

'''simple docstring'''



from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor



class 				_snake_case					:

  def __init__(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE=13      , _SCREAMING_SNAKE_CASE=30      , _SCREAMING_SNAKE_CASE=2      , _SCREAMING_SNAKE_CASE=3      , _SCREAMING_SNAKE_CASE=True      , _SCREAMING_SNAKE_CASE=True      , _SCREAMING_SNAKE_CASE=32      , _SCREAMING_SNAKE_CASE=2      , _SCREAMING_SNAKE_CASE=4      , _SCREAMING_SNAKE_CASE=37      , _SCREAMING_SNAKE_CASE="gelu"      , _SCREAMING_SNAKE_CASE=0.1      , _SCREAMING_SNAKE_CASE=0.1      , _SCREAMING_SNAKE_CASE=10      , _SCREAMING_SNAKE_CASE=0.02      , _SCREAMING_SNAKE_CASE=3      , _SCREAMING_SNAKE_CASE=0.6      , _SCREAMING_SNAKE_CASE=None      , ):





      '''simple docstring'''

      lowerCAmelCase 						=							parent
      lowerCAmelCase 						=							batch_size
      lowerCAmelCase 						=							image_size
      lowerCAmelCase 						=							patch_size
      lowerCAmelCase 						=							num_channels
      lowerCAmelCase 						=							is_training
      lowerCAmelCase 						=							use_labels
      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 						=							type_sequence_label_size
      lowerCAmelCase 						=							initializer_range
      lowerCAmelCase 						=							mask_ratio
      lowerCAmelCase 						=							scope

      # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
      # (we add 1 for the [CLS] token)
      lowerCAmelCase 						=							(image_size // patch_size) ** 2
      lowerCAmelCase 						=							int(math.ceil((1 - mask_ratio) * (num_patches + 1)	)	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

      lowerCAmelCase 						=							None
      if self.use_labels:
          lowerCAmelCase 						=							ids_tensor([self.batch_size]      , self.type_sequence_label_size	)

      lowerCAmelCase 						=							self.get_config()

      return config, pixel_values, labels

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      return ViTMAEConfig(
          image_size=self.image_size      , patch_size=self.patch_size      , num_channels=self.num_channels      , hidden_size=self.hidden_size      , num_hidden_layers=self.num_hidden_layers      , num_attention_heads=self.num_attention_heads      , intermediate_size=self.intermediate_size      , decoder_hidden_size=self.hidden_size      , decoder_num_hidden_layers=self.num_hidden_layers      , decoder_num_attention_heads=self.num_attention_heads      , decoder_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      , is_decoder=_SCREAMING_SNAKE_CASE      , initializer_range=self.initializer_range      , mask_ratio=self.mask_ratio      , )

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							TFViTMAEModel(config=_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , training=_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.last_hidden_state.shape      , (self.batch_size, self.seq_length, self.hidden_size)	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							TFViTMAEForPreTraining(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , training=_SCREAMING_SNAKE_CASE	)
      # expected sequence length = num_patches
      lowerCAmelCase 						=							(self.image_size // self.patch_size) ** 2
      lowerCAmelCase 						=							self.patch_size**2 * self.num_channels
      self.parent.assertEqual(result.logits.shape      , (self.batch_size, num_patches, expected_num_channels)	)

      # test greyscale images
      lowerCAmelCase 						=							1
      lowerCAmelCase 						=							TFViTMAEForPreTraining(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
      lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , training=_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							self.patch_size**2
      self.parent.assertEqual(result.logits.shape      , (self.batch_size, num_patches, expected_num_channels)	)


  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.prepare_config_and_inputs()
      ((lowerCAmelCase)						,   (lowerCAmelCase)						,   (lowerCAmelCase)) 						=							config_and_inputs
      lowerCAmelCase 						=							{'pixel_values': pixel_values}
      return config, inputs_dict



@require_tf
class 				_snake_case					(   a_     , a_     , unittest.TestCase			):
  SCREAMING_SNAKE_CASE			:       Union[str, Any]				=     (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
  SCREAMING_SNAKE_CASE			:       List[str]				=     {'''feature-extraction''': TFViTMAEModel} if is_tf_available() else {}

  SCREAMING_SNAKE_CASE			:       Union[str, Any]				=     False
  SCREAMING_SNAKE_CASE			:       Any				=     False
  SCREAMING_SNAKE_CASE			:       Dict				=     False
  SCREAMING_SNAKE_CASE			:       Optional[Any]				=     False

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							TFViTMAEModelTester(self	)
      lowerCAmelCase 						=							ConfigTester(self      , config_class=_SCREAMING_SNAKE_CASE      , has_text_modality=_SCREAMING_SNAKE_CASE      , hidden_size=37	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      self.config_tester.run_common_tests()

  @unittest.skip(reason='ViTMAE does not use inputs_embeds'	)
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      pass

