






"""simple docstring"""

# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

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,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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


enable_full_determinism()


class 		__lowerCamelCase				( A__ ,							A__ ,							A__ ,							unittest.TestCase			):





		'''simple docstring'''






		a_	:				List[Any]            =							StableDiffusionControlNetImgaImgPipeline
		a_	:				Optional[Any]            =							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
		a_	:				Optional[Any]            =							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
		a_	:				Union[str, Any]            =							IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"""control_image"""}			)
		a_	:				Optional[Any]            =							IMAGE_TO_IMAGE_IMAGE_PARAMS





		def    lowerCamelCase       (   self							:	int	):
				torch.manual_seed(0	)
				lowerCAmelCase_   :							Dict									=			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						,			)
				torch.manual_seed(0	)
				lowerCAmelCase_   :							List[str]									=			ControlNetModel(
				    block_out_channels=(32, 64)						,			layers_per_block=2						,			in_channels=4						,			down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")						,			cross_attention_dim=32						,			conditioning_embedding_out_channels=(16, 32)						,			)
				torch.manual_seed(0	)
				lowerCAmelCase_   :							List[str]									=			DDIMScheduler(
				    beta_start=0.00085						,			beta_end=0.012						,			beta_schedule="scaled_linear"						,			clip_sample=_snake_case						,			set_alpha_to_one=_snake_case						,			)
				torch.manual_seed(0	)
				lowerCAmelCase_   :							List[Any]									=			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	)
				lowerCAmelCase_   :							Dict									=			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=10_00						,			)
				lowerCAmelCase_   :							Dict									=			CLIPTextModel(_snake_case	)
				lowerCAmelCase_   :							Union[str, Any]									=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

				lowerCAmelCase_   :							List[str]									=			{
				    "unet": unet,
				    "controlnet": controlnet,
				    "scheduler": scheduler,
				    "vae": vae,
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "safety_checker": None,
				    "feature_extractor": None,
				}
				return components





		def    lowerCamelCase       (   self							:	Union[str, Any]						,			a_							:	Any						,			a_							:	Dict=0	):


				if str(_snake_case	).startswith("mps"	):
						lowerCAmelCase_   :							int									=			torch.manual_seed(_snake_case	)
				else:
						lowerCAmelCase_   :							str									=			torch.Generator(device=_snake_case	).manual_seed(_snake_case	)

				lowerCAmelCase_   :							Any									=			2
				lowerCAmelCase_   :							Optional[Any]									=			randn_tensor(
				    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)						,			generator=_snake_case						,			device=torch.device(_snake_case	)						,			)
				lowerCAmelCase_   :							Dict									=			floats_tensor(control_image.shape						,			rng=random.Random(_snake_case	)	).to(_snake_case	)
				lowerCAmelCase_   :							List[Any]									=			image.cpu().permute(0						,			2						,			3						,			1	)[0]
				lowerCAmelCase_   :							Optional[int]									=			Image.fromarray(np.uinta(_snake_case	)	).convert("RGB"	).resize((64, 64)	)
				lowerCAmelCase_   :							Optional[Any]									=			{
				    "prompt": "A painting of a squirrel eating a burger",
				    "generator": generator,
				    "num_inference_steps": 2,
				    "guidance_scale": 6.0,
				    "output_type": "numpy",
				    "image": image,
				    "control_image": control_image,
				}

				return inputs





		def    lowerCamelCase       (   self							:	Any	):
				return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3	)





		@unittest.skipIf(
		    torch_device != "cuda" or not is_xformers_available()						,			reason="XFormers attention is only available with CUDA and `xformers` installed"						,			)
		def    lowerCamelCase       (   self							:	Any	):
				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3	)





		def    lowerCamelCase       (   self							:	Optional[Any]	):
				self._test_inference_batch_single_identical(expected_max_diff=2e-3	)







class 		__lowerCamelCase				( A__ ,							A__ ,							unittest.TestCase			):





