

from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


_UpperCAmelCase		:       Dict									=	transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							if isinstance(UpperCamelCase__       ,							torch.Tensor				):
														return image
							elif isinstance(UpperCamelCase__       ,							PIL.Image.Image				):
														snake_case_          =   [image]

							snake_case_          =   [trans(img.convert('RGB'				)				) for img in image]
							snake_case_          =   torch.stack(UpperCamelCase__				)
							return image







class 			lowercase    (	lowercase_      ):




				def __init__( self				,       snake_case				,       snake_case	):
											super().__init__()

											# make sure scheduler can always be converted to DDIM
											snake_case_          =   DDIMScheduler.from_config(scheduler.config	)

											self.register_modules(unet=snake_case				,       scheduler=snake_case	)




				def    a							( self				,       snake_case	):
											if strength < 0 or strength > 1:
																		raise ValueError(F'''The value of strength should in [0.0, 1.0] but is {strength}'''	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case	):
											# get the original timestep using init_timestep
											snake_case_          =   min(int(num_inference_steps * strength	)				,       snake_case	)

											snake_case_          =   max(num_inference_steps - init_timestep				,       0	)
											snake_case_          =   self.scheduler.timesteps[t_start:]

											return timesteps, num_inference_steps - t_start




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case=None	):
											if not isinstance(snake_case				,       (torch.Tensor, PIL.Image.Image, list)	):
																		raise ValueError(
																		    F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(snake_case	)}'''	)

											snake_case_          =   image.to(device=snake_case				,       dtype=snake_case	)

											if isinstance(snake_case				,       snake_case	) and len(snake_case	) != batch_size:
																		raise ValueError(
																		    F'''You have passed a list of generators of length {len(snake_case	)}, but requested an effective batch'''
																		    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''	)

											snake_case_          =   init_latents.shape
											snake_case_          =   randn_tensor(snake_case				,       generator=snake_case				,       device=snake_case				,       dtype=snake_case	)

											# get latents
											print('add noise to latents at timestep'				,       snake_case	)
											snake_case_          =   self.scheduler.add_noise(snake_case				,       snake_case				,       snake_case	)
											snake_case_          =   init_latents

											return latents




				@torch.no_grad()
				def __call__( self				,       snake_case = None				,       snake_case = 0.8				,       snake_case = 1				,       snake_case = None				,       snake_case = 0.0				,       snake_case = 50				,       snake_case = None				,       snake_case = "pil"				,       snake_case = True				,       ):
											self.check_inputs(snake_case	)

											# 2. Preprocess image
											snake_case_          =   preprocess(snake_case	)

											# 3. set timesteps
											self.scheduler.set_timesteps(snake_case				,       device=self.device	)
											snake_case_    ,   snake_case_          =   self.get_timesteps(snake_case				,       snake_case				,       self.device	)
											snake_case_          =   timesteps[:1].repeat(snake_case	)

											# 4. Prepare latent variables
											snake_case_          =   self.prepare_latents(snake_case				,       snake_case				,       snake_case				,       self.unet.dtype				,       self.device				,       snake_case	)
											snake_case_          =   latents

											# 5. Denoising loop
											for t in self.progress_bar(snake_case	):
																		# 1. predict noise model_output
																		snake_case_          =   self.unet(snake_case				,       snake_case	).sample

																		# 2. predict previous mean of image x_t-1 and add variance depending on eta
																		# eta corresponds to η in paper and should be between [0, 1]
																		# do x_t -> x_t-1
																		snake_case_          =   self.scheduler.step(
																		    snake_case				,       snake_case				,       snake_case				,       eta=snake_case				,       use_clipped_model_output=snake_case				,       generator=snake_case				,       ).prev_sample

											snake_case_          =   (image / 2 + 0.5).clamp(0				,       1	)
											snake_case_          =   image.cpu().permute(0				,       2				,       3				,       1	).numpy()
											if output_type == "pil":
																		snake_case_          =   self.numpy_to_pil(snake_case	)

											if not return_dict:
																		return (image, latent_timestep.item())

											return ImagePipelineOutput(images=snake_case	)







from collections import OrderedDict
from typing import Mapping

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


_UpperCAmelCase		:       int						    =	logging.get_logger(__name__)

_UpperCAmelCase		:       Optional[Any]						    =	{
    """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 			lowercase    (	lowercase_      ):
				__SCREAMING_SNAKE_CASE :			List[Any]	      =      '''roformer'''




				def __init__( self				,       snake_case=5_0000				,       snake_case=None				,       snake_case=768				,       snake_case=12				,       snake_case=12				,       snake_case=3072				,       snake_case="gelu"				,       snake_case=0.1				,       snake_case=0.1				,       snake_case=1536				,       snake_case=2				,       snake_case=0.02				,       snake_case=1e-1_2				,       snake_case=0				,       snake_case=False				,       snake_case=True				,       **snake_case				,       ):
											super().__init__(pad_token_id=snake_case				,       **snake_case	)

											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 			lowercase    (	lowercase_      ):




				@property
				def    a							( self	):
											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),
											    ]	)