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase						,   lowerCAmelCase 						=							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          lowerCAmelCase 						=							model_class(_SCREAMING_SNAKE_CASE	)
          self.assertIsInstance(model.get_input_embeddings()      , (tf.keras.layers.Layer)	)
          lowerCAmelCase 						=							model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE      , tf.keras.layers.Layer	)	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase						,   lowerCAmelCase 						=							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          lowerCAmelCase 						=							model_class(_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase 						=							inspect.signature(model.call	)
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          lowerCAmelCase 						=							[*signature.parameters.keys()]

          lowerCAmelCase 						=							['pixel_values']
          self.assertListEqual(arg_names[:1]      , _SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_pretraining(*_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      np.random.seed(2	)

      lowerCAmelCase						,   lowerCAmelCase 						=							self.model_tester.prepare_config_and_inputs_for_common()

      lowerCAmelCase 						=							int((config.image_size // config.patch_size) ** 2	)
      lowerCAmelCase 						=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)

      for model_class in self.all_model_classes:
          lowerCAmelCase 						=							model_class(_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase 						=							self._prepare_for_class(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

          lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , noise=_SCREAMING_SNAKE_CASE	)

          lowerCAmelCase 						=							copy.deepcopy(self._prepare_for_class(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)	)
          lowerCAmelCase 						=							model(**_SCREAMING_SNAKE_CASE      , noise=_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase 						=							outputs_dict[0].numpy()
          lowerCAmelCase 						=							outputs_keywords[0].numpy()

          self.assertLess(np.sum(np.abs(output_dict - output_keywords	)	)      , 1e-6	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      np.random.seed(2	)

      lowerCAmelCase						,   lowerCAmelCase 						=							self.model_tester.prepare_config_and_inputs_for_common()

      lowerCAmelCase 						=							int((config.image_size // config.patch_size) ** 2	)
      lowerCAmelCase 						=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)

      def prepare_numpy_arrays(_SCREAMING_SNAKE_CASE	):
          lowerCAmelCase 						=							{}
          for k, v in inputs_dict.items():
              if tf.is_tensor(_SCREAMING_SNAKE_CASE	):
                  lowerCAmelCase 						=							v.numpy()
              else:
                  lowerCAmelCase 						=							np.array(_SCREAMING_SNAKE_CASE	)

          return inputs_np_dict

      for model_class in self.all_model_classes:
          lowerCAmelCase 						=							model_class(_SCREAMING_SNAKE_CASE	)

          lowerCAmelCase 						=							self._prepare_for_class(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)
          lowerCAmelCase 						=							prepare_numpy_arrays(_SCREAMING_SNAKE_CASE	)

          lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , noise=_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase 						=							model(**_SCREAMING_SNAKE_CASE      , noise=_SCREAMING_SNAKE_CASE	)
          self.assert_outputs_same(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      np.random.seed(2	)

      lowerCAmelCase 						=							int((tf_model.config.image_size // tf_model.config.patch_size) ** 2	)
      lowerCAmelCase 						=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)
      lowerCAmelCase 						=							tf.constant(_SCREAMING_SNAKE_CASE	)

      # Add `noise` argument.
      # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
      lowerCAmelCase 						=							tf_noise

      super().check_pt_tf_models(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      np.random.seed(2	)

      lowerCAmelCase						,   lowerCAmelCase 						=							self.model_tester.prepare_config_and_inputs_for_common()

      lowerCAmelCase 						=							{
          module_member
          for model_class in self.all_model_classes
          for module in (import_module(model_class.__module__	),)
          for module_member_name in dir(_SCREAMING_SNAKE_CASE	)
          if module_member_name.endswith('MainLayer'	)
          # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
          and module_member_name[: -len('MainLayer'	)] == model_class.__name__[: -len('Model'	)]
          for module_member in (getattr(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	),)
          if isinstance(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)
          and tf.keras.layers.Layer in module_member.__bases__
          and getattr(_SCREAMING_SNAKE_CASE      , '_keras_serializable'      , _SCREAMING_SNAKE_CASE	)
      }

      lowerCAmelCase 						=							int((config.image_size // config.patch_size) ** 2	)
      lowerCAmelCase 						=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)
      lowerCAmelCase 						=							tf.convert_to_tensor(_SCREAMING_SNAKE_CASE	)
      inputs_dict.update({'noise': noise}	)

      for main_layer_class in tf_main_layer_classes:
          lowerCAmelCase 						=							main_layer_class(_SCREAMING_SNAKE_CASE	)

          lowerCAmelCase 						=							{
              name: tf.keras.Input(tensor.shape[1:]      , dtype=tensor.dtype	) for name, tensor in inputs_dict.items()
          }

          lowerCAmelCase 						=							tf.keras.Model(_SCREAMING_SNAKE_CASE      , outputs=main_layer(_SCREAMING_SNAKE_CASE	)	)
          lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE	)

          with tempfile.TemporaryDirectory() as tmpdirname:
              lowerCAmelCase 						=							os.path.join(_SCREAMING_SNAKE_CASE      , 'keras_model.h5'	)
              model.save(_SCREAMING_SNAKE_CASE	)
              lowerCAmelCase 						=							tf.keras.models.load_model(
                  _SCREAMING_SNAKE_CASE      , custom_objects={main_layer_class.__name__: main_layer_class}	)
              assert isinstance(_SCREAMING_SNAKE_CASE      , tf.keras.Model	)
              lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE	)
              self.assert_outputs_same(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

