






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    ,	_lowerCAmelCase    ,	_lowerCAmelCase=13    ,	_lowerCAmelCase=7    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=99    ,	_lowerCAmelCase=32    ,	_lowerCAmelCase=5    ,	_lowerCAmelCase=4    ,	_lowerCAmelCase=37    ,	_lowerCAmelCase="gelu"    ,	_lowerCAmelCase=0.1    ,	_lowerCAmelCase=0.1    ,	_lowerCAmelCase=512    ,	_lowerCAmelCase=16    ,	_lowerCAmelCase=2    ,	_lowerCAmelCase=0.02    ,	_lowerCAmelCase=4    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :str      							=				parent
          lowerCAmelCase__   :Dict      							=				batch_size
          lowerCAmelCase__   :Optional[int]      							=				seq_length
          lowerCAmelCase__   :Union[str, Any]      							=				is_training
          lowerCAmelCase__   :Tuple      							=				use_attention_mask
          lowerCAmelCase__   :Union[str, Any]      							=				use_token_type_ids
          lowerCAmelCase__   :Optional[Any]      							=				use_labels
          lowerCAmelCase__   :int      							=				vocab_size
          lowerCAmelCase__   :int      							=				hidden_size
          lowerCAmelCase__   :Tuple      							=				num_hidden_layers
          lowerCAmelCase__   :int      							=				num_attention_heads
          lowerCAmelCase__   :Optional[Any]      							=				intermediate_size
          lowerCAmelCase__   :Any      							=				hidden_act
          lowerCAmelCase__   :int      							=				hidden_dropout_prob
          lowerCAmelCase__   :Any      							=				attention_probs_dropout_prob
          lowerCAmelCase__   :Optional[int]      							=				max_position_embeddings
          lowerCAmelCase__   :Any      							=				type_vocab_size
          lowerCAmelCase__   :Any      							=				type_sequence_label_size
          lowerCAmelCase__   :List[Any]      							=				initializer_range
          lowerCAmelCase__   :int      							=				num_choices
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Any      							=				ids_tensor([self.batch_size, self.seq_length]    ,	self.vocab_size		)

          lowerCAmelCase__   :List[str]      							=				None
          if self.use_attention_mask:
              lowerCAmelCase__   :Optional[Any]      							=				random_attention_mask([self.batch_size, self.seq_length]		)

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

          lowerCAmelCase__   :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=_lowerCAmelCase    ,	initializer_range=self.initializer_range    ,	)

          return config, input_ids, token_type_ids, attention_mask



      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				self.prepare_config_and_inputs()
          lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__   :Union[str, Any]      							=				config_and_inputs
          lowerCAmelCase__   :Optional[int]      							=				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
          return config, inputs_dict







@require_flax
class 	_UpperCAmelCase  (  _A				,							unittest.TestCase		):




      """simple docstring"""



      A          =    True

      A          =    (
          (
              FlaxRoFormerModel,
              FlaxRoFormerForMaskedLM,
              FlaxRoFormerForSequenceClassification,
              FlaxRoFormerForTokenClassification,
              FlaxRoFormerForMultipleChoice,
              FlaxRoFormerForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :str      							=				FlaxRoFormerModelTester(self		)



      @slow
      def 			snake_case_      (   self		):



          '''simple docstring'''
          for model_class_name in self.all_model_classes:
              lowerCAmelCase__   :str      							=				model_class_name.from_pretrained("junnyu/roformer_chinese_small"    ,	from_pt=_lowerCAmelCase		)
              lowerCAmelCase__   :List[Any]      							=				model(np.ones((1, 1)		)		)
              self.assertIsNotNone(_lowerCAmelCase		)







@require_flax
class 	_UpperCAmelCase  (  unittest.TestCase		):




      """simple docstring"""



      @slow
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Dict      							=				FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"		)
          lowerCAmelCase__   :Dict      							=				jnp.array([[0, 1, 2, 3, 4, 5]]		)
          lowerCAmelCase__   :Dict      							=				model(_lowerCAmelCase		)[0]

          lowerCAmelCase__   :List[Any]      							=				50_000

          lowerCAmelCase__   :Any      							=				(1, 6, vocab_size)
          self.assertEqual(output.shape    ,	_lowerCAmelCase		)

          lowerCAmelCase__   :Tuple      							=				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]    ,	_lowerCAmelCase    ,	atol=1e-4		)		)











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_a    :   str        =			{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _a    :   Optional[Any]        =			[
     """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """WavLMForAudioFrameClassification""",
     """WavLMForCTC""",
     """WavLMForSequenceClassification""",
     """WavLMForXVector""",
     """WavLMModel""",
     """WavLMPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_wavlm import (
      WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      WavLMForAudioFrameClassification,
      WavLMForCTC,
      WavLMForSequenceClassification,
      WavLMForXVector,
      WavLMModel,
      WavLMPreTrainedModel,
  )

else:
 import sys

 _a    :   str        =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