		'''simple docstring'''






		a_	:				str            =							StableDiffusionControlNetImgaImgPipeline
		a_	:				List[str]            =							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
		a_	:				Optional[Any]            =							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
		a_	:				str            =							frozenset([]			)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess





		def    lowerCamelCase       (   self							:	str	):
				torch.manual_seed(0	)
				lowerCAmelCase_   :							Union[str, 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						,			)
				torch.manual_seed(0	)

				def init_weights(a_							:	Optional[int]	):
						if isinstance(_snake_case						,			torch.nn.Convad	):
								torch.nn.init.normal(m.weight	)
								m.bias.data.fill_(1.0	)

				lowerCAmelCase_   :							List[str]									=			ControlNetModel(
				    block_out_channels=(32, 64)						,			layers_per_block=2						,			in_channels=4						,			down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")						,			cross_attention_dim=32						,			conditioning_embedding_out_channels=(16, 32)						,			)
				controlneta.controlnet_down_blocks.apply(_snake_case	)

				torch.manual_seed(0	)
				lowerCAmelCase_   :							str									=			ControlNetModel(
				    block_out_channels=(32, 64)						,			layers_per_block=2						,			in_channels=4						,			down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")						,			cross_attention_dim=32						,			conditioning_embedding_out_channels=(16, 32)						,			)
				controlneta.controlnet_down_blocks.apply(_snake_case	)

				torch.manual_seed(0	)
				lowerCAmelCase_   :							Dict									=			DDIMScheduler(
				    beta_start=0.00085						,			beta_end=0.012						,			beta_schedule="scaled_linear"						,			clip_sample=_snake_case						,			set_alpha_to_one=_snake_case						,			)
				torch.manual_seed(0	)
				lowerCAmelCase_   :							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	)
				lowerCAmelCase_   :							List[str]									=			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=10_00						,			)
				lowerCAmelCase_   :							Optional[int]									=			CLIPTextModel(_snake_case	)
				lowerCAmelCase_   :							Dict									=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

				lowerCAmelCase_   :							List[Any]									=			MultiControlNetModel([controlneta, controlneta]	)

				lowerCAmelCase_   :							List[str]									=			{
				    "unet": unet,
				    "controlnet": controlnet,
				    "scheduler": scheduler,
				    "vae": vae,
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "safety_checker": None,
				    "feature_extractor": None,
				}
				return components





		def    lowerCamelCase       (   self							:	int						,			a_							:	Union[str, Any]						,			a_							:	str=0	):


				if str(_snake_case	).startswith("mps"	):
						lowerCAmelCase_   :							Tuple									=			torch.manual_seed(_snake_case	)
				else:
						lowerCAmelCase_   :							Optional[Any]									=			torch.Generator(device=_snake_case	).manual_seed(_snake_case	)

				lowerCAmelCase_   :							Union[str, Any]									=			2

				lowerCAmelCase_   :							List[Any]									=			[
				    randn_tensor(
				        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)						,			generator=_snake_case						,			device=torch.device(_snake_case	)						,			),
				    randn_tensor(
				        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)						,			generator=_snake_case						,			device=torch.device(_snake_case	)						,			),
				]

				lowerCAmelCase_   :							Dict									=			floats_tensor(control_image[0].shape						,			rng=random.Random(_snake_case	)	).to(_snake_case	)
				lowerCAmelCase_   :							int									=			image.cpu().permute(0						,			2						,			3						,			1	)[0]
				lowerCAmelCase_   :							Any									=			Image.fromarray(np.uinta(_snake_case	)	).convert("RGB"	).resize((64, 64)	)
				lowerCAmelCase_   :							Optional[Any]									=			{
				    "prompt": "A painting of a squirrel eating a burger",
				    "generator": generator,
				    "num_inference_steps": 2,
				    "guidance_scale": 6.0,
				    "output_type": "numpy",
				    "image": image,
				    "control_image": control_image,
				}

				return inputs





		def    lowerCamelCase       (   self							:	Optional[Any]	):
				lowerCAmelCase_   :							Tuple									=			self.get_dummy_components()
				lowerCAmelCase_   :							Tuple									=			self.pipeline_class(**_snake_case	)
				pipe.to(_snake_case	)

				lowerCAmelCase_   :							Optional[int]									=			10.0
				lowerCAmelCase_   :							Optional[Any]									=			4

				lowerCAmelCase_   :							Tuple									=			self.get_dummy_inputs(_snake_case	)
				lowerCAmelCase_   :							Optional[Any]									=			steps
				lowerCAmelCase_   :							Any									=			scale
				lowerCAmelCase_   :							int									=			pipe(**_snake_case	)[0]

				lowerCAmelCase_   :							Dict									=			self.get_dummy_inputs(_snake_case	)
				lowerCAmelCase_   :							List[Any]									=			steps
				lowerCAmelCase_   :							List[Any]									=			scale
				lowerCAmelCase_   :							Dict									=			pipe(**_snake_case						,			control_guidance_start=0.1						,			control_guidance_end=0.2	)[0]

