


'''simple docstring'''

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,
)


lowercase__				     =		logging.getLogger(__name__)





@dataclass(frozen=lowerCamelCase__ )
class 						__SCREAMING_SNAKE_CASE					:
     UpperCAmelCase 					=				42
     UpperCAmelCase 					=				42
     UpperCAmelCase 					=				None
     UpperCAmelCase 					=				None
     UpperCAmelCase 					=				None







@dataclass(frozen=lowerCamelCase__ )
class 						__SCREAMING_SNAKE_CASE					:
     UpperCAmelCase 					=				42
     UpperCAmelCase 					=				None
     UpperCAmelCase 					=				None
     UpperCAmelCase 					=				None
     UpperCAmelCase 					=				None


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





   class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
        UpperCAmelCase 					=				42






        def __init__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase = None     ,							__UpperCamelCase=False     ,							__UpperCamelCase = False     ,							)       ->    List[Any]:
             _a						=		hans_processors[task]()

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

             # Make sure only the first process in distributed training processes the dataset,
             # and the others will use the cache.
             _a						=		cached_features_file + ".lock"
             with FileLock(__UpperCamelCase		):
                  if os.path.exists(__UpperCamelCase		) and not overwrite_cache:
                       logger.info(f"Loading features from cached file {cached_features_file}"		)
                       _a						=		torch.load(__UpperCamelCase		)
                  else:
                       logger.info(f"Creating features from dataset file at {data_dir}"		)

                       _a						=		(
                           processor.get_dev_examples(__UpperCamelCase		) if evaluate else processor.get_train_examples(__UpperCamelCase		)
                       )

                       logger.info("Training examples: %s"     ,							len(__UpperCamelCase		)		)
                       _a						=		hans_convert_examples_to_features(__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)
                       logger.info("Saving features into cached file %s"     ,							__UpperCamelCase		)
                       torch.save(self.features     ,							__UpperCamelCase		)






        def __len__(			self		)       ->    Tuple:
             return len(self.features		)






        def __getitem__(			self     ,							__UpperCamelCase		)       ->    InputFeatures:
             return self.features[i]






        def  a_					(			self		)       ->    List[str]:
             return self.label_list


if is_tf_available():
   import tensorflow as tf





   class 						__SCREAMING_SNAKE_CASE					:
        UpperCAmelCase 					=				42






        def __init__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase = 128     ,							__UpperCamelCase=False     ,							__UpperCamelCase = False     ,							)       ->    List[str]:
             _a						=		hans_processors[task]()
             _a						=		processor.get_labels()
             if tokenizer.__class__ in (
                 RobertaTokenizer,
                 RobertaTokenizerFast,
                 XLMRobertaTokenizer,
                 BartTokenizer,
                 BartTokenizerFast,
             ):
                  # HACK(label indices are swapped in RoBERTa pretrained model)
                  _a			,			_a						=		label_list[2], label_list[1]
             _a						=		label_list

             _a						=		processor.get_dev_examples(__UpperCamelCase		) if evaluate else processor.get_train_examples(__UpperCamelCase		)
             _a						=		hans_convert_examples_to_features(__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)

             def gen():
                  for ex_index, ex in tqdm.tqdm(enumerate(self.features		)     ,							desc="convert examples to features"		):
                       if ex_index % 10_000 == 0:
                            logger.info("Writing example %d of %d" % (ex_index, len(__UpperCamelCase		))		)

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

             _a						=		tf.data.Dataset.from_generator(
                 __UpperCamelCase     ,							(
                     {
                         "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  a_					(			self		)       ->    List[str]:
             return self.dataset






        def __len__(			self		)       ->    Optional[Any]:
             return len(self.features		)






        def __getitem__(			self     ,							__UpperCamelCase		)       ->    InputFeatures:
             return self.features[i]






        def  a_					(			self		)       ->    int:
             return self.label_list





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):






     def  a_					(			self     ,							__UpperCamelCase		)       ->    Tuple:
          return self._create_examples(self._read_tsv(os.path.join(__UpperCamelCase     ,							"heuristics_train_set.txt"		)		)     ,							"train"		)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    Tuple:
          return self._create_examples(self._read_tsv(os.path.join(__UpperCamelCase     ,							"heuristics_evaluation_set.txt"		)		)     ,							"dev"		)






     def  a_					(			self		)       ->    List[str]:
          return ["contradiction", "entailment", "neutral"]






