code: 
  
  
  '''simple docstring'''
  
  
  
  from __future__ import annotations
  
  import inspect
  import unittest
  
  import numpy as np
  
  from transformers import SwinConfig
  from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
  from transformers.utils import cached_property, is_tf_available, is_vision_available
  
  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.models.swin.modeling_tf_swin import (
            TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFSwinForImageClassification,
            TFSwinForMaskedImageModeling,
            TFSwinModel,
        )
  
  
  if is_vision_available():
        from PIL import Image
  
        from transformers import AutoImageProcessor
  
  
  
  
  
  class      UpperCAmelCase_						:
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      def __init__(							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_=13    ,  SCREAMING_SNAKE_CASE_=32    ,  SCREAMING_SNAKE_CASE_=2    ,  SCREAMING_SNAKE_CASE_=3    ,  SCREAMING_SNAKE_CASE_=16    ,  SCREAMING_SNAKE_CASE_=[1, 2, 1]    ,  SCREAMING_SNAKE_CASE_=[2, 2, 4]    ,  SCREAMING_SNAKE_CASE_=2    ,  SCREAMING_SNAKE_CASE_=2.0    ,  SCREAMING_SNAKE_CASE_=True    ,  SCREAMING_SNAKE_CASE_=0.0    ,  SCREAMING_SNAKE_CASE_=0.0    ,  SCREAMING_SNAKE_CASE_=0.1    ,  SCREAMING_SNAKE_CASE_="gelu"    ,  SCREAMING_SNAKE_CASE_=False    ,  SCREAMING_SNAKE_CASE_=True    ,  SCREAMING_SNAKE_CASE_=0.0_2    ,  SCREAMING_SNAKE_CASE_=1E-5    ,  SCREAMING_SNAKE_CASE_=True    ,  SCREAMING_SNAKE_CASE_=None    ,  SCREAMING_SNAKE_CASE_=True    ,  SCREAMING_SNAKE_CASE_=10    ,  SCREAMING_SNAKE_CASE_=8    ,  )       ->  None:
           __lowerCamelCase     :			List[Any]              =	parent
           __lowerCamelCase     :			Any              =	batch_size
           __lowerCamelCase     :			str              =	image_size
           __lowerCamelCase     :			List[str]              =	patch_size
           __lowerCamelCase     :			List[str]              =	num_channels
           __lowerCamelCase     :			Union[str, Any]              =	embed_dim
           __lowerCamelCase     :			int              =	depths
           __lowerCamelCase     :			Tuple              =	num_heads
           __lowerCamelCase     :			List[str]              =	window_size
           __lowerCamelCase     :			Union[str, Any]              =	mlp_ratio
           __lowerCamelCase     :			int              =	qkv_bias
           __lowerCamelCase     :			Any              =	hidden_dropout_prob
           __lowerCamelCase     :			Optional[Any]              =	attention_probs_dropout_prob
           __lowerCamelCase     :			Optional[int]              =	drop_path_rate
           __lowerCamelCase     :			List[str]              =	hidden_act
           __lowerCamelCase     :			List[Any]              =	use_absolute_embeddings
           __lowerCamelCase     :			List[str]              =	patch_norm
           __lowerCamelCase     :			Dict              =	layer_norm_eps
           __lowerCamelCase     :			Dict              =	initializer_range
           __lowerCamelCase     :			Tuple              =	is_training
           __lowerCamelCase     :			str              =	scope
           __lowerCamelCase     :			Optional[Any]              =	use_labels
           __lowerCamelCase     :			int              =	type_sequence_label_size
           __lowerCamelCase     :			int              =	encoder_stride
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  str:
           __lowerCamelCase     :			Dict              =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)
  
           __lowerCamelCase     :			List[Any]              =	None
           if self.use_labels:
                __lowerCamelCase     :			Any              =	ids_tensor([self.batch_size]    ,  self.type_sequence_label_size			)
  
