





'''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,
)


snake_case_     :       Optional[Any]						=  logging.getLogger(__name__)


@dataclass(frozen=__snake_case     )
class 					__a      :
							__a					:   str
							__a					:   str
							__a					:   Optional[str]									=       None
							__a					:   Optional[str]									=       None
							__a					:   Optional[str]									=       None





@dataclass(frozen=__snake_case     )
class 					__a      :
							__a					:   List[int]
							__a					:   Optional[List[int]]									=       None
							__a					:   Optional[List[int]]									=       None
							__a					:   Optional[Union[int, float]]									=       None
							__a					:   Optional[int]									=       None


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


					class 					__a      (__snake_case     ):
												__a					:   List[InputFeatures]

												def __init__(							self							:     Any       ,     __magic_name__							:     str       ,     __magic_name__							:     int       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     Optional[Any] = None       ,     __magic_name__							:     int=False       ,     __magic_name__							:     List[Any] = False       ,     )					->       Optional[Any]:







																		"""simple docstring"""
																		UpperCAmelCase_			:      Optional[Any]     						=  hans_processors[task]()

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

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

																														UpperCAmelCase_			:      List[Any]     						=  (
																														    processor.get_dev_examples(A_   ) if evaluate else processor.get_train_examples(A_   )
																														)

																														logger.info('''Training examples: %s'''       ,     len(A_   )   )
																														UpperCAmelCase_			:      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							:     Dict   )					->       Union[str, Any]:







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

												def __getitem__(							self							:     str       ,     __magic_name__							:     Tuple   )					->       Dict:







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



												def     UpperCAmelCase__	(							self							:     int   )					->       Tuple:







																		"""simple docstring"""
																		return self.label_list


if is_tf_available():
					import tensorflow as tf


					class 					__a      :
												__a					:   List[InputFeatures]

												def __init__(							self							:     Tuple       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     Union[str, Any]       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     int = 1_28       ,     __magic_name__							:     List[Any]=False       ,     __magic_name__							:     Union[str, Any] = False       ,     )					->       Dict:







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

																		UpperCAmelCase_			:      Optional[Any]     						=  processor.get_dev_examples(A_   ) if evaluate else processor.get_train_examples(A_   )
																		UpperCAmelCase_			:      str     						=  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_00_00 == 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,
																														)

																		UpperCAmelCase_			:      str     						=  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     UpperCAmelCase__	(							self							:     Tuple   )					->       Tuple:







																		"""simple docstring"""
																		return self.dataset

												def __len__(							self							:     Optional[int]   )					->       Tuple:







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

												def __getitem__(							self							:     Union[str, Any]       ,     __magic_name__							:     Union[str, Any]   )					->       List[str]:







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



												def     UpperCAmelCase__	(							self							:     List[str]   )					->       Any:







																		"""simple docstring"""
																		return self.label_list


class 					__a      (__snake_case     ):

							def     UpperCAmelCase__	(							self							:     Dict       ,     __magic_name__							:     Tuple   )					->       Optional[Any]:







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

							def     UpperCAmelCase__	(							self							:     int       ,     __magic_name__							:     str   )					->       Optional[Any]:







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

							def     UpperCAmelCase__	(							self							:     Tuple   )					->       int:







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



							def     UpperCAmelCase__	(							self							:     int       ,     __magic_name__							:     Dict       ,     __magic_name__							:     Any   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[Any]     						=  []
													for i, line in enumerate(A_   ):
																			if i == 0:
																									continue
																			UpperCAmelCase_			:      Union[str, Any]     						=  "%s-%s" % (set_type, line[0])
																			UpperCAmelCase_			:      Optional[int]     						=  line[5]
																			UpperCAmelCase_			:      Tuple     						=  line[6]
																			UpperCAmelCase_			:      int     						=  line[7][2:] if line[7].startswith('''ex'''   ) else line[7]
																			UpperCAmelCase_			:      Optional[Any]     						=  line[0]
																			examples.append(InputExample(guid=A_       ,     text_a=A_       ,     text_b=A_       ,     label=A_       ,     pairID=A_   )   )
													return examples





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Any,			SCREAMING_SNAKE_CASE__      :			Any,			SCREAMING_SNAKE_CASE__      :			Union[str, Any],			SCREAMING_SNAKE_CASE__      :			List[Any],			) ->	int:
						UpperCAmelCase_			:      Optional[int]     						=  {label: i for i, label in enumerate(_lowerCAmelCase   )}

						UpperCAmelCase_			:      List[str]     						=  []
						for ex_index, example in tqdm.tqdm(enumerate(_lowerCAmelCase   ),			desc='''convert examples to features'''   ):
												if ex_index % 10000 == 0:
																		logger.info('''Writing example %d''' % (ex_index)   )

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

												UpperCAmelCase_			:      str     						=  label_map[example.label] if example.label in label_map else 0

												UpperCAmelCase_			:      Tuple     						=  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


snake_case_     :       Tuple						=  {
    """hans""": 3,
}
snake_case_     :       List[Any]						=  {
    """hans""": HansProcessor,
}












'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 					__a      (unittest.TestCase     ):

							@property
							def     UpperCAmelCase__	(							self							:     Dict   )					->       str:







													"""simple docstring"""
													torch.manual_seed(0   )
													UpperCAmelCase_			:      Optional[Any]     						=  UNetaDModel(
													    block_out_channels=(32, 64)       ,     layers_per_block=2       ,     sample_size=32       ,     in_channels=3       ,     out_channels=3       ,     down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')       ,     up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')       ,     )
													return model



							def     UpperCAmelCase__	(							self							:     Dict   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  self.dummy_uncond_unet
													UpperCAmelCase_			:      Dict     						=  KarrasVeScheduler()

													UpperCAmelCase_			:      Union[str, Any]     						=  KarrasVePipeline(unet=__magic_name__       ,     scheduler=__magic_name__   )
													pipe.to(__magic_name__   )
													pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      Dict     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      Optional[int]     						=  pipe(num_inference_steps=2       ,     generator=__magic_name__       ,     output_type='''numpy'''   ).images

													UpperCAmelCase_			:      Tuple     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      str     						=  pipe(num_inference_steps=2       ,     generator=__magic_name__       ,     output_type='''numpy'''       ,     return_dict=__magic_name__   )[0]

													UpperCAmelCase_			:      Union[str, Any]     						=  image[0, -3:, -3:, -1]
													UpperCAmelCase_			:      Optional[Any]     						=  image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													UpperCAmelCase_			:      Dict     						=  np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

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





@slow
@require_torch
class 					__a      (unittest.TestCase     ):

							def     UpperCAmelCase__	(							self							:     int   )					->       Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      List[str]     						=  '''google/ncsnpp-celebahq-256'''
													UpperCAmelCase_			:      List[str]     						=  UNetaDModel.from_pretrained(__magic_name__   )
													UpperCAmelCase_			:      List[Any]     						=  KarrasVeScheduler()

													UpperCAmelCase_			:      Any     						=  KarrasVePipeline(unet=__magic_name__       ,     scheduler=__magic_name__   )
													pipe.to(__magic_name__   )
													pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      Dict     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      Optional[Any]     						=  pipe(num_inference_steps=20       ,     generator=__magic_name__       ,     output_type='''numpy'''   ).images

													UpperCAmelCase_			:      Any     						=  image[0, -3:, -3:, -1]
													assert image.shape == (1, 2_56, 2_56, 3)
													UpperCAmelCase_			:      Optional[Any]     						=  np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]   )

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





