




'''simple docstring'''



import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 		lowercase_   (			__lowerCAmelCase					,  __lowerCAmelCase					,  unittest.TestCase       ):


   """simple docstring"""

   lowerCamelCase_      =						StableDiffusionDiffEditPipeline
   lowerCamelCase_      =						TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
   lowerCamelCase_      =						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
   lowerCamelCase_      =						frozenset(
       []       )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   lowerCamelCase_      =						frozenset([]       )



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

      """simple docstring"""
      torch.manual_seed(0   )
      _SCREAMING_SNAKE_CASE	=				UNetaDConditionModel(
          block_out_channels=(3_2, 6_4)  ,				layers_per_block=2  ,				sample_size=3_2  ,				in_channels=4  ,				out_channels=4  ,				down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")  ,				up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")  ,				cross_attention_dim=3_2  ,				attention_head_dim=(2, 4)  ,				use_linear_projection=lowerCAmelCase_  ,				)
      _SCREAMING_SNAKE_CASE	=				DDIMScheduler(
          beta_start=0.0_0_0_8_5  ,				beta_end=0.0_1_2  ,				beta_schedule="scaled_linear"  ,				clip_sample=lowerCAmelCase_  ,				set_alpha_to_one=lowerCAmelCase_  ,				)
      _SCREAMING_SNAKE_CASE	=				DDIMInverseScheduler(
          beta_start=0.0_0_0_8_5  ,				beta_end=0.0_1_2  ,				beta_schedule="scaled_linear"  ,				clip_sample=lowerCAmelCase_  ,				set_alpha_to_zero=lowerCAmelCase_  ,				)
      torch.manual_seed(0   )
      _SCREAMING_SNAKE_CASE	=				AutoencoderKL(
          block_out_channels=[3_2, 6_4]  ,				in_channels=3  ,				out_channels=3  ,				down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]  ,				up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]  ,				latent_channels=4  ,				sample_size=1_2_8  ,				)
      torch.manual_seed(0   )
      _SCREAMING_SNAKE_CASE	=				CLIPTextConfig(
          bos_token_id=0  ,				eos_token_id=2  ,				hidden_size=3_2  ,				intermediate_size=3_7  ,				layer_norm_eps=1e-05  ,				num_attention_heads=4  ,				num_hidden_layers=5  ,				pad_token_id=1  ,				vocab_size=1_0_0_0  ,				hidden_act="gelu"  ,				projection_dim=5_1_2  ,				)
      _SCREAMING_SNAKE_CASE	=				CLIPTextModel(lowerCAmelCase_   )
      _SCREAMING_SNAKE_CASE	=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )

      _SCREAMING_SNAKE_CASE	=				{
          "unet": unet,
          "scheduler": scheduler,
          "inverse_scheduler": inverse_scheduler,
          "vae": vae,
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "safety_checker": None,
          "feature_extractor": None,
      }

      return components



   def 							lowerCAmelCase_      (   self :     Optional[Any]  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     Any=0   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				floats_tensor((1, 1_6, 1_6)  ,				rng=random.Random(lowerCAmelCase_   )   ).to(lowerCAmelCase_   )
      _SCREAMING_SNAKE_CASE	=				floats_tensor((1, 2, 4, 1_6, 1_6)  ,				rng=random.Random(lowerCAmelCase_   )   ).to(lowerCAmelCase_   )
      if str(lowerCAmelCase_   ).startswith("mps"   ):
         _SCREAMING_SNAKE_CASE	=				torch.manual_seed(lowerCAmelCase_   )
      else:
         _SCREAMING_SNAKE_CASE	=				torch.Generator(device=lowerCAmelCase_   ).manual_seed(lowerCAmelCase_   )
      _SCREAMING_SNAKE_CASE	=				{
          "prompt": "a dog and a newt",
          "mask_image": mask,
          "image_latents": latents,
          "generator": generator,
          "num_inference_steps": 2,
          "inpaint_strength": 1.0,
          "guidance_scale": 6.0,
          "output_type": "numpy",
      }

