




"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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 TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel






@require_tf
class       lowercase:







 '''simple docstring'''





 lowercase__				      =      BlenderbotConfig
 lowercase__				      =      {}
 lowercase__				      =      """gelu"""


 def __init__(       self:	List[str],   a_:	List[str],   a_:	Tuple=13,   a_:	int=7,   a_:	str=True,   a_:	Optional[Any]=False,   a_:	Union[str, Any]=99,   a_:	int=32,   a_:	Optional[int]=2,   a_:	List[Any]=4,   a_:	Any=37,   a_:	int=0.1,   a_:	str=0.1,   a_:	List[Any]=20,   a_:	Optional[int]=2,   a_:	List[str]=1,   a_:	Tuple=0,   ):




   '''simple docstring'''



   _snake_case							:	List[str]			=						parent
   _snake_case							:	List[str]			=						batch_size
   _snake_case							:	Optional[Any]			=						seq_length
   _snake_case							:	Any			=						is_training
   _snake_case							:	Optional[int]			=						use_labels
   _snake_case							:	Any			=						vocab_size
   _snake_case							:	Tuple			=						hidden_size
   _snake_case							:	Tuple			=						num_hidden_layers
   _snake_case							:	str			=						num_attention_heads
   _snake_case							:	str			=						intermediate_size

   _snake_case							:	Union[str, Any]			=						hidden_dropout_prob
   _snake_case							:	int			=						attention_probs_dropout_prob
   _snake_case							:	Optional[Any]			=						max_position_embeddings
   _snake_case							:	Any			=						eos_token_id
   _snake_case							:	Optional[int]			=						pad_token_id
   _snake_case							:	Any			=						bos_token_id


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	str			=						ids_tensor([self.batch_size, self.seq_length - 1],   self.vocab_size	)
   _snake_case							:	Tuple			=						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size	),   1	)
   _snake_case							:	Any			=						tf.concat([input_ids, eos_tensor],   axis=1	)

   _snake_case							:	List[str]			=						ids_tensor([self.batch_size, self.seq_length],   self.vocab_size	)

   _snake_case							:	str			=						self.config_cls(
       vocab_size=self.vocab_size,   d_model=self.hidden_size,   encoder_layers=self.num_hidden_layers,   decoder_layers=self.num_hidden_layers,   encoder_attention_heads=self.num_attention_heads,   decoder_attention_heads=self.num_attention_heads,   encoder_ffn_dim=self.intermediate_size,   decoder_ffn_dim=self.intermediate_size,   dropout=self.hidden_dropout_prob,   attention_dropout=self.attention_probs_dropout_prob,   max_position_embeddings=self.max_position_embeddings,   eos_token_ids=[2],   bos_token_id=self.bos_token_id,   pad_token_id=self.pad_token_id,   decoder_start_token_id=self.pad_token_id,   **self.config_updates,   )
   _snake_case							:	Optional[Any]			=						prepare_blenderbot_inputs_dict(lowerCAmelCase__,   lowerCAmelCase__,   lowerCAmelCase__	)
   return config, inputs_dict


 def 				UpperCamelCase_     (       self:	Optional[int],   a_:	Any,   a_:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						TFBlenderbotModel(config=lowerCAmelCase__	).get_decoder()
   _snake_case							:	Any			=						inputs_dict["input_ids"]

   _snake_case							:	Tuple			=						input_ids[:1, :]
   _snake_case							:	Dict			=						inputs_dict["attention_mask"][:1, :]
   _snake_case							:	List[Any]			=						inputs_dict["head_mask"]
   _snake_case							:	Any			=						1

   # first forward pass
   _snake_case							:	Optional[Any]			=						model(lowerCAmelCase__,   attention_mask=lowerCAmelCase__,   head_mask=lowerCAmelCase__,   use_cache=lowerCAmelCase__	)

   _snake_case							:	Optional[int]			=						outputs.to_tuple()

   # create hypothetical next token and extent to next_input_ids
   _snake_case							:	List[str]			=						ids_tensor((self.batch_size, 3),   config.vocab_size	)
   _snake_case							:	Any			=						tf.cast(ids_tensor((self.batch_size, 3),   2	),   tf.inta	)

   # append to next input_ids and
   _snake_case							:	int			=						tf.concat([input_ids, next_tokens],   axis=-1	)
   _snake_case							:	List[Any]			=						tf.concat([attention_mask, next_attn_mask],   axis=-1	)

