


'''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 	__lowerCAmelCase		(							unittest.TestCase    ):


  '''simple docstring'''







  def __init__(							self :       Dict	,_a :       int	,_a :       Tuple=13	,_a :       str=7	,_a :       str=True	,_a :       Optional[Any]=True	,_a :       List[Any]=True	,_a :       Optional[Any]=True	,_a :       List[Any]=99	,_a :       Union[str, Any]=32	,_a :       Optional[int]=5	,_a :       Optional[int]=4	,_a :       Union[str, Any]=37	,_a :       List[str]="gelu"	,_a :       Tuple=0.1	,_a :       str=0.1	,_a :       Optional[int]=512	,_a :       Union[str, Any]=16	,_a :       Dict=2	,_a :       Any=0.02	,_a :       List[Any]=4	,):

        '''simple docstring'''




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






  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        A_						:  Union[str, Any]          =				ids_tensor([self.batch_size, self.seq_length]	,self.vocab_size )

        A_						:  Dict          =				None
        if self.use_attention_mask:
              A_						:  Optional[Any]          =				random_attention_mask([self.batch_size, self.seq_length] )

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

        A_						:  List[Any]          =				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 :       Dict ):

        '''simple docstring'''




        A_						:  Any          =				self.prepare_config_and_inputs()
        A_   ,					A_   ,					A_   ,					A_						:  Union[str, Any]          =				config_and_inputs
        A_						:  Tuple          =				{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
        return config, inputs_dict







@require_flax
class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE  , unittest.TestCase    ):


  '''simple docstring'''







  a_  					=							True

  a_  					=							(
      (
          FlaxRoFormerModel,
          FlaxRoFormerForMaskedLM,
          FlaxRoFormerForSequenceClassification,
          FlaxRoFormerForTokenClassification,
          FlaxRoFormerForMultipleChoice,
          FlaxRoFormerForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )






  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        A_						:  Dict          =				FlaxRoFormerModelTester(self )






  @slow
  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        for model_class_name in self.all_model_classes:
              A_						:  Optional[int]          =				model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""	,from_pt=_a )
              A_						:  Union[str, Any]          =				model(np.ones((1, 1) ) )
              self.assertIsNotNone(_a )







@require_flax
class 	__lowerCAmelCase		(							unittest.TestCase    ):


  '''simple docstring'''







  @slow
  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




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

        A_						:  int          =				50000

        A_						:  str          =				(1, 6, vocab_size)
        self.assertEqual(output.shape	,_a )

        A_						:  Any          =				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'''


from typing import TYPE_CHECKING

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


__magic_name__			=      {
    'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      ['ConvNextFeatureExtractor']
     __magic_name__			=      ['ConvNextImageProcessor']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      [
         'CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'ConvNextForImageClassification',
         'ConvNextModel',
         'ConvNextPreTrainedModel',
         'ConvNextBackbone',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      [
         'TFConvNextForImageClassification',
         'TFConvNextModel',
         'TFConvNextPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_convnext import ConvNextFeatureExtractor
          from .image_processing_convnext import ConvNextImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_convnext import (
              CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConvNextBackbone,
              ConvNextForImageClassification,
              ConvNextModel,
              ConvNextPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
     import sys

     __magic_name__			=      _LazyModule(__name__, globals()['__file__'], _import_structure)
