
import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu





class 							UpperCamelCase__						(     unittest.TestCase     ):






       """simple docstring"""


       def   _UpperCamelCase     (				self     )							->							str:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()



       @property
       def   _UpperCamelCase     (				self     )							->							Optional[Any]:
          SCREAMING_SNAKE_CASE_     =	1
          SCREAMING_SNAKE_CASE_     =	3
          SCREAMING_SNAKE_CASE_     =	(32, 32)

          SCREAMING_SNAKE_CASE_     =	floats_tensor((batch_size, num_channels) + sizes      ,			rng=random.Random(0     )     ).to(_SCREAMING_SNAKE_CASE     )
          return image



       @property
       def   _UpperCamelCase     (				self     )							->							Optional[Any]:
          torch.manual_seed(0     )
          SCREAMING_SNAKE_CASE_     =	UNetaDConditionModel(
              block_out_channels=(32, 64)      ,			layers_per_block=2      ,			sample_size=32      ,			in_channels=4      ,			out_channels=4      ,			down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')      ,			up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')      ,			cross_attention_dim=32      ,			)
          return model



       @property
       def   _UpperCamelCase     (				self     )							->							Optional[int]:
          torch.manual_seed(0     )
          SCREAMING_SNAKE_CASE_     =	AutoencoderKL(
              block_out_channels=[32, 64]      ,			in_channels=3      ,			out_channels=3      ,			down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']      ,			up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']      ,			latent_channels=4      ,			)
          return model



       @property
       def   _UpperCamelCase     (				self     )							->							List[Any]:
          torch.manual_seed(0     )
          SCREAMING_SNAKE_CASE_     =	CLIPTextConfig(
              bos_token_id=0      ,			eos_token_id=2      ,			hidden_size=32      ,			intermediate_size=37      ,			layer_norm_eps=1E-05      ,			num_attention_heads=4      ,			num_hidden_layers=5      ,			pad_token_id=1      ,			vocab_size=1000      ,			)
          return CLIPTextModel(_SCREAMING_SNAKE_CASE     )



       @property
       def   _UpperCamelCase     (				self     )							->							Optional[int]:





          def extract(*_A      ,			**_A     ):





             class 							UpperCamelCase__						:






                    """simple docstring"""


                    def __init__(				self     )							->							Dict:
                       SCREAMING_SNAKE_CASE_     =	torch.ones([0]     )




                    def   _UpperCamelCase     (				self      ,			_A     )							->							Any:
                       self.pixel_values.to(_SCREAMING_SNAKE_CASE     )
                       return self







             return Out()

          return extract



       def   _UpperCamelCase     (				self     )							->							int:
          SCREAMING_SNAKE_CASE_     =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
          SCREAMING_SNAKE_CASE_     =	self.dummy_cond_unet
          SCREAMING_SNAKE_CASE_     =	DDIMScheduler(
              beta_start=0.0_0085      ,			beta_end=0.012      ,			beta_schedule='''scaled_linear'''      ,			clip_sample=_SCREAMING_SNAKE_CASE      ,			set_alpha_to_one=_SCREAMING_SNAKE_CASE      ,			)

          SCREAMING_SNAKE_CASE_     =	self.dummy_vae
          SCREAMING_SNAKE_CASE_     =	self.dummy_text_encoder
          SCREAMING_SNAKE_CASE_     =	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''     )

          # make sure here that pndm scheduler skips prk
          SCREAMING_SNAKE_CASE_     =	StableDiffusionPipeline(
              unet=_SCREAMING_SNAKE_CASE      ,			scheduler=_SCREAMING_SNAKE_CASE      ,			vae=_SCREAMING_SNAKE_CASE      ,			text_encoder=_SCREAMING_SNAKE_CASE      ,			tokenizer=_SCREAMING_SNAKE_CASE      ,			safety_checker=_SCREAMING_SNAKE_CASE      ,			feature_extractor=self.dummy_extractor      ,			)
          SCREAMING_SNAKE_CASE_     =	sd_pipe.to(_SCREAMING_SNAKE_CASE     )
          sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE     )

