





"""simple docstring"""





import math
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE = 0	,  _SCREAMING_SNAKE_CASE = 0     )   ->list:





  """simple docstring"""


  lowerCAmelCase__     :List[str]											=				end or len(_SCREAMING_SNAKE_CASE     )
  for i in range(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     ):
    lowerCAmelCase__     :List[Any]											=				i
    lowerCAmelCase__     :Any											=				array[i]
    while temp_index != start and temp_index_value < array[temp_index - 1]:
      lowerCAmelCase__     :str											=				array[temp_index - 1]
      temp_index -= 1
    lowerCAmelCase__     :Tuple											=				temp_index_value
  return array
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->None:  # Max Heap





  """simple docstring"""


  lowerCAmelCase__     :Any											=				index
  lowerCAmelCase__     :Optional[int]											=				2 * index + 1  # Left Node
  lowerCAmelCase__     :int											=				2 * index + 2  # Right Node

  if left_index < heap_size and array[largest] < array[left_index]:
    lowerCAmelCase__     :Dict											=				left_index

  if right_index < heap_size and array[largest] < array[right_index]:
    lowerCAmelCase__     :str											=				right_index

  if largest != index:
    lowerCAmelCase__				,							lowerCAmelCase__     :Dict											=				array[largest], array[index]
    heapify(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->list:





  """simple docstring"""


  lowerCAmelCase__     :Union[str, Any]											=				len(_SCREAMING_SNAKE_CASE     )

  for i in range(n // 2	,  -1	,  -1     ):
    heapify(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )

  for i in range(n - 1	,  0	,  -1     ):
    lowerCAmelCase__				,							lowerCAmelCase__     :int											=				array[0], array[i]
    heapify(_SCREAMING_SNAKE_CASE	,  0	,  _SCREAMING_SNAKE_CASE     )

  return array
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->int:





  """simple docstring"""


  if (array[first_index] > array[middle_index]) != (
      array[first_index] > array[last_index]
  ):
    return array[first_index]
  elif (array[middle_index] > array[first_index]) != (
      array[middle_index] > array[last_index]
  ):
    return array[middle_index]
  else:
    return array[last_index]
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->int:





  """simple docstring"""


  lowerCAmelCase__     :Optional[int]											=				low
  lowerCAmelCase__     :int											=				high
  while True:
    while array[i] < pivot:
      i += 1
    j -= 1
    while pivot < array[j]:
      j -= 1
    if i >= j:
      return i
    lowerCAmelCase__				,							lowerCAmelCase__     :List[str]											=				array[j], array[i]
    i += 1
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->list:





  """simple docstring"""


  if len(_SCREAMING_SNAKE_CASE     ) == 0:
    return array
  lowerCAmelCase__     :Dict											=				2 * math.ceil(math.loga(len(_SCREAMING_SNAKE_CASE     )     )     )
  lowerCAmelCase__     :Tuple											=				16
  return intro_sort(_SCREAMING_SNAKE_CASE	,  0	,  len(_SCREAMING_SNAKE_CASE     )	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->list:





  """simple docstring"""


  while end - start > size_threshold:
    if max_depth == 0:
      return heap_sort(_SCREAMING_SNAKE_CASE     )
    max_depth -= 1
    lowerCAmelCase__     :Any											=				median_of_a(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  start + ((end - start) // 2) + 1	,  end - 1     )
    lowerCAmelCase__     :Tuple											=				partition(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
    intro_sort(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
    lowerCAmelCase__     :str											=				p
  return insertion_sort(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  __A											=						input("""Enter numbers separated by a comma : """).strip()
  __A											=						[float(item) for item in user_input.split(""",""")]
  print(sort(unsorted))







"""simple docstring"""





import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
  import tensorflow as tf

if is_keras_nlp_available():
  from transformers.models.gpta import TFGPTaTokenizer


__A											=						["""gpt2"""]
__A											=						"""gpt2"""

if is_tf_available():






  class    _lowerCAmelCase  (	tf.Module			):
         """simple docstring"""




         def __init__(       self       ,					__UpperCAmelCase ):



           '''simple docstring'''

           super().__init__()
           lowerCAmelCase__     :List[str]											=				tokenizer
           lowerCAmelCase__     :Optional[int]											=				AutoConfig.from_pretrained(__UpperCAmelCase )
           lowerCAmelCase__     :Optional[Any]											=				TFGPTaLMHeadModel.from_config(__UpperCAmelCase )






         @tf.function(input_signature=(tf.TensorSpec((None,)       ,					tf.string       ,					name='text' ),) )
         def 	snake_case		(       self       ,					__UpperCAmelCase ):



           '''simple docstring'''

           lowerCAmelCase__     :str											=				self.tokenizer(__UpperCAmelCase )
           lowerCAmelCase__     :int											=				tokenized['input_ids'].to_tensor()

           lowerCAmelCase__     :Optional[Any]											=				tf.cast(input_ids_dense > 0       ,					tf.intaa )
           # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

           lowerCAmelCase__     :int											=				self.model(input_ids=__UpperCAmelCase       ,					attention_mask=__UpperCAmelCase )['logits']

           return outputs






@require_tf
@require_keras_nlp
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def 	snake_case		(       self ):



