




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 					snake_case_   (     unittest.TestCase	):



				'''simple docstring'''





				def __init__(      self							:   Optional[Any]      ,							__magic_name__							:   List[Any]      ,							__magic_name__							:   int=13      ,							__magic_name__							:   List[str]=7      ,							__magic_name__							:   int=True      ,							__magic_name__							:   List[Any]=True      ,							__magic_name__							:   Optional[Any]=True      ,							__magic_name__							:   str=True      ,							__magic_name__							:   Union[str, Any]=99      ,							__magic_name__							:   Dict=32      ,							__magic_name__							:   Union[str, Any]=5      ,							__magic_name__							:   str=4      ,							__magic_name__							:   Optional[Any]=37      ,							__magic_name__							:   List[str]="gelu"      ,							__magic_name__							:   Dict=0.1      ,							__magic_name__							:   Optional[int]=0.1      ,							__magic_name__							:   int=512      ,							__magic_name__							:   str=16      ,							__magic_name__							:   Any=2      ,							__magic_name__							:   str=0.02      ,							__magic_name__							:   Any=4      ,							)							->			Optional[int]:
											lowerCamelCase_			:    Optional[Any]   						=      parent
											lowerCamelCase_			:    List[str]   						=      batch_size
											lowerCamelCase_			:    List[str]   						=      seq_length
											lowerCamelCase_			:    Optional[Any]   						=      is_training
											lowerCamelCase_			:    Optional[Any]   						=      use_attention_mask
											lowerCamelCase_			:    int   						=      use_token_type_ids
											lowerCamelCase_			:    Tuple   						=      use_labels
											lowerCamelCase_			:    Optional[int]   						=      vocab_size
											lowerCamelCase_			:    str   						=      hidden_size
											lowerCamelCase_			:    Union[str, Any]   						=      num_hidden_layers
											lowerCamelCase_			:    str   						=      num_attention_heads
											lowerCamelCase_			:    Union[str, Any]   						=      intermediate_size
											lowerCamelCase_			:    Any   						=      hidden_act
											lowerCamelCase_			:    List[str]   						=      hidden_dropout_prob
											lowerCamelCase_			:    Optional[Any]   						=      attention_probs_dropout_prob
											lowerCamelCase_			:    List[Any]   						=      max_position_embeddings
											lowerCamelCase_			:    List[Any]   						=      type_vocab_size
											lowerCamelCase_			:    Optional[int]   						=      type_sequence_label_size
											lowerCamelCase_			:    Tuple   						=      initializer_range
											lowerCamelCase_			:    int   						=      num_choices





				def    __SCREAMING_SNAKE_CASE     (      self							:   Optional[int]							)							->			Tuple:
											lowerCamelCase_			:    Dict   						=      ids_tensor([self.batch_size, self.seq_length]      ,							self.vocab_size							)

											lowerCamelCase_			:    int   						=      None
											if self.use_attention_mask:
																		lowerCamelCase_			:    Dict   						=      random_attention_mask([self.batch_size, self.seq_length]							)

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

											lowerCamelCase_			:    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=__magic_name__      ,							initializer_range=self.initializer_range      ,							)

											return config, input_ids, token_type_ids, attention_mask





				def    __SCREAMING_SNAKE_CASE     (      self							:   Dict							)							->			List[Any]:
											lowerCamelCase_			:    str   						=      self.prepare_config_and_inputs()
											lowerCamelCase_			:    Optional[int]   						=      config_and_inputs
											lowerCamelCase_			:    Any   						=      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
											return config, inputs_dict





@require_flax
class 					snake_case_   (     __lowercase   ,	unittest.TestCase	):



				'''simple docstring'''
				lowerCamelCase       =  True

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





				def    __SCREAMING_SNAKE_CASE     (      self							:   List[str]							)							->			Union[str, Any]:
											lowerCamelCase_			:    List[Any]   						=      FlaxRoFormerModelTester(self							)





				@slow
				def    __SCREAMING_SNAKE_CASE     (      self							:   str							)							->			int:

											for model_class_name in self.all_model_classes:
																		lowerCamelCase_			:    Optional[Any]   						=      model_class_name.from_pretrained("junnyu/roformer_chinese_small"      ,							from_pt=__magic_name__							)
																		lowerCamelCase_			:    Dict   						=      model(np.ones((1, 1)							)							)
																		self.assertIsNotNone(__magic_name__							)





@require_flax
class 					snake_case_   (     unittest.TestCase	):



				'''simple docstring'''





				@slow
				def    __SCREAMING_SNAKE_CASE     (      self							:   str							)							->			Union[str, Any]:
											lowerCamelCase_			:    str   						=      FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"							)
											lowerCamelCase_			:    Optional[Any]   						=      jnp.array([[0, 1, 2, 3, 4, 5]]							)
											lowerCamelCase_			:    int   						=      model(__magic_name__							)[0]

											lowerCamelCase_			:    Union[str, Any]   						=      5_0000

											lowerCamelCase_			:    Dict   						=      (1, 6, vocab_size)
											self.assertEqual(output.shape      ,							__magic_name__							)

											lowerCamelCase_			:    Optional[int]   						=      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]      ,							__magic_name__      ,							atol=1e-4							)							)








'''simple docstring'''






def 					_a						(       _lowerCamelCase ) ->				int:



			"""simple docstring"""


			if not isinstance(_lowerCamelCase    ,	_lowerCamelCase ):
						raise TypeError("""only integers accepted as input""" )
			else:
						__snake_case					:      List[Any]							      =     str(abs(_lowerCamelCase ) )
						__snake_case					:      Union[str, Any]							      =     [list(_lowerCamelCase ) for char in range(len(_lowerCamelCase ) )]
						for index in range(len(_lowerCamelCase ) ):
									num_transpositions[index].pop(_lowerCamelCase )
						return max(
						    int("""""".join(list(_lowerCamelCase ) ) ) for transposition in num_transpositions )


if __name__ == "__main__":
	__import__("doctest").testmod()


