


from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput







@dataclass
class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						None
							_UpperCAmelCase      =						None
							_UpperCAmelCase      =						None
							_UpperCAmelCase      =						None







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							def __init__(      self			,						lowerCAmelCase__=1			,						lowerCAmelCase__=0			,						lowerCAmelCase__=2			,						lowerCAmelCase__=5_1_2			,						lowerCAmelCase__="cls"			,						lowerCAmelCase__=False			,						lowerCAmelCase__=True			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										super().__init__(pad_token_id=lowerCAmelCase__			,						bos_token_id=lowerCAmelCase__			,						eos_token_id=lowerCAmelCase__			,						**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					project_dim
										SCREAMING_SNAKE_CASE_		:     List[str]					=					pooler_fn
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					learn_encoder
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					use_attention_mask







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						[r"""pooler""", r"""logit_scale"""]
							_UpperCAmelCase      =						[r"""position_ids""", r"""predictions.decoder.bias"""]
							_UpperCAmelCase      =						"""roberta"""
							_UpperCAmelCase      =						RobertaSeriesConfig






							def __init__(      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										super().__init__(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					XLMRobertaModel(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					nn.Linear(config.hidden_size			,						config.project_dim       )
										SCREAMING_SNAKE_CASE_		:     str					=					getattr(lowerCAmelCase__			,						'has_pre_transformation'			,						lowerCAmelCase__       )
										if self.has_pre_transformation:
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					nn.Linear(config.hidden_size			,						config.project_dim       )
													SCREAMING_SNAKE_CASE_		:     List[str]					=					nn.LayerNorm(config.hidden_size			,						eps=config.layer_norm_eps       )
										self.post_init()






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					return_dict if return_dict is not None else self.config.use_return_dict

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.base_model(
										    input_ids=lowerCAmelCase__			,						attention_mask=lowerCAmelCase__			,						token_type_ids=lowerCAmelCase__			,						position_ids=lowerCAmelCase__			,						head_mask=lowerCAmelCase__			,						inputs_embeds=lowerCAmelCase__			,						encoder_hidden_states=lowerCAmelCase__			,						encoder_attention_mask=lowerCAmelCase__			,						output_attentions=lowerCAmelCase__			,						output_hidden_states=True if self.has_pre_transformation else output_hidden_states			,						return_dict=lowerCAmelCase__			,						)

										if self.has_pre_transformation:
													SCREAMING_SNAKE_CASE_		:     List[str]					=					outputs['hidden_states'][-2]
													SCREAMING_SNAKE_CASE_		:     List[str]					=					self.pre_LN(lowerCAmelCase__       )
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.transformation_pre(lowerCAmelCase__       )

													return TransformationModelOutput(
													    projection_state=lowerCAmelCase__			,						last_hidden_state=outputs.last_hidden_state			,						hidden_states=outputs.hidden_states			,						attentions=outputs.attentions			,						)
										else:
													SCREAMING_SNAKE_CASE_		:     int					=					self.transformation(outputs.last_hidden_state       )
													return TransformationModelOutput(
													    projection_state=lowerCAmelCase__			,						last_hidden_state=outputs.last_hidden_state			,						hidden_states=outputs.hidden_states			,						attentions=outputs.attentions			,						)











"""simple docstring"""

from typing import List, Optional, Tuple, Union

import torch

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





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	UNetaDModel
 UpperCAmelCase       :	KarrasVeScheduler

 def __init__(					self			:			Any       ,				_UpperCAmelCase			:			UNetaDModel       ,				_UpperCAmelCase			:			KarrasVeScheduler						):
       super().__init__()
       self.register_modules(unet=_UpperCAmelCase       ,				scheduler=_UpperCAmelCase						)



 @torch.no_grad()
 def __call__(					self			:			Optional[int]       ,				_UpperCAmelCase			:			int = 1       ,				_UpperCAmelCase			:			int = 50       ,				_UpperCAmelCase			:			Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,				_UpperCAmelCase			:			Optional[str] = "pil"       ,				_UpperCAmelCase			:			bool = True       ,				**_UpperCAmelCase			:			Optional[Any]       ,				):
       _A     =  self.unet.config.sample_size
       _A     =  (batch_size, 3, img_size, img_size)

       _A     =  self.unet

       # sample x_0 ~ N(0, sigma_0^2 * I)
       _A     =  randn_tensor(_UpperCAmelCase       ,				generator=_UpperCAmelCase       ,				device=self.device						) * self.scheduler.init_noise_sigma

       self.scheduler.set_timesteps(_UpperCAmelCase						)

       for t in self.progress_bar(self.scheduler.timesteps						):
             # here sigma_t == t_i from the paper
             _A     =  self.scheduler.schedule[t]
             _A     =  self.scheduler.schedule[t - 1] if t > 0 else 0

             # 1. Select temporarily increased noise level sigma_hat
             # 2. Add new noise to move from sample_i to sample_hat
             _A  ,       _A     =  self.scheduler.add_noise_to_input(_UpperCAmelCase       ,				_UpperCAmelCase       ,				generator=_UpperCAmelCase						)

             # 3. Predict the noise residual given the noise magnitude `sigma_hat`
             # The model inputs and output are adjusted by following eq. (213) in [1].
             _A     =  (sigma_hat / 2) * model((sample_hat + 1) / 2       ,				sigma_hat / 2						).sample

             # 4. Evaluate dx/dt at sigma_hat
             # 5. Take Euler step from sigma to sigma_prev
             _A     =  self.scheduler.step(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)

             if sigma_prev != 0:
                   # 6. Apply 2nd order correction
                   # The model inputs and output are adjusted by following eq. (213) in [1].
                   _A     =  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2       ,				sigma_prev / 2						).sample
                   _A     =  self.scheduler.step_correct(
                       _UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				step_output.prev_sample       ,				step_output['derivative']       ,				)
             _A     =  step_output.prev_sample

       _A     =  (sample / 2 + 0.5).clamp(0       ,				1						)
       _A     =  sample.cpu().permute(0       ,				2       ,				3       ,				1						).numpy()
       if output_type == "pil":
             _A     =  self.numpy_to_pil(_UpperCAmelCase						)

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=_UpperCAmelCase						)

