



import unittest

import numpy as np

from transformers import RoFormerConfig, 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.roformer.modeling_flax_roformer import (
					    FlaxRoFormerForMaskedLM,
					    FlaxRoFormerForMultipleChoice,
					    FlaxRoFormerForQuestionAnswering,
					    FlaxRoFormerForSequenceClassification,
					    FlaxRoFormerForTokenClassification,
					    FlaxRoFormerModel,
					)
class      UpperCamelCase_						(					unittest.TestCase   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_=13			,  lowerCAmelCase_=7			,  lowerCAmelCase_=True			,  lowerCAmelCase_=True			,  lowerCAmelCase_=True			,  lowerCAmelCase_=True			,  lowerCAmelCase_=99			,  lowerCAmelCase_=32			,  lowerCAmelCase_=5			,  lowerCAmelCase_=4			,  lowerCAmelCase_=37			,  lowerCAmelCase_="gelu"			,  lowerCAmelCase_=0.1			,  lowerCAmelCase_=0.1			,  lowerCAmelCase_=512			,  lowerCAmelCase_=16			,  lowerCAmelCase_=2			,  lowerCAmelCase_=0.02			,  lowerCAmelCase_=4			,  )						->							Dict:
										_snake_case													=			parent
										_snake_case													=			batch_size
										_snake_case													=			seq_length
										_snake_case													=			is_training
										_snake_case													=			use_attention_mask
										_snake_case													=			use_token_type_ids
										_snake_case													=			use_labels
										_snake_case													=			vocab_size
										_snake_case													=			hidden_size
										_snake_case													=			num_hidden_layers
										_snake_case													=			num_attention_heads
										_snake_case													=			intermediate_size
										_snake_case													=			hidden_act
										_snake_case													=			hidden_dropout_prob
										_snake_case													=			attention_probs_dropout_prob
										_snake_case													=			max_position_embeddings
										_snake_case													=			type_vocab_size
										_snake_case													=			type_sequence_label_size
										_snake_case													=			initializer_range
										_snake_case													=			num_choices




					def 		lowerCAmelCase     (						self					)						->							Tuple:
										_snake_case													=			ids_tensor([self.batch_size, self.seq_length]			,  self.vocab_size					)

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

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

										_snake_case													=			RoFormerConfig(
										    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=lowerCAmelCase_			,  initializer_range=self.initializer_range			,  )

										return config, input_ids, token_type_ids, attention_mask






					def 		lowerCAmelCase     (						self					)						->							str:
										_snake_case													=			self.prepare_config_and_inputs()
										_snake_case							,       _snake_case							,       _snake_case							,       _snake_case													=			config_and_inputs
										_snake_case													=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
										return config, inputs_dict





@require_flax
class      UpperCamelCase_						(					_lowerCamelCase							,					unittest.TestCase   ):
					lowerCAmelCase_							=		True

					lowerCAmelCase_							=		(
					    (
					        FlaxRoFormerModel,
					        FlaxRoFormerForMaskedLM,
					        FlaxRoFormerForSequenceClassification,
					        FlaxRoFormerForTokenClassification,
					        FlaxRoFormerForMultipleChoice,
					        FlaxRoFormerForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)




					def 		lowerCAmelCase     (						self					)						->							Any:
										_snake_case													=			FlaxRoFormerModelTester(self					)






					@slow
					def 		lowerCAmelCase     (						self					)						->							List[Any]:
										for model_class_name in self.all_model_classes:
															_snake_case													=			model_class_name.from_pretrained('junnyu/roformer_chinese_small'			,  from_pt=lowerCAmelCase_					)
															_snake_case													=			model(np.ones((1, 1)					)					)
															self.assertIsNotNone(lowerCAmelCase_					)





@require_flax
class      UpperCamelCase_						(					unittest.TestCase   ):




					@slow
					def 		lowerCAmelCase     (						self					)						->							str:
										_snake_case													=			FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'					)
										_snake_case													=			jnp.array([[0, 1, 2, 3, 4, 5]]					)
										_snake_case													=			model(lowerCAmelCase_					)[0]

										_snake_case													=			5_0000

										_snake_case													=			(1, 6, vocab_size)
										self.assertEqual(output.shape			,  lowerCAmelCase_					)

										_snake_case													=			jnp.array(
										    [[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]]					)

										self.assertTrue(jnp.allclose(output[:, :3, :3]			,  lowerCAmelCase_			,  atol=1E-4					)					)










def   lowerCamelCase__       (       UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    Optional[int]  )     ->	Tuple:



					'''simple docstring'''




					_snake_case													=			[0 for i in range(r + 1  )]
					# nc0 = 1
					_snake_case													=			1
					for i in range(1    ,      n + 1  ):
										# to compute current row from previous row.
										_snake_case													=			min(UpperCamelCase__    ,      UpperCamelCase__  )
										while j > 0:
															c[j] += c[j - 1]
															j -= 1
					return c[r]




print(binomial_coefficient(n=10, r=5))



