



import collections
import inspect
import unittest

from transformers import SwinvaConfig
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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
 from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor

class     _A       :



       def __init__(		self							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=13     ,		__SCREAMING_SNAKE_CASE							:     str=32     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=2     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=3     ,		__SCREAMING_SNAKE_CASE							:     str=16     ,		__SCREAMING_SNAKE_CASE							:     Tuple=[1, 2, 1]     ,		__SCREAMING_SNAKE_CASE							:     Tuple=[2, 2, 4]     ,		__SCREAMING_SNAKE_CASE							:     str=2     ,		__SCREAMING_SNAKE_CASE							:     int=2.0     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=True     ,		__SCREAMING_SNAKE_CASE							:     Tuple=0.0     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=0.0     ,		__SCREAMING_SNAKE_CASE							:     int=0.1     ,		__SCREAMING_SNAKE_CASE							:     str="gelu"     ,		__SCREAMING_SNAKE_CASE							:     int=False     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=True     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=0.02     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=1E-5     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=True     ,		__SCREAMING_SNAKE_CASE							:     Any=None     ,		__SCREAMING_SNAKE_CASE							:     List[str]=True     ,		__SCREAMING_SNAKE_CASE							:     List[str]=10     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=8     ,		):


           '''simple docstring'''

           __a						       =				parent
           __a						       =				batch_size
           __a						       =				image_size
           __a						       =				patch_size
           __a						       =				num_channels
           __a						       =				embed_dim
           __a						       =				depths
           __a						       =				num_heads
           __a						       =				window_size
           __a						       =				mlp_ratio
           __a						       =				qkv_bias
           __a						       =				hidden_dropout_prob
           __a						       =				attention_probs_dropout_prob
           __a						       =				drop_path_rate
           __a						       =				hidden_act
           __a						       =				use_absolute_embeddings
           __a						       =				patch_norm
           __a						       =				layer_norm_eps
           __a						       =				initializer_range
           __a						       =				is_training
           __a						       =				scope
           __a						       =				use_labels
           __a						       =				type_sequence_label_size
           __a						       =				encoder_stride



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

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

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

           __a						       =				self.get_config()

           return config, pixel_values, labels



       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           return SwinvaConfig(
               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 		_lowerCamelCase			(		self							:     str     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Tuple):


           '''simple docstring'''

           __a						       =				SwinvaModel(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a						       =				model(__SCREAMING_SNAKE_CASE)

           __a						       =				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
           __a						       =				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 		_lowerCamelCase			(		self							:     str     ,		__SCREAMING_SNAKE_CASE							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     Any):


           '''simple docstring'''

           __a						       =				SwinvaForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a						       =				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
           __a						       =				1
           __a						       =				SwinvaForMaskedImageModeling(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()

           __a						       =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
           __a						       =				model(__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, 1, self.image_size, self.image_size))



       def 		_lowerCamelCase			(		self							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     Any):


           '''simple docstring'''

           __a						       =				self.type_sequence_label_size
           __a						       =				SwinvaForImageClassification(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a						       =				model(__SCREAMING_SNAKE_CASE     ,		labels=__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.type_sequence_label_size))





       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

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


@require_torch
class     _A       (	__UpperCAmelCase      ,__UpperCAmelCase      ,unittest.TestCase    ):
       UpperCamelCase__  :    Tuple											=						(
           (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
       )
       UpperCamelCase__  :    List[Any]											=						(
           {'''feature-extraction''': SwinvaModel, '''image-classification''': SwinvaForImageClassification}
           if is_torch_available()
           else {}
       )

       UpperCamelCase__  :    List[Any]											=						False
       UpperCamelCase__  :    List[str]											=						False
       UpperCamelCase__  :    List[Any]											=						False
       UpperCamelCase__  :    Optional[Any]											=						False



       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           __a						       =				SwinvaModelTester(self)
           __a						       =				ConfigTester(self     ,		config_class=__SCREAMING_SNAKE_CASE     ,		embed_dim=37)



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           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 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __a						       =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE)



       @unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.''')
       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           pass



       @unittest.skip(reason='''Swinv2 does not use inputs_embeds''')
       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

           pass



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a       ,   __a						       =				self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               __a						       =				model_class(__SCREAMING_SNAKE_CASE)
               self.assertIsInstance(model.get_input_embeddings()     ,		(nn.Module))
               __a						       =				model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE     ,		nn.Linear))



       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           __a       ,   __a						       =				self.model_tester.prepare_config_and_inputs_for_common()