          SCREAMING_SNAKE_CASE_     =	'''A painting of a squirrel eating a burger'''

          SCREAMING_SNAKE_CASE_     =	torch.Generator(device=_SCREAMING_SNAKE_CASE     ).manual_seed(0     )
          SCREAMING_SNAKE_CASE_     =	sd_pipe([prompt]      ,			generator=_SCREAMING_SNAKE_CASE      ,			guidance_scale=6.0      ,			num_inference_steps=2      ,			output_type='''np'''     )
          SCREAMING_SNAKE_CASE_     =	output.images

          SCREAMING_SNAKE_CASE_     =	torch.Generator(device=_SCREAMING_SNAKE_CASE     ).manual_seed(0     )
          SCREAMING_SNAKE_CASE_     =	sd_pipe(
              [prompt]      ,			generator=_SCREAMING_SNAKE_CASE      ,			guidance_scale=6.0      ,			num_inference_steps=2      ,			output_type='''np'''      ,			return_dict=_SCREAMING_SNAKE_CASE      ,			)[0]

          SCREAMING_SNAKE_CASE_     =	image[0, -3:, -3:, -1]
          SCREAMING_SNAKE_CASE_     =	image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)
          SCREAMING_SNAKE_CASE_     =	np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864]     )

          assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2



       def   _UpperCamelCase     (				self     )							->							int:
          SCREAMING_SNAKE_CASE_     =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
          SCREAMING_SNAKE_CASE_     =	self.dummy_cond_unet
          SCREAMING_SNAKE_CASE_     =	PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE     )
          SCREAMING_SNAKE_CASE_     =	self.dummy_vae
          SCREAMING_SNAKE_CASE_     =	self.dummy_text_encoder
          SCREAMING_SNAKE_CASE_     =	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''     )

          # make sure here that pndm scheduler skips prk
          SCREAMING_SNAKE_CASE_     =	StableDiffusionPipeline(
              unet=_SCREAMING_SNAKE_CASE      ,			scheduler=_SCREAMING_SNAKE_CASE      ,			vae=_SCREAMING_SNAKE_CASE      ,			text_encoder=_SCREAMING_SNAKE_CASE      ,			tokenizer=_SCREAMING_SNAKE_CASE      ,			safety_checker=_SCREAMING_SNAKE_CASE      ,			feature_extractor=self.dummy_extractor      ,			)
          SCREAMING_SNAKE_CASE_     =	sd_pipe.to(_SCREAMING_SNAKE_CASE     )
          sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE     )

          SCREAMING_SNAKE_CASE_     =	'''A painting of a squirrel eating a burger'''
          SCREAMING_SNAKE_CASE_     =	torch.Generator(device=_SCREAMING_SNAKE_CASE     ).manual_seed(0     )
          SCREAMING_SNAKE_CASE_     =	sd_pipe([prompt]      ,			generator=_SCREAMING_SNAKE_CASE      ,			guidance_scale=6.0      ,			num_inference_steps=2      ,			output_type='''np'''     )

          SCREAMING_SNAKE_CASE_     =	output.images

          SCREAMING_SNAKE_CASE_     =	torch.Generator(device=_SCREAMING_SNAKE_CASE     ).manual_seed(0     )
          SCREAMING_SNAKE_CASE_     =	sd_pipe(
              [prompt]      ,			generator=_SCREAMING_SNAKE_CASE      ,			guidance_scale=6.0      ,			num_inference_steps=2      ,			output_type='''np'''      ,			return_dict=_SCREAMING_SNAKE_CASE      ,			)[0]

          SCREAMING_SNAKE_CASE_     =	image[0, -3:, -3:, -1]
          SCREAMING_SNAKE_CASE_     =	image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)
          SCREAMING_SNAKE_CASE_     =	np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993]     )

          assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2



       def   _UpperCamelCase     (				self     )							->							int:
          SCREAMING_SNAKE_CASE_     =	StableDiffusionPipeline.from_pretrained(
              '''hf-internal-testing/tiny-stable-diffusion-lms-pipe'''      ,			safety_checker=_SCREAMING_SNAKE_CASE     )
          assert isinstance(_SCREAMING_SNAKE_CASE      ,			_SCREAMING_SNAKE_CASE     )
          assert isinstance(pipe.scheduler      ,			_SCREAMING_SNAKE_CASE     )
          assert pipe.safety_checker is None

