


"""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    :       List[str]    ,    UpperCAmelCase__    :       Optional[int]    ,    UpperCAmelCase__    :       Optional[Any]=1_3    ,    UpperCAmelCase__    :       int=7    ,    UpperCAmelCase__    :       Union[str, Any]=True    ,    UpperCAmelCase__    :       Tuple=True    ,    UpperCAmelCase__    :       Tuple=True    ,    UpperCAmelCase__    :       Tuple=True    ,    UpperCAmelCase__    :       Dict=9_9    ,    UpperCAmelCase__    :       str=3_2    ,    UpperCAmelCase__    :       Any=5    ,    UpperCAmelCase__    :       Dict=4    ,    UpperCAmelCase__    :       int=3_7    ,    UpperCAmelCase__    :       Union[str, Any]="gelu"    ,    UpperCAmelCase__    :       str=0.1    ,    UpperCAmelCase__    :       Optional[int]=0.1    ,    UpperCAmelCase__    :       Optional[Any]=5_1_2    ,    UpperCAmelCase__    :       Optional[int]=1_6    ,    UpperCAmelCase__    :       Optional[int]=2    ,    UpperCAmelCase__    :       List[Any]=0.02    ,    UpperCAmelCase__    :       Dict=4    ,    ) ->			Dict:
						__SCREAMING_SNAKE_CASE		     =       parent
						__SCREAMING_SNAKE_CASE		     =       batch_size
						__SCREAMING_SNAKE_CASE		     =       seq_length
						__SCREAMING_SNAKE_CASE		     =       is_training
						__SCREAMING_SNAKE_CASE		     =       use_attention_mask
						__SCREAMING_SNAKE_CASE		     =       use_token_type_ids
						__SCREAMING_SNAKE_CASE		     =       use_labels
						__SCREAMING_SNAKE_CASE		     =       vocab_size
						__SCREAMING_SNAKE_CASE		     =       hidden_size
						__SCREAMING_SNAKE_CASE		     =       num_hidden_layers
						__SCREAMING_SNAKE_CASE		     =       num_attention_heads
						__SCREAMING_SNAKE_CASE		     =       intermediate_size
						__SCREAMING_SNAKE_CASE		     =       hidden_act
						__SCREAMING_SNAKE_CASE		     =       hidden_dropout_prob
						__SCREAMING_SNAKE_CASE		     =       attention_probs_dropout_prob
						__SCREAMING_SNAKE_CASE		     =       max_position_embeddings
						__SCREAMING_SNAKE_CASE		     =       type_vocab_size
						__SCREAMING_SNAKE_CASE		     =       type_sequence_label_size
						__SCREAMING_SNAKE_CASE		     =       initializer_range
						__SCREAMING_SNAKE_CASE		     =       num_choices




	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			Any:
						__SCREAMING_SNAKE_CASE		     =       ids_tensor([self.batch_size, self.seq_length]    ,    self.vocab_size     )

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

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

						__SCREAMING_SNAKE_CASE		     =       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=UpperCAmelCase__    ,    initializer_range=self.initializer_range    ,    )

						return config, input_ids, token_type_ids, attention_mask




	def 						UpperCAmelCase_   (				self    :       str     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       self.prepare_config_and_inputs()
						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       config_and_inputs
						__SCREAMING_SNAKE_CASE		     =       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
						return config, inputs_dict




@require_flax
class 				UpperCamelCase_			(				UpperCamelCase    ,						unittest.TestCase):

	"""simple docstring"""





	snake_case__       :						Optional[int]					      =    True

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




	def 						UpperCAmelCase_   (				self    :       int     ) ->			List[Any]:
						__SCREAMING_SNAKE_CASE		     =       FlaxRoFormerModelTester(self     )




	@slow
	def 						UpperCAmelCase_   (				self    :       str     ) ->			Any:
						for model_class_name in self.all_model_classes:
											__SCREAMING_SNAKE_CASE		     =       model_class_name.from_pretrained("junnyu/roformer_chinese_small"    ,    from_pt=UpperCAmelCase__     )
											__SCREAMING_SNAKE_CASE		     =       model(np.ones((1, 1)     )     )
											self.assertIsNotNone(UpperCAmelCase__     )




@require_flax
class 				UpperCamelCase_			(				unittest.TestCase):

	"""simple docstring"""





	@slow
	def 						UpperCAmelCase_   (				self    :       str     ) ->			str:
						__SCREAMING_SNAKE_CASE		     =       FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"     )
						__SCREAMING_SNAKE_CASE		     =       jnp.array([[0, 1, 2, 3, 4, 5]]     )
						__SCREAMING_SNAKE_CASE		     =       model(UpperCAmelCase__     )[0]

						__SCREAMING_SNAKE_CASE		     =       5_0_0_0_0

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

						__SCREAMING_SNAKE_CASE		     =       jnp.array(
						    [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]     )

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




"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor


a__       :							Dict        =  logging.get_logger(__name__)




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	def __init__(				self    :       str    ,    *UpperCAmelCase__    :       List[Any]    ,    **UpperCAmelCase__    :       Tuple     ) ->			None:
						warnings.warn(
						    "The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
						    " Please use VideoMAEImageProcessor instead."    ,    UpperCAmelCase__    ,    )
						super().__init__(*UpperCAmelCase__    ,    **UpperCAmelCase__     )