           __lowerCamelCase     :			List[str]              =	self.get_config()
  
           return config, pixel_values, labels
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Union[str, Any]:
           return SwinConfig(
               image_size=self.image_size    ,  patch_size=self.patch_size    ,  num_channels=self.num_channels    ,  embed_dim=self.embed_dim    ,  depths=self.depths    ,  num_heads=self.num_heads    ,  window_size=self.window_size    ,  mlp_ratio=self.mlp_ratio    ,  qkv_bias=self.qkv_bias    ,  hidden_dropout_prob=self.hidden_dropout_prob    ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,  drop_path_rate=self.drop_path_rate    ,  hidden_act=self.hidden_act    ,  use_absolute_embeddings=self.use_absolute_embeddings    ,  path_norm=self.patch_norm    ,  layer_norm_eps=self.layer_norm_eps    ,  initializer_range=self.initializer_range    ,  encoder_stride=self.encoder_stride    ,  )
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  Optional[Any]:
           __lowerCamelCase     :			Optional[Any]              =	TFSwinModel(config=SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Optional[Any]              =	model(SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			Dict              =	((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths			) - 1))
           __lowerCamelCase     :			Union[str, Any]              =	int(config.embed_dim * 2 ** (len(config.depths			) - 1)			)
  
           self.parent.assertEqual(result.last_hidden_state.shape    ,  (self.batch_size, expected_seq_len, expected_dim)			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  int:
           __lowerCamelCase     :			List[str]              =	TFSwinForMaskedImageModeling(config=SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Tuple              =	model(SCREAMING_SNAKE_CASE_			)
           self.parent.assertEqual(
               result.logits.shape    ,  (self.batch_size, self.num_channels, self.image_size, self.image_size)			)
  
           # test greyscale images
           __lowerCamelCase     :			str              =	1
           __lowerCamelCase     :			int              =	TFSwinForMaskedImageModeling(SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			Optional[int]              =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
           __lowerCamelCase     :			Dict              =	model(SCREAMING_SNAKE_CASE_			)
           self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, 1, self.image_size, self.image_size)			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  List[Any]:
           __lowerCamelCase     :			Optional[int]              =	self.type_sequence_label_size
           __lowerCamelCase     :			List[Any]              =	TFSwinForImageClassification(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			int              =	model(SCREAMING_SNAKE_CASE_    ,  labels=SCREAMING_SNAKE_CASE_			)
           self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.type_sequence_label_size)			)
  
           # test greyscale images
           __lowerCamelCase     :			Dict              =	1
           __lowerCamelCase     :			List[Any]              =	TFSwinForImageClassification(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			int              =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
           __lowerCamelCase     :			Optional[int]              =	model(SCREAMING_SNAKE_CASE_			)
           self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.type_sequence_label_size)			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[Any]:
           __lowerCamelCase     :			Tuple              =	self.prepare_config_and_inputs()
           __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase     :			List[str]              =	config_and_inputs
           __lowerCamelCase     :			List[str]              =	{'pixel_values': pixel_values}
           return config, inputs_dict
  
  
  
  
  
  
  
  @require_tf
  class      UpperCAmelCase_						(_UpperCAmelCase      ,	_UpperCAmelCase      ,	unittest.TestCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      List[str]							  =    (
          (
              TFSwinModel,
              TFSwinForImageClassification,
              TFSwinForMaskedImageModeling,
          )
          if is_tf_available()
          else ()
      )
      lowerCamelCase						:      Any							  =    (
          {'feature-extraction': TFSwinModel, 'image-classification': TFSwinForImageClassification}
          if is_tf_available()
          else {}
      )
  
      lowerCamelCase						:      Tuple							  =    False
      lowerCamelCase						:      Dict							  =    False
      lowerCamelCase						:      str							  =    False
      lowerCamelCase						:      int							  =    False
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Dict:
           __lowerCamelCase     :			Tuple              =	TFSwinModelTester(self			)
           __lowerCamelCase     :			str              =	ConfigTester(self    ,  config_class=SCREAMING_SNAKE_CASE_    ,  embed_dim=37			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[int]:
           self.create_and_test_config_common_properties()
           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           return
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[Any]:
           __lowerCamelCase     :			Tuple              =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           __lowerCamelCase     :			List[str]              =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Tuple:
           __lowerCamelCase     :			List[str]              =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      @unittest.skip(reason='Swin does not use inputs_embeds'			)
      def    lowercase_       (							self			)       ->  Any:
           pass
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           __lowerCamelCase       , __lowerCamelCase     :			Dict              =	self.model_tester.prepare_config_and_inputs_for_common()
  
