

'''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 						__magic_name__   (							unittest.TestCase ):







 def __init__(       self  , snake_case_  , snake_case_=13  , snake_case_=7  , snake_case_=True  , snake_case_=True  , snake_case_=True  , snake_case_=True  , snake_case_=99  , snake_case_=32  , snake_case_=5  , snake_case_=4  , snake_case_=37  , snake_case_="gelu"  , snake_case_=0.1  , snake_case_=0.1  , snake_case_=5_12  , snake_case_=16  , snake_case_=2  , snake_case_=0.02  , snake_case_=4  , ):
   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 						_A(       self   ):
   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 						_A(       self   ):
   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 						__magic_name__   (							__SCREAMING_SNAKE_CASE ,				unittest.TestCase ):
 UpperCamelCase__     =       True

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







 def 						_A(       self   ):
   lowercase									=FlaxRoFormerModelTester(self   )







 @slow
 def 						_A(       self   ):
   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 						__magic_name__   (							unittest.TestCase ):







 @slow
 def 						_A(       self   ):
   lowercase									=FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''   )
   lowercase									=jnp.array([[0, 1, 2, 3, 4, 5]]   )
   lowercase									=model(snake_case_   )[0]

   lowercase									=5_00_00

   lowercase									=(1, 6, vocab_size)
   self.assertEqual(output.shape  , snake_case_   )

   lowercase									=jnp.array(
       [[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]]   )

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

import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
	import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin

@require_torch
class        UpperCamelCase						(				unittest.TestCase	,	__a				):





						def        A_							(self						)				->				Tuple:
												UpperCamelCase_		:							Any  =		load_tool("""text-to-speech"""						)
												self.tool.setup()





						def        A_							(self						)				->				Dict:
												# SpeechT5 isn't deterministic
												torch.manual_seed(0						)
												UpperCamelCase_		:							Optional[Any]  =		self.tool("""hey"""						)
												UpperCamelCase_		:							Optional[Any]  =		result.to_raw()
												self.assertTrue(
												    torch.allclose(
												        resulting_tensor[:3]			,     torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]						)			,     )						)





						def        A_							(self						)				->				Dict:
												# SpeechT5 isn't deterministic
												torch.manual_seed(0						)
												UpperCamelCase_		:							Any  =		self.tool("""hey"""						)
												UpperCamelCase_		:							Tuple  =		result.to_raw()
												self.assertTrue(
												    torch.allclose(
												        resulting_tensor[:3]			,     torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]						)			,     )						)






