






"""simple docstring"""


from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFTransfoXLForSequenceClassification,
       TFTransfoXLLMHeadModel,
       TFTransfoXLModel,
   )






class 						snake_case  :





    def __init__(							self     : Dict    ,		A     : List[Any]    ,		):


      '''simple docstring'''




      a					:      Union[str, Any]    =       parent
      a					:      Dict    =       1_3
      a					:      Dict    =       7
      a					:      Dict    =       3_0
      a					:      Dict    =       self.seq_length + self.mem_len
      a					:      List[Any]    =       1_5
      a					:      List[Any]    =       True
      a					:      Union[str, Any]    =       True
      a					:      str    =       9_9
      a					:      Tuple    =       [1_0, 5_0, 8_0]
      a					:      int    =       3_2
      a					:      str    =       3_2
      a					:      Optional[Any]    =       4
      a					:      int    =       8
      a					:      Optional[Any]    =       1_2_8
      a					:      str    =       2
      a					:      List[Any]    =       2
      a					:      Dict    =       None
      a					:      int    =       1
      a					:      List[str]    =       0
      a					:      Any    =       3
      a					:      Optional[Any]    =       self.vocab_size - 1
      a					:      Union[str, Any]    =       0.01





    def     lowerCamelCase__				(							self     : Union[str, Any]      ):


      '''simple docstring'''




      a					:      Optional[int]    =       ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size      )
      a					:      Optional[Any]    =       ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size      )

      a					:      str    =       None
      if self.use_labels:
        a					:      Dict    =       ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size      )

      a					:      Union[str, Any]    =       TransfoXLConfig(
          vocab_size=self.vocab_size    ,		mem_len=self.mem_len    ,		clamp_len=self.clamp_len    ,		cutoffs=self.cutoffs    ,		d_model=self.hidden_size    ,		d_embed=self.d_embed    ,		n_head=self.num_attention_heads    ,		d_head=self.d_head    ,		d_inner=self.d_inner    ,		div_val=self.div_val    ,		n_layer=self.num_hidden_layers    ,		eos_token_id=self.eos_token_id    ,		pad_token_id=self.vocab_size - 1    ,		init_range=self.init_range    ,		num_labels=self.num_labels    ,		)

      return (config, input_ids_a, input_ids_a, lm_labels)





    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      random.seed(self.seed      )
      tf.random.set_seed(self.seed      )





    def     lowerCamelCase__				(							self     : Dict    ,		A     : List[str]    ,		A     : Dict    ,		A     : Tuple    ,		A     : str      ):


      '''simple docstring'''




      a					:      Tuple    =       TFTransfoXLModel(A      )

      a,					a					:      List[Any]    =       model(A      ).to_tuple()

      a					:      Union[str, Any]    =       {'input_ids': input_ids_a, 'mems': mems_a}

      a,					a					:      List[str]    =       model(A      ).to_tuple()

      self.parent.assertEqual(hidden_states_a.shape    ,		(self.batch_size, self.seq_length, self.hidden_size)      )
      self.parent.assertEqual(hidden_states_a.shape    ,		(self.batch_size, self.seq_length, self.hidden_size)      )
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a]    ,		[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers    ,		)
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a]    ,		[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers    ,		)





    def     lowerCamelCase__				(							self     : List[str]    ,		A     : Union[str, Any]    ,		A     : Tuple    ,		A     : str    ,		A     : Dict      ):


      '''simple docstring'''




      a					:      List[str]    =       TFTransfoXLLMHeadModel(A      )

      a,					a					:      List[str]    =       model(A      ).to_tuple()

      a					:      int    =       {'input_ids': input_ids_a, 'labels': lm_labels}
      a,					a					:      Tuple    =       model(A      ).to_tuple()

      a,					a					:      Any    =       model([input_ids_a, mems_a]      ).to_tuple()

      a					:      Optional[int]    =       {'input_ids': input_ids_a, 'mems': mems_a, 'labels': lm_labels}

      a,					a					:      Optional[Any]    =       model(A      ).to_tuple()

      self.parent.assertEqual(lm_logits_a.shape    ,		(self.batch_size, self.seq_length, self.vocab_size)      )
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a]    ,		[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers    ,		)

      self.parent.assertEqual(lm_logits_a.shape    ,		(self.batch_size, self.seq_length, self.vocab_size)      )
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a]    ,		[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers    ,		)





    def     lowerCamelCase__				(							self     : Optional[int]    ,		A     : Any    ,		A     : Dict    ,		A     : Tuple    ,		A     : str      ):


      '''simple docstring'''




      a					:      List[Any]    =       TFTransfoXLForSequenceClassification(A      )
      a					:      List[Any]    =       model(A      )
      self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels)      )





    def     lowerCamelCase__				(							self     : Any      ):


      '''simple docstring'''




      a					:      Tuple    =       self.prepare_config_and_inputs()
      ((a),					(a),					(a),					(a))					:      Tuple    =       config_and_inputs
      a					:      str    =       {'input_ids': input_ids_a}
      return config, inputs_dict






