import gc
import os
import tempfile
import unittest

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
from diffusers.models.attention_processor import (
    Attention,
    AttnProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    XFormersAttnProcessor,
)
from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device


def 							lowerCAmelCase__    (       a__      )	->Optional[Any]:




  '''simple docstring'''

  _UpperCamelCase				=  {}
  for name in unet.attn_processors.keys():
    _UpperCamelCase				=  None if name.endswith("attn1.processor"      ) else unet.config.cross_attention_dim
    if name.startswith("mid_block"      ):
      _UpperCamelCase				=  unet.config.block_out_channels[-1]
    elif name.startswith("up_blocks"      ):
      _UpperCamelCase				=  int(name[len("up_blocks."      )]      )
      _UpperCamelCase				=  list(reversed(unet.config.block_out_channels      )      )[block_id]
    elif name.startswith("down_blocks"      ):
      _UpperCamelCase				=  int(name[len("down_blocks."      )]      )
      _UpperCamelCase				=  unet.config.block_out_channels[block_id]
    _UpperCamelCase				=  (
        LoRAAttnProcessor2_0 if hasattr(a__  ,			"scaled_dot_product_attention"      ) else LoRAAttnProcessor
    )
    _UpperCamelCase				=  lora_attn_processor_class(
        hidden_size=a__  ,			cross_attention_dim=a__      )
  _UpperCamelCase				=  AttnProcsLayers(a__      )
  return lora_attn_procs, unet_lora_layers


def 							lowerCAmelCase__    (       a__      )	->str:




  '''simple docstring'''

  _UpperCamelCase				=  {}
  _UpperCamelCase				=  (
      LoRAAttnProcessor2_0 if hasattr(a__  ,			"scaled_dot_product_attention"      ) else LoRAAttnProcessor
  )
  for name, module in text_encoder.named_modules():
    if name.endswith(a__      ):
      _UpperCamelCase				=  lora_attn_processor_class(
          hidden_size=module.out_proj.out_features  ,			cross_attention_dim=a__      )
  return text_lora_attn_procs


def 							lowerCAmelCase__    (       a__      )	->Optional[Any]:




  '''simple docstring'''

  _UpperCamelCase				=  create_text_encoder_lora_attn_procs(a__      )
  _UpperCamelCase				=  AttnProcsLayers(a__      )
  return text_encoder_lora_layers


def 							lowerCAmelCase__    (       a__  ,			a__=False      )	->Optional[int]:




  '''simple docstring'''

  for _, attn_proc in text_lora_attn_procs.items():
    # set up.weights
    for layer_name, layer_module in attn_proc.named_modules():
      if layer_name.endswith("_lora"      ):
        _UpperCamelCase				=  (
            torch.randn_like(layer_module.up.weight      )
            if randn_weight
            else torch.zeros_like(layer_module.up.weight      )
        )
        _UpperCamelCase				=  torch.nn.Parameter(a__      )







class  _UpperCAmelCase       (			unittest.TestCase   ):



 '''simple docstring'''




 def 						__UpperCAmelCase							(						self							:					Tuple)		->     int:


   """simple docstring"""







   torch.manual_seed(0)
   _UpperCamelCase				=  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				,      )
   _UpperCamelCase				=  DDIMScheduler(
       beta_start=0.0_00_85				,      beta_end=0.0_12				,      beta_schedule="scaled_linear"				,      clip_sample=lowercase_				,      set_alpha_to_one=lowercase_				,      steps_offset=1				,      )
   torch.manual_seed(0)
   _UpperCamelCase				=  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				,      )
   _UpperCamelCase				=  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				,      )
   _UpperCamelCase				=  CLIPTextModel(lowercase_)
   _UpperCamelCase				=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

   _UpperCamelCase	,		_UpperCamelCase				=  create_unet_lora_layers(lowercase_)
   _UpperCamelCase				=  create_text_encoder_lora_layers(lowercase_)

   _UpperCamelCase				=  {
       "unet": unet,
       "scheduler": scheduler,
       "vae": vae,
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
       "safety_checker": None,
       "feature_extractor": None,
   }
   _UpperCamelCase				=  {
       "unet_lora_layers": unet_lora_layers,
       "text_encoder_lora_layers": text_encoder_lora_layers,
       "unet_lora_attn_procs": unet_lora_attn_procs,
   }
   return pipeline_components, lora_components







