"""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        snake_case (				unittest.TestCase       ):




     """simple docstring"""

     def __init__(       self     :			Optional[Any]					,lowerCamelCase__     :			str					,lowerCamelCase__     :			Any=13					,lowerCamelCase__     :			List[Any]=7					,lowerCamelCase__     :			Dict=True					,lowerCamelCase__     :			Dict=True					,lowerCamelCase__     :			Tuple=True					,lowerCamelCase__     :			Union[str, Any]=True					,lowerCamelCase__     :			Tuple=99					,lowerCamelCase__     :			str=32					,lowerCamelCase__     :			Optional[Any]=5					,lowerCamelCase__     :			Union[str, Any]=4					,lowerCamelCase__     :			Dict=37					,lowerCamelCase__     :			Optional[int]="gelu"					,lowerCamelCase__     :			Optional[int]=0.1					,lowerCamelCase__     :			Optional[Any]=0.1					,lowerCamelCase__     :			List[Any]=512					,lowerCamelCase__     :			List[Any]=16					,lowerCamelCase__     :			Union[str, Any]=2					,lowerCamelCase__     :			Tuple=0.0_2					,lowerCamelCase__     :			int=4					,):
         UpperCAmelCase__    =							parent
         UpperCAmelCase__    =							batch_size
         UpperCAmelCase__    =							seq_length
         UpperCAmelCase__    =							is_training
         UpperCAmelCase__    =							use_attention_mask
         UpperCAmelCase__    =							use_token_type_ids
         UpperCAmelCase__    =							use_labels
         UpperCAmelCase__    =							vocab_size
         UpperCAmelCase__    =							hidden_size
         UpperCAmelCase__    =							num_hidden_layers
         UpperCAmelCase__    =							num_attention_heads
         UpperCAmelCase__    =							intermediate_size
         UpperCAmelCase__    =							hidden_act
         UpperCAmelCase__    =							hidden_dropout_prob
         UpperCAmelCase__    =							attention_probs_dropout_prob
         UpperCAmelCase__    =							max_position_embeddings
         UpperCAmelCase__    =							type_vocab_size
         UpperCAmelCase__    =							type_sequence_label_size
         UpperCAmelCase__    =							initializer_range
         UpperCAmelCase__    =							num_choices







     def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
         UpperCAmelCase__    =							ids_tensor([self.batch_size, self.seq_length]					,self.vocab_size    )

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

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

         UpperCAmelCase__    =							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 	__lowerCAmelCase		(       self     :			Tuple    ):
         UpperCAmelCase__    =							self.prepare_config_and_inputs()
         UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__    =							config_and_inputs
         UpperCAmelCase__    =							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
         return config, inputs_dict






@require_flax
class        snake_case (				__UpperCAmelCase   ,					unittest.TestCase       ):




     """simple docstring"""

     snake_case__       					=				True

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







     def 	__lowerCAmelCase		(       self     :			Any    ):
         UpperCAmelCase__    =							FlaxRoFormerModelTester(self    )







     @slow
     def 	__lowerCAmelCase		(       self     :			Any    ):
         for model_class_name in self.all_model_classes:
             UpperCAmelCase__    =							model_class_name.from_pretrained('junnyu/roformer_chinese_small'					,from_pt=lowerCamelCase__    )
             UpperCAmelCase__    =							model(np.ones((1, 1)    )    )
             self.assertIsNotNone(lowerCamelCase__    )






@require_flax
class        snake_case (				unittest.TestCase       ):




     """simple docstring"""

     @slow
     def 	__lowerCAmelCase		(       self     :			List[Any]    ):
         UpperCAmelCase__    =							FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'    )
         UpperCAmelCase__    =							jnp.array([[0, 1, 2, 3, 4, 5]]    )
         UpperCAmelCase__    =							model(lowerCamelCase__    )[0]

         UpperCAmelCase__    =							50_000

         UpperCAmelCase__    =							(1, 6, vocab_size)
         self.assertEqual(output.shape					,lowerCamelCase__    )

         UpperCAmelCase__    =							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]					,lowerCamelCase__					,atol=1e-4    )    )




"""simple docstring"""






import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
						import ctypes
						import msvcrt  # noqa
						class        snake_case (				ctypes.Structure       ):




											"""simple docstring"""

											snake_case__       					=				[("size", ctypes.c_int), ("visible", ctypes.c_byte)]



def 						a_		(      ):
				if os.name == "nt":
								UpperCAmelCase__    =							CursorInfo()
								UpperCAmelCase__    =							ctypes.windll.kernelaa.GetStdHandle(-1_1 )
								ctypes.windll.kernelaa.GetConsoleCursorInfo(lowerCamelCase      ,    ctypes.byref(lowerCamelCase ) )
								UpperCAmelCase__    =							False
								ctypes.windll.kernelaa.SetConsoleCursorInfo(lowerCamelCase      ,    ctypes.byref(lowerCamelCase ) )
				elif os.name == "posix":
								sys.stdout.write('\033[?25l' )
								sys.stdout.flush()



def 						a_		(      ):
				if os.name == "nt":
								UpperCAmelCase__    =							CursorInfo()
								UpperCAmelCase__    =							ctypes.windll.kernelaa.GetStdHandle(-1_1 )
								ctypes.windll.kernelaa.GetConsoleCursorInfo(lowerCamelCase      ,    ctypes.byref(lowerCamelCase ) )
								UpperCAmelCase__    =							True
								ctypes.windll.kernelaa.SetConsoleCursorInfo(lowerCamelCase      ,    ctypes.byref(lowerCamelCase ) )
				elif os.name == "posix":
								sys.stdout.write('\033[?25h' )
								sys.stdout.flush()







@contextmanager
def 						a_		(      ):
				try:
								hide_cursor()
								yield
				finally:
								show_cursor()



