

import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

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






class 	__magic_name__				(       nn.Module							):






     def __init__(							self		,	_lowercase		,	_lowercase=1.0  )->    str:
         super().__init__()

         UpperCamelCase_      =	cut_size
         UpperCamelCase_      =	cut_power






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase  )->    str:
         UpperCamelCase_     ,					UpperCamelCase_      =	pixel_values.shape[2:4]
         UpperCamelCase_      =	min(_lowercase		,	_lowercase  )
         UpperCamelCase_      =	min(_lowercase		,	_lowercase		,	self.cut_size  )
         UpperCamelCase_      =	[]
         for _ in range(_lowercase  ):
             UpperCamelCase_      =	int(torch.rand([]  ) ** self.cut_power * (max_size - min_size) + min_size  )
             UpperCamelCase_      =	torch.randint(0		,	sideX - size + 1		,	()  )
             UpperCamelCase_      =	torch.randint(0		,	sideY - size + 1		,	()  )
             UpperCamelCase_      =	pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
             cutouts.append(F.adaptive_avg_poolad(_lowercase		,	self.cut_size  )  )
         return torch.cat(_lowercase  )




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	Optional[Any]:



    """simple docstring"""






    UpperCamelCase_      =	F.normalize(SCREAMING_SNAKE_CASE_ ,	dim=-1 )
    UpperCamelCase_      =	F.normalize(SCREAMING_SNAKE_CASE_ ,	dim=-1 )
    return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	Tuple:



    """simple docstring"""






    for param in model.parameters():
        UpperCamelCase_      =	value






class 	__magic_name__				(       snake_case							):






     def __init__(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	)->    List[Any]:
         super().__init__()
         self.register_modules(
             vae=_lowercase		,	text_encoder=_lowercase		,	clip_model=_lowercase		,	tokenizer=_lowercase		,	unet=_lowercase		,	scheduler=_lowercase		,	feature_extractor=_lowercase		,	)

         UpperCamelCase_      =	transforms.Normalize(mean=feature_extractor.image_mean		,	std=feature_extractor.image_std  )
         UpperCamelCase_      =	(
             feature_extractor.size
             if isinstance(feature_extractor.size		,	_lowercase  )
             else feature_extractor.size["shortest_edge"]
         )
         UpperCamelCase_      =	MakeCutouts(self.cut_out_size  )

         set_requires_grad(self.text_encoder		,	_lowercase  )
         set_requires_grad(self.clip_model		,	_lowercase  )






     def   UpperCAmelCase_							(							self		,	_lowercase = "auto"  )->    List[Any]:
         if slice_size == "auto":
             # half the attention head size is usually a good trade-off between
             # speed and memory
             UpperCamelCase_      =	self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(_lowercase  )






     def   UpperCAmelCase_							(							self  )->    List[str]:
         self.enable_attention_slicing(_lowercase  )






     def   UpperCAmelCase_							(							self  )->    Optional[int]:
         set_requires_grad(self.vae		,	_lowercase  )






     def   UpperCAmelCase_							(							self  )->    List[str]:
         set_requires_grad(self.vae		,	_lowercase  )






     def   UpperCAmelCase_							(							self  )->    Union[str, Any]:
         set_requires_grad(self.unet		,	_lowercase  )






     def   UpperCAmelCase_							(							self  )->    Union[str, Any]:
         set_requires_grad(self.unet		,	_lowercase  )






     @torch.enable_grad()
     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase=True		,	)->    Optional[int]:
         UpperCamelCase_      =	latents.detach().requires_grad_()

         UpperCamelCase_      =	self.scheduler.scale_model_input(_lowercase		,	_lowercase  )

         # predict the noise residual
         UpperCamelCase_      =	self.unet(_lowercase		,	_lowercase		,	encoder_hidden_states=_lowercase  ).sample

         if isinstance(self.scheduler		,	(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)  ):
             UpperCamelCase_      =	self.scheduler.alphas_cumprod[timestep]
             UpperCamelCase_      =	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
             UpperCamelCase_      =	(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

             UpperCamelCase_      =	torch.sqrt(_lowercase  )
             UpperCamelCase_      =	pred_original_sample * (fac) + latents * (1 - fac)
         elif isinstance(self.scheduler		,	_lowercase  ):
             UpperCamelCase_      =	self.scheduler.sigmas[index]
             UpperCamelCase_      =	latents - sigma * noise_pred
         else:
             raise ValueError(F"scheduler type {type(self.scheduler  )} not supported"  )

