import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class    snake_case       (   UpperCamelCase_ ,    UpperCamelCase_ ,    UpperCamelCase_ ,    unittest.TestCase    ):
  lowercase_								=  StableDiffusionInstructPixaPixPipeline
  lowercase_								=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
  lowercase_								=  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  lowercase_								=  IMAGE_TO_IMAGE_IMAGE_PARAMS
  lowercase_								=  IMAGE_TO_IMAGE_IMAGE_PARAMS




  def 						__lowercase(   self  :						str					)->							int:




       """simple docstring"""
       torch.manual_seed(0					)
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		UNetaDConditionModel(
           block_out_channels=(32, 64)  ,			layers_per_block=2  ,			sample_size=32  ,			in_channels=8  ,			out_channels=4  ,			down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')  ,			up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')  ,			cross_attention_dim=32  ,			)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		PNDMScheduler(skip_prk_steps=a_					)
       torch.manual_seed(0					)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		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  ,			)
       torch.manual_seed(0					)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		CLIPTextConfig(
           bos_token_id=0  ,			eos_token_id=2  ,			hidden_size=32  ,			intermediate_size=37  ,			layer_norm_eps=1e-0_5  ,			num_attention_heads=4  ,			num_hidden_layers=5  ,			pad_token_id=1  ,			vocab_size=1000  ,			)
       SCREAMING_SNAKE_CASE__					:					int      =		CLIPTextModel(a_					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

       SCREAMING_SNAKE_CASE__					:					List[str]      =		{
           'unet': unet,
           'scheduler': scheduler,
           'vae': vae,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'safety_checker': None,
           'feature_extractor': None,
       }
       return components




  def 						__lowercase(   self  :						List[Any]  ,			a_  :						Tuple  ,			a_  :						Optional[Any]=0					)->							int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		floats_tensor((1, 3, 32, 32)  ,			rng=random.Random(a_					)					).to(a_					)
       SCREAMING_SNAKE_CASE__					:					str      =		image.cpu().permute(0  ,			2  ,			3  ,			1					)[0]
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		Image.fromarray(np.uinta(a_					)					).convert('RGB'					)
       if str(a_					).startswith('mps'					):
            SCREAMING_SNAKE_CASE__					:					str      =		torch.manual_seed(a_					)
       else:
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		torch.Generator(device=a_					).manual_seed(a_					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		{
           'prompt': 'A painting of a squirrel eating a burger',
           'image': image,
           'generator': generator,
           'num_inference_steps': 2,
           'guidance_scale': 6.0,
           'image_guidance_scale': 1,
           'output_type': 'numpy',
       }
       return inputs




  def 						__lowercase(   self  :						str					)->							Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		'cpu'  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					:					List[str]      =		StableDiffusionInstructPixaPixPipeline(**a_					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		sd_pipe.to(a_					)
       sd_pipe.set_progress_bar_config(disable=a_					)

       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.get_dummy_inputs(a_					)
       SCREAMING_SNAKE_CASE__					:					int      =		sd_pipe(**a_					).images
       SCREAMING_SNAKE_CASE__					:					Dict      =		image[0, -3:, -3:, -1]
       assert image.shape == (1, 32, 32, 3)
       SCREAMING_SNAKE_CASE__					:					Dict      =		np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3




  def 						__lowercase(   self  :						Optional[Any]					)->							int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		'cpu'  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					:					Dict      =		self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		StableDiffusionInstructPixaPixPipeline(**a_					)
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		sd_pipe.to(a_					)
       sd_pipe.set_progress_bar_config(disable=a_					)

       SCREAMING_SNAKE_CASE__					:					List[str]      =		self.get_dummy_inputs(a_					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		'french fries'
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		sd_pipe(**a_  ,			negative_prompt=a_					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		output.images
       SCREAMING_SNAKE_CASE__					:					Any      =		image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3




  def 						__lowercase(   self  :						List[Any]					)->							List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		'cpu'  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		StableDiffusionInstructPixaPixPipeline(**a_					)
       SCREAMING_SNAKE_CASE__					:					int      =		sd_pipe.to(a_					)
       sd_pipe.set_progress_bar_config(disable=a_					)

