





'''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							):



    def __init__(  self     ,			__A     ,			__A=13     ,			__A=7     ,			__A=True     ,			__A=True     ,			__A=True     ,			__A=True     ,			__A=99     ,			__A=32     ,			__A=5     ,			__A=4     ,			__A=37     ,			__A="gelu"     ,			__A=0.1     ,			__A=0.1     ,			__A=512     ,			__A=16     ,			__A=2     ,			__A=0.0_2     ,			__A=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					):
     __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					):
     __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 UpperCAmelCase							(		UpperCAmelCase_					,			unittest.TestCase							):
    _A :			Optional[Any]						   =       True

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



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   FlaxRoFormerModelTester(self					)




    @slow
    def 					__lowerCamelCase						(  self					):
     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 UpperCAmelCase							(		unittest.TestCase							):



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

     __UpperCAmelCase					=   50_000

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

     __UpperCAmelCase					=   jnp.array(
         [[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]]					)

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












'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A:					Tuple							=				logging.get_logger(__name__)






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			List[Any]						   =       """timm_backbone"""



    def __init__(  self     ,			__A=None     ,			__A=3     ,			__A=True     ,			__A=True     ,			__A=None     ,			**__A     ,			):
     super().__init__(**__A					)
     __UpperCAmelCase					=   backbone
     __UpperCAmelCase					=   num_channels
     __UpperCAmelCase					=   features_only
     __UpperCAmelCase					=   use_pretrained_backbone
     __UpperCAmelCase					=   True
     __UpperCAmelCase					=   out_indices if out_indices is not None else (-1,)