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

               __a						       =				['''pixel_values''']
               self.assertListEqual(arg_names[:1]     ,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __a       ,   __a						       =				self.model_tester.prepare_config_and_inputs_for_common()
           __a						       =				True

           for model_class in self.all_model_classes:
               __a						       =				True
               __a						       =				False
               __a						       =				True
               __a						       =				model_class(__SCREAMING_SNAKE_CASE)
               model.to(__SCREAMING_SNAKE_CASE)
               model.eval()
               with torch.no_grad():
                   __a						       =				model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE))
               __a						       =				outputs.attentions
               __a						       =				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"]
               __a						       =				True
               __a						       =				config.window_size**2
               __a						       =				model_class(__SCREAMING_SNAKE_CASE)
               model.to(__SCREAMING_SNAKE_CASE)
               model.eval()
               with torch.no_grad():
                   __a						       =				model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE))
               __a						       =				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]     ,		)
               __a						       =				len(__SCREAMING_SNAKE_CASE)

               # Check attention is always last and order is fine
               __a						       =				True
               __a						       =				True
               __a						       =				model_class(__SCREAMING_SNAKE_CASE)
               model.to(__SCREAMING_SNAKE_CASE)
               model.eval()
               with torch.no_grad():
                   __a						       =				model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE))

               if hasattr(self.model_tester     ,		'''num_hidden_states_types'''):
                   __a						       =				self.model_tester.num_hidden_states_types
               else:
                   # also another +1 for reshaped_hidden_states
                   __a						       =				2
               self.assertEqual(out_len + added_hidden_states     ,		len(__SCREAMING_SNAKE_CASE))

               __a						       =				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 		_lowerCamelCase			(		self							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Any     ,		__SCREAMING_SNAKE_CASE							:     Tuple):


           '''simple docstring'''

           __a						       =				model_class(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()

           with torch.no_grad():
               __a						       =				model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE))

           __a						       =				outputs.hidden_states

           __a						       =				getattr(
               self.model_tester     ,		'''expected_num_hidden_layers'''     ,		len(self.model_tester.depths) + 1)
           self.assertEqual(len(__SCREAMING_SNAKE_CASE)     ,		__SCREAMING_SNAKE_CASE)

           # Swinv2 has a different seq_length
           __a						       =				(
               config.patch_size
               if isinstance(config.patch_size     ,		collections.abc.Iterable)
               else (config.patch_size, config.patch_size)
           )

           __a						       =				(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]     ,		)

           __a						       =				outputs.reshaped_hidden_states
           self.assertEqual(len(__SCREAMING_SNAKE_CASE)     ,		__SCREAMING_SNAKE_CASE)

           __a       ,   __a       ,   __a       ,   __a						       =				reshaped_hidden_states[0].shape
           __a						       =				(
               reshaped_hidden_states[0].view(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		height * width).permute(0     ,		2     ,		1)
           )
           self.assertListEqual(
               list(reshaped_hidden_states.shape[-2:])     ,		[num_patches, self.model_tester.embed_dim]     ,		)



       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           __a       ,   __a						       =				self.model_tester.prepare_config_and_inputs_for_common()

           __a						       =				(
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size     ,		collections.abc.Iterable)
               else (self.model_tester.image_size, self.model_tester.image_size)
           )

           for model_class in self.all_model_classes:
               __a						       =				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"]
               __a						       =				True

               self.check_hidden_states_output(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           __a       ,   __a						       =				self.model_tester.prepare_config_and_inputs_for_common()
           __a						       =				3

           __a						       =				(
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size     ,		collections.abc.Iterable)
               else (self.model_tester.image_size, self.model_tester.image_size)
           )
           __a						       =				(
               config.patch_size
               if isinstance(config.patch_size     ,		collections.abc.Iterable)
               else (config.patch_size, config.patch_size)
           )

           __a						       =				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
           __a						       =				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

           for model_class in self.all_model_classes:
               __a						       =				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"]
               __a						       =				True
               self.check_hidden_states_output(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		(padded_height, padded_width))



       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           __a						       =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           __a						       =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE)



       @slow
       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a						       =				SwinvaModel.from_pretrained(__SCREAMING_SNAKE_CASE)
               self.assertIsNotNone(__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           __a       ,   __a						       =				self.model_tester.prepare_config_and_inputs_for_common()

           __a						       =				_config_zero_init(__SCREAMING_SNAKE_CASE)
           for model_class in self.all_model_classes:
               __a						       =				model_class(config=__SCREAMING_SNAKE_CASE)
               for name, param in model.named_parameters():
                   if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
                       self.assertIn(
                           ((param.data.mean() * 1E9).round() / 1E9).item()     ,		[0.0, 1.0]     ,		msg=F'Parameter {name} of model {model_class} seems not properly initialized'     ,		)


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



       @cached_property
       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           return (
               AutoImageProcessor.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256''')
               if is_vision_available()
               else None
           )





       @slow
       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           __a						       =				SwinvaForImageClassification.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256''').to(
               __SCREAMING_SNAKE_CASE)
           __a						       =				self.default_image_processor

           __a						       =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
           __a						       =				image_processor(images=__SCREAMING_SNAKE_CASE     ,		return_tensors='''pt''').to(__SCREAMING_SNAKE_CASE)

           # forward pass
           with torch.no_grad():
               __a						       =				model(**__SCREAMING_SNAKE_CASE)

           # verify the logits
           __a						       =				torch.Size((1, 1_000))
           self.assertEqual(outputs.logits.shape     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				torch.tensor([-0.39_47, -0.43_06, 0.00_26]).to(__SCREAMING_SNAKE_CASE)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,		__SCREAMING_SNAKE_CASE     ,		atol=1E-4))










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


__snake_case						:Union[str, Any]      		=     logging.get_logger(__name__)

__snake_case						:Any      		=     {
    '''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Optional[int]											=						'''switch_transformers'''
       UpperCamelCase__  :    Optional[Any]											=						['''past_key_values''']
       UpperCamelCase__  :    Optional[Any]											=						{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}



       def __init__(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     str=32_128     ,		__SCREAMING_SNAKE_CASE							:     int=768     ,		__SCREAMING_SNAKE_CASE							:     Any=64     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=2_048     ,		__SCREAMING_SNAKE_CASE							:     List[str]=64     ,		__SCREAMING_SNAKE_CASE							:     int=12     ,		__SCREAMING_SNAKE_CASE							:     Any=3     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=12     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=3     ,		__SCREAMING_SNAKE_CASE							:     Any=12     ,		__SCREAMING_SNAKE_CASE							:     Tuple=8     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=False     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=0.01     ,		__SCREAMING_SNAKE_CASE							:     Dict="float32"     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=False     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=32     ,		__SCREAMING_SNAKE_CASE							:     int=128     ,		__SCREAMING_SNAKE_CASE							:     Any=0.1     ,		__SCREAMING_SNAKE_CASE							:     int=1E-6     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.0_01     ,		__SCREAMING_SNAKE_CASE							:     List[str]=0.0_01     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=1.0     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]="relu"     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=True     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=False     ,		__SCREAMING_SNAKE_CASE							:     str=True     ,		__SCREAMING_SNAKE_CASE							:     int=0     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=1     ,		**__SCREAMING_SNAKE_CASE							:     Dict     ,		):


           '''simple docstring'''

           __a						       =				vocab_size
           __a						       =				d_model
           __a						       =				d_kv
           __a						       =				d_ff

           __a						       =				num_sparse_encoder_layers

           __a						       =				num_layers
           __a						       =				(
               num_decoder_layers if num_decoder_layers is not None else self.num_layers
           )  # default = symmetry
           __a						       =				num_sparse_decoder_layers

           # This tells us, each how many encoder layer we'll have to set a sparse layer.
           if self.num_sparse_encoder_layers > 0:
               __a						       =				self.num_layers // self.num_sparse_encoder_layers
           else:
               __a						       =				self.num_layers  # HACK: this will create 0 sparse layers

           # This tells us, each how many encoder layer we'll have to set a sparse layer.
           if self.num_sparse_decoder_layers > 0:
               __a						       =				self.num_decoder_layers // self.num_sparse_decoder_layers
           else:
               __a						       =				self.num_decoder_layers  # HACK: this will create 0 sparse layers

           __a						       =				num_heads
           __a						       =				num_experts
           __a						       =				expert_capacity
           __a						       =				router_bias
           __a						       =				router_jitter_noise
           if router_dtype not in ["float32", "float16", "bfloat16"]:
               raise ValueError(F'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}')
           __a						       =				router_dtype

           __a						       =				router_ignore_padding_tokens
           __a						       =				relative_attention_num_buckets
           __a						       =				relative_attention_max_distance

           __a						       =				dropout_rate
           __a						       =				layer_norm_epsilon
           __a						       =				initializer_factor
           __a						       =				feed_forward_proj
           __a						       =				use_cache
           __a						       =				add_router_probs

           __a						       =				router_z_loss_coef
           __a						       =				router_aux_loss_coef

           __a						       =				self.feed_forward_proj.split('''-''')
           __a						       =				act_info[-1]
           __a						       =				act_info[0] == '''gated'''

           if len(__SCREAMING_SNAKE_CASE) > 1 and act_info[0] != "gated" or len(__SCREAMING_SNAKE_CASE) > 2:
               raise ValueError(
                   F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
                   '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
                   '''\'gated-gelu\' or \'relu\'''')

           # for backwards compatibility
           if feed_forward_proj == "gated-gelu":
               __a						       =				'''gelu_new'''

           super().__init__(
               pad_token_id=__SCREAMING_SNAKE_CASE     ,		eos_token_id=__SCREAMING_SNAKE_CASE     ,		is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE     ,		)





