



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   ):

  """simple docstring"""


  def __init__(    self			:		str ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		Any=1_3 ,  lowerCAmelCase_			:		List[str]=7 ,  lowerCAmelCase_			:		Union[str, Any]=True ,  lowerCAmelCase_			:		List[Any]=True ,  lowerCAmelCase_			:		Tuple=True ,  lowerCAmelCase_			:		Optional[Any]=True ,  lowerCAmelCase_			:		Optional[Any]=9_9 ,  lowerCAmelCase_			:		str=3_2 ,  lowerCAmelCase_			:		str=5 ,  lowerCAmelCase_			:		Union[str, Any]=4 ,  lowerCAmelCase_			:		Tuple=3_7 ,  lowerCAmelCase_			:		List[Any]="gelu" ,  lowerCAmelCase_			:		List[str]=0.1 ,  lowerCAmelCase_			:		int=0.1 ,  lowerCAmelCase_			:		Optional[int]=5_1_2 ,  lowerCAmelCase_			:		List[Any]=1_6 ,  lowerCAmelCase_			:		str=2 ,  lowerCAmelCase_			:		Tuple=0.02 ,  lowerCAmelCase_			:		str=4 ,  )							->						Optional[Any]:
     __lowerCAmelCase							     =     parent
     __lowerCAmelCase							     =     batch_size
     __lowerCAmelCase							     =     seq_length
     __lowerCAmelCase							     =     is_training
     __lowerCAmelCase							     =     use_attention_mask
     __lowerCAmelCase							     =     use_token_type_ids
     __lowerCAmelCase							     =     use_labels
     __lowerCAmelCase							     =     vocab_size
     __lowerCAmelCase							     =     hidden_size
     __lowerCAmelCase							     =     num_hidden_layers
     __lowerCAmelCase							     =     num_attention_heads
     __lowerCAmelCase							     =     intermediate_size
     __lowerCAmelCase							     =     hidden_act
     __lowerCAmelCase							     =     hidden_dropout_prob
     __lowerCAmelCase							     =     attention_probs_dropout_prob
     __lowerCAmelCase							     =     max_position_embeddings
     __lowerCAmelCase							     =     type_vocab_size
     __lowerCAmelCase							     =     type_sequence_label_size
     __lowerCAmelCase							     =     initializer_range
     __lowerCAmelCase							     =     num_choices






  def     lowercase		(    self			:		List[str]					)							->						str:
     __lowerCAmelCase							     =     ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size					)

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

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

     __lowerCAmelCase							     =     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=lowerCAmelCase_ ,  initializer_range=self.initializer_range ,  )

     return config, input_ids, token_type_ids, attention_mask






  def     lowercase		(    self			:		List[str]					)							->						Optional[Any]:
     __lowerCAmelCase							     =     self.prepare_config_and_inputs()
     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase							     =     config_and_inputs
     __lowerCAmelCase							     =     {'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   ):

  """simple docstring"""


  a_      =			True

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






  def     lowercase		(    self			:		List[Any]					)							->						int:
     __lowerCAmelCase							     =     FlaxRoFormerModelTester(self					)






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





@require_flax
class     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  @slow
  def     lowercase		(    self			:		Optional[int]					)							->						List[str]:
     __lowerCAmelCase							     =     FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'					)
     __lowerCAmelCase							     =     jnp.array([[0, 1, 2, 3, 4, 5]]					)
     __lowerCAmelCase							     =     model(lowerCAmelCase_					)[0]

     __lowerCAmelCase							     =     5_0_0_0_0

     __lowerCAmelCase							     =     (1, 6, vocab_size)
     self.assertEqual(output.shape ,  lowerCAmelCase_					)

     __lowerCAmelCase							     =     jnp.array(
         [[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]]					)

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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case  :   Optional[Any]						   =  {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   int						   =  ['FNetTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   Tuple						   =  ['FNetTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   List[str]						   =  [
         'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'FNetForMaskedLM',
         'FNetForMultipleChoice',
         'FNetForNextSentencePrediction',
         'FNetForPreTraining',
         'FNetForQuestionAnswering',
         'FNetForSequenceClassification',
         'FNetForTokenClassification',
         'FNetLayer',
         'FNetModel',
         'FNetPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet import FNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet_fast import FNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_fnet import (
              FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              FNetForMaskedLM,
              FNetForMultipleChoice,
              FNetForNextSentencePrediction,
              FNetForPreTraining,
              FNetForQuestionAnswering,
              FNetForSequenceClassification,
              FNetForTokenClassification,
              FNetLayer,
              FNetModel,
              FNetPreTrainedModel,
          )


else:
     import sys

     _snake_case  :   str						   =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