         UpperCamelCase_      =	1 / self.vae.config.scaling_factor * sample
         UpperCamelCase_      =	self.vae.decode(_lowercase  ).sample
         UpperCamelCase_      =	(image / 2 + 0.5).clamp(0		,	1  )

         if use_cutouts:
             UpperCamelCase_      =	self.make_cutouts(_lowercase		,	_lowercase  )
         else:
             UpperCamelCase_      =	transforms.Resize(self.cut_out_size  )(_lowercase  )
         UpperCamelCase_      =	self.normalize(_lowercase  ).to(latents.dtype  )

         UpperCamelCase_      =	self.clip_model.get_image_features(_lowercase  )
         UpperCamelCase_      =	image_embeddings_clip / image_embeddings_clip.norm(p=2		,	dim=-1		,	keepdim=_lowercase  )

         if use_cutouts:
             UpperCamelCase_      =	spherical_dist_loss(_lowercase		,	_lowercase  )
             UpperCamelCase_      =	dists.view([num_cutouts, sample.shape[0], -1]  )
             UpperCamelCase_      =	dists.sum(2  ).mean(0  ).sum() * clip_guidance_scale
         else:
             UpperCamelCase_      =	spherical_dist_loss(_lowercase		,	_lowercase  ).mean() * clip_guidance_scale

         UpperCamelCase_      =	-torch.autograd.grad(_lowercase		,	_lowercase  )[0]

         if isinstance(self.scheduler		,	_lowercase  ):
             UpperCamelCase_      =	latents.detach() + grads * (sigma**2)
             UpperCamelCase_      =	noise_pred_original
         else:
             UpperCamelCase_      =	noise_pred_original - torch.sqrt(_lowercase  ) * grads
         return noise_pred, latents






     @torch.no_grad()
     def __call__(							self		,	_lowercase		,	_lowercase = 512		,	_lowercase = 512		,	_lowercase = 50		,	_lowercase = 7.5		,	_lowercase = 1		,	_lowercase = 0.0		,	_lowercase = 100		,	_lowercase = None		,	_lowercase = 4		,	_lowercase = True		,	_lowercase = None		,	_lowercase = None		,	_lowercase = "pil"		,	_lowercase = True		,	)->    str:
         if isinstance(_lowercase		,	_lowercase  ):
             UpperCamelCase_      =	1
         elif isinstance(_lowercase		,	_lowercase  ):
             UpperCamelCase_      =	len(_lowercase  )
         else:
             raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(_lowercase  )}"  )

         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}."  )

         # get prompt text embeddings
         UpperCamelCase_      =	self.tokenizer(
             _lowercase		,	padding="max_length"		,	max_length=self.tokenizer.model_max_length		,	truncation=_lowercase		,	return_tensors="pt"		,	)
         UpperCamelCase_      =	self.text_encoder(text_input.input_ids.to(self.device  )  )[0]
         # duplicate text embeddings for each generation per prompt
         UpperCamelCase_      =	text_embeddings.repeat_interleave(_lowercase		,	dim=0  )

         if clip_guidance_scale > 0:
             if clip_prompt is not None:
                 UpperCamelCase_      =	self.tokenizer(
                     _lowercase		,	padding="max_length"		,	max_length=self.tokenizer.model_max_length		,	truncation=_lowercase		,	return_tensors="pt"		,	).input_ids.to(self.device  )
             else:
                 UpperCamelCase_      =	text_input.input_ids.to(self.device  )
             UpperCamelCase_      =	self.clip_model.get_text_features(_lowercase  )
             UpperCamelCase_      =	text_embeddings_clip / text_embeddings_clip.norm(p=2		,	dim=-1		,	keepdim=_lowercase  )
             # duplicate text embeddings clip for each generation per prompt
             UpperCamelCase_      =	text_embeddings_clip.repeat_interleave(_lowercase		,	dim=0  )

