'''simple docstring'''


import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
     MODEL_MAPPING,
     EfficientFormerForImageClassification,
     EfficientFormerForImageClassificationWithTeacher,
     EfficientFormerModel,
 )
 from transformers.models.efficientformer.modeling_efficientformer import (
     EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
 )


if is_vision_available():
 from PIL import Image

 from transformers import EfficientFormerImageProcessor






class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_ ,						snake_case_ = 1_3 ,						snake_case_ = 6_4 ,						snake_case_ = 2 ,						snake_case_ = 3 ,						snake_case_ = 3 ,						snake_case_ = True ,						snake_case_ = True ,						snake_case_ = 1_2_8 ,						snake_case_=[1_6, 3_2, 6_4, 1_2_8] ,						snake_case_ = 7 ,						snake_case_ = 4 ,						snake_case_ = 3_7 ,						snake_case_ = "gelu" ,						snake_case_ = 0.1 ,						snake_case_ = 0.1 ,						snake_case_ = 1_0 ,						snake_case_ = 0.02 ,						snake_case_ = 2 ,						snake_case_ = 1 ,						snake_case_ = 1_2_8 ,						snake_case_ = [2, 2, 2, 2] ,						snake_case_ = 2 ,						snake_case_ = 2 ,						):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = parent
       UpperCAmelCase_				:  List[str]  = batch_size
       UpperCAmelCase_				:  List[Any]  = image_size
       UpperCAmelCase_				:  List[Any]  = patch_size
       UpperCAmelCase_				:  Any  = num_channels
       UpperCAmelCase_				:  Dict  = is_training
       UpperCAmelCase_				:  Optional[int]  = use_labels
       UpperCAmelCase_				:  int  = hidden_size
       UpperCAmelCase_				:  Any  = num_hidden_layers
       UpperCAmelCase_				:  str  = num_attention_heads
       UpperCAmelCase_				:  Dict  = intermediate_size
       UpperCAmelCase_				:  Optional[int]  = hidden_act
       UpperCAmelCase_				:  int  = hidden_dropout_prob
       UpperCAmelCase_				:  List[str]  = attention_probs_dropout_prob
       UpperCAmelCase_				:  str  = type_sequence_label_size
       UpperCAmelCase_				:  str  = initializer_range
       UpperCAmelCase_				:  str  = encoder_stride
       UpperCAmelCase_				:  List[Any]  = num_attention_outputs
       UpperCAmelCase_				:  Optional[Any]  = embed_dim
       UpperCAmelCase_				:  List[str]  = embed_dim + 1
       UpperCAmelCase_				:  Tuple  = resolution
       UpperCAmelCase_				:  Optional[Any]  = depths
       UpperCAmelCase_				:  List[Any]  = hidden_sizes
       UpperCAmelCase_				:  Union[str, Any]  = dim
       UpperCAmelCase_				:  int  = mlp_expansion_ratio


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

       UpperCAmelCase_				:  Any  = None
       if self.use_labels:
            UpperCAmelCase_				:  int  = ids_tensor([self.batch_size] ,						self.type_sequence_label_size		)

       UpperCAmelCase_				:  Optional[int]  = self.get_config()
       return config, pixel_values, labels


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return EfficientFormerConfig(
           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 ,						hidden_act=self.hidden_act ,						hidden_dropout_prob=self.hidden_dropout_prob ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob ,						is_decoder=_lowerCAmelCase ,						initializer_range=self.initializer_range ,						encoder_stride=self.encoder_stride ,						resolution=self.resolution ,						depths=self.depths ,						hidden_sizes=self.hidden_sizes ,						dim=self.dim ,						mlp_expansion_ratio=self.mlp_expansion_ratio ,						)


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Tuple  = EfficientFormerModel(config=_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.eval()
       UpperCAmelCase_				:  Dict  = model(_lowerCAmelCase		)
       self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.hidden_size)		)


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = self.type_sequence_label_size
       UpperCAmelCase_				:  Optional[int]  = EfficientFormerForImageClassification(_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.eval()
       UpperCAmelCase_				:  Optional[int]  = model(_lowerCAmelCase ,						labels=_lowerCAmelCase		)
       self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.type_sequence_label_size)		)

