





from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


__lowerCAmelCase :List[Any]     =    8







def    A				(							UpperCAmelCase  ,  UpperCAmelCase=BITS				):
					_snake_case       :					Dict				       =   x.device

					_snake_case       :					int				       =   (x * 255).int().clamp(0  ,  255				)

					_snake_case       :					Dict				       =   2 ** torch.arange(bits - 1  ,  -1  ,  -1  ,  device=UpperCAmelCase				)
					_snake_case       :					str				       =   rearrange(UpperCAmelCase  ,  "d -> d 1 1"				)
					_snake_case       :					Any				       =   rearrange(UpperCAmelCase  ,  "b c h w -> b c 1 h w"				)

					_snake_case       :					Optional[int]				       =   ((x & mask) != 0).float()
					_snake_case       :					List[Any]				       =   rearrange(UpperCAmelCase  ,  "b c d h w -> b (c d) h w"				)
					_snake_case       :					Any				       =   bits * 2 - 1
					return bits







def    A				(							UpperCAmelCase  ,  UpperCAmelCase=BITS				):
					_snake_case       :					Optional[Any]				       =   x.device

					_snake_case       :					Union[str, Any]				       =   (x > 0).int()
					_snake_case       :					Any				       =   2 ** torch.arange(bits - 1  ,  -1  ,  -1  ,  device=UpperCAmelCase  ,  dtype=torch.intaa				)

					_snake_case       :					Any				       =   rearrange(UpperCAmelCase  ,  "d -> d 1 1"				)
					_snake_case       :					List[Any]				       =   rearrange(UpperCAmelCase  ,  "b (c d) h w -> b c d h w"  ,  d=8				)
					_snake_case       :					str				       =   reduce(x * mask  ,  "b c d h w -> b c h w"  ,  "sum"				)
					return (dec / 255).clamp(0.0  ,  1.0				)







def    A				(							self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = 0.0  ,  UpperCAmelCase = True  ,  UpperCAmelCase=None  ,  UpperCAmelCase = True  ,  ):
					if self.num_inference_steps is None:
										raise ValueError(
										    "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"				)

					# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
					# Ideally, read DDIM paper in-detail understanding

					# Notation (<variable name> -> <name in paper>
					# - pred_noise_t -> e_theta(x_t, t)
					# - pred_original_sample -> f_theta(x_t, t) or x_0
					# - std_dev_t -> sigma_t
					# - eta -> η
					# - pred_sample_direction -> "direction pointing to x_t"
					# - pred_prev_sample -> "x_t-1"

					# 1. get previous step value (=t-1)
					_snake_case       :					Optional[int]				       =   timestep - self.config.num_train_timesteps // self.num_inference_steps

					# 2. compute alphas, betas
					_snake_case       :					List[str]				       =   self.alphas_cumprod[timestep]
					_snake_case       :					Optional[Any]				       =   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

					_snake_case       :					Optional[Any]				       =   1 - alpha_prod_t

					# 3. compute predicted original sample from predicted noise also called
					# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
					_snake_case       :					Optional[int]				       =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

					# 4. Clip "predicted x_0"
					_snake_case       :					List[str]				       =   self.bit_scale
					if self.config.clip_sample:
										_snake_case       :					List[str]				       =   torch.clamp(UpperCAmelCase  ,  -scale  ,  UpperCAmelCase				)

					# 5. compute variance: "sigma_t(η)" -> see formula (16)
					# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
					_snake_case       :					str				       =   self._get_variance(UpperCAmelCase  ,  UpperCAmelCase				)
					_snake_case       :					List[str]				       =   eta * variance ** 0.5

					if use_clipped_model_output:
										# the model_output is always re-derived from the clipped x_0 in Glide
										_snake_case       :					Any				       =   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

					# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
					_snake_case       :					List[str]				       =   (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

					# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
					_snake_case       :					List[str]				       =   alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

					if eta > 0:
										# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
										_snake_case       :					List[Any]				       =   model_output.device if torch.is_tensor(UpperCAmelCase				) else "cpu"
										_snake_case       :					Optional[Any]				       =   torch.randn(model_output.shape  ,  dtype=model_output.dtype  ,  generator=UpperCAmelCase				).to(UpperCAmelCase				)
										_snake_case       :					Optional[int]				       =   self._get_variance(UpperCAmelCase  ,  UpperCAmelCase				) ** 0.5 * eta * noise

