




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       a					(       unittest.TestCase      ):

					'''simple docstring'''





					def __init__(     self       :      Tuple ,   __snake_case       :      Dict ,   __snake_case       :      Optional[int]=13 ,   __snake_case       :      int=7 ,   __snake_case       :      Optional[int]=True ,   __snake_case       :      int=True ,   __snake_case       :      List[str]=True ,   __snake_case       :      List[str]=True ,   __snake_case       :      Tuple=99 ,   __snake_case       :      Dict=32 ,   __snake_case       :      Union[str, Any]=5 ,   __snake_case       :      int=4 ,   __snake_case       :      List[Any]=37 ,   __snake_case       :      Any="gelu" ,   __snake_case       :      Dict=0.1 ,   __snake_case       :      str=0.1 ,   __snake_case       :      Optional[Any]=5_12 ,   __snake_case       :      List[str]=16 ,   __snake_case       :      Optional[int]=2 ,   __snake_case       :      List[str]=0.02 ,   __snake_case       :      Tuple=4 ,   ):
									UpperCAmelCase_										=					parent
									UpperCAmelCase_										=					batch_size
									UpperCAmelCase_										=					seq_length
									UpperCAmelCase_										=					is_training
									UpperCAmelCase_										=					use_attention_mask
									UpperCAmelCase_										=					use_token_type_ids
									UpperCAmelCase_										=					use_labels
									UpperCAmelCase_										=					vocab_size
									UpperCAmelCase_										=					hidden_size
									UpperCAmelCase_										=					num_hidden_layers
									UpperCAmelCase_										=					num_attention_heads
									UpperCAmelCase_										=					intermediate_size
									UpperCAmelCase_										=					hidden_act
									UpperCAmelCase_										=					hidden_dropout_prob
									UpperCAmelCase_										=					attention_probs_dropout_prob
									UpperCAmelCase_										=					max_position_embeddings
									UpperCAmelCase_										=					type_vocab_size
									UpperCAmelCase_										=					type_sequence_label_size
									UpperCAmelCase_										=					initializer_range
									UpperCAmelCase_										=					num_choices
					def   lowerCamelCase_       (     self       :      Any    ):
									UpperCAmelCase_										=					ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size    )

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

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

									UpperCAmelCase_										=					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=__snake_case ,   initializer_range=self.initializer_range ,   )

									return config, input_ids, token_type_ids, attention_mask




					def   lowerCamelCase_       (     self       :      List[Any]    ):
									UpperCAmelCase_										=					self.prepare_config_and_inputs()
									UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_										=					config_and_inputs
									UpperCAmelCase_										=					{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
									return config, inputs_dict



@require_flax
class       a					(       _A     ,       unittest.TestCase      ):

					'''simple docstring'''





					lowerCAmelCase    :	Union[str, Any]       		=  True

					lowerCAmelCase    :	Any       		=  (
					    (
					        FlaxRoFormerModel,
					        FlaxRoFormerForMaskedLM,
					        FlaxRoFormerForSequenceClassification,
					        FlaxRoFormerForTokenClassification,
					        FlaxRoFormerForMultipleChoice,
					        FlaxRoFormerForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)
					def   lowerCamelCase_       (     self       :      Tuple    ):
									UpperCAmelCase_										=					FlaxRoFormerModelTester(self    )




					@slow
					def   lowerCamelCase_       (     self       :      Optional[int]    ):
									for model_class_name in self.all_model_classes:
													UpperCAmelCase_										=					model_class_name.from_pretrained('''junnyu/roformer_chinese_small''' ,   from_pt=__snake_case    )
													UpperCAmelCase_										=					model(np.ones((1, 1)    )    )
													self.assertIsNotNone(__snake_case    )



@require_flax
class       a					(       unittest.TestCase      ):

					'''simple docstring'''





					@slow
					def   lowerCamelCase_       (     self       :      Tuple    ):
									UpperCAmelCase_										=					FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''    )
									UpperCAmelCase_										=					jnp.array([[0, 1, 2, 3, 4, 5]]    )
									UpperCAmelCase_										=					model(__snake_case    )[0]

									UpperCAmelCase_										=					5_00_00

									UpperCAmelCase_										=					(1, 6, vocab_size)
									self.assertEqual(output.shape ,   __snake_case    )

									UpperCAmelCase_										=					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] ,   __snake_case ,   atol=1E-4    )    )











from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase      			=						logging.get_logger(__name__)

_lowerCamelCase      			=						{
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}



class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	Optional[Any]       		=  'data2vec-text'
     def __init__(     self       :      Optional[Any] ,   __snake_case       :      Optional[int]=3_05_22 ,   __snake_case       :      List[str]=7_68 ,   __snake_case       :      Tuple=12 ,   __snake_case       :      int=12 ,   __snake_case       :      Union[str, Any]=30_72 ,   __snake_case       :      List[Any]="gelu" ,   __snake_case       :      Any=0.1 ,   __snake_case       :      Union[str, Any]=0.1 ,   __snake_case       :      Tuple=5_12 ,   __snake_case       :      str=2 ,   __snake_case       :      str=0.02 ,   __snake_case       :      List[Any]=1E-12 ,   __snake_case       :      Any=1 ,   __snake_case       :      List[Any]=0 ,   __snake_case       :      Dict=2 ,   __snake_case       :      Any="absolute" ,   __snake_case       :      Union[str, Any]=True ,   __snake_case       :      Any=None ,   **__snake_case       :      List[Any] ,   ):
         super().__init__(pad_token_id=__snake_case ,   bos_token_id=__snake_case ,   eos_token_id=__snake_case ,   **__snake_case    )

         UpperCAmelCase_										=					vocab_size
         UpperCAmelCase_										=					hidden_size
         UpperCAmelCase_										=					num_hidden_layers
         UpperCAmelCase_										=					num_attention_heads
         UpperCAmelCase_										=					hidden_act
         UpperCAmelCase_										=					intermediate_size
         UpperCAmelCase_										=					hidden_dropout_prob
         UpperCAmelCase_										=					attention_probs_dropout_prob
         UpperCAmelCase_										=					max_position_embeddings
         UpperCAmelCase_										=					type_vocab_size
         UpperCAmelCase_										=					initializer_range
         UpperCAmelCase_										=					layer_norm_eps
         UpperCAmelCase_										=					position_embedding_type
         UpperCAmelCase_										=					use_cache
         UpperCAmelCase_										=					classifier_dropout



class       a					(       _A      ):

     '''simple docstring'''





     @property
     def   lowerCamelCase_       (     self       :      str    ):
         if self.task == "multiple-choice":
             UpperCAmelCase_										=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
             UpperCAmelCase_										=					{0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
             ]    )





