

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   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







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



			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_: Dict  		=       ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size)

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

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

									SCREAMING_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=lowerCAmelCase__   ,						initializer_range=self.initializer_range   ,						)

									return config, input_ids, token_type_ids, attention_mask




			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[Any]  		=       config_and_inputs
									SCREAMING_SNAKE_CASE_: Any  		=       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
									return config, inputs_dict







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




			"""simple docstring"""







			_UpperCAmelCase   :							List[Any]										=     True

			_UpperCAmelCase   :							str										=     (
			    (
			        FlaxRoFormerModel,
			        FlaxRoFormerForMaskedLM,
			        FlaxRoFormerForSequenceClassification,
			        FlaxRoFormerForTokenClassification,
			        FlaxRoFormerForMultipleChoice,
			        FlaxRoFormerForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: Tuple  		=       FlaxRoFormerModelTester(self)




			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									for model_class_name in self.all_model_classes:
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       model_class_name.from_pretrained("junnyu/roformer_chinese_small"   ,						from_pt=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Dict  		=       model(np.ones((1, 1)))
															self.assertIsNotNone(lowerCAmelCase__)







@require_flax
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base")
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       jnp.array([[0, 1, 2, 3, 4, 5]])
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       model(lowerCAmelCase__)[0]

									SCREAMING_SNAKE_CASE_: str  		=       5_0000

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

									SCREAMING_SNAKE_CASE_: 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]   ,						lowerCAmelCase__   ,						atol=1E-4))









import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

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



class     _a						(__magic_name__		,   __magic_name__		,   __magic_name__				):


					'''simple docstring'''






					@register_to_config
					def __init__( self    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__ = False    ,       ):
						super().__init__()

						A__							:    List[str]        =		nn.Embedding(A__    ,       A__			)

						A__							:    Any        =		nn.Embedding(A__    ,       A__			)
						A__							:    Optional[int]        =		False

						A__							:    Optional[Any]        =		nn.Dropout(p=A__			)

						A__							:    int        =		TaConfig(
						    vocab_size=A__    ,       d_model=A__    ,       num_heads=A__    ,       d_kv=A__    ,       d_ff=A__    ,       dropout_rate=A__    ,       feed_forward_proj=A__    ,       is_decoder=A__    ,       is_encoder_decoder=A__    ,       )

						A__							:    Dict        =		nn.ModuleList()
						for lyr_num in range(A__			):
							A__							:    str        =		TaBlock(A__			)
							self.encoders.append(A__			)

						A__							:    Tuple        =		TaLayerNorm(A__			)
						A__							:    str        =		nn.Dropout(p=A__			)






					def   __A			( self    ,       A__    ,       A__			):
						A__							:    List[str]        =		self.token_embedder(A__			)

						A__							:    Optional[Any]        =		encoder_input_tokens.shape[1]
						A__							:    Optional[int]        =		torch.arange(A__    ,       device=encoder_input_tokens.device			)
						x += self.position_encoding(A__			)

						A__							:    Optional[Any]        =		self.dropout_pre(A__			)

						# inverted the attention mask
						A__							:    Tuple        =		encoder_input_tokens.size()
						A__							:    Union[str, Any]        =		self.get_extended_attention_mask(A__    ,       A__			)

						for lyr in self.encoders:
							A__							:    List[str]        =		lyr(A__    ,       A__			)[0]
						A__							:    List[Any]        =		self.layer_norm(A__			)

						return self.dropout_post(A__			), encoder_inputs_mask



