






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   __SCREAMING_SNAKE_CASE(	a_	):
							_UpperCAmelCase     =       4_2
							_UpperCAmelCase     =       4_2
							def __init__(     self:      Dict       ,	UpperCamelCase:      UNetaDModel       ,	UpperCamelCase:      ScoreSdeVeScheduler						)		->		int:
											super().__init__()
											self.register_modules(unet=UpperCamelCase       ,	scheduler=UpperCamelCase						)


							@torch.no_grad()
							def __call__(     self:      Optional[int]       ,	UpperCamelCase:      int = 1       ,	UpperCamelCase:      int = 20_00       ,	UpperCamelCase:      Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,	UpperCamelCase:      Optional[str] = "pil"       ,	UpperCamelCase:      bool = True       ,	**UpperCamelCase:      int       ,	)		->		Union[ImagePipelineOutput, Tuple]:
											snake_case__									=     self.unet.config.sample_size
											snake_case__									=     (batch_size, 3, img_size, img_size)

											snake_case__									=     self.unet

											snake_case__									=     randn_tensor(UpperCamelCase       ,	generator=UpperCamelCase						) * self.scheduler.init_noise_sigma
											snake_case__									=     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						)						):
															snake_case__									=     self.scheduler.sigmas[i] * torch.ones(shape[0]       ,	device=self.device						)

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

															# prediction step
															snake_case__									=     model(UpperCamelCase       ,	UpperCamelCase						).sample
															snake_case__									=     self.scheduler.step_pred(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase       ,	generator=UpperCamelCase						)

															snake_case__							,						snake_case__									=     output.prev_sample, output.prev_sample_mean

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

											if not return_dict:
															return (sample,)

											return ImagePipelineOutput(images=UpperCamelCase						)












from collections import OrderedDict
from typing import Mapping

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


__UpperCamelCase					:		Any	  =						logging.get_logger(__name__)

__UpperCamelCase					:		int	  =						{
    """junnyu/roformer_chinese_small""": """https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json""",
    """junnyu/roformer_chinese_base""": """https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json""",
    """junnyu/roformer_chinese_char_small""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"""
    ),
    """junnyu/roformer_chinese_char_base""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_discriminator""": (
        """https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_generator""": (
        """https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"""
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}


class   __SCREAMING_SNAKE_CASE(	a_	):
							_UpperCAmelCase     =       "roformer"
							def __init__(     self:      Tuple       ,	UpperCamelCase:      Optional[Any]=5_00_00       ,	UpperCamelCase:      str=None       ,	UpperCamelCase:      Any=7_68       ,	UpperCamelCase:      Dict=12       ,	UpperCamelCase:      List[Any]=12       ,	UpperCamelCase:      List[str]=30_72       ,	UpperCamelCase:      int="gelu"       ,	UpperCamelCase:      str=0.1       ,	UpperCamelCase:      Union[str, Any]=0.1       ,	UpperCamelCase:      Any=15_36       ,	UpperCamelCase:      Dict=2       ,	UpperCamelCase:      Dict=0.02       ,	UpperCamelCase:      List[str]=1e-12       ,	UpperCamelCase:      int=0       ,	UpperCamelCase:      Any=False       ,	UpperCamelCase:      int=True       ,	**UpperCamelCase:      List[Any]       ,	)		->		List[str]:
											super().__init__(pad_token_id=UpperCamelCase       ,	**UpperCamelCase						)

											snake_case__									=     vocab_size
											snake_case__									=     hidden_size if embedding_size is None else embedding_size
											snake_case__									=     hidden_size
											snake_case__									=     num_hidden_layers
											snake_case__									=     num_attention_heads
											snake_case__									=     hidden_act
											snake_case__									=     intermediate_size
											snake_case__									=     hidden_dropout_prob
											snake_case__									=     attention_probs_dropout_prob
											snake_case__									=     max_position_embeddings
											snake_case__									=     type_vocab_size
											snake_case__									=     initializer_range
											snake_case__									=     layer_norm_eps
											snake_case__									=     rotary_value
											snake_case__									=     use_cache


class   __SCREAMING_SNAKE_CASE(	a_	):
							@property
							def lowerCAmelCase_ (     self:      Any						)		->		Mapping[str, Mapping[int, str]]:
											if self.task == "multiple-choice":
															snake_case__									=     {0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
															snake_case__									=     {0: 'batch', 1: 'sequence'}
											snake_case__									=     {0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											        ('token_type_ids', dynamic_axis),
											    ]						)




