

"""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      __a							(unittest.TestCase):






     '''simple docstring'''

     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=0.02	,    _a=4	,    )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	parent
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	batch_size
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	seq_length
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	is_training
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	use_attention_mask
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	use_token_type_ids
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	use_labels
           SCREAMING_SNAKE_CASE__   :      int      		=	vocab_size
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	hidden_size
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	num_hidden_layers
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	num_attention_heads
           SCREAMING_SNAKE_CASE__   :      Dict      		=	intermediate_size
           SCREAMING_SNAKE_CASE__   :      int      		=	hidden_act
           SCREAMING_SNAKE_CASE__   :      Dict      		=	hidden_dropout_prob
           SCREAMING_SNAKE_CASE__   :      str      		=	attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	max_position_embeddings
           SCREAMING_SNAKE_CASE__   :      Dict      		=	type_vocab_size
           SCREAMING_SNAKE_CASE__   :      Any      		=	type_sequence_label_size
           SCREAMING_SNAKE_CASE__   :      int      		=	initializer_range
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	num_choices

     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size  )

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

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

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	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 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


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





@require_flax
class      __a							(UpperCamelCase_   ,	unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Any       = True

     _SCREAMING_SNAKE_CASE						:Optional[Any]       = (
         (
             FlaxRoFormerModel,
             FlaxRoFormerForMaskedLM,
             FlaxRoFormerForSequenceClassification,
             FlaxRoFormerForTokenClassification,
             FlaxRoFormerForMultipleChoice,
             FlaxRoFormerForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	FlaxRoFormerModelTester(self  )





     @slow
     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           for model_class_name in self.all_model_classes:
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""	,    from_pt=_a  )
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	model(np.ones((1, 1)  )  )
                 self.assertIsNotNone(_a  )





@require_flax
class      __a							(unittest.TestCase):






     '''simple docstring'''

     @slow
     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	jnp.array([[0, 1, 2, 3, 4, 5]]  )
           SCREAMING_SNAKE_CASE__   :      str      		=	model(_a  )[0]

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	50_000

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	(1, 6, vocab_size)
           self.assertEqual(output.shape	,    _a  )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	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]	,    _a	,    atol=1E-4  )  )







"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


a					:List[Any]    = logging.get_logger(__name__)

a					:Optional[int]    = {
    "microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}

class      __a							(UpperCamelCase_   ,	UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Any       = """focalnet"""

     def __init__(						self	,    _a=224	,    _a=4	,    _a=3	,    _a=96	,    _a=False	,    _a=[192, 384, 768, 768]	,    _a=[2, 2, 6, 2]	,    _a=[2, 2, 2, 2]	,    _a=[3, 3, 3, 3]	,    _a="gelu"	,    _a=4.0	,    _a=0.0	,    _a=0.1	,    _a=False	,    _a=1E-4	,    _a=False	,    _a=False	,    _a=False	,    _a=0.02	,    _a=1E-5	,    _a=32	,    _a=None	,    _a=None	,    **_a	,    )  ->      Optional[Any]:


           """simple docstring"""


           super().__init__(**_a  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	image_size
           SCREAMING_SNAKE_CASE__   :      str      		=	patch_size
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	num_channels
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	embed_dim
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	use_conv_embed
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	hidden_sizes
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	depths
           SCREAMING_SNAKE_CASE__   :      Any      		=	focal_levels
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	focal_windows
           SCREAMING_SNAKE_CASE__   :      Any      		=	hidden_act
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	mlp_ratio
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	hidden_dropout_prob
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	drop_path_rate
           SCREAMING_SNAKE_CASE__   :      str      		=	use_layerscale
           SCREAMING_SNAKE_CASE__   :      int      		=	layerscale_value
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	use_post_layernorm
           SCREAMING_SNAKE_CASE__   :      Any      		=	use_post_layernorm_in_modulation
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	normalize_modulator
           SCREAMING_SNAKE_CASE__   :      str      		=	initializer_range
           SCREAMING_SNAKE_CASE__   :      Any      		=	layer_norm_eps
           SCREAMING_SNAKE_CASE__   :      Any      		=	encoder_stride
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	["""stem"""] + [f'''stage{idx}''' for idx in range(1	,    len(self.depths  ) + 1  )]
           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Dict      		=	get_aligned_output_features_output_indices(
               out_features=_a	,    out_indices=_a	,    stage_names=self.stage_names  )




