
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


__a							  =						logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

__a							  =						{
    """base""": AutoModel,
    """sequence-classification""": AutoModelForSequenceClassification,
    """question-answering""": AutoModelForQuestionAnswering,
    """pretraining""": AutoModelForPreTraining,
    """token-classification""": AutoModelForTokenClassification,
    """language-modeling""": AutoModelWithLMHead,
    """summarization""": AutoModelForSeqaSeqLM,
    """translation""": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
__a							  =						{
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
__a							  =						sorted(arg_to_scheduler.keys())
__a							  =						"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""

class        UpperCamelCase__(    pl.LightningModule	):

	"""simple docstring"""

	def __init__(      self				:			Optional[Any]							,			snake_case__				:			argparse.Namespace							,			snake_case__				:			Optional[Any]=None							,			snake_case__				:			Any="base"							,			snake_case__				:			Optional[int]=None							,			snake_case__				:			Dict=None							,			snake_case__				:			str=None							,			**snake_case__				:			List[str]							,			):
			"""simple docstring"""





			super().__init__()
			# TODO: move to self.save_hyperparameters()
			# self.save_hyperparameters()
			# can also expand arguments into trainer signature for easier reading

			self.save_hyperparameters(snake_case__ )
			A				=0
			A				=Path(self.hparams.output_dir )
			A				=self.hparams.cache_dir if self.hparams.cache_dir else None
			if config is None:
					A				=AutoConfig.from_pretrained(
					    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path							,			**({"num_labels": num_labels} if num_labels is not None else {})							,			cache_dir=snake_case__							,			**snake_case__							,			)
			else:
					A				=config

			A				=("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
			for p in extra_model_params:
					if getattr(self.hparams							,			snake_case__							,			snake_case__ ):
							assert hasattr(self.config							,			snake_case__ ), f'''model config doesn\'t have a `{p}` attribute'''
							setattr(self.config							,			snake_case__							,			getattr(self.hparams							,			snake_case__ ) )

			if tokenizer is None:
					A				=AutoTokenizer.from_pretrained(
					    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path							,			cache_dir=snake_case__							,			)
			else:
					A				=tokenizer
			A				=MODEL_MODES[mode]
			if model is None:
					A				=self.model_type.from_pretrained(
					    self.hparams.model_name_or_path							,			from_tf=bool(".ckpt" in self.hparams.model_name_or_path )							,			config=self.config							,			cache_dir=snake_case__							,			)
			else:
					A				=model







	def        _a					(      self				:			List[str]							,			*snake_case__				:			int							,			**snake_case__				:			List[str] ):
			"""simple docstring"""





			A				=self.model_type.from_pretrained(*snake_case__							,			**snake_case__ )







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





			A				=arg_to_scheduler[self.hparams.lr_scheduler]
			A				=get_schedule_func(
			    self.opt							,			num_warmup_steps=self.hparams.warmup_steps							,			num_training_steps=self.total_steps() )
			A				={"scheduler": scheduler, "interval": "step", "frequency": 1}
			return scheduler







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





			A				=self.model
			A				=["bias", "LayerNorm.weight"]
			A				=[
			    {
			        "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )],
			        "weight_decay": self.hparams.weight_decay,
			    },
			    {
			        "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )],
			        "weight_decay": 0.0,
			    },
			]
			if self.hparams.adafactor:
					A				=Adafactor(
					    snake_case__							,			lr=self.hparams.learning_rate							,			scale_parameter=snake_case__							,			relative_step=snake_case__ )

			else:
					A				=AdamW(
					    snake_case__							,			lr=self.hparams.learning_rate							,			eps=self.hparams.adam_epsilon )
			A				=optimizer