      return inputs



   def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Tuple=0   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				floats_tensor((1, 3, 3_2, 3_2)  ,				rng=random.Random(lowerCAmelCase_   )   ).to(lowerCAmelCase_   )
      _SCREAMING_SNAKE_CASE	=				image.cpu().permute(0  ,				2  ,				3  ,				1   )[0]
      _SCREAMING_SNAKE_CASE	=				Image.fromarray(np.uinta(lowerCAmelCase_   )   ).convert("RGB"   )
      if str(lowerCAmelCase_   ).startswith("mps"   ):
         _SCREAMING_SNAKE_CASE	=				torch.manual_seed(lowerCAmelCase_   )
      else:
         _SCREAMING_SNAKE_CASE	=				torch.Generator(device=lowerCAmelCase_   ).manual_seed(lowerCAmelCase_   )
      _SCREAMING_SNAKE_CASE	=				{
          "image": image,
          "source_prompt": "a cat and a frog",
          "target_prompt": "a dog and a newt",
          "generator": generator,
          "num_inference_steps": 2,
          "num_maps_per_mask": 2,
          "mask_encode_strength": 1.0,
          "guidance_scale": 6.0,
          "output_type": "numpy",
      }

      return inputs



   def 							lowerCAmelCase_      (   self :     Dict  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     str=0   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				floats_tensor((1, 3, 3_2, 3_2)  ,				rng=random.Random(lowerCAmelCase_   )   ).to(lowerCAmelCase_   )
      _SCREAMING_SNAKE_CASE	=				image.cpu().permute(0  ,				2  ,				3  ,				1   )[0]
      _SCREAMING_SNAKE_CASE	=				Image.fromarray(np.uinta(lowerCAmelCase_   )   ).convert("RGB"   )
      if str(lowerCAmelCase_   ).startswith("mps"   ):
         _SCREAMING_SNAKE_CASE	=				torch.manual_seed(lowerCAmelCase_   )
      else:
         _SCREAMING_SNAKE_CASE	=				torch.Generator(device=lowerCAmelCase_   ).manual_seed(lowerCAmelCase_   )
      _SCREAMING_SNAKE_CASE	=				{
          "image": image,
          "prompt": "a cat and a frog",
          "generator": generator,
          "num_inference_steps": 2,
          "inpaint_strength": 1.0,
          "guidance_scale": 6.0,
          "decode_latents": True,
          "output_type": "numpy",
      }
      return inputs



   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      if not hasattr(self.pipeline_class  ,				"_optional_components"   ):
         return

      _SCREAMING_SNAKE_CASE	=				self.get_dummy_components()
      _SCREAMING_SNAKE_CASE	=				self.pipeline_class(**lowerCAmelCase_   )
      pipe.to(lowerCAmelCase_   )
      pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

      # set all optional components to None and update pipeline config accordingly
      for optional_component in pipe._optional_components:
         setattr(lowerCAmelCase_  ,				lowerCAmelCase_  ,				lowerCAmelCase_   )
      pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}   )

      _SCREAMING_SNAKE_CASE	=				self.get_dummy_inputs(lowerCAmelCase_   )
      _SCREAMING_SNAKE_CASE	=				pipe(**lowerCAmelCase_   )[0]

      with tempfile.TemporaryDirectory() as tmpdir:
         pipe.save_pretrained(lowerCAmelCase_   )
         _SCREAMING_SNAKE_CASE	=				self.pipeline_class.from_pretrained(lowerCAmelCase_   )
         pipe_loaded.to(lowerCAmelCase_   )
         pipe_loaded.set_progress_bar_config(disable=lowerCAmelCase_   )

      for optional_component in pipe._optional_components:
         self.assertTrue(
             getattr(lowerCAmelCase_  ,				lowerCAmelCase_   ) is None  ,				F"""`{optional_component}` did not stay set to None after loading."""  ,				)

      _SCREAMING_SNAKE_CASE	=				self.get_dummy_inputs(lowerCAmelCase_   )
      _SCREAMING_SNAKE_CASE	=				pipe_loaded(**lowerCAmelCase_   )[0]

