
import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


UpperCamelCase_   	=	logging.getLogger(__name__)

UpperCamelCase_   	=	tf.data.AUTOTUNE






def 		_UpperCAmelCase   (	):






	"""simple docstring"""


	__lowerCAmelCase         =							argparse.ArgumentParser(description="Train a masked language model on TPU."  )
	parser.add_argument(
	    "--pretrained_model_config"			,							type=UpperCamelCase			,							default="roberta-base"			,							help="The model config to use. Note that we don't copy the model's weights, only the config!"			,							)
	parser.add_argument(
	    "--tokenizer"			,							type=UpperCamelCase			,							default="unigram-tokenizer-wikitext"			,							help="The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model's vocab size."			,							)

	parser.add_argument(
	    "--per_replica_batch_size"			,							type=UpperCamelCase			,							default=8			,							help="Batch size per TPU core."			,							)

	parser.add_argument(
	    "--no_tpu"			,							action="store_true"			,							help="If set, run on CPU and don't try to initialize a TPU. Useful for debugging on non-TPU instances."			,							)

	parser.add_argument(
	    "--tpu_name"			,							type=UpperCamelCase			,							help="Name of TPU resource to initialize. Should be blank on Colab, and 'local' on TPU VMs."			,							default="local"			,							)

	parser.add_argument(
	    "--tpu_zone"			,							type=UpperCamelCase			,							help="Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes."			,							)

	parser.add_argument(
	    "--gcp_project"			,							type=UpperCamelCase			,							help="Google cloud project name. Only used for non-Colab TPU nodes."  )

	parser.add_argument(
	    "--bfloat16"			,							action="store_true"			,							help="Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU."			,							)

	parser.add_argument(
	    "--train_dataset"			,							type=UpperCamelCase			,							help="Path to training dataset to load. If the path begins with `gs://`"
	    " then the dataset will be loaded from a Google Cloud Storage bucket."			,							)

	parser.add_argument(
	    "--shuffle_buffer_size"			,							type=UpperCamelCase			,							default=2**1_8			,							help="Size of the shuffle buffer (in samples)"			,							)

	parser.add_argument(
	    "--eval_dataset"			,							type=UpperCamelCase			,							help="Path to evaluation dataset to load. If the path begins with `gs://`"
	    " then the dataset will be loaded from a Google Cloud Storage bucket."			,							)

	parser.add_argument(
	    "--num_epochs"			,							type=UpperCamelCase			,							default=1			,							help="Number of epochs to train for."			,							)

	parser.add_argument(
	    "--learning_rate"			,							type=UpperCamelCase			,							default=1e-4			,							help="Learning rate to use for training."			,							)

	parser.add_argument(
	    "--weight_decay_rate"			,							type=UpperCamelCase			,							default=1e-3			,							help="Weight decay rate to use for training."			,							)

	parser.add_argument(
	    "--max_length"			,							type=UpperCamelCase			,							default=5_1_2			,							help="Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py"			,							)

	parser.add_argument(
	    "--mlm_probability"			,							type=UpperCamelCase			,							default=0.15			,							help="Fraction of tokens to mask during training."			,							)

	parser.add_argument("--output_dir"			,							type=UpperCamelCase			,							required=UpperCamelCase			,							help="Path to save model checkpoints to."  )
	parser.add_argument("--hub_model_id"			,							type=UpperCamelCase			,							help="Model ID to upload to on the Hugging Face Hub."  )

	__lowerCAmelCase         =							parser.parse_args()
	return args






def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[Any]  ):






	"""simple docstring"""


	try:
		if args.tpu_name:
			__lowerCAmelCase         =							tf.distribute.cluster_resolver.TPUClusterResolver(
			    args.tpu_name			,							zone=args.tpu_zone			,							project=args.gcp_project  )
		else:
			__lowerCAmelCase         =							tf.distribute.cluster_resolver.TPUClusterResolver()
	except ValueError:
		raise RuntimeError(
		    "Couldn't connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or "
		    "--gcp_project. When running on a TPU VM, use --tpu_name local."  )

	tf.config.experimental_connect_to_cluster(UpperCamelCase  )
	tf.tpu.experimental.initialize_tpu_system(UpperCamelCase  )

	return tpu






def 		_UpperCAmelCase   (	UpperCamelCase:     List[str]  ):