       # test greyscale images
       UpperCAmelCase_				:  str  = 1
       UpperCAmelCase_				:  Tuple  = EfficientFormerForImageClassification(_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.eval()

       UpperCAmelCase_				:  Any  = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
       UpperCAmelCase_				:  List[str]  = model(_lowerCAmelCase		)
       self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.type_sequence_label_size)		)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = self.prepare_config_and_inputs()
       (
           (
           UpperCAmelCase_ 
       )  ,     (
           UpperCAmelCase_ 
       )  ,     (
           UpperCAmelCase_ 
       )  ,      
       )				:  Optional[int]  = config_and_inputs
       UpperCAmelCase_				:  Dict  = {'pixel_values': pixel_values}
       return config, inputs_dict







@require_torch
class 	__SCREAMING_SNAKE_CASE		(			snake_case_		,      snake_case_		,      unittest.TestCase			):







  '''simple docstring'''






  lowerCamelCase_						:Any		       =						(
      (
          EfficientFormerModel,
          EfficientFormerForImageClassificationWithTeacher,
          EfficientFormerForImageClassification,
      )
      if is_torch_available()
      else ()
  )
  lowerCamelCase_						:Union[str, Any]		       =						(
      {
          '''feature-extraction''': EfficientFormerModel,
          '''image-classification''': (
              EfficientFormerForImageClassification,
              EfficientFormerForImageClassificationWithTeacher,
          ),
      }
      if is_torch_available()
      else {}
  )
  lowerCamelCase_						:int		       =						False

  lowerCamelCase_						:str		       =						False
  lowerCamelCase_						:List[str]		       =						False
  lowerCamelCase_						:Optional[int]		       =						False


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = EfficientFormerModelTester(self		)
       UpperCAmelCase_				:  Dict  = ConfigTester(
           self ,						config_class=_lowerCAmelCase ,						has_text_modality=_lowerCAmelCase ,						hidden_size=3_7		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       self.config_tester.run_common_tests()


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




       '''simple docstring'''

       pass


  @unittest.skip(reason='EfficientFormer does not support input and output embeddings'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       pass


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_  ,     UpperCAmelCase_				:  int  = self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCAmelCase_				:  Any  = model_class(_lowerCAmelCase		)
            UpperCAmelCase_				:  int  = inspect.signature(model.forward		)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            UpperCAmelCase_				:  List[str]  = [*signature.parameters.keys()]

            UpperCAmelCase_				:  Optional[int]  = ['pixel_values']
            self.assertListEqual(arg_names[:1] ,						_lowerCAmelCase		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       def check_hidden_states_output(snake_case_ ,						snake_case_ ,						snake_case_		):
            UpperCAmelCase_				:  Any  = model_class(_lowerCAmelCase		)
            model.to(_lowerCAmelCase		)
            model.eval()

            with torch.no_grad():
                 UpperCAmelCase_				:  int  = model(**self._prepare_for_class(_lowerCAmelCase ,						_lowerCAmelCase		)		)

            UpperCAmelCase_				:  Tuple  = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            UpperCAmelCase_				:  int  = getattr(
                self.model_tester ,						'expected_num_hidden_layers' ,						self.model_tester.num_hidden_layers + 1		)
            self.assertEqual(len(_lowerCAmelCase		) ,						_lowerCAmelCase		)

            if hasattr(self.model_tester ,						'encoder_seq_length'		):
                 UpperCAmelCase_				:  Tuple  = self.model_tester.encoder_seq_length
                 if hasattr(self.model_tester ,						'chunk_length'		) and self.model_tester.chunk_length > 1:
                      UpperCAmelCase_				:  Union[str, Any]  = seq_length * self.model_tester.chunk_length
            else:
                 UpperCAmelCase_				:  Tuple  = self.model_tester.seq_length

            self.assertListEqual(
                list(hidden_states[-1].shape[-2:]		) ,						[seq_length, self.model_tester.hidden_size] ,						)

            if config.is_encoder_decoder:
                 UpperCAmelCase_				:  Tuple  = outputs.decoder_hidden_states

                 self.assertIsInstance(_lowerCAmelCase ,						(list, tuple)		)
                 self.assertEqual(len(_lowerCAmelCase		) ,						_lowerCAmelCase		)
                 UpperCAmelCase_				:  int  = getattr(self.model_tester ,						'seq_length' ,						_lowerCAmelCase		)
                 UpperCAmelCase_				:  Dict  = getattr(self.model_tester ,						'decoder_seq_length' ,						_lowerCAmelCase		)

                 self.assertListEqual(
                     list(hidden_states[-1].shape[-2:]		) ,						[decoder_seq_length, self.model_tester.hidden_size] ,						)

