



'''simple docstring'''




from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


UpperCamelCase__			:      Optional[Any]        =					logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCamelCase__			:      Optional[int]        =					'''\n    Examples:\n        ```py\n        >>> from PIL import Image\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif, load_image\n\n        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n        >>> repo = \"openai/shap-e-img2img\"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 3.0\n        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"\n        >>> image = load_image(image_url).convert(\"RGB\")\n\n        >>> images = pipe(\n        ...     image,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")\n        ```\n'''






@dataclass
class 		_UpperCamelCase    (				__SCREAMING_SNAKE_CASE       ):


			'''simple docstring'''

			_A :						Any            =						42






class 		_UpperCamelCase    (				__SCREAMING_SNAKE_CASE       ):


			'''simple docstring'''

			def __init__(				self	:					Any				,	lowerCAmelCase__	:					PriorTransformer				,	lowerCAmelCase__	:					CLIPVisionModel				,	lowerCAmelCase__	:					CLIPImageProcessor				,	lowerCAmelCase__	:					HeunDiscreteScheduler				,	lowerCAmelCase__	:					ShapERenderer				,	):





					"""simple docstring"""






					super().__init__()

					self.register_modules(
					    prior=UpperCamelCase__				,	image_encoder=UpperCamelCase__				,	image_processor=UpperCamelCase__				,	scheduler=UpperCamelCase__				,	renderer=UpperCamelCase__				,	)

			def UpperCamelCase__   (				self	:					List[Any]				,	lowerCAmelCase__	:					int				,	lowerCAmelCase__	:					int				,	lowerCAmelCase__	:					Tuple				,	lowerCAmelCase__	:					Any				,	lowerCAmelCase__	:					int				,	lowerCAmelCase__	:					Tuple		):





					"""simple docstring"""






					if latents is None:
							__SCREAMING_SNAKE_CASE			:						str       			=						randn_tensor(UpperCamelCase__				,	generator=UpperCamelCase__				,	device=UpperCamelCase__				,	dtype=UpperCamelCase__		)
					else:
							if latents.shape != shape:
									raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"		)
							__SCREAMING_SNAKE_CASE			:						Optional[Any]       			=						latents.to(UpperCamelCase__		)

					__SCREAMING_SNAKE_CASE			:						Optional[int]       			=						latents * scheduler.init_noise_sigma
					return latents

			def UpperCamelCase__   (				self	:					Optional[int]				,	lowerCAmelCase__	:					Optional[int]=0		):





					"""simple docstring"""






					if is_accelerate_available():
							from accelerate import cpu_offload
					else:
							raise ImportError("""Please install accelerate via `pip install accelerate`"""		)

					__SCREAMING_SNAKE_CASE			:						Optional[int]       			=						torch.device(F"cuda:{gpu_id}"		)

					__SCREAMING_SNAKE_CASE			:						Optional[int]       			=						[self.image_encoder, self.prior]
					for cpu_offloaded_model in models:
							if cpu_offloaded_model is not None:
									cpu_offload(UpperCamelCase__				,	UpperCamelCase__		)

			@property
			def UpperCamelCase__   (				self	:					Optional[int]		):





					"""simple docstring"""






					if self.device != torch.device("""meta"""		) or not hasattr(self.image_encoder				,	"""_hf_hook"""		):
							return self.device
					for module in self.image_encoder.modules():
							if (
							    hasattr(UpperCamelCase__				,	"""_hf_hook"""		)
							    and hasattr(module._hf_hook				,	"""execution_device"""		)
							    and module._hf_hook.execution_device is not None
							):
									return torch.device(module._hf_hook.execution_device		)
					return self.device

			def UpperCamelCase__   (				self	:					Optional[int]				,	lowerCAmelCase__	:					List[Any]				,	lowerCAmelCase__	:					Optional[Any]				,	lowerCAmelCase__	:					Optional[Any]				,	lowerCAmelCase__	:					str				,	):