										_snake_case       :					Any				       =   prev_sample + variance

					if not return_dict:
										return (prev_sample,)

					return DDIMSchedulerOutput(prev_sample=UpperCAmelCase  ,  pred_original_sample=UpperCAmelCase				)







def    A				(							self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase="epsilon"  ,  UpperCAmelCase=None  ,  UpperCAmelCase = True  ,  ):
					_snake_case       :					List[str]				       =   timestep

					if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
										_snake_case       , _snake_case       :					Any				       =   torch.split(UpperCAmelCase  ,  sample.shape[1]  ,  dim=1				)
					else:
										_snake_case       :					Tuple				       =   None

					# 1. compute alphas, betas
					_snake_case       :					Tuple				       =   self.alphas_cumprod[t]
					_snake_case       :					Optional[int]				       =   self.alphas_cumprod[t - 1] if t > 0 else self.one
					_snake_case       :					Union[str, Any]				       =   1 - alpha_prod_t
					_snake_case       :					Union[str, Any]				       =   1 - alpha_prod_t_prev

					# 2. compute predicted original sample from predicted noise also called
					# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
					if prediction_type == "epsilon":
										_snake_case       :					Any				       =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
					elif prediction_type == "sample":
										_snake_case       :					Dict				       =   model_output
					else:
										raise ValueError(F"""Unsupported prediction_type {prediction_type}."""				)

					# 3. Clip "predicted x_0"
					_snake_case       :					Dict				       =   self.bit_scale
					if self.config.clip_sample:
										_snake_case       :					int				       =   torch.clamp(UpperCAmelCase  ,  -scale  ,  UpperCAmelCase				)

					# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					_snake_case       :					Union[str, Any]				       =   (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
					_snake_case       :					Optional[int]				       =   self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

					# 5. Compute predicted previous sample µ_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					_snake_case       :					int				       =   pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

					# 6. Add noise
					_snake_case       :					Union[str, Any]				       =   0
					if t > 0:
										_snake_case       :					Tuple				       =   torch.randn(
										    model_output.size()  ,  dtype=model_output.dtype  ,  layout=model_output.layout  ,  generator=UpperCAmelCase				).to(model_output.device				)
										_snake_case       :					Dict				       =   (self._get_variance(UpperCAmelCase  ,  predicted_variance=UpperCAmelCase				) ** 0.5) * noise

					_snake_case       :					str				       =   pred_prev_sample + variance

					if not return_dict:
										return (pred_prev_sample,)

					return DDPMSchedulerOutput(prev_sample=UpperCAmelCase  ,  pred_original_sample=UpperCAmelCase				)






class        _a(    __A    ):







		def __init__(    self ,						__snake_case ,						__snake_case ,						__snake_case = 1.0 ,						)      ->      Tuple:


							'''simple docstring'''

							super().__init__()
							_snake_case       :					Dict				       =   bit_scale
							_snake_case       :					Any				       =   (
							    ddim_bit_scheduler_step if isinstance(__snake_case ,						__snake_case				) else ddpm_bit_scheduler_step
							)

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







		@torch.no_grad()
		def __call__(    self ,						__snake_case = 2_5_6 ,						__snake_case = 2_5_6 ,						__snake_case = 5_0 ,						__snake_case = None ,						__snake_case = 1 ,						__snake_case = "pil" ,						__snake_case = True ,						**__snake_case ,						)      ->      Union[Tuple, ImagePipelineOutput]:


							'''simple docstring'''

							_snake_case       :					Union[str, Any]				       =   torch.randn(
							    (batch_size, self.unet.config.in_channels, height, width) ,						generator=__snake_case ,						)
							_snake_case       :					Any				       =   decimal_to_bits(__snake_case				) * self.bit_scale
							_snake_case       :					Union[str, Any]				       =   latents.to(self.device				)

							self.scheduler.set_timesteps(__snake_case				)

							for t in self.progress_bar(self.scheduler.timesteps				):
												# predict the noise residual
												_snake_case       :					int				       =   self.unet(__snake_case ,						__snake_case				).sample

												# compute the previous noisy sample x_t -> x_t-1
												_snake_case       :					Any				       =   self.scheduler.step(__snake_case ,						__snake_case ,						__snake_case				).prev_sample

							_snake_case       :					Optional[Any]				       =   bits_to_decimal(__snake_case				)

							if output_type == "pil":
												_snake_case       :					List[Any]				       =   self.numpy_to_pil(__snake_case				)