       UpperCAmelCase_  ,     UpperCAmelCase_				:  Union[str, Any]  = self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCAmelCase_				:  Union[str, Any]  = True
            check_hidden_states_output(_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase		)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            UpperCAmelCase_				:  Union[str, Any]  = True

            check_hidden_states_output(_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase		)


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_=False		):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = super()._prepare_for_class(_lowerCAmelCase ,						_lowerCAmelCase ,						return_labels=_lowerCAmelCase		)

       if return_labels:
            if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
                 del inputs_dict["labels"]

       return inputs_dict


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCAmelCase		)


  @unittest.skip(reason='EfficientFormer does not implement masked image modeling yet'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  str  = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCAmelCase		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       if not self.model_tester.is_training:
            return

       UpperCAmelCase_  ,     UpperCAmelCase_				:  Dict  = self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_				:  Union[str, Any]  = True

       for model_class in self.all_model_classes:
            # EfficientFormerForImageClassificationWithTeacher supports inference-only
            if (
                model_class in get_values(_lowerCAmelCase		)
                or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
            ):
                 continue
            UpperCAmelCase_				:  str  = model_class(_lowerCAmelCase		)
            model.to(_lowerCAmelCase		)
            model.train()
            UpperCAmelCase_				:  List[str]  = self._prepare_for_class(_lowerCAmelCase ,						_lowerCAmelCase ,						return_labels=_lowerCAmelCase		)
            UpperCAmelCase_				:  str  = model(**_lowerCAmelCase		).loss
            loss.backward()


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_  ,     UpperCAmelCase_				:  Optional[Any]  = self.model_tester.prepare_config_and_inputs_for_common()

       UpperCAmelCase_				:  Tuple  = [
           {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
           {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
           {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
       ]

       for model_class in self.all_model_classes:
            if (
                model_class
                not in [
                    *get_values(_lowerCAmelCase		),
                ]
                or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
            ):
                 continue

            for problem_type in problem_types:
                 with self.subTest(msg=F'''Testing {model_class} with {problem_type['title']}'''		):
                      UpperCAmelCase_				:  Optional[Any]  = problem_type['title']
                      UpperCAmelCase_				:  Dict  = problem_type['num_labels']

                      UpperCAmelCase_				:  Optional[Any]  = model_class(_lowerCAmelCase		)
                      model.to(_lowerCAmelCase		)
                      model.train()

                      UpperCAmelCase_				:  Optional[int]  = self._prepare_for_class(_lowerCAmelCase ,						_lowerCAmelCase ,						return_labels=_lowerCAmelCase		)

                      if problem_type["num_labels"] > 1:
                           UpperCAmelCase_				:  List[Any]  = inputs['labels'].unsqueeze(1		).repeat(1 ,						problem_type['num_labels']		)

                      UpperCAmelCase_				:  List[str]  = inputs['labels'].to(problem_type['dtype']		)

                      # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                      # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                      # they have the same size." which is a symptom something in wrong for the regression problem.
                      # See https://github.com/huggingface/transformers/issues/11780
                      with warnings.catch_warnings(record=_lowerCAmelCase		) as warning_list:
                           UpperCAmelCase_				:  str  = model(**_lowerCAmelCase		).loss
                      for w in warning_list:
                           if "Using a target size that is different to the input size" in str(w.message		):
                                raise ValueError(
                                    F'''Something is going wrong in the regression problem: intercepted {w.message}'''		)

                      loss.backward()