	"""simple docstring"""


	__lowerCAmelCase         =							0
	for file in file_list:
		__lowerCAmelCase         =							file.split("/"  )[-1]
		__lowerCAmelCase         =							re.search(R"-\d+-(\d+)\.tfrecord"			,							UpperCamelCase  ).group(1  )
		__lowerCAmelCase         =							int(UpperCamelCase  )
		num_samples += sample_count

	return num_samples






def 		_UpperCAmelCase   (	UpperCamelCase:     List[str]			,							UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     Any			,							UpperCamelCase:     Tuple			,							UpperCamelCase:     List[Any]			,							UpperCamelCase:     str=None  ):






	"""simple docstring"""


	__lowerCAmelCase         =							count_samples(UpperCamelCase  )
	__lowerCAmelCase         =							tf.data.Dataset.from_tensor_slices(UpperCamelCase  )
	if shuffle:
		__lowerCAmelCase         =							dataset.shuffle(len(UpperCamelCase  )  )
	__lowerCAmelCase         =							tf.data.TFRecordDataset(UpperCamelCase			,							num_parallel_reads=UpperCamelCase  )
	# TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
	__lowerCAmelCase         =							dataset.apply(tf.data.experimental.assert_cardinality(UpperCamelCase  )  )
	__lowerCAmelCase         =							dataset.map(UpperCamelCase			,							num_parallel_calls=UpperCamelCase  )
	if shuffle:
		assert shuffle_buffer_size is not None
		__lowerCAmelCase         =							dataset.shuffle(args.shuffle_buffer_size  )
	__lowerCAmelCase         =							dataset.batch(UpperCamelCase			,							drop_remainder=UpperCamelCase  )
	__lowerCAmelCase         =							dataset.map(UpperCamelCase			,							num_parallel_calls=UpperCamelCase  )
	__lowerCAmelCase         =							dataset.prefetch(UpperCamelCase  )
	return dataset






def 		_UpperCAmelCase   (	UpperCamelCase:     Union[str, Any]  ):






	"""simple docstring"""


	if not args.no_tpu:
		__lowerCAmelCase         =							initialize_tpu(UpperCamelCase  )
		__lowerCAmelCase         =							tf.distribute.TPUStrategy(UpperCamelCase  )
	else:
		__lowerCAmelCase         =							tf.distribute.OneDeviceStrategy(device="/gpu:0"  )

	if args.bfloataa:
		tf.keras.mixed_precision.set_global_policy("mixed_bfloat16"  )

	__lowerCAmelCase         =							AutoTokenizer.from_pretrained(args.tokenizer  )
	__lowerCAmelCase         =							AutoConfig.from_pretrained(args.pretrained_model_config  )
	__lowerCAmelCase         =							tokenizer.vocab_size

	__lowerCAmelCase         =							tf.io.gfile.glob(os.path.join(args.train_dataset			,							"*.tfrecord"  )  )
	if not training_records:
		raise ValueError(F"No .tfrecord files found in {args.train_dataset}."  )
	__lowerCAmelCase         =							tf.io.gfile.glob(os.path.join(args.eval_dataset			,							"*.tfrecord"  )  )
	if not eval_records:
		raise ValueError(F"No .tfrecord files found in {args.eval_dataset}."  )

	__lowerCAmelCase         =							count_samples(UpperCamelCase  )

	__lowerCAmelCase         =							num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
	__lowerCAmelCase         =							steps_per_epoch * args.num_epochs

	with strategy.scope():
		__lowerCAmelCase         =							TFAutoModelForMaskedLM.from_config(UpperCamelCase  )
		model(model.dummy_inputs  )  # Pass some dummy inputs through the model to ensure all the weights are built
		__lowerCAmelCase   ,						__lowerCAmelCase         =							create_optimizer(
		    num_train_steps=UpperCamelCase			,							num_warmup_steps=total_train_steps // 2_0			,							init_lr=args.learning_rate			,							weight_decay_rate=args.weight_decay_rate			,							)