				lowerCAmelCase_   :							Dict									=			self.get_dummy_inputs(_snake_case	)
				lowerCAmelCase_   :							Dict									=			steps
				lowerCAmelCase_   :							Optional[int]									=			scale
				lowerCAmelCase_   :							List[Any]									=			pipe(**_snake_case						,			control_guidance_start=[0.1, 0.3]						,			control_guidance_end=[0.2, 0.7]	)[0]

				lowerCAmelCase_   :							List[str]									=			self.get_dummy_inputs(_snake_case	)
				lowerCAmelCase_   :							int									=			steps
				lowerCAmelCase_   :							Tuple									=			scale
				lowerCAmelCase_   :							str									=			pipe(**_snake_case						,			control_guidance_start=0.4						,			control_guidance_end=[0.5, 0.8]	)[0]

				# make sure that all outputs are different
				assert np.sum(np.abs(output_a - output_a	)	) > 1e-3
				assert np.sum(np.abs(output_a - output_a	)	) > 1e-3
				assert np.sum(np.abs(output_a - output_a	)	) > 1e-3





		def    lowerCamelCase       (   self							:	Dict	):
				return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3	)





		@unittest.skipIf(
		    torch_device != "cuda" or not is_xformers_available()						,			reason="XFormers attention is only available with CUDA and `xformers` installed"						,			)
		def    lowerCamelCase       (   self							:	int	):
				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3	)





		def    lowerCamelCase       (   self							:	int	):
				self._test_inference_batch_single_identical(expected_max_diff=2e-3	)





		def    lowerCamelCase       (   self							:	Optional[int]	):
				lowerCAmelCase_   :							List[str]									=			self.get_dummy_components()
				lowerCAmelCase_   :							List[str]									=			self.pipeline_class(**_snake_case	)
				pipe.to(_snake_case	)
				pipe.set_progress_bar_config(disable=_snake_case	)
				with tempfile.TemporaryDirectory() as tmpdir:
						try:
								# save_pretrained is not implemented for Multi-ControlNet
								pipe.save_pretrained(_snake_case	)
						except NotImplementedError:
								pass







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





		'''simple docstring'''






		def    lowerCamelCase       (   self							:	Optional[int]	):
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()





		def    lowerCamelCase       (   self							:	Optional[int]	):
				lowerCAmelCase_   :							str									=			ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"	)

				lowerCAmelCase_   :							List[str]									=			StableDiffusionControlNetImgaImgPipeline.from_pretrained(
				    "runwayml/stable-diffusion-v1-5"						,			safety_checker=_snake_case						,			controlnet=_snake_case	)
				pipe.enable_model_cpu_offload()
				pipe.set_progress_bar_config(disable=_snake_case	)

				lowerCAmelCase_   :							Optional[Any]									=			torch.Generator(device="cpu"	).manual_seed(0	)
				lowerCAmelCase_   :							List[Any]									=			"evil space-punk bird"
				lowerCAmelCase_   :							str									=			load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"	).resize((5_12, 5_12)	)
				lowerCAmelCase_   :							Optional[Any]									=			load_image(
				    "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"	).resize((5_12, 5_12)	)

				lowerCAmelCase_   :							Optional[Any]									=			pipe(
				    _snake_case						,			_snake_case						,			control_image=_snake_case						,			generator=_snake_case						,			output_type="np"						,			num_inference_steps=50						,			strength=0.6						,			)

				lowerCAmelCase_   :							List[str]									=			output.images[0]

				assert image.shape == (5_12, 5_12, 3)

				lowerCAmelCase_   :							Any									=			load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy"	)

				assert np.abs(expected_image - image	).max() < 9e-2






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


snake_case_ : List[str]    		=       logging.get_logger(__name__)


snake_case_ : Tuple    		=       {
    "Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json",
    "Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json",
    "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json",
    "Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json",
    "Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json",
    "Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json",
    "Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json",
    "Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json",
    "Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json",
    "Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json",
    "Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json",
    "Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json",
}







class __snake_case			(			a		):
   UpperCAmelCase__  :    str          =  '''codegen'''
   UpperCAmelCase__  :    int          =  {
       '''max_position_embeddings''': '''n_positions''',
       '''hidden_size''': '''n_embd''',
       '''num_attention_heads''': '''n_head''',
       '''num_hidden_layers''': '''n_layer''',
   }







