





import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


_lowerCamelCase      							=   logging.getLogger(__name__)







@dataclass(frozen=lowerCamelCase__	)
class       __A							:




      """simple docstring"""







      UpperCAmelCase__							=   42
      UpperCAmelCase__							=   42
      UpperCAmelCase__							=   None
      UpperCAmelCase__							=   None
      UpperCAmelCase__							=   None







@dataclass(frozen=lowerCamelCase__	)
class       __A							:




      """simple docstring"""







      UpperCAmelCase__							=   42
      UpperCAmelCase__							=   None
      UpperCAmelCase__							=   None
      UpperCAmelCase__							=   None
      UpperCAmelCase__							=   None


if is_torch_available():
       import torch
       from torch.utils.data import Dataset







       class       __A							(   lowerCamelCase__	):




             """simple docstring"""







             UpperCAmelCase__							=   42


             def __init__(		self   ,				a__   ,				a__   ,				a__   ,				a__ = None   ,				a__=False   ,				a__ = False   ,				):



                    """simple docstring"""





                    _lowerCamelCase				:  Tuple						=		hans_processors[task]()

                    _lowerCamelCase				:  List[Any]						=		os.path.join(
                        a__   ,				'''cached_{}_{}_{}_{}'''.format(
                            '''dev''' if evaluate else '''train'''   ,				tokenizer.__class__.__name__   ,				str(a__)   ,				a__   ,				)   ,				)
                    _lowerCamelCase				:  List[Any]						=		processor.get_labels()
                    if tokenizer.__class__ in (
                        RobertaTokenizer,
                        RobertaTokenizerFast,
                        XLMRobertaTokenizer,
                        BartTokenizer,
                        BartTokenizerFast,
                    ):
                           # HACK(label indices are swapped in RoBERTa pretrained model)
                           _lowerCamelCase,	_lowerCamelCase				:  Any						=		label_list[2], label_list[1]
                    _lowerCamelCase				:  Dict						=		label_list

                    # Make sure only the first process in distributed training processes the dataset,
                    # and the others will use the cache.
                    _lowerCamelCase				:  Union[str, Any]						=		cached_features_file + '''.lock'''
                    with FileLock(a__):
                           if os.path.exists(a__) and not overwrite_cache:
                                  logger.info(F"""Loading features from cached file {cached_features_file}""")
                                  _lowerCamelCase				:  List[Any]						=		torch.load(a__)
                           else:
                                  logger.info(F"""Creating features from dataset file at {data_dir}""")

                                  _lowerCamelCase				:  Optional[int]						=		(
                                      processor.get_dev_examples(a__) if evaluate else processor.get_train_examples(a__)
                                  )

                                  logger.info('''Training examples: %s'''   ,				len(a__))
                                  _lowerCamelCase				:  Tuple						=		hans_convert_examples_to_features(a__   ,				a__   ,				a__   ,				a__)
                                  logger.info('''Saving features into cached file %s'''   ,				a__)
                                  torch.save(self.features   ,				a__)


             def __len__(		self):



                    """simple docstring"""





                    return len(self.features)


             def __getitem__(		self   ,				a__):



                    """simple docstring"""





                    return self.features[i]



             def 					__snake_case      (		self):



                    """simple docstring"""





                    return self.label_list


if is_tf_available():
       import tensorflow as tf







       class       __A							:




             """simple docstring"""







             UpperCAmelCase__							=   42


             def __init__(		self   ,				a__   ,				a__   ,				a__   ,				a__ = 128   ,				a__=False   ,				a__ = False   ,				):



                    """simple docstring"""





                    _lowerCamelCase				:  Optional[Any]						=		hans_processors[task]()
                    _lowerCamelCase				:  Tuple						=		processor.get_labels()
                    if tokenizer.__class__ in (
                        RobertaTokenizer,
                        RobertaTokenizerFast,
                        XLMRobertaTokenizer,
                        BartTokenizer,
                        BartTokenizerFast,
                    ):
                           # HACK(label indices are swapped in RoBERTa pretrained model)
                           _lowerCamelCase,	_lowerCamelCase				:  Any						=		label_list[2], label_list[1]
                    _lowerCamelCase				:  List[Any]						=		label_list

                    _lowerCamelCase				:  Any						=		processor.get_dev_examples(a__) if evaluate else processor.get_train_examples(a__)
                    _lowerCamelCase				:  Any						=		hans_convert_examples_to_features(a__   ,				a__   ,				a__   ,				a__)

                    def gen():
                           for ex_index, ex in tqdm.tqdm(enumerate(self.features)   ,				desc='''convert examples to features'''):
                                  if ex_index % 1_0000 == 0:
                                         logger.info('''Writing example %d of %d''' % (ex_index, len(a__)))

                                  yield (
                                      {
                                          "example_id": 0,
                                          "input_ids": ex.input_ids,
                                          "attention_mask": ex.attention_mask,
                                          "token_type_ids": ex.token_type_ids,
                                      },
                                      ex.label,
                                  )