         # 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.
         UpperCamelCase_      =	guidance_scale > 1.0
         # get unconditional embeddings for classifier free guidance
         if do_classifier_free_guidance:
             UpperCamelCase_      =	text_input.input_ids.shape[-1]
             UpperCamelCase_      =	self.tokenizer([""]		,	padding="max_length"		,	max_length=_lowercase		,	return_tensors="pt"  )
             UpperCamelCase_      =	self.text_encoder(uncond_input.input_ids.to(self.device  )  )[0]
             # duplicate unconditional embeddings for each generation per prompt
             UpperCamelCase_      =	uncond_embeddings.repeat_interleave(_lowercase		,	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
             UpperCamelCase_      =	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`.
         UpperCamelCase_      =	(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
         UpperCamelCase_      =	text_embeddings.dtype
         if latents is None:
             if self.device.type == "mps":
                 # randn does not work reproducibly on mps
                 UpperCamelCase_      =	torch.randn(_lowercase		,	generator=_lowercase		,	device="cpu"		,	dtype=_lowercase  ).to(
                     self.device  )
             else:
                 UpperCamelCase_      =	torch.randn(_lowercase		,	generator=_lowercase		,	device=self.device		,	dtype=_lowercase  )
         else:
             if latents.shape != latents_shape:
                 raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"  )
             UpperCamelCase_      =	latents.to(self.device  )

         # set timesteps
         UpperCamelCase_      =	"offset" in set(inspect.signature(self.scheduler.set_timesteps  ).parameters.keys()  )
         UpperCamelCase_      =	{}
         if accepts_offset:
             UpperCamelCase_      =	1

         self.scheduler.set_timesteps(_lowercase		,	**_lowercase  )

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

         # scale the initial noise by the standard deviation required by the scheduler
         UpperCamelCase_      =	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]
         UpperCamelCase_      =	"eta" in set(inspect.signature(self.scheduler.step  ).parameters.keys()  )
         UpperCamelCase_      =	{}
         if accepts_eta:
             UpperCamelCase_      =	eta

         # check if the scheduler accepts generator
         UpperCamelCase_      =	"generator" in set(inspect.signature(self.scheduler.step  ).parameters.keys()  )
         if accepts_generator:
             UpperCamelCase_      =	generator

         for i, t in enumerate(self.progress_bar(_lowercase  )  ):
             # expand the latents if we are doing classifier free guidance
             UpperCamelCase_      =	torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents
             UpperCamelCase_      =	self.scheduler.scale_model_input(_lowercase		,	_lowercase  )

             # predict the noise residual
             UpperCamelCase_      =	self.unet(_lowercase		,	_lowercase		,	encoder_hidden_states=_lowercase  ).sample

             # perform classifier free guidance
             if do_classifier_free_guidance:
                 UpperCamelCase_     ,					UpperCamelCase_      =	noise_pred.chunk(2  )
                 UpperCamelCase_      =	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

             # perform clip guidance
             if clip_guidance_scale > 0:
                 UpperCamelCase_      =	(
                     text_embeddings.chunk(2  )[1] if do_classifier_free_guidance else text_embeddings
                 )
                 UpperCamelCase_     ,					UpperCamelCase_      =	self.cond_fn(
                     _lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	)

             # compute the previous noisy sample x_t -> x_t-1
             UpperCamelCase_      =	self.scheduler.step(_lowercase		,	_lowercase		,	_lowercase		,	**_lowercase  ).prev_sample

         # scale and decode the image latents with vae
         UpperCamelCase_      =	1 / self.vae.config.scaling_factor * latents
         UpperCamelCase_      =	self.vae.decode(_lowercase  ).sample

         UpperCamelCase_      =	(image / 2 + 0.5).clamp(0		,	1  )
         UpperCamelCase_      =	image.cpu().permute(0		,	2		,	3		,	1  ).numpy()

         if output_type == "pil":
             UpperCamelCase_      =	self.numpy_to_pil(_lowercase  )

         if not return_dict:
             return (image, None)

         return StableDiffusionPipelineOutput(images=_lowercase		,	nsfw_content_detected=_lowercase  )



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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


SCREAMING_SNAKE_CASE		:Any				  =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:Dict				  =    {
    """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json""",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}






class 	__magic_name__				(       snake_case							):
     UpperCamelCase_  :str								=			"""blenderbot"""
     UpperCamelCase_  :Union[str, Any]								=			["""past_key_values"""]
     UpperCamelCase_  :Any								=			{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






