



'''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  :				Union[str, Any]							,  lowerCAmelCase__  :				Optional[int]							,  lowerCAmelCase__  :				Optional[int]=1_3							,  lowerCAmelCase__  :				Union[str, Any]=7							,  lowerCAmelCase__  :				List[str]=True							,  lowerCAmelCase__  :				Union[str, Any]=True							,  lowerCAmelCase__  :				str=True							,  lowerCAmelCase__  :				Union[str, Any]=True							,  lowerCAmelCase__  :				Union[str, Any]=9_9							,  lowerCAmelCase__  :				Union[str, Any]=3_2							,  lowerCAmelCase__  :				List[str]=5							,  lowerCAmelCase__  :				Optional[Any]=4							,  lowerCAmelCase__  :				int=3_7							,  lowerCAmelCase__  :				Optional[int]="gelu"							,  lowerCAmelCase__  :				Optional[Any]=0.1							,  lowerCAmelCase__  :				List[str]=0.1							,  lowerCAmelCase__  :				List[str]=5_1_2							,  lowerCAmelCase__  :				str=1_6							,  lowerCAmelCase__  :				Tuple=2							,  lowerCAmelCase__  :				Dict=0.02							,  lowerCAmelCase__  :				Dict=4							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	parent
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	batch_size
					__SCREAMING_SNAKE_CASE :	Dict					 =	seq_length
					__SCREAMING_SNAKE_CASE :	Tuple					 =	is_training
					__SCREAMING_SNAKE_CASE :	List[str]					 =	use_attention_mask
					__SCREAMING_SNAKE_CASE :	str					 =	use_token_type_ids
					__SCREAMING_SNAKE_CASE :	List[str]					 =	use_labels
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	vocab_size
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	hidden_size
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	num_hidden_layers
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	num_attention_heads
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	intermediate_size
					__SCREAMING_SNAKE_CASE :	Dict					 =	hidden_act
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	hidden_dropout_prob
					__SCREAMING_SNAKE_CASE :	Tuple					 =	attention_probs_dropout_prob
					__SCREAMING_SNAKE_CASE :	int					 =	max_position_embeddings
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	type_vocab_size
					__SCREAMING_SNAKE_CASE :	Tuple					 =	type_sequence_label_size
					__SCREAMING_SNAKE_CASE :	Any					 =	initializer_range
					__SCREAMING_SNAKE_CASE :	int					 =	num_choices

			def UpperCamelCase__      (							self  :				Tuple		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Tuple					 =	ids_tensor([self.batch_size, self.seq_length]							,  self.vocab_size		)

					__SCREAMING_SNAKE_CASE :	List[str]					 =	None
					if self.use_attention_mask:
							__SCREAMING_SNAKE_CASE :	Any					 =	random_attention_mask([self.batch_size, self.seq_length]		)

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	None
					if self.use_token_type_ids:
							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	ids_tensor([self.batch_size, self.seq_length]							,  self.type_vocab_size		)

					__SCREAMING_SNAKE_CASE :	Any					 =	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 UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.prepare_config_and_inputs()
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	config_and_inputs
					__SCREAMING_SNAKE_CASE :	str					 =	{"""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'''

			_A   :	Optional[Any]    					=	True

			_A   :	Optional[int]    					=	(
			    (
			        FlaxRoFormerModel,
			        FlaxRoFormerForMaskedLM,
			        FlaxRoFormerForSequenceClassification,
			        FlaxRoFormerForTokenClassification,
			        FlaxRoFormerForMultipleChoice,
			        FlaxRoFormerForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)

			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	int					 =	FlaxRoFormerModelTester(self		)



			@slow
			def UpperCamelCase__      (							self  :				Any		):





					"""simple docstring"""






					for model_class_name in self.all_model_classes:
							__SCREAMING_SNAKE_CASE :	List[str]					 =	model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""							,  from_pt=lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	model(np.ones((1, 1)		)		)
							self.assertIsNotNone(lowerCAmelCase__		)






@require_flax
class 		_UpperCamelCase						(							unittest.TestCase					):


			'''simple docstring'''

			@slow
			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""		)
					__SCREAMING_SNAKE_CASE :	List[str]					 =	jnp.array([[0, 1, 2, 3, 4, 5]]		)
					__SCREAMING_SNAKE_CASE :	List[str]					 =	model(lowerCAmelCase__		)[0]

					__SCREAMING_SNAKE_CASE :	Tuple					 =	5_0_0_0_0

					__SCREAMING_SNAKE_CASE :	Dict					 =	(1, 6, vocab_size)
					self.assertEqual(output.shape							,  lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	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]							,  lowerCAmelCase__							,  atol=1E-4		)		)








'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


UpperCamelCase__ :			List[str]												=				logging.get_logger(__name__)






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			def __init__(							self  :				Union[str, Any]							,  *lowerCAmelCase__  :				str							,  **lowerCAmelCase__  :				Any		):





					"""simple docstring"""






					warnings.warn(
					    """The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
					    """ use DeiTImageProcessor instead."""							,  lowerCAmelCase__							,  )
					super().__init__(*lowerCAmelCase__							,  **lowerCAmelCase__		)