


'''simple docstring'''
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
		import datasets
class      lowercase			(							A__      ):

		"""simple docstring"""

		@slow
		@require_torch
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''					,     '''prajjwal1/bert-tiny'''							)
							UpperCamelCase__     :Optional[Any]							      =				BertTokenizer.from_pretrained('''bert-base-uncased'''							)

							UpperCamelCase__     :List[Any]							      =				bertabert.config.encoder.vocab_size
							UpperCamelCase__     :Union[str, Any]							      =				tokenizer.sep_token_id
							UpperCamelCase__     :int							      =				tokenizer.cls_token_id
							UpperCamelCase__     :Union[str, Any]							      =				128

							UpperCamelCase__     :Tuple							      =				datasets.load_dataset('''cnn_dailymail'''					,     '''3.0.0'''					,     split='''train[:1%]'''							)
							UpperCamelCase__     :Dict							      =				datasets.load_dataset('''cnn_dailymail'''					,     '''3.0.0'''					,     split='''validation[:1%]'''							)

							UpperCamelCase__     :int							      =				train_dataset.select(range(32							)							)
							UpperCamelCase__     :Any							      =				val_dataset.select(range(16							)							)

							UpperCamelCase__     :Optional[int]							      =				4

							def _map_to_encoder_decoder_inputs(UpperCamelCase_							):
												# Tokenizer will automatically set [BOS] <text> [EOS]
												UpperCamelCase__     :Union[str, Any]							      =				tokenizer(batch['''article''']					,     padding='''max_length'''					,     truncation=UpperCamelCase_					,     max_length=512							)
												UpperCamelCase__     :int							      =				tokenizer(batch['''highlights''']					,     padding='''max_length'''					,     truncation=UpperCamelCase_					,     max_length=128							)
												UpperCamelCase__     :List[Any]							      =				inputs.input_ids
												UpperCamelCase__     :Union[str, Any]							      =				inputs.attention_mask

												UpperCamelCase__     :Any							      =				outputs.input_ids
												UpperCamelCase__     :Optional[int]							      =				outputs.input_ids.copy()
												UpperCamelCase__     :List[Any]							      =				[
												    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
												]
												UpperCamelCase__     :Tuple							      =				outputs.attention_mask

												assert all(len(UpperCamelCase_							) == 512 for x in inputs.input_ids							)
												assert all(len(UpperCamelCase_							) == 128 for x in outputs.input_ids							)

												return batch

							def _compute_metrics(UpperCamelCase_							):
												UpperCamelCase__     :List[str]							      =				pred.label_ids
												UpperCamelCase__     :Optional[Any]							      =				pred.predictions

												# all unnecessary tokens are removed
												UpperCamelCase__     :Tuple							      =				tokenizer.batch_decode(UpperCamelCase_					,     skip_special_tokens=UpperCamelCase_							)
												UpperCamelCase__     :str							      =				tokenizer.batch_decode(UpperCamelCase_					,     skip_special_tokens=UpperCamelCase_							)

												UpperCamelCase__     :Dict							      =				sum([int(pred_str[i] == label_str[i]							) for i in range(len(UpperCamelCase_							)							)]							) / len(UpperCamelCase_							)

												return {"accuracy": accuracy}

							# map train dataset
							UpperCamelCase__     :int							      =				train_dataset.map(
							    _map_to_encoder_decoder_inputs					,     batched=UpperCamelCase_					,     batch_size=UpperCamelCase_					,     remove_columns=['''article''', '''highlights''']					,     )
							train_dataset.set_format(
							    type='''torch'''					,     columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']					,     )

							# same for validation dataset
							UpperCamelCase__     :Optional[int]							      =				val_dataset.map(
							    _map_to_encoder_decoder_inputs					,     batched=UpperCamelCase_					,     batch_size=UpperCamelCase_					,     remove_columns=['''article''', '''highlights''']					,     )
							val_dataset.set_format(
							    type='''torch'''					,     columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']					,     )

							UpperCamelCase__     :Optional[Any]							      =				self.get_auto_remove_tmp_dir()

							UpperCamelCase__     :int							      =				SeqaSeqTrainingArguments(
							    output_dir=UpperCamelCase_					,     per_device_train_batch_size=UpperCamelCase_					,     per_device_eval_batch_size=UpperCamelCase_					,     predict_with_generate=UpperCamelCase_					,     evaluation_strategy='''steps'''					,     do_train=UpperCamelCase_					,     do_eval=UpperCamelCase_					,     warmup_steps=0					,     eval_steps=2					,     logging_steps=2					,     )

							# instantiate trainer
							UpperCamelCase__     :Dict							      =				SeqaSeqTrainer(
							    model=UpperCamelCase_					,     args=UpperCamelCase_					,     compute_metrics=_compute_metrics					,     train_dataset=UpperCamelCase_					,     eval_dataset=UpperCamelCase_					,     tokenizer=UpperCamelCase_					,     )

							# start training
							trainer.train()



'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class      lowercase			(							A__			,	A__      ):

		"""simple docstring"""

		_a									=       1




		@register_to_config
		def __init__(	self					,     UpperCamelCase_=2000					,     UpperCamelCase_=0.1					,     UpperCamelCase_=20					,     UpperCamelCase_=1e-3							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				None
							UpperCamelCase__     :Any							      =				None
							UpperCamelCase__     :List[str]							      =				None




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				torch.linspace(1					,     self.config.sampling_eps					,     UpperCamelCase_					,     device=UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_=None							):

							'''simple docstring'''


							if self.timesteps is None:
												raise ValueError(
												    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''							)

							# TODO(Patrick) better comments + non-PyTorch
							# postprocess model score
							UpperCamelCase__     :Optional[int]							      =				(
							    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
							)
							UpperCamelCase__     :Tuple							      =				torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
							UpperCamelCase__     :Tuple							      =				std.flatten()
							while len(std.shape							) < len(score.shape							):
												UpperCamelCase__     :int							      =				std.unsqueeze(-1							)
							UpperCamelCase__     :List[Any]							      =				-score / std

							# compute
							UpperCamelCase__     :List[str]							      =				-1.0 / len(self.timesteps							)

							UpperCamelCase__     :Optional[int]							      =				self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
							UpperCamelCase__     :Any							      =				beta_t.flatten()
							while len(beta_t.shape							) < len(x.shape							):
												UpperCamelCase__     :Any							      =				beta_t.unsqueeze(-1							)
							UpperCamelCase__     :Optional[int]							      =				-0.5 * beta_t * x

							UpperCamelCase__     :int							      =				torch.sqrt(UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				drift - diffusion**2 * score
							UpperCamelCase__     :Dict							      =				x + drift * dt

							# add noise
							UpperCamelCase__     :List[Any]							      =				randn_tensor(x.shape					,     layout=x.layout					,     generator=UpperCamelCase_					,     device=x.device					,     dtype=x.dtype							)
							UpperCamelCase__     :int							      =				x_mean + diffusion * math.sqrt(-dt							) * noise

							return x, x_mean




		def __len__(	self							):

							'''simple docstring'''


							return self.config.num_train_timesteps