     def __init__(							self		,	_lowercase=8_008		,	_lowercase=128		,	_lowercase=2		,	_lowercase=10_240		,	_lowercase=32		,	_lowercase=24		,	_lowercase=10_240		,	_lowercase=32		,	_lowercase=0.0		,	_lowercase=0.0		,	_lowercase=True		,	_lowercase=True		,	_lowercase="gelu"		,	_lowercase=2_560		,	_lowercase=0.1		,	_lowercase=0.0		,	_lowercase=0.0		,	_lowercase=0.02		,	_lowercase=1		,	_lowercase=False		,	_lowercase=0		,	_lowercase=1		,	_lowercase=2		,	_lowercase=3		,	_lowercase=2		,	**_lowercase		,	)->    List[str]:
         UpperCamelCase_      =	vocab_size
         UpperCamelCase_      =	max_position_embeddings
         UpperCamelCase_      =	d_model
         UpperCamelCase_      =	encoder_ffn_dim
         UpperCamelCase_      =	encoder_layers
         UpperCamelCase_      =	encoder_attention_heads
         UpperCamelCase_      =	decoder_ffn_dim
         UpperCamelCase_      =	decoder_layers
         UpperCamelCase_      =	decoder_attention_heads
         UpperCamelCase_      =	dropout
         UpperCamelCase_      =	attention_dropout
         UpperCamelCase_      =	activation_dropout
         UpperCamelCase_      =	activation_function
         UpperCamelCase_      =	init_std
         UpperCamelCase_      =	encoder_layerdrop
         UpperCamelCase_      =	decoder_layerdrop
         UpperCamelCase_      =	use_cache
         UpperCamelCase_      =	encoder_layers
         UpperCamelCase_      =	scale_embedding  # scale factor will be sqrt(d_model) if True

         super().__init__(
             pad_token_id=_lowercase		,	bos_token_id=_lowercase		,	eos_token_id=_lowercase		,	is_encoder_decoder=_lowercase		,	decoder_start_token_id=_lowercase		,	encoder_no_repeat_ngram_size=_lowercase		,	forced_eos_token_id=_lowercase		,	**_lowercase		,	)






class 	__magic_name__				(       snake_case							):






     @property
     def   UpperCAmelCase_							(							self  )->    Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
             UpperCamelCase_      =	OrderedDict(
                 [
                     ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                     ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                 ]  )
             if self.use_past:
                 UpperCamelCase_      =	{0: "batch"}
                 UpperCamelCase_      =	{0: "batch", 1: "past_decoder_sequence + sequence"}
             else:
                 UpperCamelCase_      =	{0: "batch", 1: "decoder_sequence"}
                 UpperCamelCase_      =	{0: "batch", 1: "decoder_sequence"}
             if self.use_past:
                 self.fill_with_past_key_values_(_lowercase		,	direction="inputs"  )
         elif self.task == "causal-lm":
             UpperCamelCase_      =	OrderedDict(
                 [
                     ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                     ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                 ]  )
             if self.use_past:
                 UpperCamelCase_     ,					UpperCamelCase_      =	self.num_layers
                 for i in range(_lowercase  ):
                     UpperCamelCase_      =	{0: "batch", 2: "past_sequence + sequence"}
                     UpperCamelCase_      =	{0: "batch", 2: "past_sequence + sequence"}
         else:
             UpperCamelCase_      =	OrderedDict(
                 [
                     ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                     ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                     ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                     ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
                 ]  )

         return common_inputs






     @property
     # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
     def   UpperCAmelCase_							(							self  )->    Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
             UpperCamelCase_      =	super().outputs
         else:
             UpperCamelCase_      =	super(_lowercase		,	self  ).outputs
             if self.use_past:
                 UpperCamelCase_     ,					UpperCamelCase_      =	self.num_layers
                 for i in range(_lowercase  ):
                     UpperCamelCase_      =	{0: "batch", 2: "past_sequence + sequence"}
                     UpperCamelCase_      =	{0: "batch", 2: "past_sequence + sequence"}
         return common_outputs






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = -1		,	_lowercase = -1		,	_lowercase = False		,	_lowercase = None		,	)->    Mapping[str, Any]:
         UpperCamelCase_      =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )
         # Generate decoder inputs
         UpperCamelCase_      =	seq_length if not self.use_past else 1
         UpperCamelCase_      =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )
         UpperCamelCase_      =	{F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
         UpperCamelCase_      =	dict(**_lowercase		,	**_lowercase  )