           for model_class in self.all_model_classes:
                __lowerCamelCase     :			Any              =	model_class(SCREAMING_SNAKE_CASE_			)
                self.assertIsInstance(model.get_input_embeddings()    ,  tf.keras.layers.Layer			)
                __lowerCamelCase     :			str              =	model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_    ,  tf.keras.layers.Dense			)			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  str:
           __lowerCamelCase       , __lowerCamelCase     :			Optional[int]              =	self.model_tester.prepare_config_and_inputs_for_common()
  
           for model_class in self.all_model_classes:
                __lowerCamelCase     :			int              =	model_class(SCREAMING_SNAKE_CASE_			)
                __lowerCamelCase     :			List[str]              =	inspect.signature(model.call			)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                __lowerCamelCase     :			Dict              =	[*signature.parameters.keys()]
  
                __lowerCamelCase     :			Tuple              =	['pixel_values']
                self.assertListEqual(arg_names[:1]    ,  SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Union[str, Any]:
           __lowerCamelCase       , __lowerCamelCase     :			str              =	self.model_tester.prepare_config_and_inputs_for_common()
           __lowerCamelCase     :			int              =	True
  
           for model_class in self.all_model_classes:
                __lowerCamelCase     :			Dict              =	True
                __lowerCamelCase     :			Dict              =	False
                __lowerCamelCase     :			Any              =	True
                __lowerCamelCase     :			Optional[int]              =	model_class(SCREAMING_SNAKE_CASE_			)
                __lowerCamelCase     :			str              =	model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)			)
                __lowerCamelCase     :			Optional[int]              =	outputs.attentions
                __lowerCamelCase     :			int              =	len(self.model_tester.depths			)
                self.assertEqual(len(SCREAMING_SNAKE_CASE_			)    ,  SCREAMING_SNAKE_CASE_			)
  
                # check that output_attentions also work using config
                del inputs_dict["output_attentions"]
                __lowerCamelCase     :			Optional[int]              =	True
                __lowerCamelCase     :			Tuple              =	config.window_size**2
                __lowerCamelCase     :			int              =	model_class(SCREAMING_SNAKE_CASE_			)
                __lowerCamelCase     :			List[str]              =	model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)			)
                __lowerCamelCase     :			Optional[int]              =	outputs.attentions
                self.assertEqual(len(SCREAMING_SNAKE_CASE_			)    ,  SCREAMING_SNAKE_CASE_			)
  
                self.assertListEqual(
                    list(attentions[0].shape[-3:]			)    ,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]    ,  )
                __lowerCamelCase     :			Tuple              =	len(SCREAMING_SNAKE_CASE_			)
  
                # Check attention is always last and order is fine
                __lowerCamelCase     :			Union[str, Any]              =	True
                __lowerCamelCase     :			Tuple              =	True
                __lowerCamelCase     :			List[Any]              =	model_class(SCREAMING_SNAKE_CASE_			)
                __lowerCamelCase     :			str              =	model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)			)
  
                if hasattr(self.model_tester    ,  'num_hidden_states_types'			):
                     __lowerCamelCase     :			Tuple              =	self.model_tester.num_hidden_states_types
                else:
                     # also another +1 for reshaped_hidden_states
                     __lowerCamelCase     :			List[Any]              =	2
                self.assertEqual(out_len + added_hidden_states    ,  len(SCREAMING_SNAKE_CASE_			)			)
  
                __lowerCamelCase     :			List[Any]              =	outputs.attentions
  
                self.assertEqual(len(SCREAMING_SNAKE_CASE_			)    ,  SCREAMING_SNAKE_CASE_			)
  
                self.assertListEqual(
                    list(self_attentions[0].shape[-3:]			)    ,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]    ,  )
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  Optional[int]:
           __lowerCamelCase     :			Dict              =	model_class(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Tuple              =	model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)			)
           __lowerCamelCase     :			Any              =	outputs.hidden_states
  
           __lowerCamelCase     :			Union[str, Any]              =	getattr(
               self.model_tester    ,  'expected_num_hidden_layers'    ,  len(self.model_tester.depths			) + 1			)
           self.assertEqual(len(SCREAMING_SNAKE_CASE_			)    ,  SCREAMING_SNAKE_CASE_			)
  
