

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

				"""simple docstring"""

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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							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 :   Dict					  =							random_attention_mask([self.batch_size, self.seq_length]						)

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

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							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=snake_case_   ,   initializer_range=self.initializer_range   ,   )

				return config, input_ids, token_type_ids, attention_mask






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

				"""simple docstring"""

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






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







			lowerCamelCase__							     =True

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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							FlaxRoFormerModelTester(self						)






			@slow
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							Tuple:

				"""simple docstring"""

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






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







			@slow
			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							jnp.array([[0, 1, 2, 3, 4, 5]]						)
				SCREAMING_SNAKE_CASE :   int					  =							model(snake_case_						)[0]

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							5_0000

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

				SCREAMING_SNAKE_CASE :   List[Any]					  =							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]   ,   snake_case_   ,   atol=1e-4						)						)






def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> float:




    """simple docstring"""




    return price * (1 + tax_rate)


if __name__ == "__main__":
 print(f"{price_plus_tax(100, 0.2_5) = }")
 print(f"{price_plus_tax(1_2_5.5_0, 0.0_5) = }")





