






from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowercase  :							Dict			  =    transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)

def      lowerCAmelCase__	(    _a       :			Optional[int]    ):
		if isinstance(_a  ,      torch.Tensor    ):
				return image
		elif isinstance(_a  ,      PIL.Image.Image    ):
				snake_case_				:							Optional[Any]						 =       [image]

		snake_case_				:							Union[str, Any]						 =       [trans(img.convert("RGB"    )    ) for img in image]
		snake_case_				:							Optional[int]						 =       torch.stack(_a    )
		return image




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Tuple:
								super().__init__()

								# make sure scheduler can always be converted to DDIM
								snake_case_				:							Optional[int]						 =       DDIMScheduler.from_config(scheduler.config				)

								self.register_modules(unet=_SCREAMING_SNAKE_CASE		,				scheduler=_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Optional[int]:
								if strength < 0 or strength > 1:
										raise ValueError(f'''The value of strength should in [0.0, 1.0] but is {strength}'''				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Union[str, Any]:
								# get the original timestep using init_timestep
								snake_case_				:							Any						 =       min(int(num_inference_steps * strength				)		,				_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Any						 =       max(num_inference_steps - init_timestep		,				0				)
								snake_case_				:							Dict						 =       self.scheduler.timesteps[t_start:]

								return timesteps, num_inference_steps - t_start






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=None				) ->				Dict:
								if not isinstance(_SCREAMING_SNAKE_CASE		,				(torch.Tensor, PIL.Image.Image, list)				):
										raise ValueError(
										    f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_SCREAMING_SNAKE_CASE				)}'''				)

								snake_case_				:							Tuple						 =       image.to(device=_SCREAMING_SNAKE_CASE		,				dtype=_SCREAMING_SNAKE_CASE				)

								if isinstance(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) and len(_SCREAMING_SNAKE_CASE				) != batch_size:
										raise ValueError(
										    f'''You have passed a list of generators of length {len(_SCREAMING_SNAKE_CASE				)}, but requested an effective batch'''
										    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''				)

								snake_case_				:							List[str]						 =       init_latents.shape
								snake_case_				:							List[Any]						 =       randn_tensor(_SCREAMING_SNAKE_CASE		,				generator=_SCREAMING_SNAKE_CASE		,				device=_SCREAMING_SNAKE_CASE		,				dtype=_SCREAMING_SNAKE_CASE				)

								# get latents
								print("add noise to latents at timestep"		,				_SCREAMING_SNAKE_CASE				)
								snake_case_				:							int						 =       self.scheduler.add_noise(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Any						 =       init_latents

								return latents






						@torch.no_grad()
						def __call__(	self		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = 0.8		,				_SCREAMING_SNAKE_CASE = 1		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = 0.0		,				_SCREAMING_SNAKE_CASE = 50		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = "pil"		,				_SCREAMING_SNAKE_CASE = True		,				) ->				Union[ImagePipelineOutput, Tuple]:
								self.check_inputs(_SCREAMING_SNAKE_CASE				)

								# 2. Preprocess image
								snake_case_				:							str						 =       preprocess(_SCREAMING_SNAKE_CASE				)

								# 3. set timesteps
								self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		,				device=self.device				)
								snake_case_ ,       snake_case_				:							int						 =       self.get_timesteps(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				self.device				)
								snake_case_				:							int						 =       timesteps[:1].repeat(_SCREAMING_SNAKE_CASE				)

								# 4. Prepare latent variables
								snake_case_				:							Optional[Any]						 =       self.prepare_latents(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				self.unet.dtype		,				self.device		,				_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Optional[Any]						 =       latents

								# 5. Denoising loop
								for t in self.progress_bar(_SCREAMING_SNAKE_CASE				):
										# 1. predict noise model_output
										snake_case_				:							int						 =       self.unet(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				).sample

										# 2. predict previous mean of image x_t-1 and add variance depending on eta
										# eta corresponds to η in paper and should be between [0, 1]
										# do x_t -> x_t-1
										snake_case_				:							Dict						 =       self.scheduler.step(
										    _SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				eta=_SCREAMING_SNAKE_CASE		,				use_clipped_model_output=_SCREAMING_SNAKE_CASE		,				generator=_SCREAMING_SNAKE_CASE		,				).prev_sample