           # Swin has a different seq_length
           __lowerCamelCase     :			Union[str, Any]              =	to_atuple(config.patch_size			)
  
           __lowerCamelCase     :			Dict              =	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
  
           self.assertListEqual(
               list(hidden_states[0].shape[-2:]			)    ,  [num_patches, self.model_tester.embed_dim]    ,  )
  
           __lowerCamelCase     :			str              =	outputs.reshaped_hidden_states
           self.assertEqual(len(SCREAMING_SNAKE_CASE_			)    ,  SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase     :			Union[str, Any]              =	reshaped_hidden_states[0].shape
  
           __lowerCamelCase     :			Optional[Any]              =	tf.reshape(reshaped_hidden_states[0]    ,  (batch_size, num_channels, height * width)			)
           __lowerCamelCase     :			List[Any]              =	tf.transpose(SCREAMING_SNAKE_CASE_    ,  (0, 2, 1)			)
  
           self.assertListEqual(
               list(reshaped_hidden_states.shape[-2:]			)    ,  [num_patches, self.model_tester.embed_dim]    ,  )
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Union[str, Any]:
           __lowerCamelCase       , __lowerCamelCase     :			Optional[int]              =	self.model_tester.prepare_config_and_inputs_for_common()
  
           __lowerCamelCase     :			str              =	to_atuple(self.model_tester.image_size			)
  
           for model_class in self.all_model_classes:
                __lowerCamelCase     :			str              =	True
                self.check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                __lowerCamelCase     :			Optional[int]              =	True
  
                self.check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  str:
           __lowerCamelCase       , __lowerCamelCase     :			List[Any]              =	self.model_tester.prepare_config_and_inputs_for_common()
           __lowerCamelCase     :			Dict              =	3
  
           __lowerCamelCase     :			Union[str, Any]              =	to_atuple(self.model_tester.image_size			)
           __lowerCamelCase     :			List[Any]              =	to_atuple(config.patch_size			)
  
           __lowerCamelCase     :			Union[str, Any]              =	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
           __lowerCamelCase     :			Optional[int]              =	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
  
           for model_class in self.all_model_classes:
                __lowerCamelCase     :			str              =	True
                self.check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  (padded_height, padded_width)			)
  
                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                __lowerCamelCase     :			Union[str, Any]              =	True
                self.check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  (padded_height, padded_width)			)
  
  
  
  
  
  
  
      @slow
      def    lowercase_       (							self			)       ->  Dict:
           for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                __lowerCamelCase     :			Dict              =	TFSwinModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
                self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
  @require_vision
  @require_tf
  class      UpperCAmelCase_						(unittest.TestCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      @cached_property
      def    lowercase_       (							self			)       ->  Any:
           return (
               AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'			)
               if is_vision_available()
               else None
           )
  
  
  
  
  
  
  
      @slow
      def    lowercase_       (							self			)       ->  Any:
           __lowerCamelCase     :			Optional[int]              =	TFSwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'			)
           __lowerCamelCase     :			Any              =	self.default_image_processor
  
           __lowerCamelCase     :			Dict              =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
           __lowerCamelCase     :			Any              =	image_processor(images=SCREAMING_SNAKE_CASE_    ,  return_tensors='tf'			)
  
           # forward pass
           __lowerCamelCase     :			str              =	model(SCREAMING_SNAKE_CASE_			)
  
           # verify the logits
           __lowerCamelCase     :			Tuple              =	tf.TensorShape((1, 10_00)			)
           self.assertEqual(outputs.logits.shape    ,  SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			List[Any]              =	tf.constant([-0.0_9_4_8, -0.6_4_5_4, -0.0_9_2_1]			)
           self.assertTrue(np.allclose(outputs.logits[0, :3]    ,  SCREAMING_SNAKE_CASE_    ,  atol=1E-4			)			)
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  from typing import Callable, Optional, Union
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  A__	:      Any				=						logging.get_logger(__name__)
  
  A__	:      str				=						{
      """microsoft/prophetnet-large-uncased""": (
          """https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"""
      ),
  }
  
  
  