					"""simple docstring"""






					if isinstance(UpperCamelCase__				,	UpperCamelCase__		) and isinstance(image[0]				,	torch.Tensor		):
							__SCREAMING_SNAKE_CASE			:						int       			=						torch.cat(UpperCamelCase__				,	axis=0		) if image[0].ndim == 4 else torch.stack(UpperCamelCase__				,	axis=0		)

					if not isinstance(UpperCamelCase__				,	torch.Tensor		):
							__SCREAMING_SNAKE_CASE			:						Tuple       			=						self.image_processor(UpperCamelCase__				,	return_tensors="""pt"""		).pixel_values[0].unsqueeze(0		)

					__SCREAMING_SNAKE_CASE			:						Union[str, Any]       			=						image.to(dtype=self.image_encoder.dtype				,	device=UpperCamelCase__		)

					__SCREAMING_SNAKE_CASE			:						Dict       			=						self.image_encoder(UpperCamelCase__		)['''last_hidden_state''']
					__SCREAMING_SNAKE_CASE			:						Any       			=						image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

					__SCREAMING_SNAKE_CASE			:						Any       			=						image_embeds.repeat_interleave(UpperCamelCase__				,	dim=0		)

					if do_classifier_free_guidance:
							__SCREAMING_SNAKE_CASE			:						Dict       			=						torch.zeros_like(UpperCamelCase__		)

							# 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
							__SCREAMING_SNAKE_CASE			:						Any       			=						torch.cat([negative_image_embeds, image_embeds]		)

					return image_embeds



			@torch.no_grad()
			@replace_example_docstring(UpperCamelCase__		)
			def __call__(				self	:					Optional[Any]				,	lowerCAmelCase__	:					Union[PIL.Image.Image, List[PIL.Image.Image]]				,	lowerCAmelCase__	:					int = 1				,	lowerCAmelCase__	:					int = 2_5				,	lowerCAmelCase__	:					Optional[Union[torch.Generator, List[torch.Generator]]] = None				,	lowerCAmelCase__	:					Optional[torch.FloatTensor] = None				,	lowerCAmelCase__	:					float = 4.0				,	lowerCAmelCase__	:					int = 6_4				,	lowerCAmelCase__	:					Optional[str] = "pil"				,	lowerCAmelCase__	:					bool = True				,	):





					"""simple docstring"""






					if isinstance(UpperCamelCase__				,	PIL.Image.Image		):
							__SCREAMING_SNAKE_CASE			:						Union[str, Any]       			=						1
					elif isinstance(UpperCamelCase__				,	torch.Tensor		):
							__SCREAMING_SNAKE_CASE			:						Union[str, Any]       			=						image.shape[0]
					elif isinstance(UpperCamelCase__				,	UpperCamelCase__		) and isinstance(image[0]				,	(torch.Tensor, PIL.Image.Image)		):
							__SCREAMING_SNAKE_CASE			:						List[str]       			=						len(UpperCamelCase__		)
					else:
							raise ValueError(
							    F"`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(UpperCamelCase__		)}"		)

					__SCREAMING_SNAKE_CASE			:						int       			=						self._execution_device

					__SCREAMING_SNAKE_CASE			:						List[Any]       			=						batch_size * num_images_per_prompt

					__SCREAMING_SNAKE_CASE			:						Dict       			=						guidance_scale > 1.0
					__SCREAMING_SNAKE_CASE			:						Optional[int]       			=						self._encode_image(UpperCamelCase__				,	UpperCamelCase__				,	UpperCamelCase__				,	UpperCamelCase__		)

					# prior

					self.scheduler.set_timesteps(UpperCamelCase__				,	device=UpperCamelCase__		)
					__SCREAMING_SNAKE_CASE			:						List[str]       			=						self.scheduler.timesteps

					__SCREAMING_SNAKE_CASE			:						Tuple       			=						self.prior.config.num_embeddings
					__SCREAMING_SNAKE_CASE			:						int       			=						self.prior.config.embedding_dim