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


   """simple docstring"""







   _UpperCamelCase				=  1
   _UpperCamelCase				=  10
   _UpperCamelCase				=  4
   _UpperCamelCase				=  (32, 32)

   _UpperCamelCase				=  torch.manual_seed(0)
   _UpperCamelCase				=  floats_tensor((batch_size, num_channels) + sizes)
   _UpperCamelCase				=  torch.randint(1				,      lowercase_				,      size=(batch_size, sequence_length)				,      generator=lowercase_)

   _UpperCamelCase				=  {
       "prompt": "A painting of a squirrel eating a burger",
       "generator": generator,
       "num_inference_steps": 2,
       "guidance_scale": 6.0,
       "output_type": "numpy",
   }

   return noise, input_ids, pipeline_inputs

   # copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb







 def 						__UpperCAmelCase							(						self							:					Tuple)		->     Dict:


   """simple docstring"""







   _UpperCamelCase				=  77

   _UpperCamelCase				=  torch.randint(2				,      56				,      size=(1, max_seq_length)				,      generator=torch.manual_seed(0))

   _UpperCamelCase				=  {}
   _UpperCamelCase				=  inputs
   return prepared_inputs







 def 						__UpperCAmelCase							(						self							:					Optional[Any]				,      lowercase_							:					Tuple)		->     Tuple:


   """simple docstring"""







   _UpperCamelCase	,		_UpperCamelCase				=  self.get_dummy_components()
   LoraLoaderMixin.save_lora_weights(
       save_directory=lowercase_				,      unet_lora_layers=lora_components["unet_lora_layers"]				,      text_encoder_lora_layers=lora_components["text_encoder_lora_layers"]				,      )
   self.assertTrue(os.path.isfile(os.path.join(lowercase_				,      "pytorch_lora_weights.bin")))







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


   """simple docstring"""







   _UpperCamelCase	,		_UpperCamelCase				=  self.get_dummy_components()
   _UpperCamelCase				=  StableDiffusionPipeline(**lowercase_)
   _UpperCamelCase				=  sd_pipe.to(lowercase_)
   sd_pipe.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase				=  self.get_dummy_inputs()

   _UpperCamelCase				=  sd_pipe(**lowercase_).images
   _UpperCamelCase				=  original_images[0, -3:, -3:, -1]

   with tempfile.TemporaryDirectory() as tmpdirname:
     LoraLoaderMixin.save_lora_weights(
         save_directory=lowercase_				,      unet_lora_layers=lora_components["unet_lora_layers"]				,      text_encoder_lora_layers=lora_components["text_encoder_lora_layers"]				,      )
     self.assertTrue(os.path.isfile(os.path.join(lowercase_				,      "pytorch_lora_weights.bin")))
     sd_pipe.load_lora_weights(lowercase_)

   _UpperCamelCase				=  sd_pipe(**lowercase_).images
   _UpperCamelCase				=  lora_images[0, -3:, -3:, -1]

   # Outputs shouldn't match.
   self.assertFalse(torch.allclose(torch.from_numpy(lowercase_)				,      torch.from_numpy(lowercase_)))







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


   """simple docstring"""







   _UpperCamelCase	,		_UpperCamelCase				=  self.get_dummy_components()
   _UpperCamelCase				=  StableDiffusionPipeline(**lowercase_)
   _UpperCamelCase				=  sd_pipe.to(lowercase_)
   sd_pipe.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase				=  self.get_dummy_inputs()

   _UpperCamelCase				=  sd_pipe(**lowercase_).images
   _UpperCamelCase				=  original_images[0, -3:, -3:, -1]

   with tempfile.TemporaryDirectory() as tmpdirname:
     LoraLoaderMixin.save_lora_weights(
         save_directory=lowercase_				,      unet_lora_layers=lora_components["unet_lora_layers"]				,      text_encoder_lora_layers=lora_components["text_encoder_lora_layers"]				,      safe_serialization=lowercase_				,      )
     self.assertTrue(os.path.isfile(os.path.join(lowercase_				,      "pytorch_lora_weights.safetensors")))
     sd_pipe.load_lora_weights(lowercase_)