          SCREAMING_SNAKE_CASE_     =	pipe('''example prompt'''      ,			num_inference_steps=2     ).images[0]
          assert image is not None

          # check that there's no error when saving a pipeline with one of the models being None
          with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(_SCREAMING_SNAKE_CASE     )
             SCREAMING_SNAKE_CASE_     =	StableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE     )

          # sanity check that the pipeline still works
          assert pipe.safety_checker is None
          SCREAMING_SNAKE_CASE_     =	pipe('''example prompt'''      ,			num_inference_steps=2     ).images[0]
          assert image is not None




       @unittest.skipIf(torch_device != '''cuda'''      ,			'''This test requires a GPU'''     )
       def   _UpperCamelCase     (				self     )							->							Optional[Any]:
          SCREAMING_SNAKE_CASE_     =	self.dummy_cond_unet
          SCREAMING_SNAKE_CASE_     =	PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE     )
          SCREAMING_SNAKE_CASE_     =	self.dummy_vae
          SCREAMING_SNAKE_CASE_     =	self.dummy_text_encoder
          SCREAMING_SNAKE_CASE_     =	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''     )

          # put models in fp16
          SCREAMING_SNAKE_CASE_     =	unet.half()
          SCREAMING_SNAKE_CASE_     =	vae.half()
          SCREAMING_SNAKE_CASE_     =	bert.half()

          # make sure here that pndm scheduler skips prk
          SCREAMING_SNAKE_CASE_     =	StableDiffusionPipeline(
              unet=_SCREAMING_SNAKE_CASE      ,			scheduler=_SCREAMING_SNAKE_CASE      ,			vae=_SCREAMING_SNAKE_CASE      ,			text_encoder=_SCREAMING_SNAKE_CASE      ,			tokenizer=_SCREAMING_SNAKE_CASE      ,			safety_checker=_SCREAMING_SNAKE_CASE      ,			feature_extractor=self.dummy_extractor      ,			)
          SCREAMING_SNAKE_CASE_     =	sd_pipe.to(_SCREAMING_SNAKE_CASE     )
          sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE     )

          SCREAMING_SNAKE_CASE_     =	'''A painting of a squirrel eating a burger'''
          SCREAMING_SNAKE_CASE_     =	sd_pipe([prompt]      ,			num_inference_steps=2      ,			output_type='''np'''     ).images

          assert image.shape == (1, 64, 64, 3)







@nightly
@require_torch_gpu
class 							UpperCamelCase__						(     unittest.TestCase     ):






       """simple docstring"""


       def   _UpperCamelCase     (				self     )							->							Dict:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()



       def   _UpperCamelCase     (				self     )							->							int:
          SCREAMING_SNAKE_CASE_     =	StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''      ,			safety_checker=_SCREAMING_SNAKE_CASE     )
          SCREAMING_SNAKE_CASE_     =	LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config     )
          SCREAMING_SNAKE_CASE_     =	sd_pipe.to(_SCREAMING_SNAKE_CASE     )
          sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE     )

          SCREAMING_SNAKE_CASE_     =	(
              '''portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'''
              ''' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'''
              ''' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'''
              ''' children from bahnhof zoo, detailed '''
          )
          SCREAMING_SNAKE_CASE_     =	4003660346
          SCREAMING_SNAKE_CASE_     =	7

          # without safety guidance (sld_guidance_scale = 0)
          SCREAMING_SNAKE_CASE_     =	torch.manual_seed(_SCREAMING_SNAKE_CASE     )
          SCREAMING_SNAKE_CASE_     =	sd_pipe(
              [prompt]      ,			generator=_SCREAMING_SNAKE_CASE      ,			guidance_scale=_SCREAMING_SNAKE_CASE      ,			num_inference_steps=50      ,			output_type='''np'''      ,			width=512      ,			height=512      ,			sld_guidance_scale=0      ,			)

