


"""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   _UpperCamelCase  (   __SCREAMING_SNAKE_CASE		):
							'''simple docstring'''







							@slow
							@require_torch
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"    ,   "prajjwal1/bert-tiny"	)
										__lowerCAmelCase				    =			BertTokenizer.from_pretrained("bert-base-uncased"	)

										__lowerCAmelCase				    =			bertabert.config.encoder.vocab_size
										__lowerCAmelCase				    =			tokenizer.sep_token_id
										__lowerCAmelCase				    =			tokenizer.cls_token_id
										__lowerCAmelCase				    =			1_28

										__lowerCAmelCase				    =			datasets.load_dataset("cnn_dailymail"    ,   "3.0.0"    ,   split="train[:1%]"	)
										__lowerCAmelCase				    =			datasets.load_dataset("cnn_dailymail"    ,   "3.0.0"    ,   split="validation[:1%]"	)

										__lowerCAmelCase				    =			train_dataset.select(range(32	)	)
										__lowerCAmelCase				    =			val_dataset.select(range(16	)	)

										__lowerCAmelCase				    =			4

										def _map_to_encoder_decoder_inputs(__a	):
													# Tokenizer will automatically set [BOS] <text> [EOS]
													__lowerCAmelCase				    =			tokenizer(batch["article"]    ,   padding="max_length"    ,   truncation=__a    ,   max_length=5_12	)
													__lowerCAmelCase				    =			tokenizer(batch["highlights"]    ,   padding="max_length"    ,   truncation=__a    ,   max_length=1_28	)
													__lowerCAmelCase				    =			inputs.input_ids
													__lowerCAmelCase				    =			inputs.attention_mask

													__lowerCAmelCase				    =			outputs.input_ids
													__lowerCAmelCase				    =			outputs.input_ids.copy()
													__lowerCAmelCase				    =			[
													    [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
													]
													__lowerCAmelCase				    =			outputs.attention_mask

													assert all(len(__a	) == 5_12 for x in inputs.input_ids	)
													assert all(len(__a	) == 1_28 for x in outputs.input_ids	)

													return batch

										def _compute_metrics(__a	):
													__lowerCAmelCase				    =			pred.label_ids
													__lowerCAmelCase				    =			pred.predictions

													# all unnecessary tokens are removed
													__lowerCAmelCase				    =			tokenizer.batch_decode(__a    ,   skip_special_tokens=__a	)
													__lowerCAmelCase				    =			tokenizer.batch_decode(__a    ,   skip_special_tokens=__a	)

													__lowerCAmelCase				    =			sum([int(pred_str[i] == label_str[i]	) for i in range(len(__a	)	)]	) / len(__a	)

													return {"accuracy": accuracy}

										# map train dataset
										__lowerCAmelCase				    =			train_dataset.map(
										    _map_to_encoder_decoder_inputs    ,   batched=__a    ,   batch_size=__a    ,   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
										__lowerCAmelCase				    =			val_dataset.map(
										    _map_to_encoder_decoder_inputs    ,   batched=__a    ,   batch_size=__a    ,   remove_columns=["article", "highlights"]    ,   )
										val_dataset.set_format(
										    type="torch"    ,   columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]    ,   )

										__lowerCAmelCase				    =			self.get_auto_remove_tmp_dir()

										__lowerCAmelCase				    =			SeqaSeqTrainingArguments(
										    output_dir=__a    ,   per_device_train_batch_size=__a    ,   per_device_eval_batch_size=__a    ,   predict_with_generate=__a    ,   evaluation_strategy="steps"    ,   do_train=__a    ,   do_eval=__a    ,   warmup_steps=0    ,   eval_steps=2    ,   logging_steps=2    ,   )

										# instantiate trainer
										__lowerCAmelCase				    =			SeqaSeqTrainer(
										    model=__a    ,   args=__a    ,   compute_metrics=_compute_metrics    ,   train_dataset=__a    ,   eval_dataset=__a    ,   tokenizer=__a    ,   )

										# start training
										trainer.train()






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     42
	snake_case_					    =     42
	snake_case_					    =     None





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     2






	@register_to_config
	def __init__(       self					: str							,A					: float = 0.02							,A					: float = 1_00							,A					: float = 1.0_07							,A					: float = 80							,A					: float = 0.05							,A					: float = 50							,):
					# standard deviation of the initial noise distribution
					__A   			= sigma_max

					# setable values
					__A   			= None
					__A   			= None
					__A   			= None  # sigma(t_i)






	def 				UpperCamelCase_  (       self					: str							,A					: torch.FloatTensor							,A					: Optional[int] = None ):
					return sample






	def 				UpperCamelCase_  (       self					: Dict							,A					: int							,A					: Union[str, torch.device] = None ):
					__A   			= num_inference_steps
					__A   			= np.arange(0							,self.num_inference_steps )[::-1].copy()
					__A   			= torch.from_numpy(A ).to(A )
					__A   			= [
					    (
					        self.config.sigma_max**2
					        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
					    )
					    for i in self.timesteps
					]
					__A   			= torch.tensor(A							,dtype=torch.floataa							,device=A )






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: torch.FloatTensor							,A					: float							,A					: Optional[torch.Generator] = None ):
					if self.config.s_min <= sigma <= self.config.s_max:
									__A   			= min(self.config.s_churn / self.num_inference_steps							,2**0.5 - 1 )
					else:
									__A   			= 0

					# sample eps ~ N(0, S_noise^2 * I)
					__A   			= self.config.s_noise * randn_tensor(sample.shape							,generator=A ).to(sample.device )
					__A   			= sigma + gamma * sigma
					__A   			= sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

					return sample_hat, sigma_hat






	def 				UpperCamelCase_  (       self					: Dict							,A					: torch.FloatTensor							,A					: float							,A					: float							,A					: torch.FloatTensor							,A					: bool = True							,):
					__A   			= sample_hat + sigma_hat * model_output
					__A   			= (sample_hat - pred_original_sample) / sigma_hat
					__A   			= sample_hat + (sigma_prev - sigma_hat) * derivative

					if not return_dict:
									return (sample_prev, derivative)

					return KarrasVeOutput(
					    prev_sample=A							,derivative=A							,pred_original_sample=A )






	def 				UpperCamelCase_  (       self					: Optional[int]							,A					: torch.FloatTensor							,A					: float							,A					: float							,A					: torch.FloatTensor							,A					: torch.FloatTensor							,A					: torch.FloatTensor							,A					: bool = True							,):
					__A   			= sample_prev + sigma_prev * model_output
					__A   			= (sample_prev - pred_original_sample) / sigma_prev
					__A   			= sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

					if not return_dict:
									return (sample_prev, derivative)

					return KarrasVeOutput(
					    prev_sample=A							,derivative=A							,pred_original_sample=A )







	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Dict							,A					: List[str]							,A					: str ):
					raise NotImplementedError()