   _snake_case							:	Tuple			=						model(lowerCAmelCase__,   attention_mask=lowerCAmelCase__	)[0]
   _snake_case							:	List[Any]			=						model(lowerCAmelCase__,   attention_mask=lowerCAmelCase__,   past_key_values=lowerCAmelCase__	)[0]

   self.parent.assertEqual(next_tokens.shape[1],   output_from_past.shape[1]	)

   # select random slice
   _snake_case							:	int			=						int(ids_tensor((1,),   output_from_past.shape[-1]	)	)
   _snake_case							:	int			=						output_from_no_past[:, -3:, random_slice_idx]
   _snake_case							:	Any			=						output_from_past[:, :, random_slice_idx]

   # test that outputs are equal for slice
   tf.debugging.assert_near(lowerCAmelCase__,   lowerCAmelCase__,   rtol=1E-3	)







def       UpperCAmelCase__ (snake_case__							:      Dict	,     snake_case__							:      Any	,     snake_case__							:      Optional[Any]	,     snake_case__							:      Any=None	,     snake_case__							:      Union[str, Any]=None	,     snake_case__							:      Union[str, Any]=None	,     snake_case__							:      Tuple=None	,     snake_case__							:      Dict=None	,     ):
  """simple docstring"""

  if attention_mask is None:
    _snake_case							:	str			=						tf.cast(tf.math.not_equal(_lowercase	,     config.pad_token_id					)	,     tf.inta					)
  if decoder_attention_mask is None:
    _snake_case							:	Union[str, Any]			=						tf.concat(
        [
            tf.ones(decoder_input_ids[:, :1].shape	,     dtype=tf.inta					),
            tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]	,     config.pad_token_id					)	,     tf.inta					),
        ]	,     axis=-1	,     )
  if head_mask is None:
    _snake_case							:	int			=						tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
  if decoder_head_mask is None:
    _snake_case							:	Optional[int]			=						tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
  if cross_attn_head_mask is None:
    _snake_case							:	List[Any]			=						tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": decoder_attention_mask,
      "head_mask": head_mask,
      "decoder_head_mask": decoder_head_mask,
      "cross_attn_head_mask": cross_attn_head_mask,
  }






@require_tf
class       lowercase(  a__ ,    a__ ,    unittest.TestCase				):







 '''simple docstring'''





 lowercase__				      =      (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
 lowercase__				      =      (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
 lowercase__				      =      (
     {
         """conversational""": TFBlenderbotForConditionalGeneration,
         """feature-extraction""": TFBlenderbotModel,
         """summarization""": TFBlenderbotForConditionalGeneration,
         """text2text-generation""": TFBlenderbotForConditionalGeneration,
         """translation""": TFBlenderbotForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 lowercase__				      =      True
 lowercase__				      =      False
 lowercase__				      =      False


 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						TFBlenderbotModelTester(self	)
   _snake_case							:	Union[str, Any]			=						ConfigTester(self,   config_class=lowerCAmelCase__	)


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   self.config_tester.run_common_tests()


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						self.model_tester.prepare_config_and_inputs_for_common()
   self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase__	)







@require_tokenizers
@require_tf
class       lowercase(  unittest.TestCase				):







 '''simple docstring'''





 lowercase__				      =      ["""My friends are cool but they eat too many carbs."""]
 lowercase__				      =      """facebook/blenderbot-400M-distill"""


 @cached_property
 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   return BlenderbotTokenizer.from_pretrained(self.model_name	)


 @cached_property
 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name	)
   return model


 @slow
 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.tokenizer(self.src_text,   return_tensors="""tf"""	)
   _snake_case							:	Union[str, Any]			=						self.model.generate(
       model_inputs.input_ids,   )
   _snake_case							:	Optional[int]			=						self.tokenizer.batch_decode(generated_ids.numpy(),   skip_special_tokens=lowerCAmelCase__	)[0]
   assert (
       generated_words
       == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
   )








"""simple docstring"""


def       UpperCAmelCase__ (snake_case__							:      list[int]	,     snake_case__							:      str					):
  """simple docstring"""

  _snake_case							:	str			=						int(snake_case__					)

  # Initialize Result
  _snake_case							:	str			=						[]

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

  return answer


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

     if (
         input('''Do you want to enter your denominations ? (yY/n): ''').strip().lower()
         == "y"
     ):
          A_      			=       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()))
          A_      			=       input('''Enter the change you want to make in Indian Currency: ''').strip()
     else:
          # All denominations of Indian Currency if user does not enter
          A_      			=       [1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
          A_      			=       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}: ''')
          A_      			=       find_minimum_change(denominations, value)
          # Print result
          for i in range(len(answer)):
               print(answer[i], end=''' ''')


