





'''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      snake_case     (    unittest.TestCase       ):




 """simple docstring"""



 def __init__(       self       :							Tuple							,	__A       :							Union[str, Any]							,	__A       :							Tuple=1_3							,	__A       :							int=7							,	__A       :							Union[str, Any]=True							,	__A       :							Tuple=True							,	__A       :							Any=True							,	__A       :							Optional[Any]=True							,	__A       :							Union[str, Any]=9_9							,	__A       :							Dict=3_2							,	__A       :							Tuple=5							,	__A       :							Union[str, Any]=4							,	__A       :							Union[str, Any]=3_7							,	__A       :							Optional[int]="gelu"							,	__A       :							str=0.1							,	__A       :							Dict=0.1							,	__A       :							str=5_1_2							,	__A       :							Any=1_6							,	__A       :							int=2							,	__A       :							List[str]=0.02							,	__A       :							List[Any]=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       :							Union[str, 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=__A							,	initializer_range=self.initializer_range							,	)

        return config, input_ids, token_type_ids, attention_mask






 def        _lowerCamelCase					(       self       :							str	):
        __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      snake_case     (    __lowerCamelCase			,       unittest.TestCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					int					=True

 SCREAMING_SNAKE_CASE_	:					List[str]					=(
     (
         FlaxRoFormerModel,
         FlaxRoFormerForMaskedLM,
         FlaxRoFormerForSequenceClassification,
         FlaxRoFormerForTokenClassification,
         FlaxRoFormerForMultipleChoice,
         FlaxRoFormerForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )






 def        _lowerCamelCase					(       self       :							str	):
        __UpperCamelCase       =  FlaxRoFormerModelTester(self	)






 @slow
 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        for model_class_name in self.all_model_classes:
               __UpperCamelCase       =  model_class_name.from_pretrained('junnyu/roformer_chinese_small'							,	from_pt=__A	)
               __UpperCamelCase       =  model(np.ones((1, 1)	)	)
               self.assertIsNotNone(__A	)






@require_flax
class      snake_case     (    unittest.TestCase       ):




 """simple docstring"""



 @slow
 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'	)
        __UpperCamelCase       =  jnp.array([[0, 1, 2, 3, 4, 5]]	)
        __UpperCamelCase       =  model(__A	)[0]

        __UpperCamelCase       =  5_0_0_0_0

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

        __UpperCamelCase       =  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	)	)







'''simple docstring'''
import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
    'split_dict'				,  [
        SplitDict(),
        SplitDict({'train': SplitInfo(name='train'				,  num_bytes=1337				,  num_examples=42				,  dataset_name='my_dataset'     )}     ),
        SplitDict({'train': SplitInfo(name='train'				,  num_bytes=1337				,  num_examples=42     )}     ),
        SplitDict({'train': SplitInfo()}     ),
    ]				,  )
def 				lowercase__   (   __lowercase						:  SplitDict     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       =  split_dict._to_yaml_list()
       assert len(__lowercase     ) == len(__lowercase     )
       __UpperCamelCase       =  SplitDict._from_yaml_list(__lowercase     )
       for split_name, split_info in split_dict.items():
              # dataset_name field is deprecated, and is therefore not part of the YAML dump
              __UpperCamelCase       =  None
              # the split name of split_dict takes over the name of the split info object
              __UpperCamelCase       =  split_name
       assert split_dict == reloaded


@pytest.mark.parametrize(
    'split_info'				,  [SplitInfo(), SplitInfo(dataset_name=__lowercase     ), SplitInfo(dataset_name='my_dataset'     )]     )
def 				lowercase__   (   __lowercase						:  Dict     ) ->      Any:



       """simple docstring"""






       __UpperCamelCase       =  asdict(SplitDict({'train': split_info}     )     )
       assert "dataset_name" in split_dict_asdict["train"]
       assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