  
  
  class      UpperCAmelCase_						(_UpperCAmelCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Dict							  =    'prophetnet'
      lowerCamelCase						:      List[str]							  =    ['past_key_values']
      lowerCamelCase						:      List[str]							  =    {
          'num_attention_heads': 'num_encoder_attention_heads',
      }
  
  
  
  
  
  
  
      def __init__(							self    ,  SCREAMING_SNAKE_CASE_ = 0.1    ,  SCREAMING_SNAKE_CASE_ = "gelu"    ,  SCREAMING_SNAKE_CASE_ = 3_05_22    ,  SCREAMING_SNAKE_CASE_ = 10_24    ,  SCREAMING_SNAKE_CASE_ = 40_96    ,  SCREAMING_SNAKE_CASE_ = 12    ,  SCREAMING_SNAKE_CASE_ = 16    ,  SCREAMING_SNAKE_CASE_ = 40_96    ,  SCREAMING_SNAKE_CASE_ = 12    ,  SCREAMING_SNAKE_CASE_ = 16    ,  SCREAMING_SNAKE_CASE_ = 0.1    ,  SCREAMING_SNAKE_CASE_ = 0.1    ,  SCREAMING_SNAKE_CASE_ = 5_12    ,  SCREAMING_SNAKE_CASE_ = 0.0_2    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = 0    ,  SCREAMING_SNAKE_CASE_ = 2    ,  SCREAMING_SNAKE_CASE_ = 32    ,  SCREAMING_SNAKE_CASE_ = 1_28    ,  SCREAMING_SNAKE_CASE_ = False    ,  SCREAMING_SNAKE_CASE_ = 0.0    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = 0    ,  SCREAMING_SNAKE_CASE_ = 1    ,  SCREAMING_SNAKE_CASE_ = 2    ,  **SCREAMING_SNAKE_CASE_    ,  )       ->  List[Any]:
           __lowerCamelCase     :			List[Any]              =	vocab_size
           __lowerCamelCase     :			Union[str, Any]              =	hidden_size
           __lowerCamelCase     :			Tuple              =	encoder_ffn_dim
           __lowerCamelCase     :			Any              =	num_encoder_layers
           __lowerCamelCase     :			int              =	num_encoder_attention_heads
           __lowerCamelCase     :			Optional[Any]              =	decoder_ffn_dim
           __lowerCamelCase     :			str              =	num_decoder_layers
           __lowerCamelCase     :			int              =	num_decoder_attention_heads
           __lowerCamelCase     :			Union[str, Any]              =	max_position_embeddings
           __lowerCamelCase     :			Optional[Any]              =	init_std  # Normal(0, this parameter)
           __lowerCamelCase     :			Union[str, Any]              =	activation_function
  
           # parameters for prophetnet
           __lowerCamelCase     :			int              =	ngram
           __lowerCamelCase     :			Optional[int]              =	num_buckets
           __lowerCamelCase     :			str              =	relative_max_distance
           __lowerCamelCase     :			Optional[Any]              =	disable_ngram_loss
           __lowerCamelCase     :			str              =	eps
  
           # 3 Types of Dropout
           __lowerCamelCase     :			Optional[Any]              =	attention_dropout
           __lowerCamelCase     :			str              =	activation_dropout
           __lowerCamelCase     :			Optional[Any]              =	dropout
  
           __lowerCamelCase     :			Optional[Any]              =	use_cache
  
           super().__init__(
               pad_token_id=SCREAMING_SNAKE_CASE_    ,  bos_token_id=SCREAMING_SNAKE_CASE_    ,  eos_token_id=SCREAMING_SNAKE_CASE_    ,  is_encoder_decoder=SCREAMING_SNAKE_CASE_    ,  add_cross_attention=SCREAMING_SNAKE_CASE_    ,  decoder_start_token_id=SCREAMING_SNAKE_CASE_    ,  **SCREAMING_SNAKE_CASE_    ,  )
  
  
  
  
  
  
  
      @property
      def    lowercase_       (							self			)       ->  int:
           return self.num_encoder_layers + self.num_decoder_layers
  
  
  
  
  
  
  
      @num_hidden_layers.setter
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Union[str, Any]:
           raise NotImplementedError(
               'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
               ' `num_decoder_layers`.'			)
  
  
  
  
  
  
  