



'''simple docstring'''





from __future__ import annotations

import os
import tempfile
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow

from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      AutoTokenizer,
      TFAutoModelForCausalLM,
      TFAutoModelForSeqaSeqLM,
      TFAutoModelForSpeechSeqaSeq,
      TFAutoModelForVisionaSeq,
      TFBartForConditionalGeneration,
      TFLogitsProcessorList,
      TFMinLengthLogitsProcessor,
      tf_top_k_top_p_filtering,
  )

if is_tensorflow_text_available():
  import tensorflow_text as text






@require_tf
class __A			(  unittest.TestCase						):



 '''simple docstring'''







 def 					a__  (self       )    ->     Optional[Any]:


      """simple docstring"""



      _a      =  tf.convert_to_tensor(
          [
              [
                  8.2220991,  # 3rd highest value; idx. 0
                  -0.5620044,
                  5.23229752,
                  4.0386393,
                  -6.8798378,
                  -0.54785802,
                  -3.2012153,
                  2.92777176,
                  1.88171953,
                  7.35341276,  # 5th highest value; idx. 9
                  8.43207833,  # 2nd highest value; idx. 10
                  -9.85711836,
                  -5.96209236,
                  -1.13039161,
                  -7.1115294,
                  -0.8369633,
                  -5.3186408,
                  7.06427407,
                  0.81369344,
                  -0.82023817,
                  -5.9179796,
                  0.58813443,
                  -6.99778438,
                  4.71551189,
                  -0.18771637,
                  7.44020759,  # 4th highest value; idx. 25
                  9.38450987,  # 1st highest value; idx. 26
                  2.12662941,
                  -9.32562038,
                  2.35652522,
              ],  # cummulative prob of 5 highest values <= 0.6
              [
                  0.58425518,
                  4.53139238,
                  -5.57510464,
                  -6.28030699,
                  -7.19529503,
                  -4.02122551,
                  1.39337037,
                  -6.06707057,
                  1.59480517,
                  -9.643119,
                  0.03907799,
                  0.67231762,
                  -8.88206726,
                  6.27115922,  # 4th highest value; idx. 13
                  2.28520723,
                  4.82767506,
                  4.30421368,
                  8.8275313,  # 2nd highest value; idx. 17
                  5.44029958,  # 5th highest value; idx. 18
                  -4.4735794,
                  7.38579536,  # 3rd highest value; idx. 20
                  -2.91051663,
                  2.61946077,
                  -2.5674762,
                  -9.48959302,
                  -4.02922645,
                  -1.35416918,
                  9.67702323,  # 1st highest value; idx. 27
                  -5.89478553,
                  1.85370467,
              ],  # cummulative prob of 5 highest values <= 0.6
          ] ,   dtype=tf.floataa ,   )

      _a      =  tf.convert_to_tensor(
          [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]] ,   dtype=tf.intaa ,   )  # expected non filtered idx as noted above

      _a      =  tf.convert_to_tensor(
          [8.222099, 7.3534126, 8.432078, 7.4402075, 9.38451, 6.271159, 8.827531, 5.4402995, 7.3857956, 9.677023] ,   dtype=tf.floataa ,   )  # expected non filtered values as noted above

      _a      =  tf_top_k_top_p_filtering(A ,   top_k=10 ,   top_p=0.6 ,   min_tokens_to_keep=4       )

      _a      =  output[output != -float('''inf'''       )]
      _a      =  tf.cast(
          tf.where(tf.not_equal(A ,   tf.constant(-float('''inf'''       ) ,   dtype=tf.floataa       )       )       ) ,   dtype=tf.intaa ,   )

      tf.debugging.assert_near(A ,   A ,   rtol=1E-12       )
      tf.debugging.assert_equal(A ,   A       )






@require_tf
class __A			(  unittest.TestCase   ,   A						):



