


from collections import OrderedDict
from typing import Mapping

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


A	:		Dict    =       logging.get_logger(__name__)

A	:		str    =       {
    'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
    'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
    'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
    'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
    'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
    'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
    'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
    'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
    'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			'''xmod'''

					def __init__(self   :						Any				, _UpperCAmelCase   :						List[Any]=3_0522				, _UpperCAmelCase   :						int=768				, _UpperCAmelCase   :						Union[str, Any]=12				, _UpperCAmelCase   :						Dict=12				, _UpperCAmelCase   :						int=3072				, _UpperCAmelCase   :						int="gelu"				, _UpperCAmelCase   :						str=0.1				, _UpperCAmelCase   :						Optional[int]=0.1				, _UpperCAmelCase   :						List[str]=512				, _UpperCAmelCase   :						Dict=2				, _UpperCAmelCase   :						str=0.02				, _UpperCAmelCase   :						int=1E-1_2				, _UpperCAmelCase   :						Any=1				, _UpperCAmelCase   :						List[Any]=0				, _UpperCAmelCase   :						Optional[int]=2				, _UpperCAmelCase   :						Union[str, Any]="absolute"				, _UpperCAmelCase   :						List[str]=True				, _UpperCAmelCase   :						List[Any]=None				, _UpperCAmelCase   :						str=False				, _UpperCAmelCase   :						Optional[int]=2				, _UpperCAmelCase   :						Dict=False				, _UpperCAmelCase   :						Optional[int]=True				, _UpperCAmelCase   :						List[str]=True				, _UpperCAmelCase   :						Tuple=("en_XX",)				, _UpperCAmelCase   :						List[Any]=None				, **_UpperCAmelCase   :						Any				, )						->       Optional[Any]:




							"""simple docstring"""


							super().__init__(pad_token_id=_UpperCAmelCase				, bos_token_id=_UpperCAmelCase				, eos_token_id=_UpperCAmelCase				, **_UpperCAmelCase		)

							lowercase__					      =		vocab_size
							lowercase__					      =		hidden_size
							lowercase__					      =		num_hidden_layers
							lowercase__					      =		num_attention_heads
							lowercase__					      =		hidden_act
							lowercase__					      =		intermediate_size
							lowercase__					      =		hidden_dropout_prob
							lowercase__					      =		attention_probs_dropout_prob
							lowercase__					      =		max_position_embeddings
							lowercase__					      =		type_vocab_size
							lowercase__					      =		initializer_range
							lowercase__					      =		layer_norm_eps
							lowercase__					      =		position_embedding_type
							lowercase__					      =		use_cache
							lowercase__					      =		classifier_dropout
							lowercase__					      =		pre_norm
							lowercase__					      =		adapter_reduction_factor
							lowercase__					      =		adapter_layer_norm
							lowercase__					      =		adapter_reuse_layer_norm
							lowercase__					      =		ln_before_adapter
							lowercase__					      =		list(_UpperCAmelCase		)
							lowercase__					      =		default_language






class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					@property
					def   lowerCamelCase__					(self   :						str		)						->       Mapping[str, Mapping[int, str]]:




							"""simple docstring"""


							if self.task == "multiple-choice":
									lowercase__					      =		{0: """batch""", 1: """choice""", 2: """sequence"""}
							else:
									lowercase__					      =		{0: """batch""", 1: """sequence"""}
							return OrderedDict(
							    [
							        ("""input_ids""", dynamic_axis),
							        ("""attention_mask""", dynamic_axis),
							    ]		)





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			42
					A__      =			42

					def __init__(self   :						Union[str, Any]				, _UpperCAmelCase   :						UNetaDModel				, _UpperCAmelCase   :						ScoreSdeVeScheduler		)						->       List[str]:




							"""simple docstring"""


							super().__init__()
							self.register_modules(unet=_UpperCAmelCase				, scheduler=_UpperCAmelCase		)


					@torch.no_grad()
					def __call__(self   :						Optional[Any]				, _UpperCAmelCase   :						int = 1				, _UpperCAmelCase   :						int = 2000				, _UpperCAmelCase   :						Optional[Union[torch.Generator, List[torch.Generator]]] = None				, _UpperCAmelCase   :						Optional[str] = "pil"				, _UpperCAmelCase   :						bool = True				, **_UpperCAmelCase   :						Any				, )						->       Union[ImagePipelineOutput, Tuple]:




							"""simple docstring"""


							lowercase__					      =		self.unet.config.sample_size
							lowercase__					      =		(batch_size, 3, img_size, img_size)

							lowercase__					      =		self.unet

							lowercase__					      =		randn_tensor(_UpperCAmelCase				, generator=_UpperCAmelCase		) * self.scheduler.init_noise_sigma
							lowercase__					      =		sample.to(self.device		)

							self.scheduler.set_timesteps(_UpperCAmelCase		)
							self.scheduler.set_sigmas(_UpperCAmelCase		)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
									lowercase__					      =		self.scheduler.sigmas[i] * torch.ones(shape[0]				, device=self.device		)

									# correction step
									for _ in range(self.scheduler.config.correct_steps		):
											lowercase__					      =		self.unet(_UpperCAmelCase				, _UpperCAmelCase		).sample
											lowercase__					      =		self.scheduler.step_correct(_UpperCAmelCase				, _UpperCAmelCase				, generator=_UpperCAmelCase		).prev_sample

									# prediction step
									lowercase__					      =		model(_UpperCAmelCase				, _UpperCAmelCase		).sample
									lowercase__					      =		self.scheduler.step_pred(_UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase				, generator=_UpperCAmelCase		)

									lowercase__      ,     lowercase__					      =		output.prev_sample, output.prev_sample_mean

							lowercase__					      =		sample_mean.clamp(0				, 1		)
							lowercase__					      =		sample.cpu().permute(0				, 2				, 3				, 1		).numpy()
							if output_type == "pil":
									lowercase__					      =		self.numpy_to_pil(_UpperCAmelCase		)

							if not return_dict:
									return (sample,)

							return ImagePipelineOutput(images=_UpperCAmelCase		)