   def __init__(      self		:						Union[str, Any]					,						_snake_case		:						Union[str, Any]=50400					,						_snake_case		:						Optional[int]=2048					,						_snake_case		:						Union[str, Any]=2048					,						_snake_case		:						List[str]=4096					,						_snake_case		:						Any=28					,						_snake_case		:						List[str]=16					,						_snake_case		:						int=64					,						_snake_case		:						Tuple=None					,						_snake_case		:						Dict="gelu_new"					,						_snake_case		:						Union[str, Any]=0.0					,						_snake_case		:						Optional[Any]=0.0					,						_snake_case		:						List[Any]=0.0					,						_snake_case		:						List[Any]=1e-5					,						_snake_case		:						List[str]=0.0_2					,						_snake_case		:						Optional[Any]=True					,						_snake_case		:						int=50256					,						_snake_case		:						Tuple=50256					,						_snake_case		:						int=False					,						**_snake_case		:						Any					,						):

        """simple docstring"""


        UpperCAmelCase_          =							vocab_size
        UpperCAmelCase_          =							n_ctx
        UpperCAmelCase_          =							n_positions
        UpperCAmelCase_          =							n_embd
        UpperCAmelCase_          =							n_layer
        UpperCAmelCase_          =							n_head
        UpperCAmelCase_          =							n_inner
        UpperCAmelCase_          =							rotary_dim
        UpperCAmelCase_          =							activation_function
        UpperCAmelCase_          =							resid_pdrop
        UpperCAmelCase_          =							embd_pdrop
        UpperCAmelCase_          =							attn_pdrop
        UpperCAmelCase_          =							layer_norm_epsilon
        UpperCAmelCase_          =							initializer_range
        UpperCAmelCase_          =							use_cache

        UpperCAmelCase_          =							bos_token_id
        UpperCAmelCase_          =							eos_token_id

        super().__init__(
            bos_token_id=_snake_case					,						eos_token_id=_snake_case					,						tie_word_embeddings=_snake_case					,						**_snake_case)







class __snake_case			(			a		):







   def __init__(      self		:						Tuple					,						_snake_case		:						PretrainedConfig					,						_snake_case		:						str = "default"					,						_snake_case		:						List[PatchingSpec] = None					,						_snake_case		:						bool = False					,						):

        """simple docstring"""


        super().__init__(_snake_case					,						task=_snake_case					,						patching_specs=_snake_case					,						use_past=_snake_case)
        if not getattr(self._config					,						'''pad_token_id'''					,						_snake_case):
             # TODO: how to do that better?
             UpperCAmelCase_          =							0







   @property
   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}})
        if self.use_past:
             self.fill_with_past_key_values_(_snake_case					,						direction='''inputs''')
             UpperCAmelCase_          =							{0: '''batch''', 1: '''past_sequence + sequence'''}
        else:
             UpperCAmelCase_          =							{0: '''batch''', 1: '''sequence'''}

        return common_inputs







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

        """simple docstring"""


        return self._config.n_layer







   @property
   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        return self._config.n_head







   def lowerCamelCase						(      self		:						Optional[int]					,						_snake_case		:						PreTrainedTokenizer					,						_snake_case		:						int = -1					,						_snake_case		:						int = -1					,						_snake_case		:						bool = False					,						_snake_case		:						Optional[TensorType] = None					,						):

        """simple docstring"""


        UpperCAmelCase_          =							super(_snake_case					,						self).generate_dummy_inputs(
            _snake_case					,						batch_size=_snake_case					,						seq_length=_snake_case					,						is_pair=_snake_case					,						framework=_snake_case)

        # We need to order the input in the way they appears in the forward()
        UpperCAmelCase_          =							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

                  UpperCAmelCase_     ,       UpperCAmelCase_          =							common_inputs['''input_ids'''].shape
                  # Not using the same length for past_key_values
                  UpperCAmelCase_          =							seqlen + 2
                  UpperCAmelCase_          =							(
                      batch,
                      self.num_attention_heads,
                      past_key_values_length,
                      self._config.hidden_size // self.num_attention_heads,
                  )
                  UpperCAmelCase_          =							[
                      (torch.zeros(_snake_case), torch.zeros(_snake_case)) for _ in range(self.num_layers)
                  ]

        UpperCAmelCase_          =							common_inputs['''attention_mask''']
        if self.use_past:
             UpperCAmelCase_          =							ordered_inputs['''attention_mask'''].dtype
             UpperCAmelCase_          =							torch.cat(
                 [ordered_inputs['''attention_mask'''], torch.ones(_snake_case					,						_snake_case					,						dtype=_snake_case)]					,						dim=1)

        return ordered_inputs







   @property
   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        return 13






