


import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


UpperCAmelCase   :    Optional[int]     =		logging.getLogger(__name__)







@dataclass
@add_start_docstrings(TrainingArguments.__doc__  )
class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					bool			       =		field(default=a_			,							metadata={"help": "Whether to use SortishSampler or not."}  )
		UpperCamelCase     :					bool			       =		field(
		    default=a_			,							metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}  )
		UpperCamelCase     :					Optional[int]			       =		field(
		    default=a_			,							metadata={
		        "help": (
		            "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
		            "to the `max_length` value of the model configuration."
		        )
		    }			,							)
		UpperCamelCase     :					Optional[int]			       =		field(
		    default=a_			,							metadata={
		        "help": (
		            "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
		            "to the `num_beams` value of the model configuration."
		        )
		    }			,							)
		UpperCamelCase     :					Optional[Union[str, Path, GenerationConfig]]			       =		field(
		    default=a_			,							metadata={
		        "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
		    }			,							)







		def 					__A   ( self	)  -> str:







					'''simple docstring'''







					lowerCamelCase    		=  super().to_dict()
					for k, v in d.items():
								if isinstance(A		,      A	):
											lowerCamelCase    		=  v.to_dict()
					return d





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class        __lowercase       (							a_  ):



		"""simple docstring"""

		def __init__( self		,      A		,      A	)  -> List[Any]:







					'''simple docstring'''







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







		@torch.no_grad()
		def __call__( self		,      A = 1		,      A = None		,      A = 50		,      A = "pil"		,      A = True		,      **A		,      )  -> Union[ImagePipelineOutput, Tuple]:







					'''simple docstring'''







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

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

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

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

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

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