   _UpperCamelCase				=  sd_pipe(**lowercase_).images
   _UpperCamelCase				=  lora_images[0, -3:, -3:, -1]

   # Outputs shouldn't match.
   self.assertFalse(torch.allclose(torch.from_numpy(lowercase_)				,      torch.from_numpy(lowercase_)))







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


   """simple docstring"""







   _UpperCamelCase	,		_UpperCamelCase				=  self.get_dummy_components()
   _UpperCamelCase				=  lora_components["unet_lora_attn_procs"]
   _UpperCamelCase				=  StableDiffusionPipeline(**lowercase_)
   _UpperCamelCase				=  sd_pipe.to(lowercase_)
   sd_pipe.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase				=  self.get_dummy_inputs()

   _UpperCamelCase				=  sd_pipe(**lowercase_).images
   _UpperCamelCase				=  original_images[0, -3:, -3:, -1]

   with tempfile.TemporaryDirectory() as tmpdirname:
     _UpperCamelCase				=  sd_pipe.unet
     unet.set_attn_processor(lowercase_)
     unet.save_attn_procs(lowercase_)
     self.assertTrue(os.path.isfile(os.path.join(lowercase_				,      "pytorch_lora_weights.bin")))
     sd_pipe.load_lora_weights(lowercase_)

   _UpperCamelCase				=  sd_pipe(**lowercase_).images
   _UpperCamelCase				=  lora_images[0, -3:, -3:, -1]

   # Outputs shouldn't match.
   self.assertFalse(torch.allclose(torch.from_numpy(lowercase_)				,      torch.from_numpy(lowercase_)))







 def 						__UpperCAmelCase							(						self							:					int)		->     Dict:


   """simple docstring"""







   _UpperCamelCase	,		_UpperCamelCase				=  self.get_dummy_components()
   _UpperCamelCase				=  StableDiffusionPipeline(**lowercase_)

   _UpperCamelCase				=  self.get_dummy_tokens()

   # inference without lora
   _UpperCamelCase				=  pipe.text_encoder(**lowercase_)[0]
   assert outputs_without_lora.shape == (1, 77, 32)

   # create lora_attn_procs with zeroed out up.weights
   _UpperCamelCase				=  create_text_encoder_lora_attn_procs(pipe.text_encoder)
   set_lora_up_weights(lowercase_				,      randn_weight=lowercase_)

   # monkey patch
   pipe._modify_text_encoder(lowercase_)

   # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
   del text_attn_procs
   gc.collect()

   # inference with lora
   _UpperCamelCase				=  pipe.text_encoder(**lowercase_)[0]
   assert outputs_with_lora.shape == (1, 77, 32)

   assert torch.allclose(
       lowercase_				,      lowercase_), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"

   # create lora_attn_procs with randn up.weights
   _UpperCamelCase				=  create_text_encoder_lora_attn_procs(pipe.text_encoder)
   set_lora_up_weights(lowercase_				,      randn_weight=lowercase_)

   # monkey patch
   pipe._modify_text_encoder(lowercase_)

   # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
   del text_attn_procs
   gc.collect()

   # inference with lora
   _UpperCamelCase				=  pipe.text_encoder(**lowercase_)[0]
   assert outputs_with_lora.shape == (1, 77, 32)

   assert not torch.allclose(
       lowercase_				,      lowercase_), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"







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


   """simple docstring"""







   _UpperCamelCase	,		_UpperCamelCase				=  self.get_dummy_components()
   _UpperCamelCase				=  StableDiffusionPipeline(**lowercase_)

   _UpperCamelCase				=  self.get_dummy_tokens()

   # inference without lora
   _UpperCamelCase				=  pipe.text_encoder(**lowercase_)[0]
   assert outputs_without_lora.shape == (1, 77, 32)

   # create lora_attn_procs with randn up.weights
   _UpperCamelCase				=  create_text_encoder_lora_attn_procs(pipe.text_encoder)
   set_lora_up_weights(lowercase_				,      randn_weight=lowercase_)

   # monkey patch
   pipe._modify_text_encoder(lowercase_)

   # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
   del text_attn_procs
   gc.collect()

   # inference with lora
   _UpperCamelCase				=  pipe.text_encoder(**lowercase_)[0]
   assert outputs_with_lora.shape == (1, 77, 32)

   assert not torch.allclose(
       lowercase_				,      lowercase_), "lora outputs should be different to without lora outputs"