 '''simple docstring'''







 if is_tf_available():
  __lowerCamelCase    :       Any          =       {
      'AutoModelForCausalLM': TFAutoModelForCausalLM,
      'AutoModelForSpeechSeq2Seq': TFAutoModelForSpeechSeqaSeq,
      'AutoModelForSeq2SeqLM': TFAutoModelForSeqaSeqLM,
      'AutoModelForVision2Seq': TFAutoModelForVisionaSeq,
      'LogitsProcessorList': TFLogitsProcessorList,
      'MinLengthLogitsProcessor': TFMinLengthLogitsProcessor,
      'create_tensor_fn': tf.convert_to_tensor,
      'floats_tensor': floats_tensor,
      'return_tensors': 'tf',
  }



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


      """simple docstring"""



      _a      =  TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       )
      _a      =  2
      _a      =  2






      class __A			(  tf.Module						):



       '''simple docstring'''







       def __init__(self ,   A       )    ->     Dict:


            """simple docstring"""



            super(A ,   self       ).__init__()
            _a      =  model



       @tf.function(
           input_signature=(
               tf.TensorSpec((None, input_length) ,   tf.intaa ,   name='''input_ids'''       ),
               tf.TensorSpec((None, input_length) ,   tf.intaa ,   name='''attention_mask'''       ),
           ) ,   jit_compile=A ,   )
       def 					a__  (self ,   A ,   A       )    ->     int:


            """simple docstring"""



            _a      =  self.model.generate(
                input_ids=A ,   attention_mask=A ,   max_new_tokens=A ,   return_dict_in_generate=A ,   )
            return {"sequences": outputs["sequences"]}





      _a      =  [[2, 0], [102, 103]]
      _a      =  [[1, 0], [1, 1]]
      _a      =  DummyModel(model=A       )
      with tempfile.TemporaryDirectory() as tmp_dir:
           tf.saved_model.save(A ,   A ,   signatures={'''serving_default''': dummy_model.serving}       )
           _a      =  tf.saved_model.load(A       ).signatures['''serving_default''']
           for batch_size in range(1 ,   len(A       ) + 1       ):
                _a      =  {
                    '''input_ids''': tf.constant(dummy_input_ids[:batch_size]       ),
                    '''attention_mask''': tf.constant(dummy_attention_masks[:batch_size]       ),
                }
                _a      =  serving_func(**A       )['''sequences''']
                _a      =  test_model.generate(**A ,   max_new_tokens=A       )
                tf.debugging.assert_equal(A ,   A       )



 @slow
 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      _a      =  TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       )
      _a      =  1
      _a      =  2






      class __A			(  tf.Module						):



       '''simple docstring'''







       def __init__(self ,   A       )    ->     str:


            """simple docstring"""



            super(A ,   self       ).__init__()
            _a      =  model



       @tf.function(
           input_signature=(
               tf.TensorSpec((batch_size, None) ,   tf.intaa ,   name='''input_ids'''       ),
               tf.TensorSpec((batch_size, None) ,   tf.intaa ,   name='''attention_mask'''       ),
           ) ,   jit_compile=A ,   )
       def 					a__  (self ,   A ,   A       )    ->     List[str]:


            """simple docstring"""



            _a      =  self.model.generate(
                input_ids=A ,   attention_mask=A ,   max_new_tokens=A ,   return_dict_in_generate=A ,   )
            return {"sequences": outputs["sequences"]}





      _a      =  [[2], [102, 103]]
      _a      =  [[1], [1, 1]]
      _a      =  DummyModel(model=A       )
      with tempfile.TemporaryDirectory() as tmp_dir:
           tf.saved_model.save(A ,   A ,   signatures={'''serving_default''': dummy_model.serving}       )
           _a      =  tf.saved_model.load(A       ).signatures['''serving_default''']
           for input_row in range(len(A       )       ):
                _a      =  {
                    '''input_ids''': tf.constant([dummy_input_ids[input_row]]       ),
                    '''attention_mask''': tf.constant([dummy_attention_masks[input_row]]       ),
                }
                _a      =  serving_func(**A       )['''sequences''']
                _a      =  test_model.generate(**A ,   max_new_tokens=A       )
                tf.debugging.assert_equal(A ,   A       )



 @slow
 @require_tensorflow_text
 def 					a__  (self       )    ->     str:


      """simple docstring"""



      with tempfile.TemporaryDirectory() as tmp_dir:
           # file needed to load the TF tokenizer
           hf_hub_download(repo_id='''google/flan-t5-small''' ,   filename='''spiece.model''' ,   local_dir=A       )






           class __A			(  tf.keras.layers.Layer						):



            '''simple docstring'''







            def __init__(self       )    ->     Optional[Any]:


                 """simple docstring"""



                 super().__init__()
                 _a      =  text.SentencepieceTokenizer(
                     model=tf.io.gfile.GFile(os.path.join(A ,   '''spiece.model'''       ) ,   '''rb'''       ).read()       )
                 _a      =  TFAutoModelForSeqaSeqLM.from_pretrained('''hf-internal-testing/tiny-random-t5'''       )



            def 					a__  (self ,   A ,   *A ,   **A       )    ->     List[Any]:


                 """simple docstring"""



                 _a      =  self.tokenizer.tokenize(A       )
                 _a  ,    _a      =  text.pad_model_inputs(
                     A ,   max_seq_length=64 ,   pad_value=self.model.config.pad_token_id       )
                 _a      =  self.model.generate(input_ids=A ,   attention_mask=A       )
                 return self.tokenizer.detokenize(A       )





           _a      =  CompleteSentenceTransformer()
           _a      =  tf.keras.layers.Input(shape=(1,) ,   dtype=tf.string ,   name='''inputs'''       )
           _a      =  complete_model(A       )
           _a      =  tf.keras.Model(A ,   A       )
           keras_model.save(A       )



 def 					a__  (self       )    ->     str:


      """simple docstring"""



      _a      =  {
          '''do_sample''': True,
          '''num_beams''': 1,
          '''top_p''': 0.7,
          '''top_k''': 10,
          '''temperature''': 0.7,
      }
      _a      =  14

      _a      =  AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       )
      _a      =  '''Hello, my dog is cute and'''
      _a      =  tokenizer(A ,   return_tensors='''tf'''       )
      _a      =  TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       )

      _a      =  638
      # forces the generation to happen on CPU, to avoid GPU-related quirks
      with tf.device(''':/CPU:0'''       ):
           tf.random.set_seed(0       )
           _a      =  model.generate(**A ,   eos_token_id=A ,   **A       )
      self.assertTrue(expectation == len(generated_tokens[0]       )       )

      _a      =  [638, 198]
      with tf.device(''':/CPU:0'''       ):
           tf.random.set_seed(0       )
           _a      =  model.generate(**A ,   eos_token_id=A ,   **A       )
      self.assertTrue(expectation == len(generated_tokens[0]       )       )



 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      _a      =  AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bart'''       )
      _a      =  '''Hugging Face is a technology company based in New York and Paris.'''
      _a      =  bart_tokenizer(A ,   return_tensors='''tf'''       ).input_ids
      _a      =  TFBartForConditionalGeneration.from_pretrained('''hf-internal-testing/tiny-random-bart'''       )
      _a      =  bart_model.generate(A       ).numpy()






      class __A			(  A						):



       '''simple docstring'''







       def 					a__  (self ,   A ,   A=None ,   **A       )    ->     Dict:


            """simple docstring"""



            return super().call(A ,   **A       )





      _a      =  FakeBart.from_pretrained('''hf-internal-testing/tiny-random-bart'''       )
      _a      =  bart_model.generate(A ,   foo='''bar'''       ).numpy()
      self.assertTrue(np.array_equal(A ,   A       )       )






      class __A			(  bart_model.model.encoder.__class__						):



       '''simple docstring'''







       def 					a__  (self ,   A ,   **A       )    ->     int:


            """simple docstring"""



            return super().call(A ,   **A       )





      _a      =  FakeEncoder(bart_model.config ,   bart_model.model.shared       )
      _a      =  fake_encoder

      # Normal generation still works (the output will be different because the encoder weights are different)
      _a      =  bart_model.generate(A       ).numpy()
      with self.assertRaises(A       ):
           # FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
           bart_model.generate(A ,   foo='''bar'''       )








'''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=" ")