      _SCREAMING_SNAKE_CASE	=				np.abs(output - output_loaded   ).max()
      self.assertLess(lowerCAmelCase_  ,				1e-4   )



   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				"cpu"

      _SCREAMING_SNAKE_CASE	=				self.get_dummy_components()
      _SCREAMING_SNAKE_CASE	=				self.pipeline_class(**lowerCAmelCase_   )
      pipe.to(lowerCAmelCase_   )
      pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

      _SCREAMING_SNAKE_CASE	=				self.get_dummy_mask_inputs(lowerCAmelCase_   )
      _SCREAMING_SNAKE_CASE	=				pipe.generate_mask(**lowerCAmelCase_   )
      _SCREAMING_SNAKE_CASE	=				mask[0, -3:, -3:]

      self.assertEqual(mask.shape  ,				(1, 1_6, 1_6)   )
      _SCREAMING_SNAKE_CASE	=				np.array([0] * 9   )
      _SCREAMING_SNAKE_CASE	=				np.abs(mask_slice.flatten() - expected_slice   ).max()
      self.assertLessEqual(lowerCAmelCase_  ,				1e-3   )
      self.assertEqual(mask[0, -3, -4]  ,				0   )



   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				"cpu"

      _SCREAMING_SNAKE_CASE	=				self.get_dummy_components()
      _SCREAMING_SNAKE_CASE	=				self.pipeline_class(**lowerCAmelCase_   )
      pipe.to(lowerCAmelCase_   )
      pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

      _SCREAMING_SNAKE_CASE	=				self.get_dummy_inversion_inputs(lowerCAmelCase_   )
      _SCREAMING_SNAKE_CASE	=				pipe.invert(**lowerCAmelCase_   ).images
      _SCREAMING_SNAKE_CASE	=				image[0, -1, -3:, -3:]

      self.assertEqual(image.shape  ,				(2, 3_2, 3_2, 3)   )
      _SCREAMING_SNAKE_CASE	=				np.array(
          [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]  ,				)
      _SCREAMING_SNAKE_CASE	=				np.abs(image_slice.flatten() - expected_slice   ).max()
      self.assertLessEqual(lowerCAmelCase_  ,				1e-3   )



   def 							lowerCAmelCase_      (   self :     str   ):

      """simple docstring"""
      super().test_inference_batch_single_identical(expected_max_diff=5e-3   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				"cpu"

      _SCREAMING_SNAKE_CASE	=				self.get_dummy_components()

      _SCREAMING_SNAKE_CASE	=				{"beta_start": 0.0_0_0_8_5, "beta_end": 0.0_1_2, "beta_schedule": "scaled_linear"}
      _SCREAMING_SNAKE_CASE	=				DPMSolverMultistepScheduler(**lowerCAmelCase_   )
      _SCREAMING_SNAKE_CASE	=				DPMSolverMultistepInverseScheduler(**lowerCAmelCase_   )

      _SCREAMING_SNAKE_CASE	=				self.pipeline_class(**lowerCAmelCase_   )
      pipe.to(lowerCAmelCase_   )
      pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

      _SCREAMING_SNAKE_CASE	=				self.get_dummy_inversion_inputs(lowerCAmelCase_   )
      _SCREAMING_SNAKE_CASE	=				pipe.invert(**lowerCAmelCase_   ).images
      _SCREAMING_SNAKE_CASE	=				image[0, -1, -3:, -3:]

      self.assertEqual(image.shape  ,				(2, 3_2, 3_2, 3)   )
      _SCREAMING_SNAKE_CASE	=				np.array(
          [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]  ,				)
      _SCREAMING_SNAKE_CASE	=				np.abs(image_slice.flatten() - expected_slice   ).max()
      self.assertLessEqual(lowerCAmelCase_  ,				1e-3   )







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


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     List[str]   ):

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



   @classmethod
   def 							lowerCAmelCase_      (   cls :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"   )

      _SCREAMING_SNAKE_CASE	=				raw_image.convert("RGB"   ).resize((7_6_8, 7_6_8)   )

      _SCREAMING_SNAKE_CASE	=				raw_image



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				torch.manual_seed(0   )