   # remove monkey patch
   pipe._remove_text_encoder_monkey_patch()

   # inference with removed lora
   _UpperCamelCase				=  pipe.text_encoder(**lowercase_)[0]
   assert outputs_without_lora_removed.shape == (1, 77, 32)

   assert torch.allclose(
       lowercase_				,      lowercase_), "remove lora monkey patch should restore the original outputs"







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


   """simple docstring"""







   _UpperCamelCase	,		_UpperCamelCase				=  self.get_dummy_components()
   _UpperCamelCase				=  StableDiffusionPipeline(**lowercase_)
   _UpperCamelCase				=  sd_pipe.to(lowercase_)
   sd_pipe.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase				=  self.get_dummy_inputs()

   with tempfile.TemporaryDirectory() as tmpdirname:
     LoraLoaderMixin.save_lora_weights(
         save_directory=lowercase_				,      unet_lora_layers=lora_components["unet_lora_layers"]				,      text_encoder_lora_layers=lora_components["text_encoder_lora_layers"]				,      )
     self.assertTrue(os.path.isfile(os.path.join(lowercase_				,      "pytorch_lora_weights.bin")))
     sd_pipe.load_lora_weights(lowercase_)

   _UpperCamelCase				=  sd_pipe(**lowercase_).images
   _UpperCamelCase				=  lora_images[0, -3:, -3:, -1]

   _UpperCamelCase				=  sd_pipe(**lowercase_				,      cross_attention_kwargs={"scale": 0.5}).images
   _UpperCamelCase				=  lora_images_with_scale[0, -3:, -3:, -1]

   # Outputs shouldn't match.
   self.assertFalse(
       torch.allclose(torch.from_numpy(lowercase_)				,      torch.from_numpy(lowercase_)))







 def 						__UpperCAmelCase							(						self							:					Tuple)		->     str:


   """simple docstring"""







   with tempfile.TemporaryDirectory() as tmpdirname:
     self.create_lora_weight_file(lowercase_)

     _UpperCamelCase	,		_UpperCamelCase				=  self.get_dummy_components()
     _UpperCamelCase				=  StableDiffusionPipeline(**lowercase_)
     _UpperCamelCase				=  sd_pipe.to(lowercase_)
     sd_pipe.set_progress_bar_config(disable=lowercase_)

     # check if vanilla attention processors are used
     for _, module in sd_pipe.unet.named_modules():
       if isinstance(lowercase_				,      lowercase_):
         self.assertIsInstance(module.processor				,      (AttnProcessor, AttnProcessor2_0))

            # load LoRA weight file
     sd_pipe.load_lora_weights(lowercase_)

     # check if lora attention processors are used
     for _, module in sd_pipe.unet.named_modules():
       if isinstance(lowercase_				,      lowercase_):
         _UpperCamelCase				=  (
             LoRAAttnProcessor2_0 if hasattr(lowercase_				,      "scaled_dot_product_attention") else LoRAAttnProcessor
         )
         self.assertIsInstance(module.processor				,      lowercase_)







 @unittest.skipIf(torch_device != "cuda"				,      "This test is supposed to run on GPU")
 def 						__UpperCAmelCase							(						self							:					Optional[Any])		->     Any:


   """simple docstring"""







   with tempfile.TemporaryDirectory() as tmpdirname:
     self.create_lora_weight_file(lowercase_)

     _UpperCamelCase	,		_UpperCamelCase				=  self.get_dummy_components()
     _UpperCamelCase				=  StableDiffusionPipeline(**lowercase_)
     _UpperCamelCase				=  sd_pipe.to(lowercase_)
     sd_pipe.set_progress_bar_config(disable=lowercase_)

     # enable XFormers
     sd_pipe.enable_xformers_memory_efficient_attention()

     # check if xFormers attention processors are used
     for _, module in sd_pipe.unet.named_modules():
       if isinstance(lowercase_				,      lowercase_):
         self.assertIsInstance(module.processor				,      lowercase_)

            # load LoRA weight file
     sd_pipe.load_lora_weights(lowercase_)

     # check if lora attention processors are used
     for _, module in sd_pipe.unet.named_modules():
       if isinstance(lowercase_				,      lowercase_):
         self.assertIsInstance(module.processor				,      lowercase_)