					__SCREAMING_SNAKE_CASE			:						Union[str, Any]       			=						self.prepare_latents(
					    (batch_size, num_embeddings * embedding_dim)				,	image_embeds.dtype				,	UpperCamelCase__				,	UpperCamelCase__				,	UpperCamelCase__				,	self.scheduler				,	)

					# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
					__SCREAMING_SNAKE_CASE			:						Dict       			=						latents.reshape(latents.shape[0]				,	UpperCamelCase__				,	UpperCamelCase__		)

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

							__SCREAMING_SNAKE_CASE			:						Dict       			=						self.prior(
							    UpperCamelCase__				,	timestep=UpperCamelCase__				,	proj_embedding=UpperCamelCase__				,	).predicted_image_embedding

							# remove the variance
							__SCREAMING_SNAKE_CASE			:						int       			=						noise_pred.split(
							    scaled_model_input.shape[2]				,	dim=2		)  # batch_size, num_embeddings, embedding_dim

							if do_classifier_free_guidance is not None:
									__SCREAMING_SNAKE_CASE			:						Dict       			=						noise_pred.chunk(2		)
									__SCREAMING_SNAKE_CASE			:						Tuple       			=						noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

							__SCREAMING_SNAKE_CASE			:						Optional[Any]       			=						self.scheduler.step(
							    UpperCamelCase__				,	timestep=UpperCamelCase__				,	sample=UpperCamelCase__				,	).prev_sample

					if output_type == "latent":
							return ShapEPipelineOutput(images=UpperCamelCase__		)

					__SCREAMING_SNAKE_CASE			:						int       			=						[]
					for i, latent in enumerate(UpperCamelCase__		):
							print()
							__SCREAMING_SNAKE_CASE			:						int       			=						self.renderer.decode(
							    latent[None, :]				,	UpperCamelCase__				,	size=UpperCamelCase__				,	ray_batch_size=4_0_9_6				,	n_coarse_samples=6_4				,	n_fine_samples=1_2_8				,	)

							images.append(UpperCamelCase__		)

					__SCREAMING_SNAKE_CASE			:						Optional[Any]       			=						torch.stack(UpperCamelCase__		)

					if output_type not in ["np", "pil"]:
							raise ValueError(F"Only the output types `pil` and `np` are supported not output_type={output_type}"		)

					__SCREAMING_SNAKE_CASE			:						Any       			=						images.cpu().numpy()

					if output_type == "pil":
							__SCREAMING_SNAKE_CASE			:						List[Any]       			=						[self.numpy_to_pil(UpperCamelCase__		) for image in images]

					# Offload last model to CPU
					if hasattr(self				,	"""final_offload_hook"""		) and self.final_offload_hook is not None:
							self.final_offload_hook.offload()

					if not return_dict:
							return (images,)

					return ShapEPipelineOutput(images=UpperCamelCase__		)

'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional

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


lowercase__						       =      logging.get_logger(__name__)

lowercase__						       =      {
    "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json",
    # See all Marian models at https://huggingface.co/models?filter=marian
}






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						"""marian"""
		lowerCamelCase     							=						["""past_key_values"""]
		lowerCamelCase     							=						{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



		def __init__(      self				:			Any  ,	UpperCamelCase__				:			Dict=5_8101  ,	UpperCamelCase__				:			int=None  ,	UpperCamelCase__				:			Union[str, Any]=1024  ,	UpperCamelCase__				:			List[Any]=12  ,	UpperCamelCase__				:			str=4096  ,	UpperCamelCase__				:			Optional[Any]=16  ,	UpperCamelCase__				:			str=12  ,	UpperCamelCase__				:			Tuple=4096  ,	UpperCamelCase__				:			Any=16  ,	UpperCamelCase__				:			Tuple=0.0  ,	UpperCamelCase__				:			Dict=0.0  ,	UpperCamelCase__				:			Dict=True  ,	UpperCamelCase__				:			Any=True  ,	UpperCamelCase__				:			List[Any]="gelu"  ,	UpperCamelCase__				:			Tuple=1024  ,	UpperCamelCase__				:			Optional[Any]=0.1  ,	UpperCamelCase__				:			str=0.0  ,	UpperCamelCase__				:			Optional[int]=0.0  ,	UpperCamelCase__				:			Optional[Any]=0.02  ,	UpperCamelCase__				:			Tuple=5_8100  ,	UpperCamelCase__				:			Union[str, Any]=False  ,	UpperCamelCase__				:			str=5_8100  ,	UpperCamelCase__				:			List[str]=0  ,	UpperCamelCase__				:			int=0  ,	UpperCamelCase__				:			Dict=True  ,	**UpperCamelCase__				:			int  ,	)						->			int:






				"""simple docstring"""







				snake_case  :       List[Any]     			=						vocab_size
				snake_case  :       Optional[int]     			=						decoder_vocab_size or vocab_size
				snake_case  :       int     			=						max_position_embeddings
				snake_case  :       Tuple     			=						d_model
				snake_case  :       int     			=						encoder_ffn_dim
				snake_case  :       Optional[int]     			=						encoder_layers
				snake_case  :       Union[str, Any]     			=						encoder_attention_heads
				snake_case  :       List[str]     			=						decoder_ffn_dim
				snake_case  :       List[str]     			=						decoder_layers
				snake_case  :       List[str]     			=						decoder_attention_heads
				snake_case  :       Any     			=						dropout
				snake_case  :       Optional[Any]     			=						attention_dropout
				snake_case  :       Tuple     			=						activation_dropout
				snake_case  :       Union[str, Any]     			=						activation_function
				snake_case  :       int     			=						init_std
				snake_case  :       Dict     			=						encoder_layerdrop
				snake_case  :       Dict     			=						decoder_layerdrop
				snake_case  :       List[Any]     			=						use_cache
				snake_case  :       int     			=						encoder_layers
				snake_case  :       List[str]     			=						scale_embedding  # scale factor will be sqrt(d_model) if True
				snake_case  :       str     			=						share_encoder_decoder_embeddings
				super().__init__(
				    pad_token_id=UpperCamelCase__  ,	eos_token_id=UpperCamelCase__  ,	is_encoder_decoder=UpperCamelCase__  ,	decoder_start_token_id=UpperCamelCase__  ,	forced_eos_token_id=UpperCamelCase__  ,	**UpperCamelCase__  ,	)







class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		@property
		# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
		def       lowerCAmelCase				(      self				:			Optional[int]      )						->			Mapping[str, Mapping[int, str]]:






				"""simple docstring"""







				if self.task in ["default", "seq2seq-lm"]:
						snake_case  :       Any     			=						OrderedDict(
						    [
						        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
						        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
						    ]      )

						if self.use_past:
								snake_case  :       List[Any]     			=						{0: '''batch'''}
								snake_case  :       Optional[Any]     			=						{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
						else:
								snake_case  :       List[Any]     			=						{0: '''batch''', 1: '''decoder_sequence'''}
								snake_case  :       Any     			=						{0: '''batch''', 1: '''decoder_sequence'''}

						if self.use_past:
								self.fill_with_past_key_values_(UpperCamelCase__  ,	direction='''inputs'''      )
				elif self.task == "causal-lm":
						# TODO: figure this case out.
						snake_case  :       Any     			=						OrderedDict(
						    [
						        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
						        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
						    ]      )
						if self.use_past:
								snake_case			,snake_case  :       Union[str, Any]     			=						self.num_layers
								for i in range(UpperCamelCase__      ):
										snake_case  :       Optional[Any]     			=						{0: '''batch''', 2: '''past_sequence + sequence'''}
										snake_case  :       List[str]     			=						{0: '''batch''', 2: '''past_sequence + sequence'''}
				else:
						snake_case  :       Dict     			=						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       lowerCAmelCase				(      self				:			Any      )						->			Mapping[str, Mapping[int, str]]:






				"""simple docstring"""







				if self.task in ["default", "seq2seq-lm"]:
						snake_case  :       Any     			=						super().outputs
				else:
						snake_case  :       int     			=						super(UpperCamelCase__  ,	self      ).outputs
						if self.use_past:
								snake_case			,snake_case  :       Optional[Any]     			=						self.num_layers
								for i in range(UpperCamelCase__      ):
										snake_case  :       Optional[Any]     			=						{0: '''batch''', 2: '''past_sequence + sequence'''}
										snake_case  :       Optional[Any]     			=						{0: '''batch''', 2: '''past_sequence + sequence'''}
				return common_outputs



		def       lowerCAmelCase				(      self				:			Union[str, Any]  ,	UpperCamelCase__				:			PreTrainedTokenizer  ,	UpperCamelCase__				:			int = -1  ,	UpperCamelCase__				:			int = -1  ,	UpperCamelCase__				:			bool = False  ,	UpperCamelCase__				:			Optional[TensorType] = None  ,	)						->			Mapping[str, Any]:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						self._generate_dummy_inputs_for_encoder_and_decoder(
				    UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )

				# Generate decoder inputs
				snake_case  :       Optional[int]     			=						seq_length if not self.use_past else 1
				snake_case  :       Optional[int]     			=						self._generate_dummy_inputs_for_encoder_and_decoder(
				    UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )
				snake_case  :       Dict     			=						{f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
				snake_case  :       Any     			=						dict(**UpperCamelCase__  ,	**UpperCamelCase__      )

				if self.use_past:
						if not is_torch_available():
								raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''      )
						else:
								import torch
						snake_case			,snake_case  :       Dict     			=						common_inputs['''input_ids'''].shape
						snake_case  :       Any     			=						common_inputs['''decoder_input_ids'''].shape[1]
						snake_case			,snake_case  :       Tuple     			=						self.num_attention_heads
						snake_case  :       str     			=						(
						    batch,
						    num_encoder_attention_heads,
						    encoder_seq_length,
						    self._config.hidden_size // num_encoder_attention_heads,
						)
						snake_case  :       Union[str, Any]     			=						decoder_seq_length + 3
						snake_case  :       Union[str, Any]     			=						(
						    batch,
						    num_decoder_attention_heads,
						    decoder_past_length,
						    self._config.hidden_size // num_decoder_attention_heads,
						)

						snake_case  :       Optional[int]     			=						torch.cat(
						    [common_inputs['''decoder_attention_mask'''], torch.ones(UpperCamelCase__  ,	UpperCamelCase__      )]  ,	dim=1      )

						snake_case  :       Dict     			=						[]
						# If the number of encoder and decoder layers are present in the model configuration, both are considered
						snake_case			,snake_case  :       str     			=						self.num_layers
						snake_case  :       Union[str, Any]     			=						min(UpperCamelCase__  ,	UpperCamelCase__      )
						snake_case  :       Union[str, Any]     			=						max(UpperCamelCase__  ,	UpperCamelCase__      ) - min_num_layers
						snake_case  :       str     			=						'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

						for _ in range(UpperCamelCase__      ):
								common_inputs["past_key_values"].append(
								    (
								        torch.zeros(UpperCamelCase__      ),
								        torch.zeros(UpperCamelCase__      ),
								        torch.zeros(UpperCamelCase__      ),
								        torch.zeros(UpperCamelCase__      ),
								    )      )
						# TODO: test this.
						snake_case  :       Optional[int]     			=						encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
						for _ in range(UpperCamelCase__  ,	UpperCamelCase__      ):
								common_inputs["past_key_values"].append((torch.zeros(UpperCamelCase__      ), torch.zeros(UpperCamelCase__      ))      )
				return common_inputs



		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			PreTrainedTokenizer  ,	UpperCamelCase__				:			int = -1  ,	UpperCamelCase__				:			int = -1  ,	UpperCamelCase__				:			bool = False  ,	UpperCamelCase__				:			Optional[TensorType] = None  ,	)						->			Mapping[str, Any]:






				"""simple docstring"""







				snake_case  :       str     			=						self._generate_dummy_inputs_for_encoder_and_decoder(
				    UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )

				if self.use_past:
						if not is_torch_available():
								raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''      )
						else:
								import torch
						snake_case			,snake_case  :       Optional[Any]     			=						common_inputs['''input_ids'''].shape
						# Not using the same length for past_key_values
						snake_case  :       int     			=						seqlen + 2
						snake_case			,snake_case  :       int     			=						self.num_layers
						snake_case			,snake_case  :       List[Any]     			=						self.num_attention_heads
						snake_case  :       Optional[int]     			=						(
						    batch,
						    num_encoder_attention_heads,
						    past_key_values_length,
						    self._config.hidden_size // num_encoder_attention_heads,
						)

						snake_case  :       Tuple     			=						common_inputs['''attention_mask'''].dtype
						snake_case  :       Optional[int]     			=						torch.cat(
						    [common_inputs['''attention_mask'''], torch.ones(UpperCamelCase__  ,	UpperCamelCase__  ,	dtype=UpperCamelCase__      )]  ,	dim=1      )
						snake_case  :       str     			=						[
						    (torch.zeros(UpperCamelCase__      ), torch.zeros(UpperCamelCase__      )) for _ in range(UpperCamelCase__      )
						]
				return common_inputs



		def       lowerCAmelCase				(      self				:			List[Any]  ,	UpperCamelCase__				:			PreTrainedTokenizer  ,	UpperCamelCase__				:			int = -1  ,	UpperCamelCase__				:			int = -1  ,	UpperCamelCase__				:			bool = False  ,	UpperCamelCase__				:			Optional[TensorType] = None  ,	)						->			Mapping[str, Any]:






				"""simple docstring"""







				snake_case  :       Tuple     			=						compute_effective_axis_dimension(
				    UpperCamelCase__  ,	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
				snake_case  :       List[str]     			=						tokenizer.num_special_tokens_to_add(UpperCamelCase__      )
				snake_case  :       str     			=						compute_effective_axis_dimension(
				    UpperCamelCase__  ,	fixed_dimension=OnnxConfig.default_fixed_sequence  ,	num_token_to_add=UpperCamelCase__      )

				# Generate dummy inputs according to compute batch and sequence
				snake_case  :       str     			=						[''' '''.join([tokenizer.unk_token]      ) * seq_length] * batch_size
				snake_case  :       Union[str, Any]     			=						dict(tokenizer(UpperCamelCase__  ,	return_tensors=UpperCamelCase__      )      )
				return common_inputs



		def       lowerCAmelCase				(      self				:			List[Any]  ,	UpperCamelCase__				:			PreTrainedTokenizer  ,	UpperCamelCase__				:			int = -1  ,	UpperCamelCase__				:			int = -1  ,	UpperCamelCase__				:			bool = False  ,	UpperCamelCase__				:			Optional[TensorType] = None  ,	)						->			Mapping[str, Any]:






				"""simple docstring"""







				if self.task in ["default", "seq2seq-lm"]:
						snake_case  :       Dict     			=						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
						    UpperCamelCase__  ,	batch_size=UpperCamelCase__  ,	seq_length=UpperCamelCase__  ,	is_pair=UpperCamelCase__  ,	framework=UpperCamelCase__      )

				else:
						snake_case  :       Tuple     			=						self._generate_dummy_inputs_for_causal_lm(
						    UpperCamelCase__  ,	batch_size=UpperCamelCase__  ,	seq_length=UpperCamelCase__  ,	is_pair=UpperCamelCase__  ,	framework=UpperCamelCase__      )

				return common_inputs



		def       lowerCAmelCase				(      self				:			Optional[int]  ,	UpperCamelCase__				:			Optional[Any]  ,	UpperCamelCase__				:			List[str]  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			Union[str, Any]      )						->			Union[str, Any]:






				"""simple docstring"""







				if self.task in ["default", "seq2seq-lm"]:
						snake_case  :       List[Any]     			=						super()._flatten_past_key_values_(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )
				else:
						snake_case  :       Optional[int]     			=						super(UpperCamelCase__  ,	self      )._flatten_past_key_values_(
						    UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )



		@property
		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			float:






				"""simple docstring"""







				return 1e-4


