


"""simple docstring"""


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    ,   __a    ,   __a=13    ,   __a=7    ,   __a=True    ,   __a=True    ,   __a=True    ,   __a=True    ,   __a=99    ,   __a=32    ,   __a=5    ,   __a=4    ,   __a=37    ,   __a="gelu"    ,   __a=0.1    ,   __a=0.1    ,   __a=5_12    ,   __a=16    ,   __a=2    ,   __a=0.0_2    ,   __a=4    ,   ):
										__lowerCAmelCase				    =			parent
										__lowerCAmelCase				    =			batch_size
										__lowerCAmelCase				    =			seq_length
										__lowerCAmelCase				    =			is_training
										__lowerCAmelCase				    =			use_attention_mask
										__lowerCAmelCase				    =			use_token_type_ids
										__lowerCAmelCase				    =			use_labels
										__lowerCAmelCase				    =			vocab_size
										__lowerCAmelCase				    =			hidden_size
										__lowerCAmelCase				    =			num_hidden_layers
										__lowerCAmelCase				    =			num_attention_heads
										__lowerCAmelCase				    =			intermediate_size
										__lowerCAmelCase				    =			hidden_act
										__lowerCAmelCase				    =			hidden_dropout_prob
										__lowerCAmelCase				    =			attention_probs_dropout_prob
										__lowerCAmelCase				    =			max_position_embeddings
										__lowerCAmelCase				    =			type_vocab_size
										__lowerCAmelCase				    =			type_sequence_label_size
										__lowerCAmelCase				    =			initializer_range
										__lowerCAmelCase				    =			num_choices

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			ids_tensor([self.batch_size, self.seq_length]    ,   self.vocab_size	)

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

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

										__lowerCAmelCase				    =			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 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.prepare_config_and_inputs()
										__lowerCAmelCase    ,	__lowerCAmelCase    ,	__lowerCAmelCase    ,	__lowerCAmelCase				    =			config_and_inputs
										__lowerCAmelCase				    =			{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
										return config, inputs_dict
@require_flax
class   _UpperCamelCase  (   lowerCAmelCase__       ,unittest.TestCase		):
							'''simple docstring'''







							__UpperCAmelCase						:    Union[str, Any]				      =True

							__UpperCAmelCase						:    List[Any]				      =(
							    (
							        FlaxRoFormerModel,
							        FlaxRoFormerForMaskedLM,
							        FlaxRoFormerForSequenceClassification,
							        FlaxRoFormerForTokenClassification,
							        FlaxRoFormerForMultipleChoice,
							        FlaxRoFormerForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			FlaxRoFormerModelTester(self	)



							@slow
							def 			snake_case				(     self	):
										for model_class_name in self.all_model_classes:
													__lowerCAmelCase				    =			model_class_name.from_pretrained("junnyu/roformer_chinese_small"    ,   from_pt=__a	)
													__lowerCAmelCase				    =			model(np.ones((1, 1)	)	)
													self.assertIsNotNone(__a	)
@require_flax
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







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

										__lowerCAmelCase				    =			5_00_00

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

										__lowerCAmelCase				    =			jnp.array(
										    [[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]]	)

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







'''simple docstring'''
from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin







class 							A__    (       A__     ,   A__  ):





  @register_to_config
  def __init__(		self      :	Dict							,					_a      :	int = 768							,					)	->    Union[str, Any]:





       '''simple docstring'''






       super().__init__()

       _SCREAMING_SNAKE_CASE							    =nn.Parameter(torch.zeros(1							,					_a ) )
       _SCREAMING_SNAKE_CASE							    =nn.Parameter(torch.ones(1							,					_a ) )





  def 						A  (		self      :	Tuple							,					_a      :	Optional[Union[str, torch.device]] = None							,					_a      :	Optional[torch.dtype] = None							,					)	->    List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =nn.Parameter(self.mean.to(_a ).to(_a ) )
       _SCREAMING_SNAKE_CASE							    =nn.Parameter(self.std.to(_a ).to(_a ) )
       return self





  def 						A  (		self      :	Tuple							,					_a      :	str )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =(embeds - self.mean) * 1.0 / self.std
       return embeds





  def 						A  (		self      :	List[str]							,					_a      :	Optional[Any] )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =(embeds * self.std) + self.mean
       return embeds
