

import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate





@dataclass(frozen=__A					)
class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Union[str, Any]									=				field(default="text-classification"	,       metadata={"include_in_asdict_even_if_is_default": True}					)
			SCREAMING_SNAKE_CASE     :   Optional[Any]									=				Features({"text": Value("string"					)}					)
			SCREAMING_SNAKE_CASE     :   Optional[Any]									=				Features({"labels": ClassLabel}					)
			SCREAMING_SNAKE_CASE     :   str									=				"text"
			SCREAMING_SNAKE_CASE     :   str									=				"labels"







			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				Dict							)      ->Optional[int]:

									if self.label_column not in features:
															raise ValueError(f'''Column {self.label_column} is not present in features.'''							)
									if not isinstance(features[self.label_column]							,   lowerCamelCase_							):
															raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''							)
									snake_case_   =	copy.deepcopy(self							)
									snake_case_   =	self.label_schema.copy()
									snake_case_   =	features[self.label_column]
									snake_case_   =	label_schema
									return task_template







			@property
			def 				snake_case__(     self :				str							)      ->Optional[int]:
									return {
									    self.text_column: "text",
									    self.label_column: "labels",
									}




# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

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




	@torch.no_grad()
	def __call__(self		,       lowerCamelCase_ = 1		,       lowerCamelCase_ = None		,       lowerCamelCase_ = 50		,       lowerCamelCase_ = "pil"		,       lowerCamelCase_ = True		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						a					     =   torch.randn(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,       generator=lowerCamelCase_		,       )
						a					     =   image.to(self.device		)

						# set step values
						self.scheduler.set_timesteps(lowerCamelCase_		)

						for t in self.progress_bar(self.scheduler.timesteps		):
											# 1. predict noise model_output
											a					     =   self.unet(lowerCamelCase_		,       lowerCamelCase_		).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
											a					     =   self.scheduler.step(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		).prev_sample

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

						if not return_dict:
											return (image,), "This is a local test"

						return ImagePipelineOutput(images=lowerCamelCase_		), "This is a local test"
