


from __future__ import annotations

import unittest

from transformers import AlbertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
    import tensorflow as tf

    from transformers import TF_MODEL_FOR_PRETRAINING_MAPPING
    from transformers.models.albert.modeling_tf_albert import (
        TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFAlbertForMaskedLM,
        TFAlbertForMultipleChoice,
        TFAlbertForPreTraining,
        TFAlbertForQuestionAnswering,
        TFAlbertForSequenceClassification,
        TFAlbertForTokenClassification,
        TFAlbertModel,
    )







class 					lowerCAmelCase__    :




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase			,						__lowerCamelCase=1_3			,						__lowerCamelCase=7			,						__lowerCamelCase=True			,						__lowerCamelCase=True			,						__lowerCamelCase=True			,						__lowerCamelCase=True			,						__lowerCamelCase=9_9			,						__lowerCamelCase=1_6			,						__lowerCamelCase=3_2			,						__lowerCamelCase=2			,						__lowerCamelCase=4			,						__lowerCamelCase=3_7			,						__lowerCamelCase="gelu"			,						__lowerCamelCase=0.1			,						__lowerCamelCase=0.1			,						__lowerCamelCase=5_1_2			,						__lowerCamelCase=1_6			,						__lowerCamelCase=2			,						__lowerCamelCase=0.0_2			,						__lowerCamelCase=3			,						__lowerCamelCase=4			,						__lowerCamelCase=None			,						)      ->    Optional[Any]:
            _A				:	Tuple       =			parent
            _A				:	List[str]       =			1_3
            _A				:	List[str]       =			7
            _A				:	Union[str, Any]       =			True
            _A				:	Dict       =			True
            _A				:	Union[str, Any]       =			True
            _A				:	Any       =			True
            _A				:	List[Any]       =			9_9
            _A				:	Dict       =			1_6
            _A				:	int       =			3_2
            _A				:	int       =			2
            _A				:	List[str]       =			4
            _A				:	List[Any]       =			3_7
            _A				:	Dict       =			"gelu"
            _A				:	Dict       =			0.1
            _A				:	int       =			0.1
            _A				:	Tuple       =			5_1_2
            _A				:	Any       =			1_6
            _A				:	List[str]       =			2
            _A				:	Any       =			0.0_2
            _A				:	str       =			3
            _A				:	str       =			4
            _A				:	List[Any]       =			None


     def 			_lowerCamelCase			(						self)      ->    Optional[Any]:
            _A				:	List[str]       =			ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size)

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

            _A				:	List[str]       =			None
            if self.use_token_type_ids:
                   _A				:	Optional[Any]       =			ids_tensor([self.batch_size, self.seq_length]			,						self.type_vocab_size)

            _A				:	Tuple       =			None
            _A				:	Dict       =			None
            _A				:	str       =			None
            if self.use_labels:
                   _A				:	Optional[Any]       =			ids_tensor([self.batch_size]			,						self.type_sequence_label_size)
                   _A				:	Union[str, Any]       =			ids_tensor([self.batch_size, self.seq_length]			,						self.num_labels)
                   _A				:	Tuple       =			ids_tensor([self.batch_size]			,						self.num_choices)

            _A				:	Optional[int]       =			AlbertConfig(
                vocab_size=self.vocab_size			,						hidden_size=self.hidden_size			,						embedding_size=self.embedding_size			,						num_hidden_layers=self.num_hidden_layers			,						num_attention_heads=self.num_attention_heads			,						intermediate_size=self.intermediate_size			,						hidden_act=self.hidden_act			,						hidden_dropout_prob=self.hidden_dropout_prob			,						attention_probs_dropout_prob=self.attention_probs_dropout_prob			,						max_position_embeddings=self.max_position_embeddings			,						type_vocab_size=self.type_vocab_size			,						initializer_range=self.initializer_range			,						)

            return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    Tuple:
            _A				:	Optional[Any]       =			TFAlbertModel(config=__lowerCAmelCase)
            # inputs = {'input_ids': input_ids,
            #           'attention_mask': input_mask,
            #           'token_type_ids': token_type_ids}
            # sequence_output, pooled_output = model(**inputs)
            _A				:	List[Any]       =			{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
            _A				:	Optional[int]       =			model(__lowerCAmelCase)

            _A				:	str       =			[input_ids, input_mask]
            _A				:	int       =			model(__lowerCAmelCase)

            _A				:	Union[str, Any]       =			model(__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 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    List[Any]:
            _A				:	Optional[Any]       =			self.num_labels
            _A				:	Tuple       =			TFAlbertForPreTraining(config=__lowerCAmelCase)
            _A				:	Union[str, Any]       =			{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
            _A				:	List[str]       =			model(__lowerCAmelCase)
            self.parent.assertEqual(result.prediction_logits.shape			,						(self.batch_size, self.seq_length, self.vocab_size))
            self.parent.assertEqual(result.sop_logits.shape			,						(self.batch_size, self.num_labels))