                    _lowerCamelCase				:  Optional[int]						=		tf.data.Dataset.from_generator(
                        a__   ,				(
                            {
                                '''example_id''': tf.intaa,
                                '''input_ids''': tf.intaa,
                                '''attention_mask''': tf.intaa,
                                '''token_type_ids''': tf.intaa,
                            },
                            tf.intaa,
                        )   ,				(
                            {
                                '''example_id''': tf.TensorShape([]),
                                '''input_ids''': tf.TensorShape([None, None]),
                                '''attention_mask''': tf.TensorShape([None, None]),
                                '''token_type_ids''': tf.TensorShape([None, None]),
                            },
                            tf.TensorShape([]),
                        )   ,				)


             def 					__snake_case      (		self):



                    """simple docstring"""





                    return self.dataset


             def __len__(		self):



                    """simple docstring"""





                    return len(self.features)


             def __getitem__(		self   ,				a__):



                    """simple docstring"""





                    return self.features[i]



             def 					__snake_case      (		self):



                    """simple docstring"""





                    return self.label_list







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             return self._create_examples(self._read_tsv(os.path.join(a__   ,				'''heuristics_train_set.txt'''))   ,				'''train''')


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             return self._create_examples(self._read_tsv(os.path.join(a__   ,				'''heuristics_evaluation_set.txt'''))   ,				'''dev''')


      def 					__snake_case      (		self):



             """simple docstring"""





             return ["contradiction", "entailment", "neutral"]



      def 					__snake_case      (		self   ,				a__   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		[]
             for i, line in enumerate(a__):
                    if i == 0:
                           continue
                    _lowerCamelCase				:  str						=		'''%s-%s''' % (set_type, line[0])
                    _lowerCamelCase				:  Any						=		line[5]
                    _lowerCamelCase				:  Dict						=		line[6]
                    _lowerCamelCase				:  List[Any]						=		line[7][2:] if line[7].startswith('''ex''') else line[7]
                    _lowerCamelCase				:  Optional[int]						=		line[0]
                    examples.append(InputExample(guid=a__   ,				text_a=a__   ,				text_b=a__   ,				label=a__   ,				pairID=a__))
             return examples




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_			,     lowercase_			,     ):
       _lowerCamelCase				:  Dict						=		{label: i for i, label in enumerate(lowercase_   )}

       _lowerCamelCase				:  List[Any]						=		[]
       for ex_index, example in tqdm.tqdm(enumerate(lowercase_   )			,     desc='''convert examples to features'''   ):
              if ex_index % 1_00_00 == 0:
                     logger.info('''Writing example %d''' % (ex_index)   )

              _lowerCamelCase				:  List[str]						=		tokenizer(
                  example.text_a			,     example.text_b			,     add_special_tokens=lowercase_			,     max_length=lowercase_			,     padding='''max_length'''			,     truncation=lowercase_			,     return_overflowing_tokens=lowercase_			,     )

              _lowerCamelCase				:  int						=		label_map[example.label] if example.label in label_map else 0

              _lowerCamelCase				:  int						=		int(example.pairID   )

              features.append(InputFeatures(**lowercase_			,     label=lowercase_			,     pairID=lowercase_   )   )

       for i, example in enumerate(examples[:5]   ):
              logger.info('''*** Example ***'''   )
              logger.info(F"""guid: {example}"""   )
              logger.info(F"""features: {features[i]}"""   )

       return features


_lowerCamelCase      							=   {
    'hans': 3,
}



_lowerCamelCase      							=   {
    'hans': HansProcessor,
}








import inspect
from typing import List, Optional, Tuple, Union

import torch

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







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





             super().__init__()
             self.register_modules(vqvae=a__   ,				unet=a__   ,				scheduler=a__)



      @torch.no_grad()
      def __call__(		self   ,				a__ = 1   ,				a__ = None   ,				a__ = 0.0   ,				a__ = 50   ,				a__ = "pil"   ,				a__ = True   ,				**a__   ,				):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		randn_tensor(
                 (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,				generator=a__   ,				)
             _lowerCamelCase				:  Optional[int]						=		latents.to(self.device)

             # scale the initial noise by the standard deviation required by the scheduler
             _lowerCamelCase				:  Tuple						=		latents * self.scheduler.init_noise_sigma

             self.scheduler.set_timesteps(a__)

             # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
             _lowerCamelCase				:  Optional[int]						=		'''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())

             _lowerCamelCase				:  Union[str, Any]						=		{}
             if accepts_eta:
                    _lowerCamelCase				:  List[Any]						=		eta

             for t in self.progress_bar(self.scheduler.timesteps):
                    _lowerCamelCase				:  List[Any]						=		self.scheduler.scale_model_input(a__   ,				a__)
                    # predict the noise residual
                    _lowerCamelCase				:  Optional[int]						=		self.unet(a__   ,				a__).sample
                    # compute the previous noisy sample x_t -> x_t-1
                    _lowerCamelCase				:  List[str]						=		self.scheduler.step(a__   ,				a__   ,				a__   ,				**a__).prev_sample

             # decode the image latents with the VAE
             _lowerCamelCase				:  Optional[int]						=		self.vqvae.decode(a__).sample

             _lowerCamelCase				:  Dict						=		(image / 2 + 0.5).clamp(0   ,				1)
             _lowerCamelCase				:  List[str]						=		image.cpu().permute(0   ,				2   ,				3   ,				1).numpy()
             if output_type == "pil":
                    _lowerCamelCase				:  List[Any]						=		self.numpy_to_pil(a__)

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=a__)

