


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class      snake_case__	(   _A    ,							_A    ,							_A    ,							unittest.TestCase	):
				__lowerCAmelCase		=   StableDiffusionImageVariationPipeline
				__lowerCAmelCase		=   IMAGE_VARIATION_PARAMS
				__lowerCAmelCase		=   IMAGE_VARIATION_BATCH_PARAMS
				__lowerCAmelCase		=   frozenset([]	)
				# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				__lowerCAmelCase		=   frozenset([]	)
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										torch.manual_seed(0   )
										__magic_name__					:				Any				=   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  ,   )
										__magic_name__					:				Optional[Any]				=   PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE   )
										torch.manual_seed(0   )
										__magic_name__					:				Dict				=   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   )
										__magic_name__					:				Tuple				=   CLIPVisionConfig(
										    hidden_size=32  ,   projection_dim=32  ,   intermediate_size=37  ,   layer_norm_eps=1E-0_5  ,   num_attention_heads=4  ,   num_hidden_layers=5  ,   image_size=32  ,   patch_size=4  ,   )
										__magic_name__					:				Optional[Any]				=   CLIPVisionModelWithProjection(__SCREAMING_SNAKE_CASE   )
										__magic_name__					:				List[str]				=   CLIPImageProcessor(crop_size=32  ,   size=32   )

										__magic_name__					:				Optional[Any]				=   {
										    'unet': unet,
										    'scheduler': scheduler,
										    'vae': vae,
										    'image_encoder': image_encoder,
										    'feature_extractor': feature_extractor,
										    'safety_checker': None,
										}
										return components
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase=0   ):




										"""simple docstring"""




										__magic_name__					:				Union[str, Any]				=   floats_tensor((1, 3, 32, 32)  ,   rng=random.Random(__SCREAMING_SNAKE_CASE   )   )
										__magic_name__					:				List[Any]				=   image.cpu().permute(0  ,   2  ,   3  ,   1   )[0]
										__magic_name__					:				str				=   Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert('RGB'   ).resize((32, 32)   )
										if str(__SCREAMING_SNAKE_CASE   ).startswith('mps'   ):
																__magic_name__					:				List[Any]				=   torch.manual_seed(__SCREAMING_SNAKE_CASE   )
										else:
																__magic_name__					:				int				=   torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )
										__magic_name__					:				Union[str, Any]				=   {
										    'image': image,
										    'generator': generator,
										    'num_inference_steps': 2,
										    'guidance_scale': 6.0,
										    'output_type': 'numpy',
										}
										return inputs
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Union[str, Any]				=   'cpu'  # ensure determinism for the device-dependent torch.Generator
										__magic_name__					:				Optional[int]				=   self.get_dummy_components()
										__magic_name__					:				Optional[Any]				=   StableDiffusionImageVariationPipeline(**__SCREAMING_SNAKE_CASE   )
										__magic_name__					:				Tuple				=   sd_pipe.to(__SCREAMING_SNAKE_CASE   )
										sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

										__magic_name__					:				List[Any]				=   self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   )
										__magic_name__					:				Union[str, Any]				=   sd_pipe(**__SCREAMING_SNAKE_CASE   ).images
										__magic_name__					:				List[Any]				=   image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)
										__magic_name__					:				Tuple				=   np.array([0.5239, 0.5723, 0.4796, 0.5049, 0.5550, 0.4685, 0.5329, 0.4891, 0.4921]   )

										assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Any				=   'cpu'  # ensure determinism for the device-dependent torch.Generator
										__magic_name__					:				Optional[Any]				=   self.get_dummy_components()
										__magic_name__					:				int				=   StableDiffusionImageVariationPipeline(**__SCREAMING_SNAKE_CASE   )
										__magic_name__					:				List[Any]				=   sd_pipe.to(__SCREAMING_SNAKE_CASE   )
										sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

										__magic_name__					:				Tuple				=   self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   )
										__magic_name__					:				Optional[Any]				=   2 * [inputs['image']]
										__magic_name__					:				List[Any]				=   sd_pipe(**__SCREAMING_SNAKE_CASE   )

										__magic_name__					:				Optional[int]				=   output.images

										__magic_name__					:				int				=   image[-1, -3:, -3:, -1]

										assert image.shape == (2, 64, 64, 3)
										__magic_name__					:				List[Any]				=   np.array([0.6892, 0.5637, 0.5836, 0.5771, 0.6254, 0.6409, 0.5580, 0.5569, 0.5289]   )

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





				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										super().test_inference_batch_single_identical(expected_max_diff=3E-3   )






@slow
@require_torch_gpu
class      snake_case__	(   unittest.TestCase	):
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase="cpu"  ,   _UpperCAmelCase=torch.floataa  ,   _UpperCAmelCase=0   ):




										"""simple docstring"""




										__magic_name__					:				Union[str, Any]				=   torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )
										__magic_name__					:				int				=   load_image(
										    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
										    '/stable_diffusion_imgvar/input_image_vermeer.png'   )
										__magic_name__					:				List[str]				=   np.random.RandomState(__SCREAMING_SNAKE_CASE   ).standard_normal((1, 4, 64, 64)   )
										__magic_name__					:				Dict				=   torch.from_numpy(__SCREAMING_SNAKE_CASE   ).to(device=__SCREAMING_SNAKE_CASE  ,   dtype=__SCREAMING_SNAKE_CASE   )
										__magic_name__					:				Any				=   {
										    'image': init_image,
										    'latents': latents,
										    'generator': generator,
										    'num_inference_steps': 3,
										    'guidance_scale': 7.5,
										    'output_type': 'numpy',
										}
										return inputs
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Any				=   StableDiffusionImageVariationPipeline.from_pretrained(
										    'lambdalabs/sd-image-variations-diffusers'  ,   safety_checker=__SCREAMING_SNAKE_CASE   )
										__magic_name__					:				Optional[int]				=   sd_pipe.to(__SCREAMING_SNAKE_CASE   )
										sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

										__magic_name__					:				List[str]				=   self.get_inputs(__SCREAMING_SNAKE_CASE   )
										__magic_name__					:				Dict				=   sd_pipe(**__SCREAMING_SNAKE_CASE   ).images
										__magic_name__					:				Any				=   image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 512, 512, 3)
										__magic_name__					:				Union[str, Any]				=   np.array([0.84491, 0.90789, 0.75708, 0.78734, 0.83485, 0.70099, 0.66938, 0.68727, 0.61379]   )
										assert np.abs(image_slice - expected_slice   ).max() < 6E-3
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Optional[Any]				=   0

										def callback_fn(_UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase   ) -> None:
																__magic_name__					:				Any				=   True
																nonlocal number_of_steps
																number_of_steps += 1
																if step == 1:
																						__magic_name__					:				Dict				=   latents.detach().cpu().numpy()
																						assert latents.shape == (1, 4, 64, 64)
																						__magic_name__					:				Union[str, Any]				=   latents[0, -3:, -3:, -1]
																						__magic_name__					:				Tuple				=   np.array(
																						    [-0.1621, 0.2837, -0.7979, -0.1221, -1.3057, 0.7681, -2.1191, 0.0464, 1.6309]   )

																						assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 5E-2
																elif step == 2:
																						__magic_name__					:				Optional[Any]				=   latents.detach().cpu().numpy()
																						assert latents.shape == (1, 4, 64, 64)
																						__magic_name__					:				str				=   latents[0, -3:, -3:, -1]
																						__magic_name__					:				int				=   np.array([0.6299, 1.7500, 1.1992, -2.1582, -1.8994, 0.7334, -0.7090, 1.0137, 1.5273]   )

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

										__magic_name__					:				List[Any]				=   False

										__magic_name__					:				Optional[int]				=   StableDiffusionImageVariationPipeline.from_pretrained(
										    'fusing/sd-image-variations-diffusers'  ,   safety_checker=__SCREAMING_SNAKE_CASE  ,   torch_dtype=torch.floataa  ,   )
										pipe.to(__SCREAMING_SNAKE_CASE   )
										pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )
										pipe.enable_attention_slicing()

										__magic_name__					:				str				=   self.get_inputs(__SCREAMING_SNAKE_CASE  ,   dtype=torch.floataa   )
										pipe(**__SCREAMING_SNAKE_CASE  ,   callback=__SCREAMING_SNAKE_CASE  ,   callback_steps=1   )
										assert callback_fn.has_been_called
										assert number_of_steps == inputs["num_inference_steps"]





				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										__magic_name__					:				Optional[int]				=   'fusing/sd-image-variations-diffusers'
										__magic_name__					:				List[str]				=   StableDiffusionImageVariationPipeline.from_pretrained(
										    __SCREAMING_SNAKE_CASE  ,   safety_checker=__SCREAMING_SNAKE_CASE  ,   torch_dtype=torch.floataa   )
										__magic_name__					:				List[str]				=   pipe.to(__SCREAMING_SNAKE_CASE   )
										pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )
										pipe.enable_attention_slicing(1   )
										pipe.enable_sequential_cpu_offload()