		# Transformers models compute the right loss for their task by default when labels are passed, and will
		# use this for training unless you specify your own loss function in compile().
		model.compile(optimizer=UpperCamelCase			,							metrics=["accuracy"]  )

	def decode_fn(UpperCamelCase:     Optional[int]  ):
		__lowerCAmelCase         =							{
		    "input_ids": tf.io.FixedLenFeature(dtype=tf.intaa			,							shape=(args.max_length,)  ),
		    "attention_mask": tf.io.FixedLenFeature(dtype=tf.intaa			,							shape=(args.max_length,)  ),
		}
		return tf.io.parse_single_example(UpperCamelCase			,							UpperCamelCase  )

	# Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
	# use their methods in our data pipeline.
	__lowerCAmelCase         =							DataCollatorForLanguageModeling(
	    tokenizer=UpperCamelCase			,							mlm_probability=args.mlm_probability			,							mlm=UpperCamelCase			,							return_tensors="tf"  )

	def mask_with_collator(UpperCamelCase:     str  ):
		# TF really needs an isin() function
		__lowerCAmelCase         =							(
		    ~tf.cast(batch["attention_mask"]			,							tf.bool  )
		    | (batch["input_ids"] == tokenizer.cls_token_id)
		    | (batch["input_ids"] == tokenizer.sep_token_id)
		)
		__lowerCAmelCase   ,						__lowerCAmelCase         =							data_collator.tf_mask_tokens(
		    batch["input_ids"]			,							vocab_size=len(UpperCamelCase  )			,							mask_token_id=tokenizer.mask_token_id			,							special_tokens_mask=UpperCamelCase			,							)
		return batch

	__lowerCAmelCase         =							args.per_replica_batch_size * strategy.num_replicas_in_sync

	__lowerCAmelCase         =							prepare_dataset(
	    UpperCamelCase			,							decode_fn=UpperCamelCase			,							mask_fn=UpperCamelCase			,							batch_size=UpperCamelCase			,							shuffle=UpperCamelCase			,							shuffle_buffer_size=args.shuffle_buffer_size			,							)

	__lowerCAmelCase         =							prepare_dataset(
	    UpperCamelCase			,							decode_fn=UpperCamelCase			,							mask_fn=UpperCamelCase			,							batch_size=UpperCamelCase			,							shuffle=UpperCamelCase			,							)

	__lowerCAmelCase         =							[]
	if args.hub_model_id:
		callbacks.append(
		    PushToHubCallback(output_dir=args.output_dir			,							hub_model_id=args.hub_model_id			,							tokenizer=UpperCamelCase  )  )

	model.fit(
	    UpperCamelCase			,							validation_data=UpperCamelCase			,							epochs=args.num_epochs			,							callbacks=UpperCamelCase			,							)

	model.save_pretrained(args.output_dir  )


if __name__ == "__main__":
					UpperCamelCase_   	=	parse_args()
					main(args)








import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class       a		(   __UpperCAmelCase					):
							lowercase_		:						Dict   							=  (DPMSolverSinglestepScheduler,)
							lowercase_		:						Optional[Any]   							=  (('num_inference_steps', 25),)







							def       UpperCAmelCase__					(						self  :  Optional[Any]      ,				**snake_case__  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							{
								    "num_train_timesteps": 1_000,
								    "beta_start": 0.0_0_0_1,
								    "beta_end": 0.0_2,
								    "beta_schedule": "linear",
								    "solver_order": 2,
								    "prediction_type": "epsilon",
								    "thresholding": False,
								    "sample_max_value": 1.0,
								    "algorithm_type": "dpmsolver++",
								    "solver_type": "midpoint",
								    "lambda_min_clipped": -float("inf" ),
								    "variance_type": None,
								}

