

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   ):
			'''simple docstring'''







			def __init__(			self							: str      ,      a							: Any      ,      a							: Optional[Any]=13      ,      a							: Any=7      ,      a							: Optional[int]=True      ,      a							: Dict=True      ,      a							: Optional[Any]=True      ,      a							: int=True      ,      a							: int=99      ,      a							: Any=32      ,      a							: Any=5      ,      a							: int=4      ,      a							: Tuple=37      ,      a							: List[Any]="gelu"      ,      a							: Any=0.1      ,      a							: str=0.1      ,      a							: List[Any]=512      ,      a							: Union[str, Any]=16      ,      a							: Tuple=2      ,      a							: Optional[Any]=0.02      ,      a							: List[str]=4      ,      )							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       parent
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       batch_size
				SCREAMING_SNAKE_CASE   :      Tuple				     =       seq_length
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       is_training
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       use_attention_mask
				SCREAMING_SNAKE_CASE   :      str				     =       use_token_type_ids
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       use_labels
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       vocab_size
				SCREAMING_SNAKE_CASE   :      str				     =       hidden_size
				SCREAMING_SNAKE_CASE   :      Any				     =       num_hidden_layers
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       num_attention_heads
				SCREAMING_SNAKE_CASE   :      int				     =       intermediate_size
				SCREAMING_SNAKE_CASE   :      Any				     =       hidden_act
				SCREAMING_SNAKE_CASE   :      Any				     =       hidden_dropout_prob
				SCREAMING_SNAKE_CASE   :      List[str]				     =       attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE   :      int				     =       max_position_embeddings
				SCREAMING_SNAKE_CASE   :      List[str]				     =       type_vocab_size
				SCREAMING_SNAKE_CASE   :      Tuple				     =       type_sequence_label_size
				SCREAMING_SNAKE_CASE   :      int				     =       initializer_range
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       num_choices
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Any:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       None
				if self.use_attention_mask:
					SCREAMING_SNAKE_CASE   :      List[str]				     =       random_attention_mask([self.batch_size, self.seq_length]	)

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

				SCREAMING_SNAKE_CASE   :      List[str]				     =       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=a      ,      initializer_range=self.initializer_range      ,      )

				return config, input_ids, token_type_ids, attention_mask






			def 							__UpperCamelCase (			self							: str	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       config_and_inputs
				SCREAMING_SNAKE_CASE   :      int				     =       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
				return config, inputs_dict






@require_flax
class _UpperCamelCase							(							__A							,	unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =True

			lowerCamelCase__           =(
			    (
			        FlaxRoFormerModel,
			        FlaxRoFormerForMaskedLM,
			        FlaxRoFormerForSequenceClassification,
			        FlaxRoFormerForTokenClassification,
			        FlaxRoFormerForMultipleChoice,
			        FlaxRoFormerForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)
			def 							__UpperCamelCase (			self							: List[Any]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       FlaxRoFormerModelTester(self	)






			@slow
			def 							__UpperCamelCase (			self							: Dict	)							->       Optional[Any]:

				"""simple docstring"""

				for model_class_name in self.all_model_classes:
					SCREAMING_SNAKE_CASE   :      int				     =       model_class_name.from_pretrained("junnyu/roformer_chinese_small"      ,      from_pt=a	)
					SCREAMING_SNAKE_CASE   :      Any				     =       model(np.ones((1, 1)	)	)
					self.assertIsNotNone(a	)






@require_flax
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			@slow
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       jnp.array([[0, 1, 2, 3, 4, 5]]	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       model(a	)[0]

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       5_0000

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (1, 6, vocab_size)
				self.assertEqual(output.shape      ,      a	)

				SCREAMING_SNAKE_CASE   :      List[str]				     =       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]      ,      a      ,      atol=1e-4	)	)


import math

def  lowerCamelCase__ (					_a							,			_a):
	if (
	    not isinstance(_a							,			(int, float))
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError("power_factor must be a valid float value between -1 and 1.")
	return apparent_power * power_factor

def  lowerCamelCase__ (					_a							,			_a):
	if (
	    not isinstance(_a							,			(int, float))
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError("power_factor must be a valid float value between -1 and 1.")
	return apparent_power * math.sqrt(1 - power_factor**2)


if __name__ == "__main__":
			import doctest

			doctest.testmod()