  @slow
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCAmelCase_				:  Union[str, Any]  = EfficientFormerModel.from_pretrained(_lowerCAmelCase		)
            self.assertIsNotNone(_lowerCAmelCase		)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_  ,     UpperCAmelCase_				:  Union[str, Any]  = self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_				:  int  = True

       UpperCAmelCase_				:  Optional[int]  = getattr(self.model_tester ,						'seq_length' ,						_lowerCAmelCase		)
       UpperCAmelCase_				:  Dict  = getattr(self.model_tester ,						'encoder_seq_length' ,						_lowerCAmelCase		)
       UpperCAmelCase_				:  Dict  = getattr(self.model_tester ,						'key_length' ,						_lowerCAmelCase		)
       UpperCAmelCase_				:  Optional[Any]  = getattr(self.model_tester ,						'chunk_length' ,						_lowerCAmelCase		)

       if chunk_length is not None and hasattr(self.model_tester ,						'num_hashes'		):
            UpperCAmelCase_				:  str  = encoder_seq_length * self.model_tester.num_hashes

       for model_class in self.all_model_classes:
            UpperCAmelCase_				:  str  = True
            UpperCAmelCase_				:  List[str]  = False
            UpperCAmelCase_				:  str  = True
            UpperCAmelCase_				:  Any  = model_class(_lowerCAmelCase		)
            model.to(_lowerCAmelCase		)
            model.eval()
            with torch.no_grad():
                 UpperCAmelCase_				:  List[Any]  = model(**self._prepare_for_class(_lowerCAmelCase ,						_lowerCAmelCase		)		)
            UpperCAmelCase_				:  Dict  = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
            self.assertEqual(len(_lowerCAmelCase		) ,						self.model_tester.num_attention_outputs		)

            # check that output_attentions also work using config
            del inputs_dict["output_attentions"]
            UpperCAmelCase_				:  Union[str, Any]  = True
            UpperCAmelCase_				:  str  = model_class(_lowerCAmelCase		)
            model.to(_lowerCAmelCase		)
            model.eval()
            with torch.no_grad():
                 UpperCAmelCase_				:  List[str]  = model(**self._prepare_for_class(_lowerCAmelCase ,						_lowerCAmelCase		)		)
            UpperCAmelCase_				:  List[Any]  = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
            self.assertEqual(len(_lowerCAmelCase		) ,						self.model_tester.num_attention_outputs		)

            if chunk_length is not None:
                 self.assertListEqual(
                     list(attentions[0].shape[-4:]		) ,						[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length] ,						)
            else:
                 self.assertListEqual(
                     list(attentions[0].shape[-3:]		) ,						[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length] ,						)







def    _lowerCamelCase       ( ):



     """simple docstring"""





     UpperCAmelCase_				:  Optional[Any]  = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
     return image






@require_torch
@require_vision
class 	__SCREAMING_SNAKE_CASE		(			unittest.TestCase			):







  '''simple docstring'''






  @cached_property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return (
           EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300'		)
           if is_vision_available()
           else None
       )


  @slow
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = EfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300'		).to(
           _lowerCAmelCase		)

       UpperCAmelCase_				:  Any  = self.default_image_processor
       UpperCAmelCase_				:  Optional[int]  = prepare_img()
       UpperCAmelCase_				:  Optional[Any]  = image_processor(images=_lowerCAmelCase ,						return_tensors='pt'		).to(_lowerCAmelCase		)

       # forward pass
       with torch.no_grad():
            UpperCAmelCase_				:  int  = model(**_lowerCAmelCase		)

       # verify the logits
       UpperCAmelCase_				:  Any  = (1, 1_0_0_0)
       self.assertEqual(outputs.logits.shape ,						_lowerCAmelCase		)

       UpperCAmelCase_				:  int  = torch.tensor([-0.05_55, 0.48_25, -0.08_52]		).to(_lowerCAmelCase		)
       self.assertTrue(torch.allclose(outputs.logits[0][:3] ,						_lowerCAmelCase ,						atol=1E-4		)		)






  @slow
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = EfficientFormerForImageClassificationWithTeacher.from_pretrained(
           'snap-research/efficientformer-l1-300'		).to(_lowerCAmelCase		)

