit += weight[index]
               # Adding profit gained for the given weight 1 ===
               # weight[index]/weight[index]
               gain += 1 * profit[index]
          else:
               # Since the weight encountered is greater than limit, therefore take the
               # required number of remaining kgs and calculate profit for it.
               # weight remaining / weight[index]
               gain += (max_weight - limit) / weight[index] * profit[index]
               break
          i += 1
     return gain


if __name__ == "__main__":
 print(
     'Input profits, weights, and then max_weight (all positive ints) separated by '
     'spaces.'
 )

 lowerCAmelCase_						=			[int(x) for x in input('Input profits separated by spaces: ').split()]
 lowerCAmelCase_						=			[int(x) for x in input('Input weights separated by spaces: ').split()]
 lowerCAmelCase_						=			int(input('Max weight allowed: '))

 # Function Call
 calc_profit(profit, weight, max_weight)