

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   lowercase_   (  unittest.TestCase					):



						def __init__(      self				:    Optional[Any]      , A__				:    Dict      , A__				:    int=13      , A__				:    str=7      , A__				:    Optional[Any]=True      , A__				:    int=True      , A__				:    Union[str, Any]=True      , A__				:    int=True      , A__				:    List[str]=99      , A__				:    List[str]=32      , A__				:    Tuple=5      , A__				:    Any=4      , A__				:    Dict=37      , A__				:    Tuple="gelu"      , A__				:    List[str]=0.1      , A__				:    Any=0.1      , A__				:    Union[str, Any]=512      , A__				:    int=16      , A__				:    Optional[int]=2      , A__				:    Optional[Any]=0.02      , A__				:    Optional[Any]=4      , )   ->					List[str]:
								_snake_case						=							parent
								_snake_case						=							batch_size
								_snake_case						=							seq_length
								_snake_case						=							is_training
								_snake_case						=							use_attention_mask
								_snake_case						=							use_token_type_ids
								_snake_case						=							use_labels
								_snake_case						=							vocab_size
								_snake_case						=							hidden_size
								_snake_case						=							num_hidden_layers
								_snake_case						=							num_attention_heads
								_snake_case						=							intermediate_size
								_snake_case						=							hidden_act
								_snake_case						=							hidden_dropout_prob
								_snake_case						=							attention_probs_dropout_prob
								_snake_case						=							max_position_embeddings
								_snake_case						=							type_vocab_size
								_snake_case						=							type_sequence_label_size
								_snake_case						=							initializer_range
								_snake_case						=							num_choices



						def 						UpperCamelCase_		(      self				:    str		)   ->					Dict:
								_snake_case						=							ids_tensor([self.batch_size, self.seq_length]      , self.vocab_size		)

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

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

								_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=__A      , initializer_range=self.initializer_range      , )

								return config, input_ids, token_type_ids, attention_mask







						def 						UpperCamelCase_		(      self				:    Any		)   ->					int:
								_snake_case						=							self.prepare_config_and_inputs()
								_snake_case						=							config_and_inputs
								_snake_case						=							{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
								return config, inputs_dict



@require_flax
class   lowercase_   (  A__       ,							unittest.TestCase					):
						UpperCamelCase_      :		int										=    True

						UpperCamelCase_      :		int										=    (
						    (
						        FlaxRoFormerModel,
						        FlaxRoFormerForMaskedLM,
						        FlaxRoFormerForSequenceClassification,
						        FlaxRoFormerForTokenClassification,
						        FlaxRoFormerForMultipleChoice,
						        FlaxRoFormerForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)



						def 						UpperCamelCase_		(      self				:    List[str]		)   ->					int:
								_snake_case						=							FlaxRoFormerModelTester(self		)







						@slow
						def 						UpperCamelCase_		(      self				:    Dict		)   ->					str:
								for model_class_name in self.all_model_classes:
										_snake_case						=							model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''      , from_pt=__A		)
										_snake_case						=							model(np.ones((1, 1)		)		)
										self.assertIsNotNone(__A		)



@require_flax
class   lowercase_   (  unittest.TestCase					):



						@slow
						def 						UpperCamelCase_		(      self				:    int		)   ->					Any:
								_snake_case						=							FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''		)
								_snake_case						=							jnp.array([[0, 1, 2, 3, 4, 5]]		)
								_snake_case						=							model(__A		)[0]

								_snake_case						=							50000

								_snake_case						=							(1, 6, vocab_size)
								self.assertEqual(output.shape      , __A		)

								_snake_case						=							jnp.array(
								    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]		)

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




import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


__A      		=     logging.get_logger(__name__)



class   lowercase_   (  __lowercase					):



      def __init__(      self				:    Optional[Any]      , *A__				:    List[Any]      , **A__				:    int		)   ->					None:
        warnings.warn(
            '''The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
            ''' Please use DeformableDetrImageProcessor instead.'''      , A__      , )
        super().__init__(*A__      , **A__		)