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    Optional[int]:
          _a						=		[]
          for i, line in enumerate(__UpperCamelCase		):
               if i == 0:
                    continue
               _a						=		"%s-%s" % (set_type, line[0])
               _a						=		line[5]
               _a						=		line[6]
               _a						=		line[7][2:] if line[7].startswith("ex"		) else line[7]
               _a						=		line[0]
               examples.append(InputExample(guid=__UpperCamelCase     ,							text_a=__UpperCamelCase     ,							text_b=__UpperCamelCase     ,							label=__UpperCamelCase     ,							pairID=__UpperCamelCase		)		)
          return examples







def __UpperCamelCase   (			__lowerCamelCase		: List[InputExample]      ,  __lowerCamelCase		: List[str]      ,  __lowerCamelCase		: int      ,  __lowerCamelCase		: PreTrainedTokenizer      ,  )      ->     str:







     '''simple docstring'''






     _a						=		{label: i for i, label in enumerate(__lowerCamelCase				)}

     _a						=		[]
     for ex_index, example in tqdm.tqdm(enumerate(__lowerCamelCase				)      ,  desc="convert examples to features"				):
          if ex_index % 1_0000 == 0:
               logger.info("Writing example %d" % (ex_index)				)

          _a						=		tokenizer(
              example.text_a      ,  example.text_b      ,  add_special_tokens=__lowerCamelCase      ,  max_length=__lowerCamelCase      ,  padding="max_length"      ,  truncation=__lowerCamelCase      ,  return_overflowing_tokens=__lowerCamelCase      ,  )

          _a						=		label_map[example.label] if example.label in label_map else 0

          _a						=		int(example.pairID				)

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

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

     return features


lowercase__				     =		{
    "hans": 3,
}






lowercase__				     =		{
    "hans": HansProcessor,
}





'''simple docstring'''

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


lowercase__				     =		False





class 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ):
     pass







@nightly
@require_torch_gpu
class 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ):






     def  a_					(			self		)       ->    Dict:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






     def  a_					(			self		)       ->    List[str]:
          _a						=		VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"		)
          # remove text_unet
          pipe.remove_unused_weights()
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a						=		"A painting of a squirrel eating a burger "
          _a						=		torch.manual_seed(0		)
          _a						=		pipe(
              prompt=__UpperCamelCase     ,							generator=__UpperCamelCase     ,							guidance_scale=7.5     ,							num_inference_steps=2     ,							output_type="numpy"		).images

          with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__UpperCamelCase		)
               _a						=		VersatileDiffusionTextToImagePipeline.from_pretrained(__UpperCamelCase		)
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a						=		generator.manual_seed(0		)
          _a						=		pipe(
              prompt=__UpperCamelCase     ,							generator=__UpperCamelCase     ,							guidance_scale=7.5     ,							num_inference_steps=2     ,							output_type="numpy"		).images

          assert np.abs(image - new_image		).sum() < 1e-5, "Models don't have the same forward pass"






     def  a_					(			self		)       ->    List[Any]:
          _a						=		VersatileDiffusionTextToImagePipeline.from_pretrained(
              "shi-labs/versatile-diffusion"     ,							torch_dtype=torch.floataa		)
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a						=		"A painting of a squirrel eating a burger "
          _a						=		torch.manual_seed(0		)
          _a						=		pipe(
              prompt=__UpperCamelCase     ,							generator=__UpperCamelCase     ,							guidance_scale=7.5     ,							num_inference_steps=50     ,							output_type="numpy"		).images

          _a						=		image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 3)
          _a						=		np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]		)

          assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2

