'''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     __magic_name__						(    unittest.TestCase):







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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										lowercase_					:			Optional[Any]										=       ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size    )

										lowercase_					:			Union[str, Any]										=       None
										if self.use_attention_mask:
																	lowercase_					:			int										=       random_attention_mask([self.batch_size, self.seq_length]    )

										lowercase_					:			Tuple										=       None
										if self.use_token_type_ids:
																	lowercase_					:			Optional[Any]										=       ids_tensor([self.batch_size, self.seq_length]				,			self.type_vocab_size    )

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





@require_flax
class     __magic_name__						(    UpperCamelCase__,     unittest.TestCase):
			UpperCamelCase__				=       True

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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict    ):
										lowercase_					:			Optional[Any]										=       FlaxRoFormerModelTester(self    )







			@slow
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										for model_class_name in self.all_model_classes:
																	lowercase_					:			Union[str, Any]										=       model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""				,			from_pt=__a    )
																	lowercase_					:			Optional[int]										=       model(np.ones((1, 1)    )    )
																	self.assertIsNotNone(__a    )





@require_flax
class     __magic_name__						(    unittest.TestCase):







			@slow
			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										lowercase_					:			Any										=       FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""    )
										lowercase_					:			Tuple										=       jnp.array([[0, 1, 2, 3, 4, 5]]    )
										lowercase_					:			Union[str, Any]										=       model(__a    )[0]

										lowercase_					:			int										=       50000

										lowercase_					:			str										=       (1, 6, vocab_size)
										self.assertEqual(output.shape				,			__a    )

										lowercase_					:			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]				,			__a				,			atol=1E-4    )    )






'''simple docstring'''





import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


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




class        __A      ( UpperCamelCase__	):



    def __init__(self    :    int   ,			*__a    :    Dict   ,			**__a    :    str ):
         warnings.warn(
             "The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
             " use CLIPImageProcessor instead."   ,			__a   ,			)
         super().__init__(*__a   ,			**__a )




