





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 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			@slow
			@require_torch
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				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_2_8

				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(3_2							)							)
				lowerCamelCase__								=		val_dataset.select(range(1_6							)							)

				lowerCamelCase__								=		4

				def _map_to_encoder_decoder_inputs(__lowerCAmelCase							):
					# Tokenizer will automatically set [BOS] <text> [EOS]
					lowerCamelCase__								=		tokenizer(batch['''article''']       ,  padding='''max_length'''       ,  truncation=__lowerCAmelCase       ,  max_length=5_1_2							)
					lowerCamelCase__								=		tokenizer(batch['''highlights''']       ,  padding='''max_length'''       ,  truncation=__lowerCAmelCase       ,  max_length=1_2_8							)
					lowerCamelCase__								=		inputs.input_ids
					lowerCamelCase__								=		inputs.attention_mask

					lowerCamelCase__								=		outputs.input_ids
					lowerCamelCase__								=		outputs.input_ids.copy()
					lowerCamelCase__								=		[
					    [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
					]
					lowerCamelCase__								=		outputs.attention_mask

					assert all(len(__lowerCAmelCase							) == 5_1_2 for x in inputs.input_ids							)
					assert all(len(__lowerCAmelCase							) == 1_2_8 for x in outputs.input_ids							)

					return batch

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

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

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

					return {"accuracy": accuracy}

				# map train dataset
				lowerCamelCase__								=		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
				lowerCamelCase__								=		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''']       ,  )

				lowerCamelCase__								=		self.get_auto_remove_tmp_dir()

				lowerCamelCase__								=		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
				lowerCamelCase__								=		SeqaSeqTrainer(
				    model=__lowerCAmelCase       ,  args=__lowerCAmelCase       ,  compute_metrics=_compute_metrics       ,  train_dataset=__lowerCAmelCase       ,  eval_dataset=__lowerCAmelCase       ,  tokenizer=__lowerCAmelCase       ,  )

				# start training
				trainer.train()










import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						(UnCLIPScheduler,)





			def 		__lowerCamelCase				(    self       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		{
				    '''num_train_timesteps''': 1_0_0_0,
				    '''variance_type''': '''fixed_small_log''',
				    '''clip_sample''': True,
				    '''clip_sample_range''': 1.0,
				    '''prediction_type''': '''epsilon''',
				}

				config.update(**__lowerCAmelCase							)
				return config





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
					self.check_over_configs(num_train_timesteps=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for variance in ["fixed_small_log", "learned_range"]:
					self.check_over_configs(variance_type=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for clip_sample in [True, False]:
					self.check_over_configs(clip_sample=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for clip_sample_range in [1, 5, 1_0, 2_0]:
					self.check_over_configs(clip_sample_range=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for prediction_type in ["epsilon", "sample"]:
					self.check_over_configs(prediction_type=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for time_step in [0, 5_0_0, 9_9_9]:
					for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
						if prev_timestep is not None and prev_timestep >= time_step:
							continue

						self.check_over_forward(time_step=__lowerCAmelCase       ,  prev_timestep=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.scheduler_classes[0]
				lowerCamelCase__								=		self.get_scheduler_config(variance_type='''fixed_small_log'''							)
				lowerCamelCase__								=		scheduler_class(**__lowerCAmelCase							)

				assert torch.sum(torch.abs(scheduler._get_variance(0							) - 1.0000E-10							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(4_8_7							) - 0.054_9625							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(9_9_9							) - 0.999_4987							)							) < 1E-5





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.scheduler_classes[0]
				lowerCamelCase__								=		self.get_scheduler_config(variance_type='''learned_range'''							)
				lowerCamelCase__								=		scheduler_class(**__lowerCAmelCase							)

				lowerCamelCase__								=		0.5

				assert scheduler._get_variance(1       ,  predicted_variance=__lowerCAmelCase							) - -10.171_2790 < 1E-5
				assert scheduler._get_variance(4_8_7       ,  predicted_variance=__lowerCAmelCase							) - -5.799_8052 < 1E-5
				assert scheduler._get_variance(9_9_9       ,  predicted_variance=__lowerCAmelCase							) - -0.001_0011 < 1E-5





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.scheduler_classes[0]
				lowerCamelCase__								=		self.get_scheduler_config()
				lowerCamelCase__								=		scheduler_class(**__lowerCAmelCase							)

				lowerCamelCase__								=		scheduler.timesteps

				lowerCamelCase__								=		self.dummy_model()
				lowerCamelCase__								=		self.dummy_sample_deter
				lowerCamelCase__								=		torch.manual_seed(0							)

				for i, t in enumerate(__lowerCAmelCase							):
					# 1. predict noise residual
					lowerCamelCase__								=		model(__lowerCAmelCase       ,  __lowerCAmelCase							)

					# 2. predict previous mean of sample x_t-1
					lowerCamelCase__								=		scheduler.step(__lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  generator=__lowerCAmelCase							).prev_sample

					lowerCamelCase__								=		pred_prev_sample

				lowerCamelCase__								=		torch.sum(torch.abs(__lowerCAmelCase							)							)
				lowerCamelCase__								=		torch.mean(torch.abs(__lowerCAmelCase							)							)

				assert abs(result_sum.item() - 252.268_2495							) < 1E-2
				assert abs(result_mean.item() - 0.328_4743							) < 1E-3





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.scheduler_classes[0]
				lowerCamelCase__								=		self.get_scheduler_config()
				lowerCamelCase__								=		scheduler_class(**__lowerCAmelCase							)

				scheduler.set_timesteps(2_5							)

				lowerCamelCase__								=		scheduler.timesteps

				lowerCamelCase__								=		self.dummy_model()
				lowerCamelCase__								=		self.dummy_sample_deter
				lowerCamelCase__								=		torch.manual_seed(0							)

				for i, t in enumerate(__lowerCAmelCase							):
					# 1. predict noise residual
					lowerCamelCase__								=		model(__lowerCAmelCase       ,  __lowerCAmelCase							)

					if i + 1 == timesteps.shape[0]:
						lowerCamelCase__								=		None
					else:
						lowerCamelCase__								=		timesteps[i + 1]

					# 2. predict previous mean of sample x_t-1
					lowerCamelCase__								=		scheduler.step(
					    __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  prev_timestep=__lowerCAmelCase       ,  generator=__lowerCAmelCase							).prev_sample

					lowerCamelCase__								=		pred_prev_sample

				lowerCamelCase__								=		torch.sum(torch.abs(__lowerCAmelCase							)							)
				lowerCamelCase__								=		torch.mean(torch.abs(__lowerCAmelCase							)							)

				assert abs(result_sum.item() - 258.204_4983							) < 1E-2
				assert abs(result_mean.item() - 0.336_2038							) < 1E-3





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass



