




import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				if isinstance(lowerCAmelCase__	,     torch.Tensor   ):
								return image
				elif isinstance(lowerCAmelCase__	,     PIL.Image.Image   ):
								__magic_name__	:	List[Any]								=[image]

				if isinstance(image[0]	,     PIL.Image.Image   ):
								__magic_name__	:	List[str]								=[np.array(i.resize((w, h)	,     resample=PIL_INTERPOLATION["""lanczos"""]   )   )[None, :] for i in image]
								__magic_name__	:	str								=np.concatenate(lowerCAmelCase__	,     axis=0   )
								__magic_name__	:	str								=np.array(lowerCAmelCase__   ).astype(np.floataa   ) / 255.0
								__magic_name__	:	Optional[Any]								=image.transpose(0	,     3	,     1	,     2   )
								__magic_name__	:	int								=2.0 * image - 1.0
								__magic_name__	:	List[str]								=torch.from_numpy(lowerCAmelCase__   )
				elif isinstance(image[0]	,     torch.Tensor   ):
								__magic_name__	:	List[Any]								=torch.cat(lowerCAmelCase__	,     dim=0   )
				return image




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase=0.9_9_9_5   ):
				if not isinstance(lowerCAmelCase__	,     np.ndarray   ):
								__magic_name__	:	str								=True
								__magic_name__	:	int								=va.device
								__magic_name__	:	int								=va.cpu().numpy()
								__magic_name__	:	Union[str, Any]								=va.cpu().numpy()

				__magic_name__	:	Tuple								=np.sum(va * va / (np.linalg.norm(lowerCAmelCase__   ) * np.linalg.norm(lowerCAmelCase__   ))   )
				if np.abs(lowerCAmelCase__   ) > DOT_THRESHOLD:
								__magic_name__	:	Optional[Any]								=(1 - t) * va + t * va
				else:
								__magic_name__	:	str								=np.arccos(lowerCAmelCase__   )
								__magic_name__	:	Union[str, Any]								=np.sin(lowerCAmelCase__   )
								__magic_name__	:	Dict								=theta_a * t
								__magic_name__	:	Union[str, Any]								=np.sin(lowerCAmelCase__   )
								__magic_name__	:	int								=np.sin(theta_a - theta_t   ) / sin_theta_a
								__magic_name__	:	Optional[Any]								=sin_theta_t / sin_theta_a
								__magic_name__	:	Tuple								=sa * va + sa * va

				if inputs_are_torch:
								__magic_name__	:	Tuple								=torch.from_numpy(lowerCAmelCase__   ).to(lowerCAmelCase__   )

				return va




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	Optional[Any]								=F.normalize(lowerCAmelCase__	,     dim=-1   )
				__magic_name__	:	Any								=F.normalize(lowerCAmelCase__	,     dim=-1   )
				return (x - y).norm(dim=-1   ).div(2   ).arcsin().pow(2   ).mul(2   )




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				for param in model.parameters():
								__magic_name__	:	List[str]								=value




class 		__A      (			__SCREAMING_SNAKE_CASE			):

						def __init__(		self   :List[str]    , __snake_case   :Optional[int]    , __snake_case   :Any    , __snake_case   :Union[str, Any]    , __snake_case   :Optional[int]    , __snake_case   :Any    , __snake_case   :Union[str, Any]    , __snake_case   :int    , __snake_case   :Union[str, Any]=None    , __snake_case   :Union[str, Any]=None    , __snake_case   :str=None    , ):
										'''simple docstring'''

										super().__init__()
										self.register_modules(
										    vae=_a    , text_encoder=_a    , clip_model=_a    , tokenizer=_a    , unet=_a    , scheduler=_a    , feature_extractor=_a    , coca_model=_a    , coca_tokenizer=_a    , coca_transform=_a    , )
										__magic_name__	:	int								=(
										    feature_extractor.size
										    if isinstance(feature_extractor.size    , _a )
										    else feature_extractor.size["""shortest_edge"""]
										)
										__magic_name__	:	List[str]								=transforms.Normalize(mean=feature_extractor.image_mean    , std=feature_extractor.image_std )
										set_requires_grad(self.text_encoder    , _a )
										set_requires_grad(self.clip_model    , _a )

						def     A__						(		self   :int    , __snake_case   :Dict = "auto" ):
										'''simple docstring'''

										if slice_size == "auto":
														# half the attention head size is usually a good trade-off between
														# speed and memory
														__magic_name__	:	int								=self.unet.config.attention_head_dim // 2
										self.unet.set_attention_slice(_a )

						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										self.enable_attention_slicing(_a )

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

										set_requires_grad(self.vae    , _a )

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

										set_requires_grad(self.vae    , _a )

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										set_requires_grad(self.unet    , _a )

						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										set_requires_grad(self.unet    , _a )

						def     A__						(		self   :Optional[Any]    , __snake_case   :Optional[int]    , __snake_case   :Tuple    , __snake_case   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	Dict								=min(int(num_inference_steps * strength )    , _a )

										__magic_name__	:	str								=max(num_inference_steps - init_timestep    , 0 )
										__magic_name__	:	Optional[int]								=self.scheduler.timesteps[t_start:]

										return timesteps, num_inference_steps - t_start

						def     A__						(		self   :Any    , __snake_case   :Union[str, Any]    , __snake_case   :Optional[int]    , __snake_case   :Optional[int]    , __snake_case   :str    , __snake_case   :str    , __snake_case   :str=None ):
										'''simple docstring'''

										if not isinstance(_a    , torch.Tensor ):
														raise ValueError(f"`image` has to be of type `torch.Tensor` but is {type(_a )}" )

										__magic_name__	:	List[Any]								=image.to(device=_a    , dtype=_a )

										if isinstance(_a    , _a ):
														__magic_name__	:	Optional[int]								=[
														    self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(_a )
														]
														__magic_name__	:	Optional[Any]								=torch.cat(_a    , dim=0 )
										else:
														__magic_name__	:	int								=self.vae.encode(_a ).latent_dist.sample(_a )

										# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
										__magic_name__	:	List[Any]								=0.18215 * init_latents
										__magic_name__	:	List[str]								=init_latents.repeat_interleave(_a    , dim=0 )

										__magic_name__	:	str								=randn_tensor(init_latents.shape    , generator=_a    , device=_a    , dtype=_a )

										# get latents
										__magic_name__	:	int								=self.scheduler.add_noise(_a    , _a    , _a )
										__magic_name__	:	Any								=init_latents

										return latents

						def     A__						(		self   :str    , __snake_case   :Dict ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=self.coca_transform(_a ).unsqueeze(0 )
										with torch.no_grad(), torch.cuda.amp.autocast():
														__magic_name__	:	Optional[int]								=self.coca_model.generate(transformed_image.to(device=self.device    , dtype=self.coca_model.dtype ) )
										__magic_name__	:	str								=self.coca_tokenizer.decode(generated[0].cpu().numpy() )
										return generated.split("""<end_of_text>""" )[0].replace("""<start_of_text>"""    , """""" ).rstrip(""" .,""" )

						def     A__						(		self   :Optional[Any]    , __snake_case   :Union[str, Any]    , __snake_case   :Tuple ):
										'''simple docstring'''

										__magic_name__	:	Any								=self.feature_extractor.preprocess(_a )
										__magic_name__	:	List[Any]								=torch.from_numpy(clip_image_input["""pixel_values"""][0] ).unsqueeze(0 ).to(self.device ).half()
										__magic_name__	:	Union[str, Any]								=self.clip_model.get_image_features(_a )
										__magic_name__	:	List[str]								=image_embeddings_clip / image_embeddings_clip.norm(p=2    , dim=-1    , keepdim=_a )
										__magic_name__	:	Any								=image_embeddings_clip.repeat_interleave(_a    , dim=0 )
										return image_embeddings_clip

						@torch.enable_grad()
						def     A__						(		self   :Any    , __snake_case   :int    , __snake_case   :Any    , __snake_case   :List[str]    , __snake_case   :List[str]    , __snake_case   :Any    , __snake_case   :List[Any]    , __snake_case   :List[str]    , ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=latents.detach().requires_grad_()

										__magic_name__	:	List[Any]								=self.scheduler.scale_model_input(_a    , _a )

										# predict the noise residual
										__magic_name__	:	Optional[Any]								=self.unet(_a    , _a    , encoder_hidden_states=_a ).sample

										if isinstance(self.scheduler    , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
														__magic_name__	:	Optional[Any]								=self.scheduler.alphas_cumprod[timestep]
														__magic_name__	:	Any								=1 - alpha_prod_t
														# compute predicted original sample from predicted noise also called
														# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
														__magic_name__	:	Any								=(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

														__magic_name__	:	Optional[Any]								=torch.sqrt(_a )
														__magic_name__	:	Dict								=pred_original_sample * (fac) + latents * (1 - fac)
										elif isinstance(self.scheduler    , _a ):
														__magic_name__	:	List[Any]								=self.scheduler.sigmas[index]
														__magic_name__	:	List[Any]								=latents - sigma * noise_pred
										else:
														raise ValueError(f"scheduler type {type(self.scheduler )} not supported" )

										# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
										__magic_name__	:	List[str]								=1 / 0.18215 * sample
										__magic_name__	:	Optional[int]								=self.vae.decode(_a ).sample
										__magic_name__	:	str								=(image / 2 + 0.5).clamp(0    , 1 )

										__magic_name__	:	Optional[int]								=transforms.Resize(self.feature_extractor_size )(_a )
										__magic_name__	:	List[str]								=self.normalize(_a ).to(latents.dtype )

										__magic_name__	:	Union[str, Any]								=self.clip_model.get_image_features(_a )
										__magic_name__	:	List[str]								=image_embeddings_clip / image_embeddings_clip.norm(p=2    , dim=-1    , keepdim=_a )

										__magic_name__	:	Optional[Any]								=spherical_dist_loss(_a    , _a ).mean() * clip_guidance_scale

										__magic_name__	:	int								=-torch.autograd.grad(_a    , _a )[0]

										if isinstance(self.scheduler    , _a ):
														__magic_name__	:	Optional[Any]								=latents.detach() + grads * (sigma**2)
														__magic_name__	:	str								=noise_pred_original
										else:
														__magic_name__	:	Dict								=noise_pred_original - torch.sqrt(_a ) * grads
										return noise_pred, latents


						@torch.no_grad()
						def __call__(		self   :Optional[int]    , __snake_case   :Optional[int]    , __snake_case   :Tuple    , __snake_case   :Optional[Any] = None    , __snake_case   :List[Any] = None    , __snake_case   :Any = 5_12    , __snake_case   :List[str] = 5_12    , __snake_case   :Union[str, Any] = 0.6    , __snake_case   :Union[str, Any] = 50    , __snake_case   :List[Any] = 7.5    , __snake_case   :Optional[Any] = 1    , __snake_case   :Dict = 0.0    , __snake_case   :List[Any] = 1_00    , __snake_case   :int = None    , __snake_case   :Optional[int] = "pil"    , __snake_case   :List[Any] = True    , __snake_case   :Optional[Any] = 0.8    , __snake_case   :Any = 0.1    , __snake_case   :int = 0.1    , ):
										'''simple docstring'''

										if isinstance(_a    , _a ) and len(_a ) != batch_size:
														raise ValueError(f"You have passed {batch_size} batch_size, but only {len(_a )} generators." )

										if height % 8 != 0 or width % 8 != 0:
														raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}." )

										if isinstance(_a    , torch.Generator ) and batch_size > 1:
														__magic_name__	:	Dict								=[generator] + [None] * (batch_size - 1)

										__magic_name__	:	Optional[Any]								=[
										    ("""model""", self.coca_model is None),
										    ("""tokenizer""", self.coca_tokenizer is None),
										    ("""transform""", self.coca_transform is None),
										]
										__magic_name__	:	Union[str, Any]								=[x[0] for x in coca_is_none if x[1]]
										__magic_name__	:	List[str]								=""", """.join(_a )
										# generate prompts with coca model if prompt is None
										if content_prompt is None:
														if len(_a ):
																		raise ValueError(
																		    f"Content prompt is None and CoCa [{coca_is_none_str}] is None."
																		    f"Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline." )
														__magic_name__	:	Tuple								=self.get_image_description(_a )
										if style_prompt is None:
														if len(_a ):
																		raise ValueError(
																		    f"Style prompt is None and CoCa [{coca_is_none_str}] is None."
																		    f" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline." )
														__magic_name__	:	Tuple								=self.get_image_description(_a )

										# get prompt text embeddings for content and style
										__magic_name__	:	str								=self.tokenizer(
										    _a    , padding="""max_length"""    , max_length=self.tokenizer.model_max_length    , truncation=_a    , return_tensors="""pt"""    , )
										__magic_name__	:	Optional[int]								=self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]

										__magic_name__	:	Union[str, Any]								=self.tokenizer(
										    _a    , padding="""max_length"""    , max_length=self.tokenizer.model_max_length    , truncation=_a    , return_tensors="""pt"""    , )
										__magic_name__	:	int								=self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]

										__magic_name__	:	Tuple								=slerp(_a    , _a    , _a )

										# duplicate text embeddings for each generation per prompt
										__magic_name__	:	Union[str, Any]								=text_embeddings.repeat_interleave(_a    , dim=0 )