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    int:
            _A				:	Union[str, Any]       =			TFAlbertForMaskedLM(config=__lowerCAmelCase)
            _A				:	List[str]       =			{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
            _A				:	Any       =			model(__lowerCAmelCase)
            self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.vocab_size))


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    Any:
            _A				:	int       =			self.num_labels
            _A				:	Dict       =			TFAlbertForSequenceClassification(config=__lowerCAmelCase)
            _A				:	Optional[Any]       =			{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
            _A				:	Tuple       =			model(__lowerCAmelCase)
            self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels))


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    Tuple:
            _A				:	Optional[int]       =			TFAlbertForQuestionAnswering(config=__lowerCAmelCase)
            _A				:	Any       =			{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
            _A				:	int       =			model(__lowerCAmelCase)
            self.parent.assertEqual(result.start_logits.shape			,						(self.batch_size, self.seq_length))
            self.parent.assertEqual(result.end_logits.shape			,						(self.batch_size, self.seq_length))


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    Optional[Any]:
            _A				:	Any       =			self.num_choices
            _A				:	List[str]       =			TFAlbertForMultipleChoice(config=__lowerCAmelCase)
            _A				:	int       =			tf.tile(tf.expand_dims(__lowerCAmelCase			,						1)			,						(1, self.num_choices, 1))
            _A				:	str       =			tf.tile(tf.expand_dims(__lowerCAmelCase			,						1)			,						(1, self.num_choices, 1))
            _A				:	Tuple       =			tf.tile(tf.expand_dims(__lowerCAmelCase			,						1)			,						(1, self.num_choices, 1))
            _A				:	Union[str, Any]       =			{
                "input_ids": multiple_choice_inputs_ids,
                "attention_mask": multiple_choice_input_mask,
                "token_type_ids": multiple_choice_token_type_ids,
            }
            _A				:	List[Any]       =			model(__lowerCAmelCase)
            self.parent.assertListEqual(list(result["logits"].shape)			,						[self.batch_size, self.num_choices])


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    Any:
            _A				:	int       =			self.num_labels
            _A				:	Tuple       =			TFAlbertForTokenClassification(config=__lowerCAmelCase)
            _A				:	Any       =			{
                "input_ids": input_ids,
                "attention_mask": input_mask,
                "token_type_ids": token_type_ids,
            }
            _A				:	int       =			model(__lowerCAmelCase)
            self.parent.assertListEqual(list(result["logits"].shape)			,						[self.batch_size, self.seq_length, self.num_labels])


     def 			_lowerCamelCase			(						self)      ->    Dict:
            _A				:	Union[str, Any]       =			self.prepare_config_and_inputs()
            (
                (
                _A					
            )   ,      (
                _A					
            )   ,      (
                _A					
            )   ,      (
                _A					
            )   ,      (
                _A					
            )   ,      (
                _A					
            )   ,      (
                _A					
            )   ,      					
            )				:	Dict       =			config_and_inputs
            _A				:	List[str]       =			{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
            return config, inputs_dict







@require_tf
class 					lowerCAmelCase__    (    lowerCAmelCase__					,   lowerCAmelCase__					,   unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			(
         (
             TFAlbertModel,
             TFAlbertForPreTraining,
             TFAlbertForMaskedLM,
             TFAlbertForSequenceClassification,
             TFAlbertForQuestionAnswering,
             TFAlbertForTokenClassification,
             TFAlbertForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     __SCREAMING_SNAKE_CASE        =			(
         {
             '''feature-extraction''': TFAlbertModel,
             '''fill-mask''': TFAlbertForMaskedLM,
             '''question-answering''': TFAlbertForQuestionAnswering,
             '''text-classification''': TFAlbertForSequenceClassification,
             '''token-classification''': TFAlbertForTokenClassification,
             '''zero-shot''': TFAlbertForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     __SCREAMING_SNAKE_CASE        =			False
     __SCREAMING_SNAKE_CASE        =			False


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase=False)      ->    str:
            _A				:	List[str]       =			super()._prepare_for_class(__lowerCAmelCase			,						__lowerCAmelCase			,						return_labels=__lowerCAmelCase)

            if return_labels:
                   if model_class in get_values(__lowerCAmelCase):
                          _A				:	List[str]       =			tf.zeros(self.model_tester.batch_size			,						dtype=tf.intaa)

            return inputs_dict


     def 			_lowerCamelCase			(						self)      ->    Union[str, Any]:
            _A				:	Optional[Any]       =			TFAlbertModelTester(self)
            _A				:	int       =			ConfigTester(self			,						config_class=__lowerCAmelCase			,						hidden_size=3_7)


     def 			_lowerCamelCase			(						self)      ->    str:
            self.config_tester.run_common_tests()