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=__snake_case				)






import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


__lowerCAmelCase :Tuple     =    logging.get_logger(__name__)






@dataclass
class        _a:
		lowerCamelCase__     :str   				=		field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys()    )}    )
		lowerCamelCase__     :str   				=		field(
		    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}    )
		lowerCamelCase__     :int   				=		field(
		    default=128	,   metadata={
		        'help': (
		            'The maximum total input sequence length after tokenization. Sequences longer '
		            'than this will be truncated, sequences shorter will be padded.'
		        )
		    }	,   )
		lowerCamelCase__     :bool   				=		field(
		    default=__A	,   metadata={'help': 'Overwrite the cached training and evaluation sets'}    )







		def        lowercase       (    self				)      ->      Optional[Any]:


							'''simple docstring'''

							_snake_case       :					Dict				       =   self.task_name.lower()






class        _a(    __A    ):
		lowerCamelCase__     :Optional[Any]   				=		'train'
		lowerCamelCase__     :List[str]   				=		'dev'
		lowerCamelCase__     :Any   				=		'test'






class        _a(    __A    ):
		lowerCamelCase__     :GlueDataTrainingArguments
		lowerCamelCase__     :str
		lowerCamelCase__     :List[InputFeatures]







		def __init__(    self ,						__snake_case ,						__snake_case ,						__snake_case = None ,						__snake_case = Split.train ,						__snake_case = None ,						)      ->      Any:


							'''simple docstring'''

							warnings.warn(
							    "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
							    "library. You can have a look at this example script for pointers: "
							    "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py" ,						__snake_case ,						)
							_snake_case       :					Optional[Any]				       =   args
							_snake_case       :					Optional[int]				       =   glue_processors[args.task_name]()
							_snake_case       :					int				       =   glue_output_modes[args.task_name]
							if isinstance(__snake_case ,						__snake_case				):
												try:
																	_snake_case       :					Tuple				       =   Split[mode]
												except KeyError:
																	raise KeyError("mode is not a valid split name"				)
        # Load data features from cache or dataset file
							_snake_case       :					List[str]				       =   os.path.join(
							    cache_dir if cache_dir is not None else args.data_dir ,						f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}""" ,						)
							_snake_case       :					List[Any]				       =   self.processor.get_labels()
							if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
							    "RobertaTokenizer",
							    "RobertaTokenizerFast",
							    "XLMRobertaTokenizer",
							    "BartTokenizer",
							    "BartTokenizerFast",
							):
												# HACK(label indices are swapped in RoBERTa pretrained model)
												_snake_case       , _snake_case       :					int				       =   label_list[2], label_list[1]
							_snake_case       :					str				       =   label_list

							# Make sure only the first process in distributed training processes the dataset,
							# and the others will use the cache.
							_snake_case       :					Optional[Any]				       =   cached_features_file + ".lock"
							with FileLock(__snake_case				):
												if os.path.exists(__snake_case				) and not args.overwrite_cache:
																	_snake_case       :					List[str]				       =   time.time()
																	_snake_case       :					str				       =   torch.load(__snake_case				)
																	logger.info(
																	    f"""Loading features from cached file {cached_features_file} [took %.3f s]""" ,						time.time() - start				)
												else:
																	logger.info(f"""Creating features from dataset file at {args.data_dir}"""				)

																	if mode == Split.dev:
																						_snake_case       :					List[Any]				       =   self.processor.get_dev_examples(args.data_dir				)
																	elif mode == Split.test:
																						_snake_case       :					List[Any]				       =   self.processor.get_test_examples(args.data_dir				)
																	else:
																						_snake_case       :					str				       =   self.processor.get_train_examples(args.data_dir				)
																	if limit_length is not None:
																						_snake_case       :					Tuple				       =   examples[:limit_length]
																	_snake_case       :					List[str]				       =   glue_convert_examples_to_features(
																	    __snake_case ,						__snake_case ,						max_length=args.max_seq_length ,						label_list=__snake_case ,						output_mode=self.output_mode ,						)
																	_snake_case       :					Optional[Any]				       =   time.time()
																	torch.save(self.features ,						__snake_case				)
																	# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																	logger.info(
																	    f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""				)







		def __len__(    self				)      ->      List[str]:


							'''simple docstring'''

							return len(self.features				)







		def __getitem__(    self ,						__snake_case				)      ->      InputFeatures:


							'''simple docstring'''

							return self.features[i]







		def        lowercase       (    self				)      ->      Optional[Any]:


							'''simple docstring'''

							return self.label_list