										# set timesteps
										__magic_name__	:	Tuple								="""offset""" in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
										__magic_name__	:	Union[str, Any]								={}
										if accepts_offset:
														__magic_name__	:	Union[str, Any]								=1

										self.scheduler.set_timesteps(_a    , **_a )
										# Some schedulers like PNDM have timesteps as arrays
										# It's more optimized to move all timesteps to correct device beforehand
										self.scheduler.timesteps.to(self.device )

										__magic_name__				, __magic_name__	:	Tuple								=self.get_timesteps(_a    , _a    , self.device )
										__magic_name__	:	List[Any]								=timesteps[:1].repeat(_a )

										# Preprocess image
										__magic_name__	:	str								=preprocess(_a    , _a    , _a )
										__magic_name__	:	Union[str, Any]								=self.prepare_latents(
										    _a    , _a    , _a    , text_embeddings.dtype    , self.device    , _a )

										__magic_name__	:	Any								=preprocess(_a    , _a    , _a )
										__magic_name__	:	Union[str, Any]								=self.prepare_latents(
										    _a    , _a    , _a    , text_embeddings.dtype    , self.device    , _a )

										__magic_name__	:	List[str]								=slerp(_a    , _a    , _a )

										if clip_guidance_scale > 0:
														__magic_name__	:	Dict								=self.get_clip_image_embeddings(_a    , _a )
														__magic_name__	:	Dict								=self.get_clip_image_embeddings(_a    , _a )
														__magic_name__	:	Dict								=slerp(
														    _a    , _a    , _a )

										# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
										# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
										# corresponds to doing no classifier free guidance.
										__magic_name__	:	int								=guidance_scale > 1.0
										# get unconditional embeddings for classifier free guidance
										if do_classifier_free_guidance:
														__magic_name__	:	Union[str, Any]								=content_text_input.input_ids.shape[-1]
														__magic_name__	:	str								=self.tokenizer([""""""]    , padding="""max_length"""    , max_length=_a    , return_tensors="""pt""" )
														__magic_name__	:	List[Any]								=self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
														# duplicate unconditional embeddings for each generation per prompt
														__magic_name__	:	int								=uncond_embeddings.repeat_interleave(_a    , dim=0 )

														# For classifier free guidance, we need to do two forward passes.
														# Here we concatenate the unconditional and text embeddings into a single batch
														# to avoid doing two forward passes
														__magic_name__	:	List[Any]								=torch.cat([uncond_embeddings, text_embeddings] )

										# get the initial random noise unless the user supplied it

										# Unlike in other pipelines, latents need to be generated in the target device
										# for 1-to-1 results reproducibility with the CompVis implementation.
										# However this currently doesn't work in `mps`.
										__magic_name__	:	Any								=(batch_size, self.unet.config.in_channels, height // 8, width // 8)
										__magic_name__	:	Optional[Any]								=text_embeddings.dtype
										if latents is None:
														if self.device.type == "mps":
																		# randn does not work reproducibly on mps
																		__magic_name__	:	List[Any]								=torch.randn(_a    , generator=_a    , device="""cpu"""    , dtype=_a ).to(
																		    self.device )
														else:
																		__magic_name__	:	int								=torch.randn(_a    , generator=_a    , device=self.device    , dtype=_a )
										else:
														if latents.shape != latents_shape:
																		raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}" )
														__magic_name__	:	Union[str, Any]								=latents.to(self.device )

										# scale the initial noise by the standard deviation required by the scheduler
										__magic_name__	:	Union[str, Any]								=latents * self.scheduler.init_noise_sigma

										# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
										# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
										# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
										# and should be between [0, 1]
										__magic_name__	:	Optional[Any]								="""eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
										__magic_name__	:	Any								={}
										if accepts_eta:
														__magic_name__	:	str								=eta

										# check if the scheduler accepts generator
										__magic_name__	:	List[str]								="""generator""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
										if accepts_generator:
														__magic_name__	:	Any								=generator

										with self.progress_bar(total=_a ):
														for i, t in enumerate(_a ):
																		# expand the latents if we are doing classifier free guidance
																		__magic_name__	:	List[str]								=torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
																		__magic_name__	:	Union[str, Any]								=self.scheduler.scale_model_input(_a    , _a )

																		# predict the noise residual
																		__magic_name__	:	Tuple								=self.unet(_a    , _a    , encoder_hidden_states=_a ).sample