          SCREAMING_SNAKE_CASE_     =	output.images
          SCREAMING_SNAKE_CASE_     =	image[0, -3:, -3:, -1]
          SCREAMING_SNAKE_CASE_     =	[0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

          assert image.shape == (1, 512, 512, 3)

          assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2

          # without safety guidance (strong configuration)
          SCREAMING_SNAKE_CASE_     =	torch.manual_seed(_SCREAMING_SNAKE_CASE     )
          SCREAMING_SNAKE_CASE_     =	sd_pipe(
              [prompt]      ,			generator=_SCREAMING_SNAKE_CASE      ,			guidance_scale=_SCREAMING_SNAKE_CASE      ,			num_inference_steps=50      ,			output_type='''np'''      ,			width=512      ,			height=512      ,			sld_guidance_scale=2000      ,			sld_warmup_steps=7      ,			sld_threshold=0.025      ,			sld_momentum_scale=0.5      ,			sld_mom_beta=0.7      ,			)

          SCREAMING_SNAKE_CASE_     =	output.images
          SCREAMING_SNAKE_CASE_     =	image[0, -3:, -3:, -1]
          SCREAMING_SNAKE_CASE_     =	[0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

          assert image.shape == (1, 512, 512, 3)

          assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2



       def   _UpperCamelCase     (				self     )							->							Optional[int]:
          SCREAMING_SNAKE_CASE_     =	StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''      ,			safety_checker=_SCREAMING_SNAKE_CASE     )
          SCREAMING_SNAKE_CASE_     =	LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config     )
          SCREAMING_SNAKE_CASE_     =	sd_pipe.to(_SCREAMING_SNAKE_CASE     )
          sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE     )

          SCREAMING_SNAKE_CASE_     =	'''padme amidala taking a bath artwork, safe for work, no nudity'''
          SCREAMING_SNAKE_CASE_     =	2734971755
          SCREAMING_SNAKE_CASE_     =	7

          SCREAMING_SNAKE_CASE_     =	torch.manual_seed(_SCREAMING_SNAKE_CASE     )
          SCREAMING_SNAKE_CASE_     =	sd_pipe(
              [prompt]      ,			generator=_SCREAMING_SNAKE_CASE      ,			guidance_scale=_SCREAMING_SNAKE_CASE      ,			num_inference_steps=50      ,			output_type='''np'''      ,			width=512      ,			height=512      ,			sld_guidance_scale=0      ,			)

          SCREAMING_SNAKE_CASE_     =	output.images
          SCREAMING_SNAKE_CASE_     =	image[0, -3:, -3:, -1]
          SCREAMING_SNAKE_CASE_     =	[0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

          assert image.shape == (1, 512, 512, 3)

          assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2

          SCREAMING_SNAKE_CASE_     =	torch.manual_seed(_SCREAMING_SNAKE_CASE     )
          SCREAMING_SNAKE_CASE_     =	sd_pipe(
              [prompt]      ,			generator=_SCREAMING_SNAKE_CASE      ,			guidance_scale=_SCREAMING_SNAKE_CASE      ,			num_inference_steps=50      ,			output_type='''np'''      ,			width=512      ,			height=512      ,			sld_guidance_scale=2000      ,			sld_warmup_steps=7      ,			sld_threshold=0.025      ,			sld_momentum_scale=0.5      ,			sld_mom_beta=0.7      ,			)

          SCREAMING_SNAKE_CASE_     =	output.images
          SCREAMING_SNAKE_CASE_     =	image[0, -3:, -3:, -1]
          SCREAMING_SNAKE_CASE_     =	[0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

          assert image.shape == (1, 512, 512, 3)

          assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2




       def   _UpperCamelCase     (				self     )							->							Tuple:
          SCREAMING_SNAKE_CASE_     =	StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''     )
          SCREAMING_SNAKE_CASE_     =	sd_pipe.to(_SCREAMING_SNAKE_CASE     )
          sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE     )

          SCREAMING_SNAKE_CASE_     =	(
              '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
              ''' leyendecker'''
          )
          SCREAMING_SNAKE_CASE_     =	1044355234
          SCREAMING_SNAKE_CASE_     =	12

