


from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
       import tensorflow as tf

       from transformers import TFBlipTextModel
       from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST

class 					__magic_name__		:


     def __init__(					self							:   int							,		lowerCamelCase__							:   List[str]							,		lowerCamelCase__							:   Optional[Any]=12							,		lowerCamelCase__							:   Optional[Any]=7							,		lowerCamelCase__							:   List[str]=True							,		lowerCamelCase__							:   Dict=True							,		lowerCamelCase__							:   Dict=True							,		lowerCamelCase__							:   Optional[int]=99							,		lowerCamelCase__							:   int=32							,		lowerCamelCase__							:   Any=32							,		lowerCamelCase__							:   List[Any]=2							,		lowerCamelCase__							:   Tuple=4							,		lowerCamelCase__							:   Union[str, Any]=37							,		lowerCamelCase__							:   Dict=0.1							,		lowerCamelCase__							:   int=0.1							,		lowerCamelCase__							:   Tuple=512							,		lowerCamelCase__							:   Dict=0.02							,		lowerCamelCase__							:   List[Any]=0							,		lowerCamelCase__							:   Optional[int]=None							,		)     ->					str:





      '''simple docstring'''






      UpperCamelCase__    :			Union[str, Any]      				=      parent
      UpperCamelCase__    :			Union[str, Any]      				=      batch_size
      UpperCamelCase__    :			Optional[Any]      				=      seq_length
      UpperCamelCase__    :			Any      				=      is_training
      UpperCamelCase__    :			Dict      				=      use_input_mask
      UpperCamelCase__    :			str      				=      use_labels
      UpperCamelCase__    :			Dict      				=      vocab_size
      UpperCamelCase__    :			int      				=      hidden_size
      UpperCamelCase__    :			List[Any]      				=      projection_dim
      UpperCamelCase__    :			Union[str, Any]      				=      num_hidden_layers
      UpperCamelCase__    :			Union[str, Any]      				=      num_attention_heads
      UpperCamelCase__    :			Optional[Any]      				=      intermediate_size
      UpperCamelCase__    :			Optional[int]      				=      dropout
      UpperCamelCase__    :			Optional[Any]      				=      attention_dropout
      UpperCamelCase__    :			int      				=      max_position_embeddings
      UpperCamelCase__    :			int      				=      initializer_range
      UpperCamelCase__    :			Dict      				=      scope
      UpperCamelCase__    :			Any      				=      bos_token_id


     def    UpperCAmelCase__		(					self							:   Tuple	)     ->					List[str]:





      '''simple docstring'''






      UpperCamelCase__    :			Dict      				=      ids_tensor([self.batch_size, self.seq_length]							,		self.vocab_size	)

      UpperCamelCase__    :			List[str]      				=      None
      if self.use_input_mask:
       UpperCamelCase__    :			Tuple      				=      random_attention_mask([self.batch_size, self.seq_length]	)

      if input_mask is not None:
       UpperCamelCase__    :			Any      				=      input_mask.numpy()
       UpperCamelCase__				, UpperCamelCase__    :			Any      				=      input_mask.shape
       UpperCamelCase__    :			Union[str, Any]      				=      np.random.randint(1							,		seq_length - 1							,		size=(batch_size,)	)
       for batch_idx, start_index in enumerate(lowerCamelCase__	):
        UpperCamelCase__    :			List[Any]      				=      1
        UpperCamelCase__    :			str      				=      0

      UpperCamelCase__    :			Tuple      				=      self.get_config()

      return config, input_ids, tf.convert_to_tensor(lowerCamelCase__	)


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





      '''simple docstring'''






      return BlipTextConfig(
          vocab_size=self.vocab_size							,		hidden_size=self.hidden_size							,		projection_dim=self.projection_dim							,		num_hidden_layers=self.num_hidden_layers							,		num_attention_heads=self.num_attention_heads							,		intermediate_size=self.intermediate_size							,		dropout=self.dropout							,		attention_dropout=self.attention_dropout							,		max_position_embeddings=self.max_position_embeddings							,		initializer_range=self.initializer_range							,		bos_token_id=self.bos_token_id							,		)


     def    UpperCAmelCase__		(					self							:   Dict							,		lowerCamelCase__							:   Tuple							,		lowerCamelCase__							:   int							,		lowerCamelCase__							:   Optional[int]	)     ->					List[Any]:





      '''simple docstring'''






