





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,
)


UpperCamelCase__           =			logging.getLogger(__name__)

@dataclass(frozen=_a							)
class 							__SCREAMING_SNAKE_CASE       :
							snake_case  :   str
							snake_case  :   str
							snake_case  :   Optional[str]      =							None
							snake_case  :   Optional[str]      =							None
							snake_case  :   Optional[str]      =							None






@dataclass(frozen=_a							)
class 							__SCREAMING_SNAKE_CASE       :
							snake_case  :   List[int]
							snake_case  :   Optional[List[int]]      =							None
							snake_case  :   Optional[List[int]]      =							None
							snake_case  :   Optional[Union[int, float]]      =							None
							snake_case  :   Optional[int]      =							None


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

					class 							__SCREAMING_SNAKE_CASE       (  _a							):
												snake_case  :   List[InputFeatures]






												def __init__(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase = None	,			__lowerCAmelCase=False	,			__lowerCAmelCase = False	,			):
																			UpperCamelCase__					  =      hans_processors[task]()

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

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

																																	UpperCamelCase__					  =      (
																																	    processor.get_dev_examples(__lowerCAmelCase	) if evaluate else processor.get_train_examples(__lowerCAmelCase	)
																																	)

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






												def __len__(    self	):
																			return len(self.features	)






												def __getitem__(    self	,			__lowerCAmelCase	):
																			return self.features[i]







												def 					_lowerCamelCase		(    self	):
																			return self.label_list


if is_tf_available():
					import tensorflow as tf

					class 							__SCREAMING_SNAKE_CASE       :
												snake_case  :   List[InputFeatures]






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

																			UpperCamelCase__					  =      processor.get_dev_examples(__lowerCAmelCase	) if evaluate else processor.get_train_examples(__lowerCAmelCase	)
																			UpperCamelCase__					  =      hans_convert_examples_to_features(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	)

																			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(__lowerCAmelCase	))	)

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

																			UpperCamelCase__					  =      tf.data.Dataset.from_generator(
																			    __lowerCAmelCase	,			(
																			        {
																			            """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	):
																			return self.dataset






												def __len__(    self	):
																			return len(self.features	)






												def __getitem__(    self	,			__lowerCAmelCase	):
																			return self.features[i]







												def 					_lowerCamelCase		(    self	):
																			return self.label_list

class 							__SCREAMING_SNAKE_CASE       (  _a							):






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														return self._create_examples(self._read_tsv(os.path.join(__lowerCAmelCase	,			"""heuristics_train_set.txt"""	)	)	,			"""train"""	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														return self._create_examples(self._read_tsv(os.path.join(__lowerCAmelCase	,			"""heuristics_evaluation_set.txt"""	)	)	,			"""dev"""	)






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







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






def      _UpperCamelCase  (a__ :List[InputExample] ,      a__ :List[str] ,      a__ :int ,      a__ :PreTrainedTokenizer ,      ):






							"""simple docstring"""





							UpperCamelCase__					  =      {label: i for i, label in enumerate(a__							)}

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

														UpperCamelCase__					  =      tokenizer(
														    example.text_a ,      example.text_b ,      add_special_tokens=a__ ,      max_length=a__ ,      padding="""max_length""" ,      truncation=a__ ,      return_overflowing_tokens=a__ ,      )

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

														UpperCamelCase__					  =      int(example.pairID							)

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

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

							return features


UpperCamelCase__           =			{
    "hans": 3,
}
UpperCamelCase__           =			{
    "hans": HansProcessor,
}











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


UpperCamelCase__           =			False

class 							__SCREAMING_SNAKE_CASE       (  unittest.TestCase							):
							pass






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






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






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      VersatileDiffusionTextToImagePipeline.from_pretrained("""shi-labs/versatile-diffusion"""	)
														# remove text_unet
														pipe.remove_unused_weights()
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

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

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

														UpperCamelCase__					  =      generator.manual_seed(0	)
														UpperCamelCase__					  =      pipe(
														    prompt=__lowerCAmelCase	,			generator=__lowerCAmelCase	,			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	):
														UpperCamelCase__					  =      VersatileDiffusionTextToImagePipeline.from_pretrained(
														    """shi-labs/versatile-diffusion"""	,			torch_dtype=torch.floataa	)
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

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

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

														assert image.shape == (1, 512, 512, 3)
														UpperCamelCase__					  =      np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]	)

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