			A				=self.get_lr_scheduler()

			return [optimizer], [scheduler]







	def        _a					(      self				:			List[str]							,			snake_case__				:			str							,			snake_case__				:			List[Any] ):
			"""simple docstring"""





			return self.validation_step(snake_case__							,			snake_case__ )







	def        _a					(      self				:			Optional[Any]							,			snake_case__				:			str ):
			"""simple docstring"""





			return self.validation_end(snake_case__ )







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





			A				=max(1							,			self.hparams.gpus )  # TODO: consider num_tpu_cores
			A				=self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
			return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs







	def        _a					(      self				:			Any							,			snake_case__				:			Optional[int] ):
			"""simple docstring"""





			if mode == "test":
					A				=len(self.test_dataloader().dataset )
			else:
					A				=self.get_dataloader("train"							,			self.hparams.train_batch_size							,			shuffle=snake_case__ )
					A				=len(self.train_dataloader().dataset )







	def        _a					(      self				:			List[Any]							,			snake_case__				:			str							,			snake_case__				:			int							,			snake_case__				:			bool = False ):
			"""simple docstring"""





			raise NotImplementedError("You must implement this for your task" )







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





			return self.train_loader







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





			return self.get_dataloader("dev"							,			self.hparams.eval_batch_size							,			shuffle=snake_case__ )







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





			return self.get_dataloader("test"							,			self.hparams.eval_batch_size							,			shuffle=snake_case__ )







	def        _a					(      self				:			List[Any]							,			snake_case__				:			List[str] ):
			"""simple docstring"""





			return os.path.join(
			    self.hparams.data_dir							,			"cached_{}_{}_{}".format(
			        snake_case__							,			list(filter(snake_case__							,			self.hparams.model_name_or_path.split("/" ) ) ).pop()							,			str(self.hparams.max_seq_length )							,			)							,			)







	@pl.utilities.rank_zero_only
	def        _a					(      self				:			int							,			snake_case__				:			Dict[str, Any] ):
			"""simple docstring"""





			A				=self.output_dir.joinpath("best_tfmr" )
			A				=self.step_count
			self.model.save_pretrained(snake_case__ )
			self.tokenizer.save_pretrained(snake_case__ )







	@staticmethod
	def        _a					(      snake_case__				:			Optional[Any]							,			snake_case__				:			Tuple ):
			"""simple docstring"""





			parser.add_argument(
			    "--model_name_or_path"							,			default=snake_case__							,			type=snake_case__							,			required=snake_case__							,			help="Path to pretrained model or model identifier from huggingface.co/models"							,			)
			parser.add_argument(
			    "--config_name"							,			default=""							,			type=snake_case__							,			help="Pretrained config name or path if not the same as model_name" )
			parser.add_argument(
			    "--tokenizer_name"							,			default=snake_case__							,			type=snake_case__							,			help="Pretrained tokenizer name or path if not the same as model_name"							,			)
			parser.add_argument(
			    "--cache_dir"							,			default=""							,			type=snake_case__							,			help="Where do you want to store the pre-trained models downloaded from huggingface.co"							,			)
			parser.add_argument(
			    "--encoder_layerdrop"							,			type=snake_case__							,			help="Encoder layer dropout probability (Optional). Goes into model.config"							,			)
			parser.add_argument(
			    "--decoder_layerdrop"							,			type=snake_case__							,			help="Decoder layer dropout probability (Optional). Goes into model.config"							,			)
			parser.add_argument(
			    "--dropout"							,			type=snake_case__							,			help="Dropout probability (Optional). Goes into model.config"							,			)
			parser.add_argument(
			    "--attention_dropout"							,			type=snake_case__							,			help="Attention dropout probability (Optional). Goes into model.config"							,			)
			parser.add_argument("--learning_rate"							,			default=5E-5							,			type=snake_case__							,			help="The initial learning rate for Adam." )
			parser.add_argument(
			    "--lr_scheduler"							,			default="linear"							,			choices=snake_case__							,			metavar=snake_case__							,			type=snake_case__							,			help="Learning rate scheduler"							,			)
			parser.add_argument("--weight_decay"							,			default=0.0							,			type=snake_case__							,			help="Weight decay if we apply some." )
			parser.add_argument("--adam_epsilon"							,			default=1E-8							,			type=snake_case__							,			help="Epsilon for Adam optimizer." )
			parser.add_argument("--warmup_steps"							,			default=0							,			type=snake_case__							,			help="Linear warmup over warmup_steps." )
			parser.add_argument("--num_workers"							,			default=4							,			type=snake_case__							,			help="kwarg passed to DataLoader" )
			parser.add_argument("--num_train_epochs"							,			dest="max_epochs"							,			default=3							,			type=snake_case__ )
			parser.add_argument("--train_batch_size"							,			default=32							,			type=snake_case__ )
			parser.add_argument("--eval_batch_size"							,			default=32							,			type=snake_case__ )
			parser.add_argument("--adafactor"							,			action="store_true" )