     def 			_lowerCamelCase			(						self)      ->    Optional[Any]:
            _A				:	Union[str, Any]       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_albert_model(*__lowerCAmelCase)


     def 			_lowerCamelCase			(						self)      ->    Dict:
            _A				:	Any       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_albert_for_pretraining(*__lowerCAmelCase)


     def 			_lowerCamelCase			(						self)      ->    str:
            _A				:	Any       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_albert_for_masked_lm(*__lowerCAmelCase)


     def 			_lowerCamelCase			(						self)      ->    Union[str, Any]:
            _A				:	Tuple       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_albert_for_multiple_choice(*__lowerCAmelCase)


     def 			_lowerCamelCase			(						self)      ->    List[str]:
            _A				:	Tuple       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_albert_for_sequence_classification(*__lowerCAmelCase)


     def 			_lowerCamelCase			(						self)      ->    int:
            _A				:	Optional[Any]       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_albert_for_question_answering(*__lowerCAmelCase)


     @slow
     def 			_lowerCamelCase			(						self)      ->    Any:


            for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _A				:	Dict       =			TFAlbertModel.from_pretrained(__lowerCAmelCase)
                   self.assertIsNotNone(__lowerCAmelCase)







@require_tf
class 					lowerCAmelCase__    (    unittest.TestCase):




     '''simple docstring'''


     @slow
     def 			_lowerCamelCase			(						self)      ->    int:
            _A				:	Union[str, Any]       =			TFAlbertForPreTraining.from_pretrained("albert-base-v2")
            _A				:	int       =			tf.constant([[0, 1, 2, 3, 4, 5]])
            _A				:	Tuple       =			model(__lowerCAmelCase)[0]

            _A				:	Optional[int]       =			[1, 6, 3_0_0_0_0]
            self.assertEqual(output.shape			,						__lowerCAmelCase)

            _A				:	List[str]       =			tf.constant(
                [
                    [
                        [4.5_9_5_6_6_8, 0.7_4_4_6_2_7_5_4, -1.8_1_8_1_4_7],
                        [4.5_9_5_4_3_4_7, 0.7_4_5_4_1_8_4, -1.8_1_8_8_2_5_8],
                        [4.5_9_5_4_9_0_5, 0.7_4_4_8_2_3_5, -1.8_1_8_2_3_1_6],
                    ]
                ])
            tf.debugging.assert_near(output[:, :3, :3]			,						__lowerCAmelCase			,						atol=1e-4)




def 							_UpperCAmelCase    (UpperCamelCase__					:  list    ,							UpperCamelCase__					:  int    ,							UpperCamelCase__					:  int						):
       if right <= left:
              return
       _A				:	str       =			left
       _A				:	List[str]       =			right
       _A				:	Union[str, Any]       =			sorting[left]
       while i <= b:
              if sorting[i] < pivot:
                     _A   ,      _A				:	Union[str, Any]       =			sorting[i], sorting[a]
                     a += 1
                     i += 1
              elif sorting[i] > pivot:
                     _A   ,      _A				:	str       =			sorting[i], sorting[b]
                     b -= 1
              else:
                     i += 1
       quick_sort_apartition(UpperCamelCase__    ,							UpperCamelCase__    ,							a - 1						)
       quick_sort_apartition(UpperCamelCase__    ,							b + 1    ,							UpperCamelCase__						)
def 							_UpperCAmelCase    (UpperCamelCase__					:  list    ,							UpperCamelCase__					:  int    ,							UpperCamelCase__					:  int						):
       if left < right:
              _A				:	Tuple       =			lomuto_partition(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__						)
              quick_sort_lomuto_partition(UpperCamelCase__    ,							UpperCamelCase__    ,							pivot_index - 1						)
              quick_sort_lomuto_partition(UpperCamelCase__    ,							pivot_index + 1    ,							UpperCamelCase__						)
def 							_UpperCAmelCase    (UpperCamelCase__					:  list    ,							UpperCamelCase__					:  int    ,							UpperCamelCase__					:  int						):
       _A				:	Tuple       =			sorting[right]
       _A				:	Tuple       =			left
       for i in range(UpperCamelCase__    ,							UpperCamelCase__						):
              if sorting[i] < pivot:
                     _A   ,      _A				:	int       =			sorting[i], sorting[store_index]
                     store_index += 1
       _A   ,      _A				:	Dict       =			sorting[store_index], sorting[right]
       return store_index
def 							_UpperCAmelCase    (UpperCamelCase__					:  list						):
       if len(UpperCamelCase__						) <= 1:
              return sorting
       return (
           three_way_radix_quicksort([i for i in sorting if i < sorting[0]]						)
           + [i for i in sorting if i == sorting[0]]
           + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]						)
       )


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)

    lowerCAmelCase__						=			input('Enter numbers separated by a comma:\n').strip()
    lowerCAmelCase__						=			[int(item) for item in user_input.split(',')]
    quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
    print(unsorted)