  @slow
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      np.random.seed(2	)

      lowerCAmelCase						,   lowerCAmelCase 						=							self.model_tester.prepare_config_and_inputs_for_common()

      lowerCAmelCase 						=							int((config.image_size // config.patch_size) ** 2	)
      lowerCAmelCase 						=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)

      for model_class in self.all_model_classes:
          lowerCAmelCase 						=							model_class(_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase 						=							self._prepare_for_class(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)
          lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , noise=_SCREAMING_SNAKE_CASE	)

          if model_class.__name__ == "TFViTMAEModel":
              lowerCAmelCase 						=							outputs.last_hidden_state.numpy()
              lowerCAmelCase 						=							0
          else:
              lowerCAmelCase 						=							outputs.logits.numpy()
              lowerCAmelCase 						=							0

          with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_pretrained(_SCREAMING_SNAKE_CASE      , saved_model=_SCREAMING_SNAKE_CASE	)
              lowerCAmelCase 						=							model_class.from_pretrained(_SCREAMING_SNAKE_CASE	)
              lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , noise=_SCREAMING_SNAKE_CASE	)

              if model_class.__name__ == "TFViTMAEModel":
                  lowerCAmelCase 						=							after_outputs['last_hidden_state'].numpy()
                  lowerCAmelCase 						=							0
              else:
                  lowerCAmelCase 						=							after_outputs['logits'].numpy()
                  lowerCAmelCase 						=							0

              lowerCAmelCase 						=							np.amax(np.abs(out_a - out_a	)	)
              self.assertLessEqual(_SCREAMING_SNAKE_CASE      , 1e-5	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      np.random.seed(2	)

      lowerCAmelCase						,   lowerCAmelCase 						=							self.model_tester.prepare_config_and_inputs_for_common()

      lowerCAmelCase 						=							int((config.image_size // config.patch_size) ** 2	)
      lowerCAmelCase 						=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)	)

      for model_class in self.all_model_classes:
          lowerCAmelCase 						=							model_class(_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase 						=							self._prepare_for_class(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

          lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , noise=_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase 						=							model.get_config()
          # make sure that returned config is jsonifiable, which is required by keras
          json.dumps(_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase 						=							model_class.from_config(model.get_config()	)
          # make sure it also accepts a normal config
          lowerCAmelCase 						=							model_class.from_config(model.config	)
          lowerCAmelCase 						=							new_model(_SCREAMING_SNAKE_CASE	)  # Build model
          new_model.set_weights(model.get_weights()	)
          lowerCAmelCase 						=							new_model(_SCREAMING_SNAKE_CASE      , noise=_SCREAMING_SNAKE_CASE	)

          self.assert_outputs_same(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

  @unittest.skip(
      reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'	)
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      pass

  @unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'	)
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      pass


  @slow
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							TFViTMAEModel.from_pretrained('google/vit-base-patch16-224'	)
      self.assertIsNotNone(_SCREAMING_SNAKE_CASE	)



def       snake_case   (	) ->  Optional[int]:


    """simple docstring"""


    lowerCAmelCase 						=							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
    return image



@require_tf
@require_vision
class 				_snake_case					(   unittest.TestCase			):

  @cached_property
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'	) if is_vision_available() else None


  @slow
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      np.random.seed(2	)

      lowerCAmelCase 						=							TFViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'	)

      lowerCAmelCase 						=							self.default_image_processor
      lowerCAmelCase 						=							prepare_img()
      lowerCAmelCase 						=							image_processor(images=_SCREAMING_SNAKE_CASE      , return_tensors='tf'	)

      # prepare a noise vector that will be also used for testing the TF model
      # (this way we can ensure that the PT and TF models operate on the same inputs)
      lowerCAmelCase 						=							ViTMAEConfig()
      lowerCAmelCase 						=							int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2	)
      lowerCAmelCase 						=							np.random.uniform(size=(1, num_patches)	)

      # forward pass
      lowerCAmelCase 						=							model(**_SCREAMING_SNAKE_CASE      , noise=_SCREAMING_SNAKE_CASE	)

      # verify the logits
      lowerCAmelCase 						=							tf.convert_to_tensor([1, 1_96, 7_68]	)
      self.assertEqual(outputs.logits.shape      , _SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							tf.convert_to_tensor(
          [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]	)

      tf.debugging.assert_near(outputs.logits[0, :3, :3]      , _SCREAMING_SNAKE_CASE      , atol=1e-4	)








'''simple docstring'''



from ..utils import DummyObject, requires_backends



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       int				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       List[Any]				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       int				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       Dict				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       List[Any]				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       Optional[Any]				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)





