'''simple docstring'''







import os

from ...utils import logging
from .utils import DataProcessor, InputExample


lowercase__     :  str    					=			logging.get_logger(__name__)

class       lowerCamelCase_							(	A__  ):






   '''simple docstring'''

   def __init__(  self     ,						__UpperCamelCase     ,						__UpperCamelCase=None ):
       UpperCamelCase__      =			language
       UpperCamelCase__      =			train_language


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			self.language if self.train_language is None else self.train_language
       UpperCamelCase__      =			self._read_tsv(os.path.join(_lowerCAmelCase     ,						f'XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv' ) )
       UpperCamelCase__      =			[]
       for i, line in enumerate(_lowerCAmelCase ):
           if i == 0:
               continue
           UpperCamelCase__      =			f'train-{i}'
           UpperCamelCase__      =			line[0]
           UpperCamelCase__      =			line[1]
           UpperCamelCase__      =			"contradiction" if line[2] == "contradictory" else line[2]
           if not isinstance(_lowerCAmelCase     ,						_lowerCAmelCase ):
               raise ValueError(f'Training input {text_a} is not a string' )
           if not isinstance(_lowerCAmelCase     ,						_lowerCAmelCase ):
               raise ValueError(f'Training input {text_b} is not a string' )
           if not isinstance(_lowerCAmelCase     ,						_lowerCAmelCase ):
               raise ValueError(f'Training label {label} is not a string' )
           examples.append(InputExample(guid=_lowerCAmelCase     ,						text_a=_lowerCAmelCase     ,						text_b=_lowerCAmelCase     ,						label=_lowerCAmelCase ) )
       return examples


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			self._read_tsv(os.path.join(_lowerCAmelCase     ,						"XNLI-1.0/xnli.test.tsv" ) )
       UpperCamelCase__      =			[]
       for i, line in enumerate(_lowerCAmelCase ):
           if i == 0:
               continue
           UpperCamelCase__      =			line[0]
           if language != self.language:
               continue
           UpperCamelCase__      =			f'test-{i}'
           UpperCamelCase__      =			line[6]
           UpperCamelCase__      =			line[7]
           UpperCamelCase__      =			line[1]
           if not isinstance(_lowerCAmelCase     ,						_lowerCAmelCase ):
               raise ValueError(f'Training input {text_a} is not a string' )
           if not isinstance(_lowerCAmelCase     ,						_lowerCAmelCase ):
               raise ValueError(f'Training input {text_b} is not a string' )
           if not isinstance(_lowerCAmelCase     ,						_lowerCAmelCase ):
               raise ValueError(f'Training label {label} is not a string' )
           examples.append(InputExample(guid=_lowerCAmelCase     ,						text_a=_lowerCAmelCase     ,						text_b=_lowerCAmelCase     ,						label=_lowerCAmelCase ) )
       return examples







   def 	_UpperCamelCase       (  self ):
       return ["contradiction", "entailment", "neutral"]

lowercase__     :  str    					=			{
    'xnli': XnliProcessor,
}

lowercase__     :  Optional[Any]    					=			{
    'xnli': 'classification',
}

lowercase__     :  Optional[int]    					=			{
    'xnli': 3,
}









'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

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

class       lowerCamelCase_							(	lowerCAmelCase_  ):






   '''simple docstring'''

   A :     UNetaDModel
   A :     PNDMScheduler


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

       UpperCamelCase__      =			PNDMScheduler.from_config(scheduler.config )

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







   @torch.no_grad()
   def __call__(  self     ,						__UpperCamelCase = 1     ,						__UpperCamelCase = 50     ,						__UpperCamelCase = None     ,						__UpperCamelCase = "pil"     ,						__UpperCamelCase = True     ,						**__UpperCamelCase     ,						):
       UpperCamelCase__      =			randn_tensor(
           (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     ,						generator=__UpperCamelCase     ,						device=self.device     ,						)

       self.scheduler.set_timesteps(__UpperCamelCase )
       for t in self.progress_bar(self.scheduler.timesteps ):
           UpperCamelCase__      =			self.unet(__UpperCamelCase     ,						__UpperCamelCase ).sample

           UpperCamelCase__      =			self.scheduler.step(__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase ).prev_sample

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

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=__UpperCamelCase )




