



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 			_lowerCAmelCase   ( unittest.TestCase			):






			"""simple docstring"""





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







									__lowercase					=				parent
									__lowercase					=				batch_size
									__lowercase					=				seq_length
									__lowercase					=				is_training
									__lowercase					=				use_attention_mask
									__lowercase					=				use_token_type_ids
									__lowercase					=				use_labels
									__lowercase					=				vocab_size
									__lowercase					=				hidden_size
									__lowercase					=				num_hidden_layers
									__lowercase					=				num_attention_heads
									__lowercase					=				intermediate_size
									__lowercase					=				hidden_act
									__lowercase					=				hidden_dropout_prob
									__lowercase					=				attention_probs_dropout_prob
									__lowercase					=				max_position_embeddings
									__lowercase					=				type_vocab_size
									__lowercase					=				type_sequence_label_size
									__lowercase					=				initializer_range
									__lowercase					=				num_choices





			def 		snake_case__			(				self	:       List[str]   )						->					List[str]:
									"""simple docstring"""







									__lowercase					=				ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size   )

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

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

									__lowercase					=				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 		snake_case__			(				self	:       Any   )						->					Optional[Any]:
									"""simple docstring"""







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





@require_flax
class 			_lowerCAmelCase   ( __a    ,					unittest.TestCase			):






			"""simple docstring"""





			lowercase__      :  List[Any]      =			True

			lowercase__      :  Union[str, Any]      =			(
			    (
			        FlaxRoFormerModel,
			        FlaxRoFormerForMaskedLM,
			        FlaxRoFormerForSequenceClassification,
			        FlaxRoFormerForTokenClassification,
			        FlaxRoFormerForMultipleChoice,
			        FlaxRoFormerForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)





			def 		snake_case__			(				self	:       int   )						->					Tuple:
									"""simple docstring"""







									__lowercase					=				FlaxRoFormerModelTester(self   )







			@slow
			def 		snake_case__			(				self	:       Optional[int]   )						->					int:
									"""simple docstring"""







									for model_class_name in self.all_model_classes:
															__lowercase					=				model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""			,     from_pt=snake_case__   )
															__lowercase					=				model(np.ones((1, 1)   )   )
															self.assertIsNotNone(snake_case__   )





@require_flax
class 			_lowerCAmelCase   ( unittest.TestCase			):






			"""simple docstring"""





			@slow
			def 		snake_case__			(				self	:       List[Any]   )						->					str:
									"""simple docstring"""







									__lowercase					=				FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""   )
									__lowercase					=				jnp.array([[0, 1, 2, 3, 4, 5]]   )
									__lowercase					=				model(snake_case__   )[0]

									__lowercase					=				50_000

									__lowercase					=				(1, 6, vocab_size)
									self.assertEqual(output.shape			,     snake_case__   )

									__lowercase					=				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  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      int:
						if len(lowercase__   ) != len(lowercase__   ):
												raise ValueError("""String lengths must match!"""   )

						__lowercase					=				0

						for chara, chara in zip(lowercase__						,       lowercase__   ):
												if chara != chara:
																		count += 1

						return count


if __name__ == "__main__":
			import doctest

			doctest.testmod()





