





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







       def  __UpperCAmelCase			(     self						:						List[str]	)    ->       Any:
         _lowercase						    =			ids_tensor([self.batch_size, self.seq_length]    ,self.vocab_size	)

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

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

         _lowercase						    =			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  __UpperCAmelCase			(     self						:						List[Any]	)    ->       Any:
         _lowercase						    =			self.prepare_config_and_inputs()
         _lowercase    ,		_lowercase    ,		_lowercase    ,		_lowercase						    =			config_and_inputs
         _lowercase						    =			{'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_   :			Dict  			=	True

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







       def  __UpperCAmelCase			(     self						:						int	)    ->       List[str]:
         _lowercase						    =			FlaxRoFormerModelTester(self	)







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





@require_flax
class 					A_					(   unittest.TestCase						):







       """simple docstring"""





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

         _lowercase						    =			5_0000

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

         _lowercase						    =			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	)	)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowercase		:  Dict			       ={
    """configuration_resnet""": ["""RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ResNetConfig""", """ResNetOnnxConfig"""]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:  Union[str, Any]			       =[
		    """RESNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """ResNetForImageClassification""",
		    """ResNetModel""",
		    """ResNetPreTrainedModel""",
		    """ResNetBackbone""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:  Union[str, Any]			       =[
		    """TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFResNetForImageClassification""",
		    """TFResNetModel""",
		    """TFResNetPreTrainedModel""",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:  Tuple			       =[
		    """FlaxResNetForImageClassification""",
		    """FlaxResNetModel""",
		    """FlaxResNetPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_resnet import (
				    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ResNetBackbone,
				    ResNetForImageClassification,
				    ResNetModel,
				    ResNetPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_resnet import (
				    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFResNetForImageClassification,
				    TFResNetModel,
				    TFResNetPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
		import sys

		_lowercase		:  Union[str, Any]			       =_LazyModule(__name__, globals()["""__file__"""], _import_structure)