      _SCREAMING_SNAKE_CASE	=				StableDiffusionDiffEditPipeline.from_pretrained(
          "stabilityai/stable-diffusion-2-1"  ,				safety_checker=lowerCAmelCase_  ,				torch_dtype=torch.floataa   )
      _SCREAMING_SNAKE_CASE	=				DDIMScheduler.from_config(pipe.scheduler.config   )
      _SCREAMING_SNAKE_CASE	=				DDIMInverseScheduler.from_config(pipe.scheduler.config   )
      pipe.enable_model_cpu_offload()
      pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

      _SCREAMING_SNAKE_CASE	=				"a bowl of fruit"
      _SCREAMING_SNAKE_CASE	=				"a bowl of pears"

      _SCREAMING_SNAKE_CASE	=				pipe.generate_mask(
          image=self.raw_image  ,				source_prompt=lowerCAmelCase_  ,				target_prompt=lowerCAmelCase_  ,				generator=lowerCAmelCase_  ,				)

      _SCREAMING_SNAKE_CASE	=				pipe.invert(
          prompt=lowerCAmelCase_  ,				image=self.raw_image  ,				inpaint_strength=0.7  ,				generator=lowerCAmelCase_   ).latents

      _SCREAMING_SNAKE_CASE	=				pipe(
          prompt=lowerCAmelCase_  ,				mask_image=lowerCAmelCase_  ,				image_latents=lowerCAmelCase_  ,				generator=lowerCAmelCase_  ,				negative_prompt=lowerCAmelCase_  ,				inpaint_strength=0.7  ,				output_type="numpy"  ,				).images[0]

      _SCREAMING_SNAKE_CASE	=				(
          np.array(
              load_image(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                  "/diffedit/pears.png"   ).resize((7_6_8, 7_6_8)   )   )
          / 2_5_5
      )
      assert np.abs((expected_image - image).max()   ) < 5e-1



   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				torch.manual_seed(0   )

      _SCREAMING_SNAKE_CASE	=				StableDiffusionDiffEditPipeline.from_pretrained(
          "stabilityai/stable-diffusion-2-1"  ,				safety_checker=lowerCAmelCase_  ,				torch_dtype=torch.floataa   )
      _SCREAMING_SNAKE_CASE	=				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
      _SCREAMING_SNAKE_CASE	=				DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config   )
      pipe.enable_model_cpu_offload()
      pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

      _SCREAMING_SNAKE_CASE	=				"a bowl of fruit"
      _SCREAMING_SNAKE_CASE	=				"a bowl of pears"

      _SCREAMING_SNAKE_CASE	=				pipe.generate_mask(
          image=self.raw_image  ,				source_prompt=lowerCAmelCase_  ,				target_prompt=lowerCAmelCase_  ,				generator=lowerCAmelCase_  ,				)

      _SCREAMING_SNAKE_CASE	=				pipe.invert(
          prompt=lowerCAmelCase_  ,				image=self.raw_image  ,				inpaint_strength=0.7  ,				generator=lowerCAmelCase_  ,				num_inference_steps=2_5  ,				).latents

      _SCREAMING_SNAKE_CASE	=				pipe(
          prompt=lowerCAmelCase_  ,				mask_image=lowerCAmelCase_  ,				image_latents=lowerCAmelCase_  ,				generator=lowerCAmelCase_  ,				negative_prompt=lowerCAmelCase_  ,				inpaint_strength=0.7  ,				num_inference_steps=2_5  ,				output_type="numpy"  ,				).images[0]

      _SCREAMING_SNAKE_CASE	=				(
          np.array(
              load_image(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                  "/diffedit/pears.png"   ).resize((7_6_8, 7_6_8)   )   )
          / 2_5_5
      )
      assert np.abs((expected_image - image).max()   ) < 5e-1







'''simple docstring'''



from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
 from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


lowerCamelCase_			=						logging.get_logger(__name__)

lowerCamelCase_			=						{
    'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
    ),
    'microsoft/deberta-v2-xxlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
    ),
}







