



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       a							(			unittest.TestCase				):



		def __init__(     self     :List[str]   ,__lowercase     :Optional[Any]   ,__lowercase     :Dict=1_3   ,__lowercase     :str=7   ,__lowercase     :int=True   ,__lowercase     :Optional[int]=True   ,__lowercase     :List[Any]=True   ,__lowercase     :int=True   ,__lowercase     :Optional[Any]=9_9   ,__lowercase     :List[Any]=3_2   ,__lowercase     :Optional[int]=5   ,__lowercase     :Tuple=4   ,__lowercase     :List[Any]=3_7   ,__lowercase     :Tuple="gelu"   ,__lowercase     :Optional[int]=0.1   ,__lowercase     :Optional[int]=0.1   ,__lowercase     :Tuple=5_1_2   ,__lowercase     :Dict=1_6   ,__lowercase     :List[str]=2   ,__lowercase     :int=0.02   ,__lowercase     :Union[str, Any]=4   ,):
						snake_case__	:      int     		=	parent
						snake_case__	:      Optional[Any]     		=	batch_size
						snake_case__	:      Union[str, Any]     		=	seq_length
						snake_case__	:      Optional[int]     		=	is_training
						snake_case__	:      Union[str, Any]     		=	use_attention_mask
						snake_case__	:      int     		=	use_token_type_ids
						snake_case__	:      Dict     		=	use_labels
						snake_case__	:      str     		=	vocab_size
						snake_case__	:      int     		=	hidden_size
						snake_case__	:      Union[str, Any]     		=	num_hidden_layers
						snake_case__	:      int     		=	num_attention_heads
						snake_case__	:      List[str]     		=	intermediate_size
						snake_case__	:      Dict     		=	hidden_act
						snake_case__	:      Dict     		=	hidden_dropout_prob
						snake_case__	:      Tuple     		=	attention_probs_dropout_prob
						snake_case__	:      int     		=	max_position_embeddings
						snake_case__	:      int     		=	type_vocab_size
						snake_case__	:      Optional[Any]     		=	type_sequence_label_size
						snake_case__	:      Union[str, Any]     		=	initializer_range
						snake_case__	:      Optional[int]     		=	num_choices



		def 	__lowerCamelCase			(     self     :List[Any]  ):
						snake_case__	:      Tuple     		=	ids_tensor([self.batch_size, self.seq_length]   ,self.vocab_size  )

						snake_case__	:      Optional[int]     		=	None
						if self.use_attention_mask:
										snake_case__	:      Optional[Any]     		=	random_attention_mask([self.batch_size, self.seq_length]  )

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

						snake_case__	:      int     		=	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=__lowercase   ,initializer_range=self.initializer_range   ,)

						return config, input_ids, token_type_ids, attention_mask






		def 	__lowerCamelCase			(     self     :List[str]  ):
						snake_case__	:      Any     		=	self.prepare_config_and_inputs()
						snake_case__		, snake_case__		, snake_case__		, snake_case__	:      str     		=	config_and_inputs
						snake_case__	:      List[Any]     		=	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
						return config, inputs_dict


@require_flax
class       a							(			__lowerCamelCase  ,							unittest.TestCase				):
		__lowerCAmelCase	:    str											=					True

		__lowerCAmelCase	:    int											=					(
		    (
		        FlaxRoFormerModel,
		        FlaxRoFormerForMaskedLM,
		        FlaxRoFormerForSequenceClassification,
		        FlaxRoFormerForTokenClassification,
		        FlaxRoFormerForMultipleChoice,
		        FlaxRoFormerForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)



		def 	__lowerCamelCase			(     self     :Optional[Any]  ):
						snake_case__	:      Dict     		=	FlaxRoFormerModelTester(self  )






		@slow
		def 	__lowerCamelCase			(     self     :List[str]  ):
						for model_class_name in self.all_model_classes:
										snake_case__	:      Optional[int]     		=	model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''   ,from_pt=__lowercase  )
										snake_case__	:      Optional[Any]     		=	model(np.ones((1, 1)  )  )
										self.assertIsNotNone(__lowercase  )


@require_flax
class       a							(			unittest.TestCase				):



		@slow
		def 	__lowerCamelCase			(     self     :Union[str, Any]  ):
						snake_case__	:      str     		=	FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''  )
						snake_case__	:      str     		=	jnp.array([[0, 1, 2, 3, 4, 5]]  )
						snake_case__	:      Any     		=	model(__lowercase  )[0]

						snake_case__	:      List[str]     		=	5_0_0_0_0

						snake_case__	:      Union[str, Any]     		=	(1, 6, vocab_size)
						self.assertEqual(output.shape   ,__lowercase  )

						snake_case__	:      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]   ,__lowercase   ,atol=1e-4  )  )






from torch import nn


class       a							(			nn.Module				):



		def __init__(     self     :Tuple   ,__lowercase     :Optional[int]   ,__lowercase     :int  ):
						super().__init__()
						snake_case__	:      Optional[Any]     		=	class_size
						snake_case__	:      Dict     		=	embed_size
						# self.mlp1 = nn.Linear(embed_size, embed_size)
						# self.mlp2 = (nn.Linear(embed_size, class_size))
						snake_case__	:      Dict     		=	nn.Linear(__lowercase   ,__lowercase  )






		def 	__lowerCamelCase			(     self     :str   ,__lowercase     :int  ):
						# hidden_state = nn.functional.relu(self.mlp1(hidden_state))
						# hidden_state = self.mlp2(hidden_state)
						snake_case__	:      Optional[Any]     		=	self.mlp(__lowercase  )
						return logits