								config.update(**snake_case__ )
								return config







							def       UpperCAmelCase__					(						self  :  Any      ,				snake_case__  :  Tuple=0      ,				**snake_case__  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							dict(self.forward_default_kwargs )
								__lowerCAmelCase         =							kwargs.pop("num_inference_steps"      ,				snake_case__ )
								__lowerCAmelCase         =							self.dummy_sample
								__lowerCAmelCase         =							0.1 * sample
								__lowerCAmelCase         =							[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
									__lowerCAmelCase         =							self.get_scheduler_config(**snake_case__ )
									__lowerCAmelCase         =							scheduler_class(**snake_case__ )
									scheduler.set_timesteps(snake_case__ )
									# copy over dummy past residuals
									__lowerCAmelCase         =							dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(snake_case__ )
										__lowerCAmelCase         =							scheduler_class.from_pretrained(snake_case__ )
										new_scheduler.set_timesteps(snake_case__ )
										# copy over dummy past residuals
										__lowerCAmelCase         =							dummy_past_residuals[: new_scheduler.config.solver_order]

									__lowerCAmelCase   ,						__lowerCAmelCase         =							sample, sample
									for t in range(snake_case__      ,				time_step + scheduler.config.solver_order + 1 ):
										__lowerCAmelCase         =							scheduler.step(snake_case__      ,				snake_case__      ,				snake_case__      ,				**snake_case__ ).prev_sample
										__lowerCAmelCase         =							new_scheduler.step(snake_case__      ,				snake_case__      ,				snake_case__      ,				**snake_case__ ).prev_sample

										assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								pass







							def       UpperCAmelCase__					(						self  :  str      ,				snake_case__  :  Tuple=0      ,				**snake_case__  :  Tuple ):
								"""simple docstring"""







								__lowerCAmelCase         =							dict(self.forward_default_kwargs )
								__lowerCAmelCase         =							kwargs.pop("num_inference_steps"      ,				snake_case__ )
								__lowerCAmelCase         =							self.dummy_sample
								__lowerCAmelCase         =							0.1 * sample
								__lowerCAmelCase         =							[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
									__lowerCAmelCase         =							self.get_scheduler_config()
									__lowerCAmelCase         =							scheduler_class(**snake_case__ )
									scheduler.set_timesteps(snake_case__ )

									# copy over dummy past residuals (must be after setting timesteps)
									__lowerCAmelCase         =							dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(snake_case__ )
										__lowerCAmelCase         =							scheduler_class.from_pretrained(snake_case__ )
										# copy over dummy past residuals
										new_scheduler.set_timesteps(snake_case__ )

										# copy over dummy past residual (must be after setting timesteps)
										__lowerCAmelCase         =							dummy_past_residuals[: new_scheduler.config.solver_order]

									__lowerCAmelCase         =							scheduler.step(snake_case__      ,				snake_case__      ,				snake_case__      ,				**snake_case__ ).prev_sample
									__lowerCAmelCase         =							new_scheduler.step(snake_case__      ,				snake_case__      ,				snake_case__      ,				**snake_case__ ).prev_sample

									assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"







							def       UpperCAmelCase__					(						self  :  Optional[Any]      ,				snake_case__  :  Optional[Any]=None      ,				**snake_case__  :  Optional[int] ):
								"""simple docstring"""







								if scheduler is None:
									__lowerCAmelCase         =							self.scheduler_classes[0]
									__lowerCAmelCase         =							self.get_scheduler_config(**snake_case__ )
									__lowerCAmelCase         =							scheduler_class(**snake_case__ )

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

								__lowerCAmelCase         =							10
								__lowerCAmelCase         =							self.dummy_model()
								__lowerCAmelCase         =							self.dummy_sample_deter
								scheduler.set_timesteps(snake_case__ )

								for i, t in enumerate(scheduler.timesteps ):
									__lowerCAmelCase         =							model(snake_case__      ,				snake_case__ )
									__lowerCAmelCase         =							scheduler.step(snake_case__      ,				snake_case__      ,				snake_case__ ).prev_sample

								return sample







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
								__lowerCAmelCase         =							50
								__lowerCAmelCase         =							self.dummy_model()
								__lowerCAmelCase         =							self.dummy_sample_deter
								scheduler.set_timesteps(snake_case__ )