      UpperCamelCase__    :			List[str]      				=      TFBlipTextModel(config=lowerCamelCase__	)
      UpperCamelCase__    :			List[Any]      				=      model(lowerCamelCase__							,		attention_mask=lowerCamelCase__							,		training=lowerCamelCase__	)
      UpperCamelCase__    :			Union[str, Any]      				=      model(lowerCamelCase__							,		training=lowerCamelCase__	)
      self.parent.assertEqual(result.last_hidden_state.shape							,		(self.batch_size, self.seq_length, self.hidden_size)	)
      self.parent.assertEqual(result.pooler_output.shape							,		(self.batch_size, self.hidden_size)	)


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





      '''simple docstring'''






      UpperCamelCase__    :			List[Any]      				=      self.prepare_config_and_inputs()
      UpperCamelCase__				, UpperCamelCase__				, UpperCamelCase__    :			List[str]      				=      config_and_inputs
      UpperCamelCase__    :			Optional[Any]      				=      {'''input_ids''': input_ids, '''attention_mask''': input_mask}
      return config, inputs_dict



@require_tf
class 					__magic_name__		(   __lowerCAmelCase    , unittest.TestCase):
     A:						Tuple								=					(TFBlipTextModel,) if is_tf_available() else ()
     A:						List[Any]								=					False
     A:						List[Any]								=					False
     A:						List[str]								=					False


     def    UpperCAmelCase__		(					self							:   Dict	)     ->					Tuple:





      '''simple docstring'''






      UpperCamelCase__    :			int      				=      BlipTextModelTester(self	)
      UpperCamelCase__    :			Optional[Any]      				=      ConfigTester(self							,		config_class=lowerCamelCase__							,		hidden_size=37	)


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





      '''simple docstring'''






      self.config_tester.run_common_tests()


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





      '''simple docstring'''






      UpperCamelCase__    :			Union[str, Any]      				=      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase__	)


     def    UpperCAmelCase__		(					self							:   Optional[Any]	)     ->					Dict:





      '''simple docstring'''






      pass


     def    UpperCAmelCase__		(					self							:   str	)     ->					Dict:





      '''simple docstring'''






      pass


     @unittest.skip(reason='''Blip does not use inputs_embeds'''	)
     def    UpperCAmelCase__		(					self							:   List[Any]	)     ->					Dict:





      '''simple docstring'''






      pass


     @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''	)
     def    UpperCAmelCase__		(					self							:   Optional[Any]	)     ->					str:





      '''simple docstring'''






      pass


     @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''	)
     def    UpperCAmelCase__		(					self							:   List[Any]	)     ->					Tuple:





      '''simple docstring'''






      pass


     @slow
     def    UpperCAmelCase__		(					self							:   List[str]	)     ->					int:





      '''simple docstring'''






      for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCamelCase__    :			List[Any]      				=      TFBlipTextModel.from_pretrained(lowerCamelCase__	)
       self.assertIsNotNone(lowerCamelCase__	)


     def    UpperCAmelCase__		(					self							:   str							,		lowerCamelCase__							:   Optional[Any]=True	)     ->					Any:





      '''simple docstring'''






      super().test_pt_tf_model_equivalence(allow_missing_keys=lowerCamelCase__	)







'''simple docstring'''





def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     _a      =  int(__A)

     # Initialize Result
     _a      =  []

     # Traverse through all denomination
     for denomination in reversed(__A):
          # Find denominations
          while int(__A) >= int(__A):
               total_value -= int(__A)
               answer.append(__A)  # Append the "answers" array

     return answer


# Driver Code
if __name__ == "__main__":
  lowercase_		       =  []
  lowercase_		       =  "0"

  if (
      input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
      == "y"
  ):
    lowercase_		       =  int(input("Enter the number of denominations you want to add: ").strip())

    for i in range(0, n):
      denominations.append(int(input(F"""Denomination {i}: """).strip()))
    lowercase_		       =  input("Enter the change you want to make in Indian Currency: ").strip()
  else:
    # All denominations of Indian Currency if user does not enter
    lowercase_		       =  [1, 2, 5, 10, 20, 50, 100, 500, 2_000]
    lowercase_		       =  input("Enter the change you want to make: ").strip()

  if int(value) == 0 or int(value) < 0:
    print("The total value cannot be zero or negative.")

  else:
    print(F"""Following is minimal change for {value}: """)
    lowercase_		       =  find_minimum_change(denominations, value)
    # Print result
    for i in range(len(answer)):
      print(answer[i], end=" ")