         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
             UpperCamelCase_      =	common_inputs["decoder_input_ids"].shape[1]
             UpperCamelCase_     ,					UpperCamelCase_      =	self.num_attention_heads
             UpperCamelCase_      =	(
                 batch,
                 num_encoder_attention_heads,
                 encoder_seq_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             UpperCamelCase_      =	decoder_seq_length
             UpperCamelCase_      =	(
                 batch,
                 num_decoder_attention_heads,
                 decoder_past_length,
                 self._config.hidden_size // num_decoder_attention_heads,
             )
             UpperCamelCase_      =	torch.cat(
                 [common_inputs["decoder_attention_mask"], torch.ones(_lowercase		,	_lowercase  )]		,	dim=1  )
             UpperCamelCase_      =	[]
             UpperCamelCase_     ,					UpperCamelCase_      =	self.num_layers

             for _ in range(_lowercase  ):
                 common_inputs["past_key_values"].append(
                     (
                         torch.zeros(_lowercase  ),
                         torch.zeros(_lowercase  ),
                         torch.zeros(_lowercase  ),
                         torch.zeros(_lowercase  ),
                     )  )
         return common_inputs






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = -1		,	_lowercase = -1		,	_lowercase = False		,	_lowercase = None		,	)->    Mapping[str, Any]:
         UpperCamelCase_      =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )

         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
             UpperCamelCase_      =	seqlen
             UpperCamelCase_     ,					UpperCamelCase_      =	self.num_layers
             UpperCamelCase_     ,					UpperCamelCase_      =	self.num_attention_heads
             UpperCamelCase_      =	(
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             UpperCamelCase_      =	common_inputs["attention_mask"].dtype
             UpperCamelCase_      =	torch.cat(
                 [common_inputs["attention_mask"], torch.ones(_lowercase		,	_lowercase		,	dtype=_lowercase  )]		,	dim=1  )
             UpperCamelCase_      =	[
                 (torch.zeros(_lowercase  ), torch.zeros(_lowercase  )) for _ in range(_lowercase  )
             ]
         return common_inputs






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = -1		,	_lowercase = -1		,	_lowercase = False		,	_lowercase = None		,	)->    Mapping[str, Any]:
         # Copied from OnnxConfig.generate_dummy_inputs
         # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         UpperCamelCase_      =	compute_effective_axis_dimension(
             _lowercase		,	fixed_dimension=OnnxConfig.default_fixed_batch		,	num_token_to_add=0  )

         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         UpperCamelCase_      =	tokenizer.num_special_tokens_to_add(_lowercase  )
         UpperCamelCase_      =	compute_effective_axis_dimension(
             _lowercase		,	fixed_dimension=OnnxConfig.default_fixed_sequence		,	num_token_to_add=_lowercase  )

         # Generate dummy inputs according to compute batch and sequence
         UpperCamelCase_      =	[" ".join([tokenizer.unk_token]  ) * seq_length] * batch_size
         UpperCamelCase_      =	dict(tokenizer(_lowercase		,	return_tensors=_lowercase  )  )
         return common_inputs






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = -1		,	_lowercase = -1		,	_lowercase = False		,	_lowercase = None		,	)->    Mapping[str, Any]:
         if self.task in ["default", "seq2seq-lm"]:
             UpperCamelCase_      =	self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                 _lowercase		,	batch_size=_lowercase		,	seq_length=_lowercase		,	is_pair=_lowercase		,	framework=_lowercase  )

         elif self.task == "causal-lm":
             UpperCamelCase_      =	self._generate_dummy_inputs_for_causal_lm(
                 _lowercase		,	batch_size=_lowercase		,	seq_length=_lowercase		,	is_pair=_lowercase		,	framework=_lowercase  )
         else:
             UpperCamelCase_      =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 _lowercase		,	batch_size=_lowercase		,	seq_length=_lowercase		,	is_pair=_lowercase		,	framework=_lowercase  )

         return common_inputs






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )->    List[Any]:
         if self.task in ["default", "seq2seq-lm"]:
             UpperCamelCase_      =	super()._flatten_past_key_values_(_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )
         else:
             UpperCamelCase_      =	super(_lowercase		,	self  )._flatten_past_key_values_(
                 _lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase  )->    Union[str, Any]:
         if direction not in ["inputs", "outputs"]:
             raise ValueError(F"direction must either be \"inputs\" or \"outputs\", but {direction} was given"  )

         UpperCamelCase_      =	"past_key_values" if direction == "inputs" else "present"
         UpperCamelCase_     ,					UpperCamelCase_      =	self.num_layers

         UpperCamelCase_      =	"past_encoder_sequence"
         UpperCamelCase_      =	"past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"

         for i in range(_lowercase  ):
             UpperCamelCase_      =	{0: "batch", 2: decoder_sequence}
             UpperCamelCase_      =	{0: "batch", 2: decoder_sequence}
             UpperCamelCase_      =	{0: "batch", 2: encoder_sequence}
             UpperCamelCase_      =	{0: "batch", 2: encoder_sequence}