 @unittest.skipIf(torch_device != "cuda"				,      "This test is supposed to run on GPU")
 def 						__UpperCAmelCase							(						self							:					List[Any])		->     int:


   """simple docstring"""







   _UpperCamelCase	,		_UpperCamelCase				=  self.get_dummy_components()
   _UpperCamelCase				=  StableDiffusionPipeline(**lowercase_)
   _UpperCamelCase				=  sd_pipe.to(lowercase_)
   sd_pipe.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase				=  self.get_dummy_inputs()

   # enable XFormers
   sd_pipe.enable_xformers_memory_efficient_attention()

   _UpperCamelCase				=  sd_pipe(**lowercase_).images
   _UpperCamelCase				=  original_images[0, -3:, -3:, -1]

   with tempfile.TemporaryDirectory() as tmpdirname:
     LoraLoaderMixin.save_lora_weights(
         save_directory=lowercase_				,      unet_lora_layers=lora_components["unet_lora_layers"]				,      text_encoder_lora_layers=lora_components["text_encoder_lora_layers"]				,      )
     self.assertTrue(os.path.isfile(os.path.join(lowercase_				,      "pytorch_lora_weights.bin")))
     sd_pipe.load_lora_weights(lowercase_)

   _UpperCamelCase				=  sd_pipe(**lowercase_).images
   _UpperCamelCase				=  lora_images[0, -3:, -3:, -1]

   # Outputs shouldn't match.
   self.assertFalse(torch.allclose(torch.from_numpy(lowercase_)				,      torch.from_numpy(lowercase_)))





from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase__   = logging.get_logger(__name__)

lowerCamelCase__   = {
    '''nghuyong/ernie-1.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-base-en''': '''https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-large-en''': '''https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-medium-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-mini-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-micro-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-nano-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json''',
    '''nghuyong/ernie-gram-zh''': '''https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json''',
    '''nghuyong/ernie-health-zh''': '''https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json''',
}




class  _UpperCAmelCase       (			lowerCAmelCase   ):



 '''simple docstring'''




 __A           = '''ernie'''







 def __init__(						self							:					Dict				,      lowercase_							:					List[Any]=30522				,      lowercase_							:					Optional[int]=768				,      lowercase_							:					Tuple=12				,      lowercase_							:					Optional[int]=12				,      lowercase_							:					Any=3072				,      lowercase_							:					int="gelu"				,      lowercase_							:					List[Any]=0.1				,      lowercase_							:					Any=0.1				,      lowercase_							:					str=512				,      lowercase_							:					Any=2				,      lowercase_							:					Any=3				,      lowercase_							:					List[Any]=False				,      lowercase_							:					List[Any]=0.02				,      lowercase_							:					str=1e-1_2				,      lowercase_							:					int=0				,      lowercase_							:					str="absolute"				,      lowercase_							:					Dict=True				,      lowercase_							:					List[str]=None				,      **lowercase_							:					Dict				,      )		->     Union[str, Any]:


   """simple docstring"""







   super().__init__(pad_token_id=lowercase_				,      **lowercase_)

   _UpperCamelCase				=  vocab_size
   _UpperCamelCase				=  hidden_size
   _UpperCamelCase				=  num_hidden_layers
   _UpperCamelCase				=  num_attention_heads
   _UpperCamelCase				=  hidden_act
   _UpperCamelCase				=  intermediate_size
   _UpperCamelCase				=  hidden_dropout_prob
   _UpperCamelCase				=  attention_probs_dropout_prob
   _UpperCamelCase				=  max_position_embeddings
   _UpperCamelCase				=  type_vocab_size
   _UpperCamelCase				=  task_type_vocab_size
   _UpperCamelCase				=  use_task_id
   _UpperCamelCase				=  initializer_range
   _UpperCamelCase				=  layer_norm_eps
   _UpperCamelCase				=  position_embedding_type
   _UpperCamelCase				=  use_cache
   _UpperCamelCase				=  classifier_dropout




class  _UpperCAmelCase       (			lowerCAmelCase   ):



 '''simple docstring'''




 @property
 def 						__UpperCAmelCase							(						self							:					Optional[int])		->     Mapping[str, Mapping[int, str]]:


   """simple docstring"""







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