         '''simple docstring'''

         super().setUp()

         lowerCAmelCase__     :List[str]											=				[GPTaTokenizer.from_pretrained(__UpperCAmelCase ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
         lowerCAmelCase__     :Union[str, Any]											=				[TFGPTaTokenizer.from_pretrained(__UpperCAmelCase ) for checkpoint in TOKENIZER_CHECKPOINTS]
         assert len(self.tokenizers ) == len(self.tf_tokenizers )

         lowerCAmelCase__     :int											=				[
             'This is a straightforward English test sentence.',
             'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
             'Now we\'re going to add some Chinese: 一 二 三 一二三',
             'And some much more rare Chinese: 齉 堃 齉堃',
             'Je vais aussi écrire en français pour tester les accents',
             'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
         ]
         lowerCAmelCase__     :str											=				list(zip(self.test_sentences       ,					self.test_sentences[::-1] ) )






       def 	snake_case		(       self ):



         '''simple docstring'''

         for tokenizer, tf_tokenizer in zip(self.tokenizers       ,					self.tf_tokenizers ):
           for test_inputs in self.test_sentences:
             lowerCAmelCase__     :int											=				tokenizer([test_inputs]       ,					return_tensors='tf' )
             lowerCAmelCase__     :Optional[int]											=				tf_tokenizer([test_inputs] )

             for key in python_outputs.keys():
               # convert them to numpy to avoid messing with ragged tensors
               lowerCAmelCase__     :Optional[int]											=				python_outputs[key].numpy()
               lowerCAmelCase__     :List[str]											=				tf_outputs[key].numpy()

               self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape ) )
               self.assertTrue(tf.reduce_all(tf.cast(__UpperCAmelCase       ,					tf.intaa ) == tf_outputs_values ) )






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         for tf_tokenizer in self.tf_tokenizers:
           lowerCAmelCase__     :Optional[int]											=				tf.function(__UpperCAmelCase )
           for test_inputs in self.test_sentences:
             lowerCAmelCase__     :Any											=				tf.constant(__UpperCAmelCase )
             lowerCAmelCase__     :int											=				compiled_tokenizer(__UpperCAmelCase )
             lowerCAmelCase__     :Any											=				tf_tokenizer(__UpperCAmelCase )

             for key in eager_outputs.keys():
               self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         for tf_tokenizer in self.tf_tokenizers:
           lowerCAmelCase__     :List[str]											=				ModelToSave(tokenizer=__UpperCAmelCase )
           lowerCAmelCase__     :Union[str, Any]											=				tf.convert_to_tensor([self.test_sentences[0]] )
           lowerCAmelCase__     :List[str]											=				model.serving(__UpperCAmelCase )  # Build model with some sample inputs
           with TemporaryDirectory() as tempdir:
             lowerCAmelCase__     :Union[str, Any]											=				Path(__UpperCAmelCase ) / 'saved.model'
             tf.saved_model.save(__UpperCAmelCase       ,					__UpperCAmelCase       ,					signatures={'serving_default': model.serving} )
             lowerCAmelCase__     :str											=				tf.saved_model.load(__UpperCAmelCase )
           lowerCAmelCase__     :Dict											=				loaded_model.signatures['serving_default'](__UpperCAmelCase )['output_0']
           # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
           self.assertTrue(tf.reduce_all(out == loaded_output ) )






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         for tf_tokenizer in self.tf_tokenizers:
           lowerCAmelCase__     :List[str]											=				tf.convert_to_tensor([self.test_sentences[0]] )
           lowerCAmelCase__     :List[str]											=				tf_tokenizer(__UpperCAmelCase )  # Build model with some sample inputs

           lowerCAmelCase__     :Union[str, Any]											=				tf_tokenizer.get_config()
           lowerCAmelCase__     :Tuple											=				TFGPTaTokenizer.from_config(__UpperCAmelCase )
           lowerCAmelCase__     :Tuple											=				model_from_config(__UpperCAmelCase )

           for key in from_config_output.keys():
             self.assertTrue(tf.reduce_all(from_config_output[key] == out[key] ) )






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         for tf_tokenizer in self.tf_tokenizers:
           # for the test to run
           lowerCAmelCase__     :int											=				1_2_3_1_2_3

           for max_length in [3, 5, 1_0_2_4]:
             lowerCAmelCase__     :Tuple											=				tf.convert_to_tensor([self.test_sentences[0]] )
             lowerCAmelCase__     :List[str]											=				tf_tokenizer(__UpperCAmelCase       ,					max_length=__UpperCAmelCase )

             lowerCAmelCase__     :Union[str, Any]											=				out['input_ids'].numpy().shape[1]

             assert out_length == max_length
