"""simple docstring"""

import unittest

import numpy as np

from transformers import DistilBertConfig, 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.distilbert.modeling_flax_distilbert import (
			    FlaxDistilBertForMaskedLM,
			    FlaxDistilBertForMultipleChoice,
			    FlaxDistilBertForQuestionAnswering,
			    FlaxDistilBertForSequenceClassification,
			    FlaxDistilBertForTokenClassification,
			    FlaxDistilBertModel,
			)







class    __lowercase     (					unittest.TestCase):







						"""simple docstring"""







						def __init__(self   ,		lowercase__   ,		lowercase__=13   ,		lowercase__=7   ,		lowercase__=True   ,		lowercase__=True   ,		lowercase__=True   ,		lowercase__=True   ,		lowercase__=99   ,		lowercase__=32   ,		lowercase__=5   ,		lowercase__=4   ,		lowercase__=37   ,		lowercase__="gelu"   ,		lowercase__=0.1   ,		lowercase__=0.1   ,		lowercase__=5_12   ,		lowercase__=16   ,		lowercase__=2   ,		lowercase__=0.02   ,		lowercase__=4   ,		):
											snake_case_					:		List[Any] 					=					parent
											snake_case_					:		List[Any] 					=					batch_size
											snake_case_					:		Optional[Any] 					=					seq_length
											snake_case_					:		Optional[int] 					=					is_training
											snake_case_					:		Any 					=					use_attention_mask
											snake_case_					:		Dict 					=					use_token_type_ids
											snake_case_					:		Tuple 					=					use_labels
											snake_case_					:		Tuple 					=					vocab_size
											snake_case_					:		int 					=					hidden_size
											snake_case_					:		List[str] 					=					num_hidden_layers
											snake_case_					:		Optional[int] 					=					num_attention_heads
											snake_case_					:		Any 					=					intermediate_size
											snake_case_					:		Tuple 					=					hidden_act
											snake_case_					:		Optional[int] 					=					hidden_dropout_prob
											snake_case_					:		Any 					=					attention_probs_dropout_prob
											snake_case_					:		Optional[Any] 					=					max_position_embeddings
											snake_case_					:		Optional[Any] 					=					type_vocab_size
											snake_case_					:		Optional[int] 					=					type_sequence_label_size
											snake_case_					:		List[str] 					=					initializer_range
											snake_case_					:		Optional[int] 					=					num_choices







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		str 					=					ids_tensor([self.batch_size, self.seq_length]   ,		self.vocab_size       )

											snake_case_					:		Any 					=					None
											if self.use_attention_mask:
																snake_case_					:		Tuple 					=					random_attention_mask([self.batch_size, self.seq_length]       )

											snake_case_					:		int 					=					DistilBertConfig(
											    vocab_size=self.vocab_size   ,		dim=self.hidden_size   ,		n_layers=self.num_hidden_layers   ,		n_heads=self.num_attention_heads   ,		hidden_dim=self.intermediate_size   ,		hidden_act=self.hidden_act   ,		dropout=self.hidden_dropout_prob   ,		attention_dropout=self.attention_probs_dropout_prob   ,		max_position_embeddings=self.max_position_embeddings   ,		initializer_range=self.initializer_range   ,		tie_weights_=_a   ,		)

											return config, input_ids, attention_mask







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		int 					=					self.prepare_config_and_inputs()
											snake_case_					,			snake_case_					,			snake_case_					:		int 					=					config_and_inputs
											snake_case_					:		Tuple 					=					{"""input_ids""": input_ids, """attention_mask""": attention_mask}
											return config, inputs_dict







@require_flax
class    __lowercase     (					__SCREAMING_SNAKE_CASE					,    unittest.TestCase):







						"""simple docstring"""







						_A		:							str							       =		(
						    (
						        FlaxDistilBertModel,
						        FlaxDistilBertForMaskedLM,
						        FlaxDistilBertForMultipleChoice,
						        FlaxDistilBertForQuestionAnswering,
						        FlaxDistilBertForSequenceClassification,
						        FlaxDistilBertForTokenClassification,
						        FlaxDistilBertForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Tuple 					=					FlaxDistilBertModelTester(self       )







						@slow
						def 						__UpperCamelCase							(self       ):
											for model_class_name in self.all_model_classes:
																snake_case_					:		List[Any] 					=					model_class_name.from_pretrained("""distilbert-base-uncased"""       )
																snake_case_					:		Any 					=					model(np.ones((1, 1)       )       )
																self.assertIsNotNone(_a       )







@require_flax
class    __lowercase     (					unittest.TestCase):







						"""simple docstring"""







						@slow
						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[Any] 					=					FlaxDistilBertModel.from_pretrained("""distilbert-base-uncased"""       )
											snake_case_					:		Tuple 					=					np.array([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]       )
											snake_case_					:		Optional[int] 					=					np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
											snake_case_					:		Dict 					=					model(_a   ,		attention_mask=_a       )[0]
											snake_case_					:		Dict 					=					(1, 11, 7_68)
											self.assertEqual(output.shape   ,		_a       )
											snake_case_					:		List[str] 					=					np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]       )

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









"""simple docstring"""

from random import randint
from tempfile import TemporaryFile

import numpy as np







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  Optional[int]			):



					"""simple docstring"""





					snake_case_					:		Union[str, Any] 					=					0
					if start < end:
										snake_case_					:		Union[str, Any] 					=					randint(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
										snake_case_					:		List[Any] 					=					a[end]
										snake_case_					:		Dict 					=					a[pivot]
										snake_case_					:		Any 					=					temp

										snake_case_					,			snake_case_					:		Dict 					=					_in_place_partition(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    p - 1			)
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE__				,    p + 1				,    SCREAMING_SNAKE_CASE__			)
					return count







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Dict			):



					"""simple docstring"""





					snake_case_					:		Tuple 					=					0
					snake_case_					:		List[Any] 					=					randint(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					snake_case_					:		Dict 					=					a[end]
					snake_case_					:		List[Any] 					=					a[pivot]
					snake_case_					:		Optional[Any] 					=					temp
					snake_case_					:		List[str] 					=					start - 1
					for index in range(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			):
										count += 1
										if a[index] < a[end]:  # check if current val is less than pivot value
															snake_case_					:		Any 					=					new_pivot_index + 1
															snake_case_					:		Tuple 					=					a[new_pivot_index]
															snake_case_					:		Optional[int] 					=					a[index]
															snake_case_					:		Tuple 					=					temp

					snake_case_					:		Union[str, Any] 					=					a[new_pivot_index + 1]
					snake_case_					:		Union[str, Any] 					=					a[end]
					snake_case_					:		Union[str, Any] 					=					temp
					return new_pivot_index + 1, count


a_			   =					TemporaryFile()
a_			   =					100  # 1000 elements are to be sorted


a_   ,    a_			   =					0, 1  # mean and standard deviation
a_			   =					np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)


outfile.seek(0)  # using the same array
a_			   =					np.load(outfile)
a_			   =					len(M) - 1
a_			   =					_in_place_quick_sort(M, 0, r)

print(
    '''No of Comparisons for 100 elements selected from a standard normal distribution'''
    '''is :'''
)


print(z)