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						'''deberta-v2'''



   def __init__(   self :     str  ,				__lowerCamelCase :     Union[str, Any]=1_2_8_1_0_0  ,				__lowerCamelCase :     Optional[int]=1_5_3_6  ,				__lowerCamelCase :     Optional[int]=2_4  ,				__lowerCamelCase :     Optional[int]=2_4  ,				__lowerCamelCase :     Tuple=6_1_4_4  ,				__lowerCamelCase :     List[str]="gelu"  ,				__lowerCamelCase :     int=0.1  ,				__lowerCamelCase :     Optional[Any]=0.1  ,				__lowerCamelCase :     Union[str, Any]=5_1_2  ,				__lowerCamelCase :     Optional[Any]=0  ,				__lowerCamelCase :     str=0.0_2  ,				__lowerCamelCase :     int=1e-7  ,				__lowerCamelCase :     Any=False  ,				__lowerCamelCase :     Any=-1  ,				__lowerCamelCase :     Tuple=0  ,				__lowerCamelCase :     str=True  ,				__lowerCamelCase :     List[Any]=None  ,				__lowerCamelCase :     Optional[int]=0  ,				__lowerCamelCase :     Any="gelu"  ,				**__lowerCamelCase :     Union[str, Any]  ,				):

      """simple docstring"""
      super().__init__(**__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				hidden_size
      _SCREAMING_SNAKE_CASE	=				num_hidden_layers
      _SCREAMING_SNAKE_CASE	=				num_attention_heads
      _SCREAMING_SNAKE_CASE	=				intermediate_size
      _SCREAMING_SNAKE_CASE	=				hidden_act
      _SCREAMING_SNAKE_CASE	=				hidden_dropout_prob
      _SCREAMING_SNAKE_CASE	=				attention_probs_dropout_prob
      _SCREAMING_SNAKE_CASE	=				max_position_embeddings
      _SCREAMING_SNAKE_CASE	=				type_vocab_size
      _SCREAMING_SNAKE_CASE	=				initializer_range
      _SCREAMING_SNAKE_CASE	=				relative_attention
      _SCREAMING_SNAKE_CASE	=				max_relative_positions
      _SCREAMING_SNAKE_CASE	=				pad_token_id
      _SCREAMING_SNAKE_CASE	=				position_biased_input

      # Backwards compatibility
      if type(__lowerCamelCase   ) == str:
         _SCREAMING_SNAKE_CASE	=				[x.strip() for x in pos_att_type.lower().split("|"   )]

      _SCREAMING_SNAKE_CASE	=				pos_att_type
      _SCREAMING_SNAKE_CASE	=				vocab_size
      _SCREAMING_SNAKE_CASE	=				layer_norm_eps

      _SCREAMING_SNAKE_CASE	=				kwargs.get("pooler_hidden_size"  ,				__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				pooler_dropout
      _SCREAMING_SNAKE_CASE	=				pooler_hidden_act







class 		lowercase_   (			A       ):


   """simple docstring"""

   @property
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      if self.task == "multiple-choice":
         _SCREAMING_SNAKE_CASE	=				{0: "batch", 1: "choice", 2: "sequence"}
      else:
         _SCREAMING_SNAKE_CASE	=				{0: "batch", 1: "sequence"}
      if self._config.type_vocab_size > 0:
         return OrderedDict(
             [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)]   )
      else:
         return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)]   )



   @property
   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      return 1_2



   def 							lowerCAmelCase_      (   self :     List[str]  ,				__lowerCamelCase :     Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]  ,				__lowerCamelCase :     int = -1  ,				__lowerCamelCase :     int = -1  ,				__lowerCamelCase :     int = -1  ,				__lowerCamelCase :     bool = False  ,				__lowerCamelCase :     Optional["TensorType"] = None  ,				__lowerCamelCase :     int = 3  ,				__lowerCamelCase :     int = 4_0  ,				__lowerCamelCase :     int = 4_0  ,				__lowerCamelCase :     "PreTrainedTokenizerBase" = None  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				super().generate_dummy_inputs(preprocessor=__lowerCamelCase  ,				framework=__lowerCamelCase   )
      if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
         del dummy_inputs["token_type_ids"]
      return dummy_inputs