       UpperCAmelCase_				:  int  = self.default_image_processor
       UpperCAmelCase_				:  str  = prepare_img()
       UpperCAmelCase_				:  Optional[int]  = image_processor(images=_lowerCAmelCase ,						return_tensors='pt'		).to(_lowerCAmelCase		)

       # forward pass
       with torch.no_grad():
            UpperCAmelCase_				:  Any  = model(**_lowerCAmelCase		)

       # verify the logits
       UpperCAmelCase_				:  int  = (1, 1_0_0_0)
       self.assertEqual(outputs.logits.shape ,						_lowerCAmelCase		)

       UpperCAmelCase_				:  Tuple  = torch.tensor([-0.13_12, 0.43_53, -1.04_99]		).to(_lowerCAmelCase		)
       self.assertTrue(torch.allclose(outputs.logits[0][:3] ,						_lowerCAmelCase ,						atol=1E-4		)		)











from typing import List, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_          =				logging.get_logger(__name__)

UpperCAmelCase_          =				{
    "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/config.json",
    # See all LED models at https://huggingface.co/models?filter=led
}





class 		UpperCAmelCase    (    snake_case_					):
   SCREAMING_SNAKE_CASE__					      =  '''led'''
   SCREAMING_SNAKE_CASE__					      =  {
       '''num_attention_heads''': '''encoder_attention_heads''',
       '''hidden_size''': '''d_model''',
       '''attention_probs_dropout_prob''': '''attention_dropout''',
       '''initializer_range''': '''init_std''',
   }





   def __init__(						self							,  _lowerCAmelCase=50_265							,  _lowerCAmelCase=16_384							,  _lowerCAmelCase=1_024							,  _lowerCAmelCase=12							,  _lowerCAmelCase=4_096							,  _lowerCAmelCase=16							,  _lowerCAmelCase=12							,  _lowerCAmelCase=4_096							,  _lowerCAmelCase=16							,  _lowerCAmelCase=0.0							,  _lowerCAmelCase=0.0							,  _lowerCAmelCase=True							,  _lowerCAmelCase=True							,  _lowerCAmelCase="gelu"							,  _lowerCAmelCase=1_024							,  _lowerCAmelCase=0.1							,  _lowerCAmelCase=0.0							,  _lowerCAmelCase=0.0							,  _lowerCAmelCase=0.02							,  _lowerCAmelCase=2							,  _lowerCAmelCase=0.0							,  _lowerCAmelCase=1							,  _lowerCAmelCase=0							,  _lowerCAmelCase=2							,  _lowerCAmelCase = 512							,  **_lowerCAmelCase							,  ):
         _lowerCAmelCase			      =	vocab_size
         _lowerCAmelCase			      =	max_encoder_position_embeddings
         _lowerCAmelCase			      =	max_decoder_position_embeddings
         _lowerCAmelCase			      =	d_model
         _lowerCAmelCase			      =	encoder_ffn_dim
         _lowerCAmelCase			      =	encoder_layers
         _lowerCAmelCase			      =	encoder_attention_heads
         _lowerCAmelCase			      =	decoder_ffn_dim
         _lowerCAmelCase			      =	decoder_layers
         _lowerCAmelCase			      =	decoder_attention_heads
         _lowerCAmelCase			      =	dropout
         _lowerCAmelCase			      =	attention_dropout
         _lowerCAmelCase			      =	activation_dropout
         _lowerCAmelCase			      =	activation_function
         _lowerCAmelCase			      =	init_std
         _lowerCAmelCase			      =	encoder_layerdrop
         _lowerCAmelCase			      =	decoder_layerdrop
         _lowerCAmelCase			      =	classifier_dropout
         _lowerCAmelCase			      =	use_cache
         _lowerCAmelCase			      =	encoder_layers
         _lowerCAmelCase			      =	attention_window

         super().__init__(
             pad_token_id=_lowerCAmelCase							,  bos_token_id=_lowerCAmelCase							,  eos_token_id=_lowerCAmelCase							,  is_encoder_decoder=_lowerCAmelCase							,  decoder_start_token_id=_lowerCAmelCase							,  **_lowerCAmelCase							,  )