



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,
)


a_			=					logging.getLogger(__name__)



@dataclass(frozen=lowerCAmelCase__   )
class  __lowerCAmelCase     :
 lowerCAmelCase__       					=  42
 lowerCAmelCase__       					=  42
 lowerCAmelCase__       					=  None
 lowerCAmelCase__       					=  None
 lowerCAmelCase__       					=  None



@dataclass(frozen=lowerCAmelCase__   )
class  __lowerCAmelCase     :
 lowerCAmelCase__       					=  42
 lowerCAmelCase__       					=  None
 lowerCAmelCase__       					=  None
 lowerCAmelCase__       					=  None
 lowerCAmelCase__       					=  None


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



  class  __lowerCAmelCase     (	lowerCAmelCase__   ):
   lowerCAmelCase__       					=  42




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



      '''simple docstring'''
      __lowerCamelCase													=    hans_processors[task]()

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

      # Make sure only the first process in distributed training processes the dataset,
      # and the others will use the cache.
      __lowerCamelCase													=    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}"""				)
            __lowerCamelCase													=    torch.load(__UpperCAmelCase				)
         else:
            logger.info(F"""Creating features from dataset file at {data_dir}"""				)

            __lowerCamelCase													=    (
                processor.get_dev_examples(__UpperCAmelCase				) if evaluate else processor.get_train_examples(__UpperCAmelCase				)
            )

            logger.info('''Training examples: %s'''	,				len(__UpperCAmelCase				)				)
            __lowerCamelCase													=    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				):



      '''simple docstring'''
      return len(self.features				)




   def __getitem__(						self	,				__UpperCAmelCase				):



      '''simple docstring'''
      return self.features[i]




   def        lowerCamelCase      (						self				):



      '''simple docstring'''
      return self.label_list


if is_tf_available():
  import tensorflow as tf



  class  __lowerCAmelCase     :
   lowerCAmelCase__       					=  42




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



      '''simple docstring'''
      __lowerCamelCase													=    hans_processors[task]()
      __lowerCamelCase													=    processor.get_labels()
      if tokenizer.__class__ in (
          RobertaTokenizer,
          RobertaTokenizerFast,
          XLMRobertaTokenizer,
          BartTokenizer,
          BartTokenizerFast,
      ):
         # HACK(label indices are swapped in RoBERTa pretrained model)
         __lowerCamelCase							,__lowerCamelCase													=    label_list[2], label_list[1]
      __lowerCamelCase													=    label_list

      __lowerCamelCase													=    processor.get_dev_examples(__UpperCAmelCase				) if evaluate else processor.get_train_examples(__UpperCAmelCase				)
      __lowerCamelCase													=    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 % 10000 == 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,
            )

      __lowerCamelCase													=    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        lowerCamelCase      (						self				):



      '''simple docstring'''
      return self.dataset




   def __len__(						self				):



      '''simple docstring'''
      return len(self.features				)




   def __getitem__(						self	,				__UpperCAmelCase				):



      '''simple docstring'''
      return self.features[i]




   def        lowerCamelCase      (						self				):



      '''simple docstring'''
      return self.label_list



class  __lowerCAmelCase     (	lowerCAmelCase__   ):




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    return self._create_examples(self._read_tsv(os.path.join(__UpperCAmelCase	,				'''heuristics_train_set.txt'''				)				)	,				'''train'''				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    return self._create_examples(self._read_tsv(os.path.join(__UpperCAmelCase	,				'''heuristics_evaluation_set.txt'''				)				)	,				'''dev'''				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return ["contradiction", "entailment", "neutral"]