																		# perform classifier free guidance
																		if do_classifier_free_guidance:
																						__magic_name__				, __magic_name__	:	Optional[int]								=noise_pred.chunk(2 )
																						__magic_name__	:	Dict								=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																		# perform clip guidance
																		if clip_guidance_scale > 0:
																						__magic_name__	:	Dict								=(
																						    text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
																						)
																						__magic_name__				, __magic_name__	:	Union[str, Any]								=self.cond_fn(
																						    _a    , _a    , _a    , _a    , _a    , _a    , _a    , )

																		# compute the previous noisy sample x_t -> x_t-1
																		__magic_name__	:	List[Any]								=self.scheduler.step(_a    , _a    , _a    , **_a ).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
										__magic_name__	:	Optional[int]								=1 / 0.18215 * latents
										__magic_name__	:	List[Any]								=self.vae.decode(_a ).sample

										__magic_name__	:	str								=(image / 2 + 0.5).clamp(0    , 1 )
										__magic_name__	:	int								=image.cpu().permute(0    , 2    , 3    , 1 ).numpy()

										if output_type == "pil":
														__magic_name__	:	str								=self.numpy_to_pil(_a )

										if not return_dict:
														return (image, None)

										return StableDiffusionPipelineOutput(images=_a    , nsfw_content_detected=_a )










from typing import Mapping

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


UpperCAmelCase_		:				str									=     logging.get_logger(__name__)

UpperCAmelCase_		:				Tuple									=     {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""t5"""
						UpperCamelCase     						=					["""past_key_values"""]
						UpperCamelCase     						=					{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

						def __init__(		self   :Optional[Any]    , __snake_case   :Optional[Any]=3_21_28    , __snake_case   :Optional[int]=5_12    , __snake_case   :str=64    , __snake_case   :int=20_48    , __snake_case   :Union[str, Any]=6    , __snake_case   :Union[str, Any]=None    , __snake_case   :Union[str, Any]=8    , __snake_case   :List[str]=32    , __snake_case   :Dict=1_28    , __snake_case   :List[Any]=0.1    , __snake_case   :int=1E-6    , __snake_case   :List[str]=1.0    , __snake_case   :Optional[int]="relu"    , __snake_case   :List[str]=True    , __snake_case   :Union[str, Any]=True    , __snake_case   :Union[str, Any]=0    , __snake_case   :str=1    , **__snake_case   :int    , ):
										'''simple docstring'''

										__magic_name__	:	str								=vocab_size
										__magic_name__	:	str								=d_model
										__magic_name__	:	Optional[Any]								=d_kv
										__magic_name__	:	int								=d_ff
										__magic_name__	:	Union[str, Any]								=num_layers
										__magic_name__	:	Tuple								=(
										    num_decoder_layers if num_decoder_layers is not None else self.num_layers
										)  # default = symmetry
										__magic_name__	:	List[str]								=num_heads
										__magic_name__	:	Optional[Any]								=relative_attention_num_buckets
										__magic_name__	:	Union[str, Any]								=relative_attention_max_distance
										__magic_name__	:	Dict								=dropout_rate
										__magic_name__	:	Dict								=layer_norm_epsilon
										__magic_name__	:	List[str]								=initializer_factor
										__magic_name__	:	Any								=feed_forward_proj
										__magic_name__	:	Optional[int]								=use_cache

										__magic_name__	:	Tuple								=self.feed_forward_proj.split("""-""" )
										__magic_name__	:	int								=act_info[-1]
										__magic_name__	:	int								=act_info[0] == """gated"""

										if len(__snake_case ) > 1 and act_info[0] != "gated" or len(__snake_case ) > 2:
														raise ValueError(
														    f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
														    """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
														    """'gated-gelu' or 'relu'""" )

										# for backwards compatibility
										if feed_forward_proj == "gated-gelu":
														__magic_name__	:	str								="""gelu_new"""

										super().__init__(
										    pad_token_id=__snake_case    , eos_token_id=__snake_case    , is_encoder_decoder=__snake_case    , **__snake_case    , )




class 		__A      (			UpperCamelCase__			):

						@property
						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	int								={
										    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
										    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
										}
										if self.use_past:
														__magic_name__	:	List[str]								="""past_encoder_sequence + sequence"""
														__magic_name__	:	List[Any]								={0: """batch"""}
														__magic_name__	:	Union[str, Any]								={0: """batch""", 1: """past_decoder_sequence + sequence"""}
										else:
														__magic_name__	:	int								={0: """batch""", 1: """decoder_sequence"""}
														__magic_name__	:	Tuple								={0: """batch""", 1: """decoder_sequence"""}

										if self.use_past:
														self.fill_with_past_key_values_(__snake_case    , direction="""inputs""" )

										return common_inputs


						@property
						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										return 13