										__magic_name__					:				Tuple				=   self.get_inputs(__SCREAMING_SNAKE_CASE  ,   dtype=torch.floataa   )
										__magic_name__					:				Union[str, Any]				=   pipe(**__SCREAMING_SNAKE_CASE   )

										__magic_name__					:				Any				=   torch.cuda.max_memory_allocated()
										# make sure that less than 2.6 GB is allocated
										assert mem_bytes < 2.6 * 10**9






@nightly
@require_torch_gpu
class      snake_case__	(   unittest.TestCase	):
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase="cpu"  ,   _UpperCAmelCase=torch.floataa  ,   _UpperCAmelCase=0   ):




										"""simple docstring"""




										__magic_name__					:				List[Any]				=   torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )
										__magic_name__					:				str				=   load_image(
										    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
										    '/stable_diffusion_imgvar/input_image_vermeer.png'   )
										__magic_name__					:				Optional[Any]				=   np.random.RandomState(__SCREAMING_SNAKE_CASE   ).standard_normal((1, 4, 64, 64)   )
										__magic_name__					:				Dict				=   torch.from_numpy(__SCREAMING_SNAKE_CASE   ).to(device=__SCREAMING_SNAKE_CASE  ,   dtype=__SCREAMING_SNAKE_CASE   )
										__magic_name__					:				Optional[Any]				=   {
										    'image': init_image,
										    'latents': latents,
										    'generator': generator,
										    'num_inference_steps': 50,
										    'guidance_scale': 7.5,
										    'output_type': 'numpy',
										}
										return inputs
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Any				=   StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'   )
										sd_pipe.to(__SCREAMING_SNAKE_CASE   )
										sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

										__magic_name__					:				Optional[Any]				=   self.get_inputs(__SCREAMING_SNAKE_CASE   )
										__magic_name__					:				List[Any]				=   sd_pipe(**__SCREAMING_SNAKE_CASE   ).images[0]

										__magic_name__					:				Tuple				=   load_numpy(
										    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
										    '/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy'   )
										__magic_name__					:				Any				=   np.abs(expected_image - image   ).max()
										assert max_diff < 1E-3





				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				int				=   StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'   )
										__magic_name__					:				str				=   DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config   )
										sd_pipe.to(__SCREAMING_SNAKE_CASE   )
										sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

										__magic_name__					:				List[Any]				=   self.get_inputs(__SCREAMING_SNAKE_CASE   )
										__magic_name__					:				Dict				=   25
										__magic_name__					:				Optional[Any]				=   sd_pipe(**__SCREAMING_SNAKE_CASE   ).images[0]

										__magic_name__					:				Optional[Any]				=   load_numpy(
										    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
										    '/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy'   )
										__magic_name__					:				Optional[Any]				=   np.abs(expected_image - image   ).max()
										assert max_diff < 1E-3




'''simple docstring'''

from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__UpperCAmelCase												=   logging.get_logger(__name__)

__UpperCAmelCase												=   {
    'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
    'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
    'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
    'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
    'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
}


class 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   A		= 'gpt2'
   A		= ['past_key_values']
   A		= {
       'hidden_size': 'n_embd',
       'max_position_embeddings': 'n_positions',
       'num_attention_heads': 'n_head',
       'num_hidden_layers': 'n_layer',
   }






   def __init__(       self    ,__SCREAMING_SNAKE_CASE=50257    ,__SCREAMING_SNAKE_CASE=1024    ,__SCREAMING_SNAKE_CASE=768    ,__SCREAMING_SNAKE_CASE=12    ,__SCREAMING_SNAKE_CASE=12    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE="gelu_new"    ,__SCREAMING_SNAKE_CASE=0.1    ,__SCREAMING_SNAKE_CASE=0.1    ,__SCREAMING_SNAKE_CASE=0.1    ,__SCREAMING_SNAKE_CASE=1e-5    ,__SCREAMING_SNAKE_CASE=0.02    ,__SCREAMING_SNAKE_CASE="cls_index"    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=0.1    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=50256    ,__SCREAMING_SNAKE_CASE=50256    ,__SCREAMING_SNAKE_CASE=False    ,__SCREAMING_SNAKE_CASE=False    ,**__SCREAMING_SNAKE_CASE    ,):
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		vocab_size
    SCREAMING_SNAKE_CASE  :  Tuple								=		n_positions
    SCREAMING_SNAKE_CASE  :  int								=		n_embd
    SCREAMING_SNAKE_CASE  :  Dict								=		n_layer
    SCREAMING_SNAKE_CASE  :  Dict								=		n_head
    SCREAMING_SNAKE_CASE  :  List[Any]								=		n_inner
    SCREAMING_SNAKE_CASE  :  Dict								=		activation_function
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		resid_pdrop
    SCREAMING_SNAKE_CASE  :  str								=		embd_pdrop
    SCREAMING_SNAKE_CASE  :  int								=		attn_pdrop
    SCREAMING_SNAKE_CASE  :  Tuple								=		layer_norm_epsilon
    SCREAMING_SNAKE_CASE  :  Tuple								=		initializer_range
    SCREAMING_SNAKE_CASE  :  Any								=		summary_type
    SCREAMING_SNAKE_CASE  :  List[Any]								=		summary_use_proj
    SCREAMING_SNAKE_CASE  :  int								=		summary_activation
    SCREAMING_SNAKE_CASE  :  int								=		summary_first_dropout
    SCREAMING_SNAKE_CASE  :  List[Any]								=		summary_proj_to_labels
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		scale_attn_weights
    SCREAMING_SNAKE_CASE  :  Any								=		use_cache
    SCREAMING_SNAKE_CASE  :  List[str]								=		scale_attn_by_inverse_layer_idx
    SCREAMING_SNAKE_CASE  :  Any								=		reorder_and_upcast_attn