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

								if not return_dict:
										return (image, latent_timestep.item())

								return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE				)








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




@dataclass(frozen=SCREAMING_SNAKE_CASE__					)
class 		UpperCAmelCase_						:


						'''simple docstring'''




						A     :   str
						A     :   str
						A     :   Optional[str]						=					None
						A     :   Optional[str]						=					None
						A     :   Optional[str]						=					None




@dataclass(frozen=SCREAMING_SNAKE_CASE__					)
class 		UpperCAmelCase_						:


						'''simple docstring'''




						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 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


													'''simple docstring'''




													A     :   List[InputFeatures]






													def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE = False		,				) ->				Union[str, Any]:
															snake_case_				:							str						 =       hans_processors[task]()

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

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

																			snake_case_				:							Any						 =       (
																			    processor.get_dev_examples(_SCREAMING_SNAKE_CASE				) if evaluate else processor.get_train_examples(_SCREAMING_SNAKE_CASE				)
																			)

																			logger.info("Training examples: %s"		,				len(_SCREAMING_SNAKE_CASE				)				)
																			snake_case_				:							Union[str, Any]						 =       hans_convert_examples_to_features(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
																			logger.info("Saving features into cached file %s"		,				_SCREAMING_SNAKE_CASE				)
																			torch.save(self.features		,				_SCREAMING_SNAKE_CASE				)






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






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






													def       _lowerCAmelCase       (	self				) ->				Dict:
															return self.label_list


if is_tf_available():
							import tensorflow as tf




							class 		UpperCAmelCase_						:


													'''simple docstring'''




													A     :   List[InputFeatures]






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

															snake_case_				:							int						 =       processor.get_dev_examples(_SCREAMING_SNAKE_CASE				) if evaluate else processor.get_train_examples(_SCREAMING_SNAKE_CASE				)
															snake_case_				:							str						 =       hans_convert_examples_to_features(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

															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(_SCREAMING_SNAKE_CASE				))				)

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

															snake_case_				:							List[Any]						 =       tf.data.Dataset.from_generator(
															    _SCREAMING_SNAKE_CASE		,				(
															        {
															            "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				) ->				int:
															return self.dataset






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






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






													def       _lowerCAmelCase       (	self				) ->				Dict:
															return self.label_list




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				int:
								return self._create_examples(self._read_tsv(os.path.join(_SCREAMING_SNAKE_CASE		,				"heuristics_train_set.txt"				)				)		,				"train"				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				List[Any]:
								return self._create_examples(self._read_tsv(os.path.join(_SCREAMING_SNAKE_CASE		,				"heuristics_evaluation_set.txt"				)				)		,				"dev"				)






						def       _lowerCAmelCase       (	self				) ->				Dict:
								return ["contradiction", "entailment", "neutral"]






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




def      lowerCAmelCase__	(    _a       :			List[InputExample]  ,      _a       :			List[str]  ,      _a       :			int  ,      _a       :			PreTrainedTokenizer  ,      ):
		snake_case_				:							Optional[Any]						 =       {label: i for i, label in enumerate(_a    )}

		snake_case_				:							Optional[Any]						 =       []
		for ex_index, example in tqdm.tqdm(enumerate(_a    )  ,      desc="convert examples to features"    ):
				if ex_index % 1_00_00 == 0:
						logger.info("Writing example %d" % (ex_index)    )

				snake_case_				:							Optional[Any]						 =       tokenizer(
				    example.text_a  ,      example.text_b  ,      add_special_tokens=_a  ,      max_length=_a  ,      padding="max_length"  ,      truncation=_a  ,      return_overflowing_tokens=_a  ,      )

				snake_case_				:							Dict						 =       label_map[example.label] if example.label in label_map else 0

				snake_case_				:							Optional[Any]						 =       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


lowercase  :							List[str]			  =    {
    '''hans''': 3,
}

lowercase  :							int			  =    {
    '''hans''': HansProcessor,
}
