


from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
    from .features import FeatureType

@dataclass
class 							lowercase  :
     lowerCamelCase			:			List[str]
     lowerCamelCase			:			Optional[str]      = None
     # Automatically constructed
     lowerCamelCase			:			ClassVar[str]      = "dict"
     lowerCamelCase			:			ClassVar[Any]      = None
     lowerCamelCase			:			str      = field(default='''Translation'''    ,       init=_UpperCAmelCase    ,       repr=_UpperCAmelCase			)







     def __call__(						self :						List[Any]      ):
           return pa.struct({lang: pa.string() for lang in sorted(self.languages      )}      )







     def 							lowercase__       (						self :						int      ):
           from .features import Value

           return {k: Value('''string'''      ) for k in sorted(self.languages      )}




@dataclass
class 							lowercase  :
     lowerCamelCase			:			Optional[List]      = None
     lowerCamelCase			:			Optional[int]      = None
     lowerCamelCase			:			Optional[str]      = None
     # Automatically constructed
     lowerCamelCase			:			ClassVar[str]      = "dict"
     lowerCamelCase			:			ClassVar[Any]      = None
     lowerCamelCase			:			str      = field(default='''TranslationVariableLanguages'''    ,       init=_UpperCAmelCase    ,       repr=_UpperCAmelCase			)







     def 							lowercase__       (						self :						Any      ):
           SCREAMING_SNAKE_CASE__       :  int						     =		sorted(set(self.languages      )      ) if self.languages else None
           SCREAMING_SNAKE_CASE__       :  Dict						     =		len(self.languages      ) if self.languages else None







     def __call__(						self :						List[Any]      ):
           return pa.struct({'''language''': pa.list_(pa.string()      ), '''translation''': pa.list_(pa.string()      )}      )







     def 							lowercase__       (						self :						Any   ,   _lowercase :						Dict      ):
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		set(self.languages      )
           if self.languages and set(_lowercase      ) - lang_set:
                 raise ValueError(
                     f"""Some languages in example ({', '.join(sorted(set(_lowercase      ) - lang_set      )      )}) are not in valid set ({', '.join(_lowercase      )})."""      )

           # Convert dictionary into tuples, splitting out cases where there are
           # multiple translations for a single language.
           SCREAMING_SNAKE_CASE__       :  Any						     =		[]
           for lang, text in translation_dict.items():
                 if isinstance(_lowercase   ,   _lowercase      ):
                       translation_tuples.append((lang, text)      )
                 else:
                       translation_tuples.extend([(lang, el) for el in text]      )

        # Ensure translations are in ascending order by language code.
           SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Any						     =		zip(*sorted(_lowercase      )      )

           return {"language": languages, "translation": translations}







     def 							lowercase__       (						self :						int      ):
           from .features import Sequence, Value

           return {
               "language": Sequence(Value('''string'''      )      ),
               "translation": Sequence(Value('''string'''      )      ),
           }













'''simple docstring'''




from typing import List, Optional, Tuple, Union

import torch

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






class 				__snake_case     (					__magic_name__							):





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		int:
		super().__init__()

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

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





	@torch.no_grad()
	def __call__(		self      ,       UpperCamelCase_ = 1      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = 0.0      ,       UpperCamelCase_ = 50      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = "pil"      ,       UpperCamelCase_ = True      ,       )      ->		Union[ImagePipelineOutput, Tuple]:

		# Sample gaussian noise to begin loop
		if isinstance(self.unet.config.sample_size      ,       UpperCamelCase_		):
			snake_case__	       =   (
			    batch_size,
			    self.unet.config.in_channels,
			    self.unet.config.sample_size,
			    self.unet.config.sample_size,
			)
		else:
			snake_case__	       =   (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

		snake_case__	       =   randn_tensor(UpperCamelCase_      ,       generator=UpperCamelCase_      ,       device=self.device      ,       dtype=self.unet.dtype		)

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

		for t in self.progress_bar(self.scheduler.timesteps		):
			# 1. predict noise model_output
			snake_case__	       =   self.unet(UpperCamelCase_      ,       UpperCamelCase_		).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(
			    UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       eta=UpperCamelCase_      ,       use_clipped_model_output=UpperCamelCase_      ,       generator=UpperCamelCase_		).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(UpperCamelCase_		)

		if not return_dict:
			return (image,)

		return ImagePipelineOutput(images=UpperCamelCase_		)



