

'''simple docstring'''




import unittest

import numpy as np

from transformers import DistilBertConfig, 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.distilbert.modeling_flax_distilbert import (
          FlaxDistilBertForMaskedLM,
          FlaxDistilBertForMultipleChoice,
          FlaxDistilBertForQuestionAnswering,
          FlaxDistilBertForSequenceClassification,
          FlaxDistilBertForTokenClassification,
          FlaxDistilBertModel,
      )






class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):



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



 def 			snake_case__  (   self		:   Tuple      ):
      __magic_name__						       =    ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size      )

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

      __magic_name__						       =    DistilBertConfig(
          vocab_size=self.vocab_size			,				dim=self.hidden_size			,				n_layers=self.num_hidden_layers			,				n_heads=self.num_attention_heads			,				hidden_dim=self.intermediate_size			,				hidden_act=self.hidden_act			,				dropout=self.hidden_dropout_prob			,				attention_dropout=self.attention_probs_dropout_prob			,				max_position_embeddings=self.max_position_embeddings			,				initializer_range=self.initializer_range			,				tie_weights_=_SCREAMING_SNAKE_CASE			,				)

      return config, input_ids, attention_mask



 def 			snake_case__  (   self		:   Dict      ):
      __magic_name__						       =    self.prepare_config_and_inputs()
      __magic_name__						       =    config_and_inputs
      __magic_name__						       =    {"input_ids": input_ids, "attention_mask": attention_mask}
      return config, inputs_dict






@require_flax
class   _SCREAMING_SNAKE_CASE   (   SCREAMING_SNAKE_CASE__		,unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:Union[str, Any]      	=					(
     (
         FlaxDistilBertModel,
         FlaxDistilBertForMaskedLM,
         FlaxDistilBertForMultipleChoice,
         FlaxDistilBertForQuestionAnswering,
         FlaxDistilBertForSequenceClassification,
         FlaxDistilBertForTokenClassification,
         FlaxDistilBertForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )



 def 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    FlaxDistilBertModelTester(self      )



 @slow
 def 			snake_case__  (   self		:   List[str]      ):
      for model_class_name in self.all_model_classes:
           __magic_name__						       =    model_class_name.from_pretrained('''distilbert-base-uncased'''      )
           __magic_name__						       =    model(np.ones((1, 1)      )      )
           self.assertIsNotNone(_SCREAMING_SNAKE_CASE      )






@require_flax
class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):



 @slow
 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    FlaxDistilBertModel.from_pretrained('''distilbert-base-uncased'''      )
      __magic_name__						       =    np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]      )
      __magic_name__						       =    np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
      __magic_name__						       =    model(_SCREAMING_SNAKE_CASE			,				attention_mask=_SCREAMING_SNAKE_CASE      )[0]
      __magic_name__						       =    (1, 11, 768)
      self.assertEqual(output.shape			,				_SCREAMING_SNAKE_CASE      )
      __magic_name__						       =    np.array([[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]]      )

      self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]			,				_SCREAMING_SNAKE_CASE			,				atol=1E-4      )      )









'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase         =						{
    "configuration_clap": [
        "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
        "ClapAudioConfig",
        "ClapConfig",
        "ClapTextConfig",
    ],
    "processing_clap": ["ClapProcessor"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase         =						[
          "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
          "ClapModel",
          "ClapPreTrainedModel",
          "ClapTextModel",
          "ClapTextModelWithProjection",
          "ClapAudioModel",
          "ClapAudioModelWithProjection",
      ]
      _lowerCAmelCase         =						["ClapFeatureExtractor"]

if TYPE_CHECKING:
      from .configuration_clap import (
          CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
          ClapAudioConfig,
          ClapConfig,
          ClapTextConfig,
      )
      from .processing_clap import ClapProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_clap import ClapFeatureExtractor
            from .modeling_clap import (
                CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
                ClapAudioModel,
                ClapAudioModelWithProjection,
                ClapModel,
                ClapPreTrainedModel,
                ClapTextModel,
                ClapTextModelWithProjection,
            )


else:
      import sys

      _lowerCAmelCase         =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