@require_tf
class 						snake_case  (  UpperCAmelCase       ,			UpperCAmelCase       ,			unittest.TestCase	):
    __magic_name__   					=					(
        (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
    )
    __magic_name__   					=					() if is_tf_available() else ()
    __magic_name__   					=					(
        {
            '''feature-extraction''': TFTransfoXLModel,
            '''text-classification''': TFTransfoXLForSequenceClassification,
            '''text-generation''': TFTransfoXLLMHeadModel,
            '''zero-shot''': TFTransfoXLForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
    __magic_name__   					=					False
    __magic_name__   					=					False
    __magic_name__   					=					False
    __magic_name__   					=					False





    def     lowerCamelCase__				(							self     : Tuple    ,		A     : Dict    ,		A     : List[str]    ,		A     : Optional[Any]    ,		A     : Dict    ,		A     : Optional[int]      ):


      '''simple docstring'''




      if pipeline_test_casse_name == "TextGenerationPipelineTests":
        # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
        # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
        # tokenizer.
        return True

      return False





    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      a					:      Tuple    =       TFTransfoXLModelTester(self      )
      a					:      str    =       ConfigTester(self    ,		config_class=A    ,		d_embed=3_7      )





    def     lowerCamelCase__				(							self     : Tuple      ):


      '''simple docstring'''




      self.config_tester.run_common_tests()





    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      self.model_tester.set_seed()
      a					:      List[Any]    =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_model(*A      )





    def     lowerCamelCase__				(							self     : int      ):


      '''simple docstring'''




      self.model_tester.set_seed()
      a					:      List[Any]    =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_lm_head(*A      )





    def     lowerCamelCase__				(							self     : Tuple      ):


      '''simple docstring'''




      a					:      Dict    =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*A      )





    def     lowerCamelCase__				(							self     : int      ):


      '''simple docstring'''




      a,					a					:      Tuple    =       self.model_tester.prepare_config_and_inputs_for_common()
      a					:      int    =       [TFTransfoXLForSequenceClassification]

      for model_class in self.all_model_classes:
        a					:      Optional[int]    =       model_class(A      )
        assert isinstance(model.get_input_embeddings()    ,		tf.keras.layers.Layer      )
        if model_class in list_other_models_with_output_ebd:
          a					:      Optional[Any]    =       model.get_output_embeddings()
          assert isinstance(A    ,		tf.keras.layers.Layer      )
          a					:      Optional[Any]    =       model.get_bias()
          assert name is None
        else:
          a					:      Dict    =       model.get_output_embeddings()
          assert x is None
          a					:      Any    =       model.get_bias()
          assert name is None





    def     lowerCamelCase__				(							self     : Dict      ):


      '''simple docstring'''




      pass





    @slow
    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a					:      int    =       TFTransfoXLModel.from_pretrained(A      )
        self.assertIsNotNone(A      )





    @unittest.skip(reason='This model doesn\'t play well with fit() due to not returning a single loss.'      )
    def     lowerCamelCase__				(							self     : int      ):


      '''simple docstring'''




      pass






@require_tf
class 						snake_case  (  unittest.TestCase	):





    @unittest.skip('Skip test until #12651 is resolved.'      )
    @slow
    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      a					:      List[Any]    =       TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103'      )
      # fmt: off
      a					:      List[Any]    =       tf.convert_to_tensor([[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0]]    ,		dtype=tf.intaa      )  # noqa: E231
      # fmt: on
      #  In 1991 , the remains of Russian Tsar Nicholas II and his family
      #  ( except for Alexei and Maria ) are discovered .
      #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
      #  remainder of the story . 1883 Western Siberia ,
      #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
      #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
      #  father initially slaps him for making such an accusation , Rasputin watches as the
      #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
      #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
      #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

      # fmt: off
      a					:      Optional[Any]    =       [3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0,3_3,1,1_8_5_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_8,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,0]  # noqa: E231
      # fmt: on
      #  In 1991, the remains of Russian Tsar Nicholas II and his family (
      #  except for Alexei and Maria ) are discovered. The voice of young son,
      #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
      #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
      #  and a group of men to perform magic. Rasputin has a vision and
      #  denounces one of the men as a horse thief. Although his father initially
      #  slaps him for making such an accusation, Rasputin watches as the man
      #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
      #  of the Virgin Mary, prompting him to become a priest.
      #  Rasputin quickly becomes famous, with people, even a bishop, begging for
      #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
      # Nicholas II and his family were discovered. The voice of <unk> young son,
      # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

      a					:      str    =       model.generate(A    ,		max_length=2_0_0    ,		do_sample=A      )
      self.assertListEqual(output_ids[0].numpy().tolist()    ,		A      )












"""simple docstring"""


def       snake_case  (A_				:list[int]     ,      A_				:str				):







  '''simple docstring'''

  a					:      Optional[int]    =       int(A_				)

  # Initialize Result
  a					:      int    =       []

  # 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__":
   _UpperCamelCase    :   Dict   			=						[]
   _UpperCamelCase    :   str   			=						'0'

   if (
       input('Do you want to enter your denominations ? (yY/n): ').strip().lower()
       == "y"
   ):
      _UpperCamelCase    :   Dict   			=						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()))
      _UpperCamelCase    :   Optional[int]   			=						input('Enter the change you want to make in Indian Currency: ').strip()
   else:
      # All denominations of Indian Currency if user does not enter
      _UpperCamelCase    :   Union[str, Any]   			=						[1, 2, 5, 10, 20, 50, 100, 500, 2000]
      _UpperCamelCase    :   Dict   			=						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}: ''')
      _UpperCamelCase    :   Tuple   			=						find_minimum_change(denominations, value)
      # Print result
      for i in range(len(answer)):
         print(answer[i], end=' ')