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    []
    for i, line in enumerate(__UpperCAmelCase				):
       if i == 0:
          continue
       __lowerCamelCase													=    '''%s-%s''' % (set_type, line[0])
       __lowerCamelCase													=    line[5]
       __lowerCamelCase													=    line[6]
       __lowerCamelCase													=    line[7][2:] if line[7].startswith('''ex'''				) else line[7]
       __lowerCamelCase													=    line[0]
       examples.append(InputExample(guid=__UpperCAmelCase	,				text_a=__UpperCAmelCase	,				text_b=__UpperCAmelCase	,				label=__UpperCAmelCase	,				pairID=__UpperCAmelCase				)				)
    return examples





def 		a__			(		_UpperCamelCase			: List[InputExample] ,_UpperCamelCase			: List[str] ,_UpperCamelCase			: int ,_UpperCamelCase			: PreTrainedTokenizer ,):
   __lowerCamelCase													=    {label: i for i, label in enumerate(_UpperCamelCase							)}

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

      __lowerCamelCase													=    tokenizer(
          example.text_a ,example.text_b ,add_special_tokens=_UpperCamelCase ,max_length=_UpperCamelCase ,padding='''max_length''' ,truncation=_UpperCamelCase ,return_overflowing_tokens=_UpperCamelCase ,)

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

      __lowerCamelCase													=    int(example.pairID							)

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

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

   return features


a_			=					{
    """hans""": 3,
}





a_			=					{
    """hans""": HansProcessor,
}











import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


a_			=					False



class  __lowerCAmelCase     (	unittest.TestCase   ):
 pass



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




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''	,				torch_dtype=torch.floataa				)
    pipe.to(__UpperCAmelCase				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    __lowerCamelCase													=    load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)

    __lowerCamelCase													=    torch.manual_seed(0				)
    __lowerCamelCase													=    pipe.dual_guided(
        prompt='''first prompt'''	,				image=__UpperCAmelCase	,				text_to_image_strength=0.75	,				generator=__UpperCAmelCase	,				guidance_scale=7.5	,				num_inference_steps=2	,				output_type='''numpy'''	,				).images

    with tempfile.TemporaryDirectory() as tmpdirname:
       pipe.save_pretrained(__UpperCAmelCase				)
       __lowerCamelCase													=    VersatileDiffusionPipeline.from_pretrained(__UpperCAmelCase	,				torch_dtype=torch.floataa				)
    pipe.to(__UpperCAmelCase				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    __lowerCamelCase													=    generator.manual_seed(0				)
    __lowerCamelCase													=    pipe.dual_guided(
        prompt='''first prompt'''	,				image=__UpperCAmelCase	,				text_to_image_strength=0.75	,				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        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''	,				torch_dtype=torch.floataa				)
    pipe.to(__UpperCAmelCase				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    __lowerCamelCase													=    '''cyberpunk 2077'''
    __lowerCamelCase													=    load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)
    __lowerCamelCase													=    torch.manual_seed(0				)
    __lowerCamelCase													=    pipe.dual_guided(
        prompt=__UpperCAmelCase	,				image=__UpperCAmelCase	,				text_to_image_strength=0.75	,				generator=__UpperCAmelCase	,				guidance_scale=7.5	,				num_inference_steps=50	,				output_type='''numpy'''	,				).images

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

    assert image.shape == (1, 512, 512, 3)
    __lowerCamelCase													=    np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001]				)

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

    __lowerCamelCase													=    '''A painting of a squirrel eating a burger '''
    __lowerCamelCase													=    torch.manual_seed(0				)
    __lowerCamelCase													=    pipe.text_to_image(
        prompt=__UpperCAmelCase	,				generator=__UpperCAmelCase	,				guidance_scale=7.5	,				num_inference_steps=50	,				output_type='''numpy'''				).images

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

    assert image.shape == (1, 512, 512, 3)
    __lowerCamelCase													=    np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]				)

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

    __lowerCamelCase													=    pipe.image_variation(__UpperCAmelCase	,				generator=__UpperCAmelCase	,				output_type='''numpy'''				).images

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

    assert image.shape == (1, 512, 512, 3)
    __lowerCamelCase													=    np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456]				)

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