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.get_dummy_inputs(a_					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		[inputs['prompt']] * 2

       SCREAMING_SNAKE_CASE__					:					List[str]      =		np.array(inputs['image']					).astype(np.floataa					) / 255.0
       SCREAMING_SNAKE_CASE__					:					Tuple      =		torch.from_numpy(a_					).unsqueeze(0					).to(a_					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		image / 2 + 0.5
       SCREAMING_SNAKE_CASE__					:					Tuple      =		image.permute(0  ,			3  ,			1  ,			2					)
       SCREAMING_SNAKE_CASE__					:					int      =		image.repeat(2  ,			1  ,			1  ,			1					)

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		sd_pipe(**a_					).images
       SCREAMING_SNAKE_CASE__					:					Any      =		image[-1, -3:, -3:, -1]

       assert image.shape == (2, 32, 32, 3)
       SCREAMING_SNAKE_CASE__					:					int      =		np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3




  def 						__lowercase(   self  :						List[Any]					)->							List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		'cpu'  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					:					str      =		self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		EulerAncestralDiscreteScheduler(
           beta_start=0.0_0085  ,			beta_end=0.012  ,			beta_schedule='scaled_linear'					)
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		StableDiffusionInstructPixaPixPipeline(**a_					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		sd_pipe.to(a_					)
       sd_pipe.set_progress_bar_config(disable=a_					)

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		self.get_dummy_inputs(a_					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		sd_pipe(**a_					).images
       SCREAMING_SNAKE_CASE__					:					Any      =		image[0, -3:, -3:, -1]

       SCREAMING_SNAKE_CASE__					:					Any      =		[round(a_  ,			4					) for x in image_slice.flatten().tolist()]
       print(','.join([str(a_					) for x in slice]					)					)

       assert image.shape == (1, 32, 32, 3)
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3




  def 						__lowercase(   self  :						Union[str, Any]					)->							Any:




       """simple docstring"""
       super().test_inference_batch_single_identical(expected_max_diff=3e-3					)




  def 						__lowercase(   self  :						List[Any]					)->							Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[str]      =		self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					:					List[str]      =		StableDiffusionInstructPixaPixPipeline(**a_					)
       SCREAMING_SNAKE_CASE__					:					int      =		VaeImageProcessor(do_resize=a_  ,			do_normalize=a_					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		pipe.to(a_					)
       pipe.set_progress_bar_config(disable=a_					)

       SCREAMING_SNAKE_CASE__					:					Any      =		pipe(**self.get_dummy_inputs_by_type(a_  ,			input_image_type='pt'					)					)[0]

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		components['vae']
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.get_dummy_inputs_by_type(a_  ,			input_image_type='pt'					)

       for image_param in self.image_latents_params:
            if image_param in inputs.keys():
                 SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		vae.encode(inputs[image_param]					).latent_dist.mode()

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		pipe(**a_					)[0]

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		np.abs(out - out_latents_inputs					).max()
       self.assertLess(a_  ,			1e-4  ,			'passing latents as image input generate different result from passing image'					)




@slow
@require_torch_gpu
class    snake_case       (   unittest.TestCase    ):




  def 						__lowercase(   self  :						Tuple					)->							Dict:




       """simple docstring"""
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




  def 						__lowercase(   self  :						List[Any]  ,			a_  :						Dict=0					)->							Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[str]      =		torch.manual_seed(a_					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		load_image(
           'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		{
           'prompt': 'turn him into a cyborg',
           'image': image,
           'generator': generator,
           'num_inference_steps': 3,
           'guidance_scale': 7.5,
           'image_guidance_scale': 1.0,
           'output_type': 'numpy',
       }
       return inputs




  def 						__lowercase(   self  :						int					)->							Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		StableDiffusionInstructPixaPixPipeline.from_pretrained(
           'timbrooks/instruct-pix2pix'  ,			safety_checker=a_					)
       pipe.to(a_					)
       pipe.set_progress_bar_config(disable=a_					)
       pipe.enable_attention_slicing()

       SCREAMING_SNAKE_CASE__					:					str      =		self.get_inputs()
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		pipe(**a_					).images
       SCREAMING_SNAKE_CASE__					:					List[str]      =		image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]					)

       assert np.abs(expected_slice - image_slice					).max() < 1e-3




  def 						__lowercase(   self  :						Dict					)->							str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					int      =		StableDiffusionInstructPixaPixPipeline.from_pretrained(
           'timbrooks/instruct-pix2pix'  ,			safety_checker=a_					)
       SCREAMING_SNAKE_CASE__					:					str      =		LMSDiscreteScheduler.from_config(pipe.scheduler.config					)
       pipe.to(a_					)
       pipe.set_progress_bar_config(disable=a_					)
       pipe.enable_attention_slicing()

       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.get_inputs()
       SCREAMING_SNAKE_CASE__					:					Dict      =		pipe(**a_					).images
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]					)

       assert np.abs(expected_slice - image_slice					).max() < 1e-3




  def 						__lowercase(   self  :						Optional[int]					)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[str]      =		StableDiffusionInstructPixaPixPipeline.from_pretrained(
           'timbrooks/instruct-pix2pix'  ,			safety_checker=a_					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		DDIMScheduler.from_config(pipe.scheduler.config					)
       pipe.to(a_					)
       pipe.set_progress_bar_config(disable=a_					)
       pipe.enable_attention_slicing()

       SCREAMING_SNAKE_CASE__					:					str      =		self.get_inputs()
       SCREAMING_SNAKE_CASE__					:					Tuple      =		pipe(**a_					).images
       SCREAMING_SNAKE_CASE__					:					List[str]      =		image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]					)

       assert np.abs(expected_slice - image_slice					).max() < 1e-3




  def 						__lowercase(   self  :						int					)->							List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					str      =		0

       def callback_fn(a_  :						int  ,			a_  :						int  ,			a_  :						torch.FloatTensor					) -> None:
            SCREAMING_SNAKE_CASE__					:					Tuple      =		True
            nonlocal number_of_steps
            number_of_steps += 1
            if step == 1:
                 SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		latents.detach().cpu().numpy()
                 assert latents.shape == (1, 4, 64, 64)
                 SCREAMING_SNAKE_CASE__					:					List[Any]      =		latents[0, -3:, -3:, -1]
                 SCREAMING_SNAKE_CASE__					:					Optional[int]      =		np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]					)

                 assert np.abs(latents_slice.flatten() - expected_slice					).max() < 5e-2
            elif step == 2:
                 SCREAMING_SNAKE_CASE__					:					Optional[int]      =		latents.detach().cpu().numpy()
                 assert latents.shape == (1, 4, 64, 64)
                 SCREAMING_SNAKE_CASE__					:					Tuple      =		latents[0, -3:, -3:, -1]
                 SCREAMING_SNAKE_CASE__					:					Dict      =		np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]					)

                 assert np.abs(latents_slice.flatten() - expected_slice					).max() < 5e-2

       SCREAMING_SNAKE_CASE__					:					List[str]      =		False

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		StableDiffusionInstructPixaPixPipeline.from_pretrained(
           'timbrooks/instruct-pix2pix'  ,			safety_checker=a_  ,			torch_dtype=torch.floataa					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		pipe.to(a_					)
       pipe.set_progress_bar_config(disable=a_					)
       pipe.enable_attention_slicing()

       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.get_inputs()
       pipe(**a_  ,			callback=a_  ,			callback_steps=1					)
       assert callback_fn.has_been_called
       assert number_of_steps == 3




  def 						__lowercase(   self  :						int					)->							Any:




       """simple docstring"""
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		StableDiffusionInstructPixaPixPipeline.from_pretrained(
           'timbrooks/instruct-pix2pix'  ,			safety_checker=a_  ,			torch_dtype=torch.floataa					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		pipe.to(a_					)
       pipe.set_progress_bar_config(disable=a_					)
       pipe.enable_attention_slicing(1					)
       pipe.enable_sequential_cpu_offload()

       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.get_inputs()
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		pipe(**a_					)

       SCREAMING_SNAKE_CASE__					:					Any      =		torch.cuda.max_memory_allocated()
       # make sure that less than 2.2 GB is allocated
       assert mem_bytes < 2.2 * 10**9




  def 						__lowercase(   self  :						Tuple					)->							List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					str      =		self.get_inputs()
       # resize to resolution that is divisible by 8 but not 16 or 32
       SCREAMING_SNAKE_CASE__					:					Dict      =		inputs['image'].resize((504, 504)					)

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		'timbrooks/instruct-pix2pix'
       SCREAMING_SNAKE_CASE__					:					str      =		StableDiffusionInstructPixaPixPipeline.from_pretrained(
           a_  ,			safety_checker=a_  ,			)
       pipe.to(a_					)
       pipe.set_progress_bar_config(disable=a_					)
       pipe.enable_attention_slicing()

       SCREAMING_SNAKE_CASE__					:					Any      =		pipe(**a_					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		output.images[0]

       SCREAMING_SNAKE_CASE__					:					Any      =		image[255:258, 383:386, -1]

       assert image.shape == (504, 504, 3)
       SCREAMING_SNAKE_CASE__					:					str      =		np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 5e-3









from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_		:					Tuple          =  logging.get_logger(__name__)

UpperCAmelCase_		:					List[str]          =  {
    "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
    "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """xlm-roberta-xl"""

						def __init__(		self :Dict     ,			__snake_case :Optional[Any]=25_08_80     ,			__snake_case :List[Any]=25_60     ,			__snake_case :Optional[Any]=36     ,			__snake_case :Any=32     ,			__snake_case :int=1_02_40     ,			__snake_case :List[Any]="gelu"     ,			__snake_case :Union[str, Any]=0.1     ,			__snake_case :Optional[Any]=0.1     ,			__snake_case :str=5_14     ,			__snake_case :Union[str, Any]=1     ,			__snake_case :Optional[int]=0.02     ,			__snake_case :str=1E-05     ,			__snake_case :str=1     ,			__snake_case :int=0     ,			__snake_case :Tuple=2     ,			__snake_case :Optional[int]="absolute"     ,			__snake_case :str=True     ,			__snake_case :Any=None     ,			**__snake_case :Dict     ,			):
										'''simple docstring'''

										super().__init__(pad_token_id=__snake_case     ,			bos_token_id=__snake_case     ,			eos_token_id=__snake_case     ,			**__snake_case			)
										__magic_name__						:						List[str]										=vocab_size
										__magic_name__						:						List[str]										=hidden_size
										__magic_name__						:						Union[str, Any]										=num_hidden_layers
										__magic_name__						:						Any										=num_attention_heads
										__magic_name__						:						Any										=hidden_act
										__magic_name__						:						List[str]										=intermediate_size
										__magic_name__						:						Any										=hidden_dropout_prob
										__magic_name__						:						Union[str, Any]										=attention_probs_dropout_prob
										__magic_name__						:						Any										=max_position_embeddings
										__magic_name__						:						Any										=type_vocab_size
										__magic_name__						:						List[str]										=initializer_range
										__magic_name__						:						Optional[int]										=layer_norm_eps
										__magic_name__						:						Dict										=position_embedding_type
										__magic_name__						:						Any										=use_cache
										__magic_name__						:						Dict										=classifier_dropout




class 		__A   (      UpperCamelCase__      ):

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										if self.task == "multiple-choice":
														__magic_name__						:						str										={0: """batch""", 1: """choice""", 2: """sequence"""}
										else:
														__magic_name__						:						Optional[Any]										={0: """batch""", 1: """sequence"""}
										return OrderedDict(
										    [
										        ("""input_ids""", dynamic_axis),
										        ("""attention_mask""", dynamic_axis),
										    ]			)