    SCREAMING_SNAKE_CASE  :  Tuple								=		bos_token_id
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		eos_token_id

    super().__init__(bos_token_id=__SCREAMING_SNAKE_CASE    ,eos_token_id=__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      )



class 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE = "default"    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE = False    ,):
    super().__init__(__SCREAMING_SNAKE_CASE    ,task=__SCREAMING_SNAKE_CASE    ,patching_specs=__SCREAMING_SNAKE_CASE    ,use_past=__SCREAMING_SNAKE_CASE      )
    if not getattr(self._config    ,'pad_token_id'    ,__SCREAMING_SNAKE_CASE      ):
     # TODO: how to do that better?
     SCREAMING_SNAKE_CASE  :  int								=		0






   @property
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Dict								=		OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}      )
    if self.use_past:
     self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE    ,direction='inputs'      )
     SCREAMING_SNAKE_CASE  :  Dict								=		{0: 'batch', 1: 'past_sequence + sequence'}
    else:
     SCREAMING_SNAKE_CASE  :  Optional[Any]								=		{0: 'batch', 1: 'sequence'}

    return common_inputs






   @property
   def 					__a			(       self      ):
    return self._config.n_layer






   @property
   def 					__a			(       self      ):
    return self._config.n_head






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE = -1    ,__SCREAMING_SNAKE_CASE = -1    ,__SCREAMING_SNAKE_CASE = False    ,__SCREAMING_SNAKE_CASE = None    ,):
    SCREAMING_SNAKE_CASE  :  List[str]								=		super(__SCREAMING_SNAKE_CASE    ,self      ).generate_dummy_inputs(
        __SCREAMING_SNAKE_CASE    ,batch_size=__SCREAMING_SNAKE_CASE    ,seq_length=__SCREAMING_SNAKE_CASE    ,is_pair=__SCREAMING_SNAKE_CASE    ,framework=__SCREAMING_SNAKE_CASE      )

    # We need to order the input in the way they appears in the forward()
    SCREAMING_SNAKE_CASE  :  int								=		OrderedDict({'input_ids': common_inputs['input_ids']}      )

    # Need to add the past_keys
    if self.use_past:
     if not is_torch_available():
      raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'      )
     else:
      import torch

      SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  List[str]								=		common_inputs['input_ids'].shape
      # Not using the same length for past_key_values
      SCREAMING_SNAKE_CASE  :  str								=		seqlen + 2
      SCREAMING_SNAKE_CASE  :  Dict								=		(
          batch,
          self.num_attention_heads,
          past_key_values_length,
          self._config.hidden_size // self.num_attention_heads,
      )
      SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		[
          (torch.zeros(__SCREAMING_SNAKE_CASE      ), torch.zeros(__SCREAMING_SNAKE_CASE      )) for _ in range(self.num_layers      )
      ]

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		common_inputs['attention_mask']
    if self.use_past:
     SCREAMING_SNAKE_CASE  :  Tuple								=		ordered_inputs['attention_mask'].dtype
     SCREAMING_SNAKE_CASE  :  Any								=		torch.cat(
         [ordered_inputs['attention_mask'], torch.ones(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,dtype=__SCREAMING_SNAKE_CASE      )]    ,dim=1      )

    return ordered_inputs






   @property
   def 					__a			(       self      ):
    return 13