								# make sure that the first t is uneven
								for i, t in enumerate(scheduler.timesteps[3:] ):
									__lowerCAmelCase         =							model(snake_case__      ,				snake_case__ )
									__lowerCAmelCase         =							scheduler.step(snake_case__      ,				snake_case__      ,				snake_case__ ).prev_sample

								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.2_5_7_4 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								for timesteps in [25, 50, 100, 999, 1_000]:
									self.check_over_configs(num_train_timesteps=snake_case__ )







							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
								__lowerCAmelCase         =							self.full_loop(scheduler=snake_case__ )
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.2_7_9_1 ) < 1E-3

								__lowerCAmelCase         =							DEISMultistepScheduler.from_config(scheduler.config )
								__lowerCAmelCase         =							DPMSolverMultistepScheduler.from_config(scheduler.config )
								__lowerCAmelCase         =							UniPCMultistepScheduler.from_config(scheduler.config )
								__lowerCAmelCase         =							DPMSolverSinglestepScheduler.from_config(scheduler.config )

								__lowerCAmelCase         =							self.full_loop(scheduler=snake_case__ )
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.2_7_9_1 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







								self.check_over_configs(thresholding=snake_case__ )
								for order in [1, 2, 3]:
									for solver_type in ["midpoint", "heun"]:
										for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "sample"]:
												self.check_over_configs(
												    thresholding=snake_case__      ,				prediction_type=snake_case__      ,				sample_max_value=snake_case__      ,				algorithm_type="dpmsolver++"      ,				solver_order=snake_case__      ,				solver_type=snake_case__      ,				)







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=snake_case__ )







							def       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







								for algorithm_type in ["dpmsolver", "dpmsolver++"]:
									for solver_type in ["midpoint", "heun"]:
										for order in [1, 2, 3]:
											for prediction_type in ["epsilon", "sample"]:
												self.check_over_configs(
												    solver_order=snake_case__      ,				solver_type=snake_case__      ,				prediction_type=snake_case__      ,				algorithm_type=snake_case__      ,				)
												__lowerCAmelCase         =							self.full_loop(
												    solver_order=snake_case__      ,				solver_type=snake_case__      ,				prediction_type=snake_case__      ,				algorithm_type=snake_case__      ,				)
												assert not torch.isnan(snake_case__ ).any(), "Samples have nan numbers"







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								self.check_over_configs(lower_order_final=snake_case__ )
								self.check_over_configs(lower_order_final=snake_case__ )







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								self.check_over_configs(lambda_min_clipped=-float("inf" ) )
								self.check_over_configs(lambda_min_clipped=-5.1 )







							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								self.check_over_configs(variance_type=snake_case__ )
								self.check_over_configs(variance_type="learned_range" )







							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







								for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
									self.check_over_forward(num_inference_steps=snake_case__      ,				time_step=0 )







							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.full_loop()
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.2_7_9_1 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.full_loop(use_karras_sigmas=snake_case__ )
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.2_2_4_8 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.full_loop(prediction_type="v_prediction" )
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.1_4_5_3 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.full_loop(prediction_type="v_prediction"      ,				use_karras_sigmas=snake_case__ )
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.0_6_4_9 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  int ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.scheduler_classes[0]
								__lowerCAmelCase         =							self.get_scheduler_config(thresholding=snake_case__      ,				dynamic_thresholding_ratio=0 )
								__lowerCAmelCase         =							scheduler_class(**snake_case__ )

								__lowerCAmelCase         =							10
								__lowerCAmelCase         =							self.dummy_model()
								__lowerCAmelCase         =							self.dummy_sample_deter.half()
								scheduler.set_timesteps(snake_case__ )

								for i, t in enumerate(scheduler.timesteps ):
									__lowerCAmelCase         =							model(snake_case__      ,				snake_case__ )
									__lowerCAmelCase         =							scheduler.step(snake_case__      ,				snake_case__      ,				snake_case__ ).prev_sample

								assert sample.dtype == torch.floataa






