
'''simple docstring'''




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		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE=13		,     SCREAMING_SNAKE_CASE=7		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=99		,     SCREAMING_SNAKE_CASE=32		,     SCREAMING_SNAKE_CASE=5		,     SCREAMING_SNAKE_CASE=4		,     SCREAMING_SNAKE_CASE=37		,     SCREAMING_SNAKE_CASE="gelu"		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE=512		,     SCREAMING_SNAKE_CASE=16		,     SCREAMING_SNAKE_CASE=2		,     SCREAMING_SNAKE_CASE=0.02		,     SCREAMING_SNAKE_CASE=4		,     )					->							Optional[int]:






         """simple docstring"""






         A			:  str   =		parent
         A			:  str   =		batch_size
         A			:  Union[str, Any]   =		seq_length
         A			:  int   =		is_training
         A			:  str   =		use_attention_mask
         A			:  str   =		use_token_type_ids
         A			:  str   =		use_labels
         A			:  Union[str, Any]   =		vocab_size
         A			:  List[Any]   =		hidden_size
         A			:  Optional[int]   =		num_hidden_layers
         A			:  Union[str, Any]   =		num_attention_heads
         A			:  List[str]   =		intermediate_size
         A			:  Any   =		hidden_act
         A			:  Optional[Any]   =		hidden_dropout_prob
         A			:  Optional[int]   =		attention_probs_dropout_prob
         A			:  Union[str, Any]   =		max_position_embeddings
         A			:  List[str]   =		type_vocab_size
         A			:  Union[str, Any]   =		type_sequence_label_size
         A			:  Union[str, Any]   =		initializer_range
         A			:  str   =		num_choices





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A			:  Union[str, Any]   =		ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size      )

         A			:  Optional[int]   =		None
         if self.use_attention_mask:
             A			:  Union[str, Any]   =		random_attention_mask([self.batch_size, self.seq_length]      )

         A			:  Optional[Any]   =		None
         if self.use_token_type_ids:
             A			:  Tuple   =		ids_tensor([self.batch_size, self.seq_length]		,     self.type_vocab_size      )

         A			:  Tuple   =		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=SCREAMING_SNAKE_CASE		,     initializer_range=self.initializer_range		,     )

         return config, input_ids, token_type_ids, attention_mask





     def 							__lowerCAmelCase				(				self      )					->							Union[str, Any]:






         """simple docstring"""






         A			:  int   =		self.prepare_config_and_inputs()
         A,    A,    A,    A			:  List[str]   =		config_and_inputs
         A			:  List[str]   =		{'''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		):
     __magic_name__          =						(
         (
             FlaxAlbertModel,
             FlaxAlbertForPreTraining,
             FlaxAlbertForMaskedLM,
             FlaxAlbertForMultipleChoice,
             FlaxAlbertForQuestionAnswering,
             FlaxAlbertForSequenceClassification,
             FlaxAlbertForTokenClassification,
             FlaxAlbertForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         A			:  Tuple   =		FlaxAlbertModelTester(self      )





     @slow
     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         for model_class_name in self.all_model_classes:
             A			:  List[Any]   =		model_class_name.from_pretrained('''albert-base-v2'''      )
             A			:  List[Any]   =		model(np.ones((1, 1)      )      )
             self.assertIsNotNone(SCREAMING_SNAKE_CASE      )





@require_flax
class 					A							(				unittest.TestCase		):





     @slow
     def 							__lowerCAmelCase				(				self      )					->							List[Any]:






         """simple docstring"""






         A			:  Any   =		FlaxAlbertModel.from_pretrained('''albert-base-v2'''      )
         A			:  Tuple   =		np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]      )
         A			:  Optional[int]   =		np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
         A			:  str   =		model(SCREAMING_SNAKE_CASE		,     attention_mask=SCREAMING_SNAKE_CASE      )[0]
         A			:  Dict   =		(1, 11, 768)
         self.assertEqual(output.shape		,     SCREAMING_SNAKE_CASE      )
         A			:  List[str]   =		np.array(
             [[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]]      )

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






'''simple docstring'''




import random







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    A,    A,    A			:  Any   =		[], [], []
    for element in data:
        if element < pivot:
            less.append(snake_case__		)
        elif element > pivot:
            greater.append(snake_case__		)
        else:
            equal.append(snake_case__		)
    return less, equal, greater







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    if index >= len(snake_case__		) or index < 0:
        return None

    A			:  List[Any]   =		items[random.randint(0						,							len(snake_case__		) - 1		)]
    A			:  Optional[int]   =		0
    A,    A,    A			:  Dict   =		_partition(snake_case__						,							snake_case__		)
    A			:  int   =		len(snake_case__		)
    A			:  List[Any]   =		len(snake_case__		)

    # index is the pivot
    if m <= index < m + count:
        return pivot
    # must be in smaller
    elif m > index:
        return quick_select(snake_case__						,							snake_case__		)
    # must be in larger
    else:
        return quick_select(snake_case__						,							index - (m + count)		)




