




import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.albert.modeling_flax_albert import (
					    FlaxAlbertForMaskedLM,
					    FlaxAlbertForMultipleChoice,
					    FlaxAlbertForPreTraining,
					    FlaxAlbertForQuestionAnswering,
					    FlaxAlbertForSequenceClassification,
					    FlaxAlbertForTokenClassification,
					    FlaxAlbertModel,
					)
class 			a__    (					unittest.TestCase      ):






						def __init__(   self		,			_A		,			_A=1_3		,			_A=7		,			_A=True		,			_A=True		,			_A=True		,			_A=True		,			_A=9_9		,			_A=3_2		,			_A=5		,			_A=4		,			_A=3_7		,			_A="gelu"		,			_A=0.1		,			_A=0.1		,			_A=5_1_2		,			_A=1_6		,			_A=2		,			_A=0.02		,			_A=4		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       parent
											__lowerCAmelCase				   =       batch_size
											__lowerCAmelCase				   =       seq_length
											__lowerCAmelCase				   =       is_training
											__lowerCAmelCase				   =       use_attention_mask
											__lowerCAmelCase				   =       use_token_type_ids
											__lowerCAmelCase				   =       use_labels
											__lowerCAmelCase				   =       vocab_size
											__lowerCAmelCase				   =       hidden_size
											__lowerCAmelCase				   =       num_hidden_layers
											__lowerCAmelCase				   =       num_attention_heads
											__lowerCAmelCase				   =       intermediate_size
											__lowerCAmelCase				   =       hidden_act
											__lowerCAmelCase				   =       hidden_dropout_prob
											__lowerCAmelCase				   =       attention_probs_dropout_prob
											__lowerCAmelCase				   =       max_position_embeddings
											__lowerCAmelCase				   =       type_vocab_size
											__lowerCAmelCase				   =       type_sequence_label_size
											__lowerCAmelCase				   =       initializer_range
											__lowerCAmelCase				   =       num_choices






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size			)

											__lowerCAmelCase				   =       None
											if self.use_attention_mask:
																__lowerCAmelCase				   =       random_attention_mask([self.batch_size, self.seq_length]			)

											__lowerCAmelCase				   =       None
											if self.use_token_type_ids:
																__lowerCAmelCase				   =       ids_tensor([self.batch_size, self.seq_length]		,			self.type_vocab_size			)

											__lowerCAmelCase				   =       AlbertConfig(
											    vocab_size=self.vocab_size		,			hidden_size=self.hidden_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		,			is_decoder=A_		,			initializer_range=self.initializer_range		,			)

											return config, input_ids, token_type_ids, attention_mask






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.prepare_config_and_inputs()
											__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase				   =       config_and_inputs
											__lowerCAmelCase				   =       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
											return config, inputs_dict






@require_flax
class 			a__    (					snake_case__ ,				unittest.TestCase      ):
						_a							:    str					=    (
						    (
						        FlaxAlbertModel,
						        FlaxAlbertForPreTraining,
						        FlaxAlbertForMaskedLM,
						        FlaxAlbertForMultipleChoice,
						        FlaxAlbertForQuestionAnswering,
						        FlaxAlbertForSequenceClassification,
						        FlaxAlbertForTokenClassification,
						        FlaxAlbertForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       FlaxAlbertModelTester(self			)






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											for model_class_name in self.all_model_classes:
																__lowerCAmelCase				   =       model_class_name.from_pretrained("albert-base-v2"			)
																__lowerCAmelCase				   =       model(np.ones((1, 1)			)			)
																self.assertIsNotNone(A_			)






@require_flax
class 			a__    (					unittest.TestCase      ):






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       FlaxAlbertModel.from_pretrained("albert-base-v2"			)
											__lowerCAmelCase				   =       np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]			)
											__lowerCAmelCase				   =       np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
											__lowerCAmelCase				   =       model(A_		,			attention_mask=A_			)[0]
											__lowerCAmelCase				   =       (1, 1_1, 7_6_8)
											self.assertEqual(output.shape		,			A_			)
											__lowerCAmelCase				   =       np.array(
											    [[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]]			)

											self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]		,			A_		,			atol=1E-4			)			)










def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       list     ):


      '''simple docstring'''



      for i in range(len(lowercase     ) - 1	,							0	,							-1     ):
            lowerCamelCase_		     =    False

            for j in range(lowercase	,							0	,							-1     ):
                  if unsorted[j] < unsorted[j - 1]:
                        lowerCamelCase_     ,				lowerCamelCase_		     =    unsorted[j - 1], unsorted[j]
                        lowerCamelCase_		     =    True

            for j in range(lowercase     ):
                  if unsorted[j] > unsorted[j + 1]:
                        lowerCamelCase_     ,				lowerCamelCase_		     =    unsorted[j + 1], unsorted[j]
                        lowerCamelCase_		     =    True

            if not swapped:
                  break
      return unsorted


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 lowerCamelCase				:		Any     =   input("Enter numbers separated by a comma:\n").strip()
 lowerCamelCase				:		Dict     =   [int(item) for item in user_input.split(",")]
 print(F"""{cocktail_shaker_sort(unsorted) = }""")