class        UpperCamelCase__(    pl.Callback	):

	"""simple docstring"""

	def        _a					(      self				:			Dict							,			snake_case__				:			Optional[int]							,			snake_case__				:			Union[str, Any] ):
			"""simple docstring"""





			A				=trainer.lr_schedulers[0]["scheduler"]
			A				={f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr() )}
			pl_module.logger.log_metrics(snake_case__ )







	def        _a					(      self				:			List[Any]							,			snake_case__				:			pl.Trainer							,			snake_case__				:			pl.LightningModule ):
			"""simple docstring"""





			rank_zero_info("***** Validation results *****" )
			A				=trainer.callback_metrics
			# Log results
			for key in sorted(snake_case__ ):
					if key not in ["log", "progress_bar"]:
							rank_zero_info("{} = {}\n".format(snake_case__							,			str(metrics[key] ) ) )







	def        _a					(      self				:			Any							,			snake_case__				:			pl.Trainer							,			snake_case__				:			pl.LightningModule ):
			"""simple docstring"""





			rank_zero_info("***** Test results *****" )
			A				=trainer.callback_metrics
			# Log and save results to file
			A				=os.path.join(pl_module.hparams.output_dir							,			"test_results.txt" )
			with open(snake_case__							,			"w" ) as writer:
					for key in sorted(snake_case__ ):
							if key not in ["log", "progress_bar"]:
									rank_zero_info("{} = {}\n".format(snake_case__							,			str(metrics[key] ) ) )
									writer.write("{} = {}\n".format(snake_case__							,			str(metrics[key] ) ) )


def    UpperCamelCase_ (	a_  ,				a_		)     ->None:
		#  To allow all pl args uncomment the following line
		#  parser = pl.Trainer.add_argparse_args(parser)
		parser.add_argument(
		    "--output_dir"  ,				default=a_  ,				type=a_  ,				required=a_  ,				help="The output directory where the model predictions and checkpoints will be written."  ,				)
		parser.add_argument(
		    "--fp16"  ,				action="store_true"  ,				help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"  ,				)

		parser.add_argument(
		    "--fp16_opt_level"  ,				type=a_  ,				default="O2"  ,				help=(
		        "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
		        "See details at https://nvidia.github.io/apex/amp.html"
		    )  ,				)
		parser.add_argument("--n_tpu_cores"  ,				dest="tpu_cores"  ,				type=a_		)
		parser.add_argument("--max_grad_norm"  ,				dest="gradient_clip_val"  ,				default=1.0  ,				type=a_  ,				help="Max gradient norm"		)
		parser.add_argument("--do_train"  ,				action="store_true"  ,				help="Whether to run training."		)
		parser.add_argument("--do_predict"  ,				action="store_true"  ,				help="Whether to run predictions on the test set."		)
		parser.add_argument(
		    "--gradient_accumulation_steps"  ,				dest="accumulate_grad_batches"  ,				type=a_  ,				default=1  ,				help="Number of updates steps to accumulate before performing a backward/update pass."  ,				)
		parser.add_argument("--seed"  ,				type=a_  ,				default=42  ,				help="random seed for initialization"		)
		parser.add_argument(
		    "--data_dir"  ,				default=a_  ,				type=a_  ,				required=a_  ,				help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."  ,				)






def    UpperCamelCase_ (	a_  ,				a_  ,				a_=None  ,				a_=True  ,				a_=[]  ,				a_=None  ,				a_=None  ,				**a_  ,				)     ->Dict:
		pl.seed_everything(args.seed		)

		# init model
		A				=Path(model.hparams.output_dir		)
		odir.mkdir(exist_ok=a_		)

		# add custom checkpoints
		if checkpoint_callback is None:
				A				=pl.callbacks.ModelCheckpoint(
				    filepath=args.output_dir  ,				prefix="checkpoint"  ,				monitor="val_loss"  ,				mode="min"  ,				save_top_k=1		)
		if early_stopping_callback:
				extra_callbacks.append(a_		)
		if logging_callback is None:
				A				=LoggingCallback()

		A				={}

		# TODO: remove with PyTorch 1.6 since pl uses native amp
		if args.fpaa:
				A				=16
				A				=args.fpaa_opt_level

		if args.gpus > 1:
				A				="ddp"

		A				=args.accumulate_grad_batches
		A				=extra_train_kwargs.get("accelerator"  ,				a_		)
		A				=extra_train_kwargs.get("profiler"  ,				a_		)

		A				=pl.Trainer.from_argparse_args(
		    a_  ,				weights_summary=a_  ,				callbacks=[logging_callback] + extra_callbacks  ,				logger=a_  ,				checkpoint_callback=a_  ,				**a_  ,				)

		if args.do_train:
				trainer.fit(a_		)

		return trainer






import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel

def    UpperCamelCase_ (	a_  ,				a_		)     ->Optional[int]:
		A				=UNetaDModel(**a_		)
		A				=checkpoint["all_modules.0.W"].data
		A				=checkpoint["all_modules.0.W"].data
		A				=checkpoint["all_modules.1.weight"].data
		A				=checkpoint["all_modules.1.bias"].data

		A				=checkpoint["all_modules.2.weight"].data
		A				=checkpoint["all_modules.2.bias"].data

		A				=checkpoint["all_modules.3.weight"].data
		A				=checkpoint["all_modules.3.bias"].data

		A				=checkpoint[list(checkpoint.keys()		)[-4]].data
		A				=checkpoint[list(checkpoint.keys()		)[-3]].data
		A				=checkpoint[list(checkpoint.keys()		)[-2]].data
		A				=checkpoint[list(checkpoint.keys()		)[-1]].data

		A				=4

		def set_attention_weights(a_  ,				a_  ,				a_		):
				A				=old_checkpoint[f'''all_modules.{index}.NIN_0.W'''].data.T
				A				=old_checkpoint[f'''all_modules.{index}.NIN_1.W'''].data.T
				A				=old_checkpoint[f'''all_modules.{index}.NIN_2.W'''].data.T

				A				=old_checkpoint[f'''all_modules.{index}.NIN_0.b'''].data
				A				=old_checkpoint[f'''all_modules.{index}.NIN_1.b'''].data
				A				=old_checkpoint[f'''all_modules.{index}.NIN_2.b'''].data

				A				=old_checkpoint[f'''all_modules.{index}.NIN_3.W'''].data.T
				A				=old_checkpoint[f'''all_modules.{index}.NIN_3.b'''].data

				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

		def set_resnet_weights(a_  ,				a_  ,				a_		):
				A				=old_checkpoint[f'''all_modules.{index}.Conv_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.Conv_0.bias'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

				A				=old_checkpoint[f'''all_modules.{index}.Conv_1.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.Conv_1.bias'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_1.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_1.bias'''].data

				A				=old_checkpoint[f'''all_modules.{index}.Dense_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.Dense_0.bias'''].data

				if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
						A				=old_checkpoint[f'''all_modules.{index}.Conv_2.weight'''].data
						A				=old_checkpoint[f'''all_modules.{index}.Conv_2.bias'''].data

		for i, block in enumerate(new_model_architecture.downsample_blocks		):
				A				=hasattr(a_  ,				"attentions"		)
				for j in range(len(block.resnets		)		):
						set_resnet_weights(block.resnets[j]  ,				a_  ,				a_		)
						module_index += 1
						if has_attentions:
								set_attention_weights(block.attentions[j]  ,				a_  ,				a_		)
								module_index += 1

				if hasattr(a_  ,				"downsamplers"		) and block.downsamplers is not None:
						set_resnet_weights(block.resnet_down  ,				a_  ,				a_		)
						module_index += 1
						A				=checkpoint[f'''all_modules.{module_index}.Conv_0.weight'''].data
						A				=checkpoint[f'''all_modules.{module_index}.Conv_0.bias'''].data
						module_index += 1

		set_resnet_weights(new_model_architecture.mid_block.resnets[0]  ,				a_  ,				a_		)
		module_index += 1
		set_attention_weights(new_model_architecture.mid_block.attentions[0]  ,				a_  ,				a_		)
		module_index += 1
		set_resnet_weights(new_model_architecture.mid_block.resnets[1]  ,				a_  ,				a_		)
		module_index += 1

		for i, block in enumerate(new_model_architecture.up_blocks		):
				A				=hasattr(a_  ,				"attentions"		)
				for j in range(len(block.resnets		)		):
						set_resnet_weights(block.resnets[j]  ,				a_  ,				a_		)
						module_index += 1
				if has_attentions:
						set_attention_weights(
						    block.attentions[0]  ,				a_  ,				a_		)  # why can there only be a single attention layer for up?
						module_index += 1

				if hasattr(a_  ,				"resnet_up"		) and block.resnet_up is not None:
						A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
						A				=checkpoint[f'''all_modules.{module_index}.bias'''].data
						module_index += 1
						A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
						A				=checkpoint[f'''all_modules.{module_index}.bias'''].data
						module_index += 1
						set_resnet_weights(block.resnet_up  ,				a_  ,				a_		)
						module_index += 1

		A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
		A				=checkpoint[f'''all_modules.{module_index}.bias'''].data
		module_index += 1
		A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
		A				=checkpoint[f'''all_modules.{module_index}.bias'''].data

		return new_model_architecture.state_dict()


if __name__ == "__main__":
	__a							  =						argparse.ArgumentParser()

	parser.add_argument(
	    """--checkpoint_path""",
	    default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin""",
	    type=str,
	    required=False,
	    help="""Path to the checkpoint to convert.""",
	)

	parser.add_argument(
	    """--config_file""",
	    default="""/Users/arthurzucker/Work/diffusers/ArthurZ/config.json""",
	    type=str,
	    required=False,
	    help="""The config json file corresponding to the architecture.""",
	)

	parser.add_argument(
	    """--dump_path""",
	    default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt""",
	    type=str,
	    required=False,
	    help="""Path to the output model.""",
	)

	__a							  =						parser.parse_args()

	__a							  =						torch.load(args.checkpoint_path, map_location="""cpu""")

	with open(args.config_file) as f:
		__a							  =						json.loads(f.read())

	__a							  =						convert_ncsnpp_checkpoint(
	    checkpoint,
	    config,
	)

	if "sde" in config:
		del config["sde"]

	__a							  =						UNetaDModel(**config)
	model.load_state_dict(converted_checkpoint)

	try:
		__a							  =						ScoreSdeVeScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

		__a							  =						ScoreSdeVePipeline(unet=model, scheduler=scheduler)
		pipe.save_pretrained(args.dump_path)
	except:  # noqa: E722
		model.save_pretrained(args.dump_path)




