





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        __SCREAMING_SNAKE_CASE					(     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  , ):
														UpperCamelCase__				     =			parent
														UpperCamelCase__				     =			batch_size
														UpperCamelCase__				     =			seq_length
														UpperCamelCase__				     =			is_training
														UpperCamelCase__				     =			use_attention_mask
														UpperCamelCase__				     =			use_token_type_ids
														UpperCamelCase__				     =			use_labels
														UpperCamelCase__				     =			vocab_size
														UpperCamelCase__				     =			hidden_size
														UpperCamelCase__				     =			num_hidden_layers
														UpperCamelCase__				     =			num_attention_heads
														UpperCamelCase__				     =			intermediate_size
														UpperCamelCase__				     =			hidden_act
														UpperCamelCase__				     =			hidden_dropout_prob
														UpperCamelCase__				     =			attention_probs_dropout_prob
														UpperCamelCase__				     =			max_position_embeddings
														UpperCamelCase__				     =			type_vocab_size
														UpperCamelCase__				     =			type_sequence_label_size
														UpperCamelCase__				     =			initializer_range
														UpperCamelCase__				     =			num_choices






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			ids_tensor([self.batch_size, self.seq_length]  , self.vocab_size  )

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

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

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






@require_flax
class        __SCREAMING_SNAKE_CASE					(     _a   ,		unittest.TestCase       ):
							snake_case     :      Optional[Any]						     =       True

							snake_case     :      int						     =       (
							    (
							        FlaxRoFormerModel,
							        FlaxRoFormerForMaskedLM,
							        FlaxRoFormerForSequenceClassification,
							        FlaxRoFormerForTokenClassification,
							        FlaxRoFormerForMultipleChoice,
							        FlaxRoFormerForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			FlaxRoFormerModelTester(self  )







							@slow
							def 				_lowerCamelCase		(						self  ):
														for model_class_name in self.all_model_classes:
																					UpperCamelCase__				     =			model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""  , from_pt=__lowerCAmelCase  )
																					UpperCamelCase__				     =			model(np.ones((1, 1)  )  )
																					self.assertIsNotNone(__lowerCAmelCase  )






@require_flax
class        __SCREAMING_SNAKE_CASE					(     unittest.TestCase       ):






							@slow
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""  )
														UpperCamelCase__				     =			jnp.array([[0, 1, 2, 3, 4, 5]]  )
														UpperCamelCase__				     =			model(__lowerCAmelCase  )[0]

														UpperCamelCase__				     =			50000

														UpperCamelCase__				     =			(1, 6, vocab_size)
														self.assertEqual(output.shape  , __lowerCAmelCase  )

														UpperCamelCase__				     =			jnp.array(
														    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]  )

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











from math import ceil, sqrt





def     _UpperCamelCase     (a__   :int = 100_0000       ):






							"""simple docstring"""





							UpperCamelCase__				     =			0

							for outer_width in range(3   ,			(limit // 4) + 2       ):
														if outer_width**2 > limit:
																					UpperCamelCase__				     =			max(ceil(sqrt(outer_width**2 - limit       )       )   ,			1       )
														else:
																					UpperCamelCase__				     =			1
														if (outer_width - hole_width_lower_bound) % 2:
																					hole_width_lower_bound += 1

														answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

							return answer


if __name__ == "__main__":
					print(f"""{solution() = }""")