          SCREAMING_SNAKE_CASE_     =	torch.manual_seed(_SCREAMING_SNAKE_CASE     )
          SCREAMING_SNAKE_CASE_     =	sd_pipe(
              [prompt]      ,			generator=_SCREAMING_SNAKE_CASE      ,			guidance_scale=_SCREAMING_SNAKE_CASE      ,			num_inference_steps=50      ,			output_type='''np'''      ,			width=512      ,			height=512      ,			sld_guidance_scale=0      ,			)

          SCREAMING_SNAKE_CASE_     =	output.images
          SCREAMING_SNAKE_CASE_     =	image[0, -3:, -3:, -1]
          SCREAMING_SNAKE_CASE_     =	np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]     )

          assert image.shape == (1, 512, 512, 3)

          assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-7

          SCREAMING_SNAKE_CASE_     =	torch.manual_seed(_SCREAMING_SNAKE_CASE     )
          SCREAMING_SNAKE_CASE_     =	sd_pipe(
              [prompt]      ,			generator=_SCREAMING_SNAKE_CASE      ,			guidance_scale=_SCREAMING_SNAKE_CASE      ,			num_inference_steps=50      ,			output_type='''np'''      ,			width=512      ,			height=512      ,			sld_guidance_scale=2000      ,			sld_warmup_steps=7      ,			sld_threshold=0.025      ,			sld_momentum_scale=0.5      ,			sld_mom_beta=0.7      ,			)

          SCREAMING_SNAKE_CASE_     =	output.images
          SCREAMING_SNAKE_CASE_     =	image[0, -3:, -3:, -1]
          SCREAMING_SNAKE_CASE_     =	np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561]     )
          assert image.shape == (1, 512, 512, 3)

          assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2






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


UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	{
    """vinvino02/glpn-kitti""": """https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json""",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    "glpn"



       def __init__(      self				,       _SCREAMING_SNAKE_CASE=3				,       _SCREAMING_SNAKE_CASE=4				,       _SCREAMING_SNAKE_CASE=[2, 2, 2, 2]				,       _SCREAMING_SNAKE_CASE=[8, 4, 2, 1]				,       _SCREAMING_SNAKE_CASE=[32, 64, 160, 256]				,       _SCREAMING_SNAKE_CASE=[7, 3, 3, 3]				,       _SCREAMING_SNAKE_CASE=[4, 2, 2, 2]				,       _SCREAMING_SNAKE_CASE=[1, 2, 5, 8]				,       _SCREAMING_SNAKE_CASE=[4, 4, 4, 4]				,       _SCREAMING_SNAKE_CASE="gelu"				,       _SCREAMING_SNAKE_CASE=0.0				,       _SCREAMING_SNAKE_CASE=0.0				,       _SCREAMING_SNAKE_CASE=0.0_2				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=1e-6				,       _SCREAMING_SNAKE_CASE=64				,       _SCREAMING_SNAKE_CASE=10				,       _SCREAMING_SNAKE_CASE=-1				,       **_SCREAMING_SNAKE_CASE				,       )->Any:




         '''simple docstring'''





         super().__init__(**_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[int]		     =					num_channels
         A_      :				Union[str, Any]		     =					num_encoder_blocks
         A_      :				int		     =					depths
         A_      :				Dict		     =					sr_ratios
         A_      :				Any		     =					hidden_sizes
         A_      :				int		     =					patch_sizes
         A_      :				Optional[int]		     =					strides
         A_      :				str		     =					mlp_ratios
         A_      :				List[str]		     =					num_attention_heads
         A_      :				str		     =					hidden_act
         A_      :				int		     =					hidden_dropout_prob
         A_      :				List[Any]		     =					attention_probs_dropout_prob
         A_      :				Optional[Any]		     =					initializer_range
         A_      :				Tuple		     =					drop_path_rate
         A_      :				Optional[int]		     =					layer_norm_eps
         A_      :				List[str]		     =					decoder_hidden_size
         A_      :				List[Any]		     =					max_depth
         A_      :				List[Any]		     =					head_in_index




