



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 ,       __lowercase ,       __lowercase=1_3 ,       __lowercase=7 ,       __lowercase=True ,       __lowercase=True ,       __lowercase=True ,       __lowercase=True ,       __lowercase=9_9 ,       __lowercase=3_2 ,       __lowercase=5 ,       __lowercase=4 ,       __lowercase=3_7 ,       __lowercase="gelu" ,       __lowercase=0.1 ,       __lowercase=0.1 ,       __lowercase=5_1_2 ,       __lowercase=1_6 ,       __lowercase=2 ,       __lowercase=0.0_2 ,       __lowercase=4 ,       ) ->				Optional[int]:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




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

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

									a__       :						Union[str, Any]							=  None
									if self.use_token_type_ids:
												a__       :						Optional[int]							=  ids_tensor([self.batch_size, self.seq_length] ,       self.type_vocab_size  )

									a__       :						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 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									a__       :						List[Any]							=  self.prepare_config_and_inputs()
									a__	,						a__	,						a__	,						a__       :						int							=  config_and_inputs
									a__       :						str							=  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
									return config, inputs_dict




@require_flax
class        snake_case__    (A__ ,     unittest.TestCase		):







						"""simple docstring"""




						__lowerCAmelCase					:str      =					True

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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




									a__       :						Any							=  FlaxRoFormerModelTester(self  )

						@slow
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




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




@require_flax
class        snake_case__    (unittest.TestCase		):







						"""simple docstring"""




						@slow
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
									"""simple docstring"""




									a__       :						int							=  FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""  )
									a__       :						Union[str, Any]							=  jnp.array([[0, 1, 2, 3, 4, 5]]  )
									a__       :						Any							=  model(__lowercase  )[0]

									a__       :						List[str]							=  5_0_0_0_0

									a__       :						Optional[Any]							=  (1, 6, vocab_size)
									self.assertEqual(output.shape ,       __lowercase  )

									a__       :						Optional[int]							=  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] ,       __lowercase ,       atol=1E-4  )  )









import warnings

from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor


_lowercase		:				Union[str, Any]      		=logging.get_logger(__name__)

class        snake_case__    (A__		):







						"""simple docstring"""




						def __init__(       self ,       *__lowercase ,       **__lowercase  ) ->				None:
									"""simple docstring"""




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




