






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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
class  _UpperCAmelCase   :

				"""simple docstring"""




				lowercase__											=			None
				lowercase__											=			None
				lowercase__											=			None  # sigma(t_i)




				@classmethod
				def 							lowercase__  (	cls		:       Tuple    ):

								'''simple docstring'''
								return cls()



@dataclass
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			42
				lowercase__											=			42
				lowercase__											=			42



class  _UpperCAmelCase   (			A__						,A__      ):

				"""simple docstring"""




				@property
				def 							lowercase__  (	self		:       List[Any]    ):

								'''simple docstring'''
								return True




				@register_to_config
				def __init__(	self		:       Dict,    lowerCamelCase		:       float = 0.02,    lowerCamelCase		:       float = 100,    lowerCamelCase		:       float = 1.007,    lowerCamelCase		:       float = 80,    lowerCamelCase		:       float = 0.05,    lowerCamelCase		:       float = 50,    ):

								'''simple docstring'''
								pass




				def 							lowercase__  (	self		:       int    ):

								'''simple docstring'''
								return KarrasVeSchedulerState.create()




				def 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       KarrasVeSchedulerState,    lowerCamelCase		:       int,    lowerCamelCase		:       Tuple = ()    ):

								'''simple docstring'''
								lowercase__  					=     jnp.arange(0,    lowerCamelCase    )[::-1].copy()
								lowercase__  					=     [
								    (
								        self.config.sigma_max**2
								        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
								    )
								    for i in timesteps
								]

								return state.replace(
								    num_inference_steps=lowerCamelCase,    schedule=jnp.array(lowerCamelCase,    dtype=jnp.floataa    ),    timesteps=lowerCamelCase,    )




				def 							lowercase__  (	self		:       str,    lowerCamelCase		:       KarrasVeSchedulerState,    lowerCamelCase		:       jnp.ndarray,    lowerCamelCase		:       float,    lowerCamelCase		:       random.KeyArray,    ):

								'''simple docstring'''
								if self.config.s_min <= sigma <= self.config.s_max:
												lowercase__  					=     min(self.config.s_churn / state.num_inference_steps,    2**0.5 - 1    )
								else:
												lowercase__  					=     0

								# sample eps ~ N(0, S_noise^2 * I)
								lowercase__  					=     random.split(lowerCamelCase,    num=1    )
								lowercase__  					=     self.config.s_noise * random.normal(key=lowerCamelCase,    shape=sample.shape    )
								lowercase__  					=     sigma + gamma * sigma
								lowercase__  					=     sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

								return sample_hat, sigma_hat




				def 							lowercase__  (	self		:       Union[str, Any],    lowerCamelCase		:       KarrasVeSchedulerState,    lowerCamelCase		:       jnp.ndarray,    lowerCamelCase		:       float,    lowerCamelCase		:       float,    lowerCamelCase		:       jnp.ndarray,    lowerCamelCase		:       bool = True,    ):

								'''simple docstring'''
								lowercase__  					=     sample_hat + sigma_hat * model_output
								lowercase__  					=     (sample_hat - pred_original_sample) / sigma_hat
								lowercase__  					=     sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
												return (sample_prev, derivative, state)

								return FlaxKarrasVeOutput(prev_sample=lowerCamelCase,    derivative=lowerCamelCase,    state=lowerCamelCase    )




				def 							lowercase__  (	self		:       int,    lowerCamelCase		:       KarrasVeSchedulerState,    lowerCamelCase		:       jnp.ndarray,    lowerCamelCase		:       float,    lowerCamelCase		:       float,    lowerCamelCase		:       jnp.ndarray,    lowerCamelCase		:       jnp.ndarray,    lowerCamelCase		:       jnp.ndarray,    lowerCamelCase		:       bool = True,    ):

								'''simple docstring'''
								lowercase__  					=     sample_prev + sigma_prev * model_output
								lowercase__  					=     (sample_prev - pred_original_sample) / sigma_prev
								lowercase__  					=     sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

								if not return_dict:
												return (sample_prev, derivative, state)

								return FlaxKarrasVeOutput(prev_sample=lowerCamelCase,    derivative=lowerCamelCase,    state=lowerCamelCase    )




				def 							lowercase__  (	self		:       Dict,    lowerCamelCase		:       KarrasVeSchedulerState,    lowerCamelCase		:       Optional[int],    lowerCamelCase		:       Any,    lowerCamelCase		:       str    ):

								'''simple docstring'''
								raise NotImplementedError()












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   (			A__      ):

				"""simple docstring"""




				@slow
				@require_torch
				def 							lowercase__  (	self		:       Optional[int]    ):

								'''simple docstring'''
								lowercase__  					=     EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny''',    '''prajjwal1/bert-tiny'''    )
								lowercase__  					=     BertTokenizer.from_pretrained('''bert-base-uncased'''    )

								lowercase__  					=     bertabert.config.encoder.vocab_size
								lowercase__  					=     tokenizer.sep_token_id
								lowercase__  					=     tokenizer.cls_token_id
								lowercase__  					=     128

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

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

								lowercase__  					=     4

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

												lowercase__  					=     outputs.input_ids
												lowercase__  					=     outputs.input_ids.copy()
												lowercase__  					=     [
												    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
												]
												lowercase__  					=     outputs.attention_mask

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

												return batch

								def _compute_metrics(lowerCamelCase		:       Any    ):
												lowercase__  					=     pred.label_ids
												lowercase__  					=     pred.predictions

												# all unnecessary tokens are removed
												lowercase__  					=     tokenizer.batch_decode(lowerCamelCase,    skip_special_tokens=lowerCamelCase    )
												lowercase__  					=     tokenizer.batch_decode(lowerCamelCase,    skip_special_tokens=lowerCamelCase    )

												lowercase__  					=     sum([int(pred_str[i] == label_str[i]    ) for i in range(len(lowerCamelCase    )    )]    ) / len(lowerCamelCase    )

												return {"accuracy": accuracy}

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

								lowercase__  					=     self.get_auto_remove_tmp_dir()

								lowercase__  					=     SeqaSeqTrainingArguments(
								    output_dir=lowerCamelCase,    per_device_train_batch_size=lowerCamelCase,    per_device_eval_batch_size=lowerCamelCase,    predict_with_generate=lowerCamelCase,    evaluation_strategy='''steps''',    do_train=lowerCamelCase,    do_eval=lowerCamelCase,    warmup_steps=0,    eval_steps=2,    logging_steps=2,    )

								# instantiate trainer
								lowercase__  					=     SeqaSeqTrainer(
								    model=lowerCamelCase,    args=lowerCamelCase,    compute_metrics=_compute_metrics,    train_dataset=lowerCamelCase,    eval_dataset=lowerCamelCase,    tokenizer=lowerCamelCase,    )

								# start training
								trainer.train()




