

import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


A_	:			List[str]  	=      '\\n\n'

A_	:			Any  	=      '\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n'

A_	:			List[Any]  	=      '\nArgs:\n    model_id (str): model used for calculating Perplexity\n            NOTE: Perplexity can only be calculated for causal language models.\n                    This includes models such as gpt2, causal variations of bert,\n                    causal versions of t5, and more (the full list can be found\n                    in the AutoModelForCausalLM documentation here:\n                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n    input_texts (list of str): input text, each separate text snippet\n        is one list entry.\n    batch_size (int): the batch size to run texts through the model. Defaults to 16.\n    add_start_token (bool): whether to add the start token to the texts,\n        so the perplexity can include the probability of the first word. Defaults to True.\n    device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n    perplexity: dictionary containing the perplexity scores for the texts\n        in the input list, as well as the mean perplexity. If one of the input texts is\n        longer than the max input length of the model, then it is truncated to the\n        max length for the perplexity computation.\nExamples:\n    Example 1:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              add_start_token=False,\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        78.22\n        >>> print(round(results["perplexities"][0], 2))\n        11.11\n\n    Example 2:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = datasets.load_dataset("wikitext",\n        ...                                     "wikitext-2-raw-v1",\n        ...                                     split="test")["text"][:50] # doctest:+ELLIPSIS\n        [...]\n        >>> input_texts = [s for s in input_texts if s!=\'\']\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        60.35\n        >>> print(round(results["perplexities"][0], 2))\n        81.12\n'



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,       _KWARGS_DESCRIPTION    )
class    _a						(datasets.Metric    ):


					'''simple docstring'''






					def   __A      (			self      ):
						return datasets.MetricInfo(
						    description=_DESCRIPTION   ,	citation=_CITATION   ,	inputs_description=_KWARGS_DESCRIPTION   ,	features=datasets.Features(
						        {
						            """input_texts""": datasets.Value("""string"""      ),
						        }      )   ,	reference_urls=["""https://huggingface.co/docs/transformers/perplexity"""]   ,	)






					def   __A      (			self   ,	A__   ,	A__   ,	A__ = 16   ,	A__ = True   ,	A__=None      ):
						if device is not None:
							assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
							if device == "gpu":
								A__							:   Optional[Any]					       =		"""cuda"""
						else:
							A__							:   Union[str, Any]					       =		"""cuda""" if torch.cuda.is_available() else """cpu"""

						A__							:   int					       =		AutoModelForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE_      )
						A__							:   Optional[Any]					       =		model.to(SCREAMING_SNAKE_CASE_      )

						A__							:   List[Any]					       =		AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_      )

						# if batch_size > 1 (which generally leads to padding being required), and
						# if there is not an already assigned pad_token, assign an existing
						# special token to also be the padding token
						if tokenizer.pad_token is None and batch_size > 1:
							A__							:   int					       =		list(tokenizer.special_tokens_map_extended.values()      )
							# check that the model already has at least one special token defined
							assert (
							    len(SCREAMING_SNAKE_CASE_      ) > 0
							), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
							# assign one of the special tokens to also be the pad token
							tokenizer.add_special_tokens({"""pad_token""": existing_special_tokens[0]}      )

						if add_start_token:
							# leave room for <BOS> token to be added:
							assert (
							    tokenizer.bos_token is not None
							), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
							A__							:   Any					       =		model.config.max_length - 1
						else:
							A__							:   int					       =		model.config.max_length

						A__							:   List[Any]					       =		tokenizer(
						    SCREAMING_SNAKE_CASE_   ,	add_special_tokens=SCREAMING_SNAKE_CASE_   ,	padding=SCREAMING_SNAKE_CASE_   ,	truncation=SCREAMING_SNAKE_CASE_   ,	max_length=SCREAMING_SNAKE_CASE_   ,	return_tensors="""pt"""   ,	return_attention_mask=SCREAMING_SNAKE_CASE_   ,	).to(SCREAMING_SNAKE_CASE_      )

						A__							:   int					       =		encodings["""input_ids"""]
						A__							:   Optional[int]					       =		encodings["""attention_mask"""]

						# check that each input is long enough:
						if add_start_token:
							assert torch.all(torch.ge(attn_masks.sum(1      )   ,	1      )      ), "Each input text must be at least one token long."
						else:
							assert torch.all(
							    torch.ge(attn_masks.sum(1      )   ,	2      )      ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

						A__							:   Union[str, Any]					       =		[]
						A__							:   int					       =		CrossEntropyLoss(reduction="""none"""      )

						for start_index in logging.tqdm(range(0   ,	len(SCREAMING_SNAKE_CASE_      )   ,	SCREAMING_SNAKE_CASE_      )      ):
							A__							:   str					       =		min(start_index + batch_size   ,	len(SCREAMING_SNAKE_CASE_      )      )
							A__							:   Any					       =		encoded_texts[start_index:end_index]
							A__							:   str					       =		attn_masks[start_index:end_index]

							if add_start_token:
								A__							:   Tuple					       =		torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0      )      ).to(SCREAMING_SNAKE_CASE_      )
								A__							:   Dict					       =		torch.cat([bos_tokens_tensor, encoded_batch]   ,	dim=1      )
								A__							:   Optional[int]					       =		torch.cat(
								    [torch.ones(bos_tokens_tensor.size()   ,	dtype=torch.intaa      ).to(SCREAMING_SNAKE_CASE_      ), attn_mask]   ,	dim=1      )

							A__							:   Dict					       =		encoded_batch

							with torch.no_grad():
								A__							:   Tuple					       =		model(SCREAMING_SNAKE_CASE_   ,	attention_mask=SCREAMING_SNAKE_CASE_      ).logits

							A__							:   Optional[int]					       =		out_logits[..., :-1, :].contiguous()
							A__							:   Any					       =		labels[..., 1:].contiguous()
							A__							:   List[str]					       =		attn_mask[..., 1:].contiguous()

							A__							:   List[Any]					       =		torch.expa(
							    (loss_fct(shift_logits.transpose(1   ,	2      )   ,	SCREAMING_SNAKE_CASE_      ) * shift_attention_mask_batch).sum(1      )
							    / shift_attention_mask_batch.sum(1      )      )

							ppls += perplexity_batch.tolist()

						return {"perplexities": ppls, "mean_perplexity": np.mean(SCREAMING_SNAKE_CASE_      )}






import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


A_	:			Optional[Any]  	=      16
A_	:			Optional[int]  	=      32

def 						UpperCamelCase (lowercase_:			Accelerator   ,				lowercase_:			int = 16   ,				lowercase_:			str = "bert-base-cased" )					->				List[str]:
	A__							:   int					       =		AutoTokenizer.from_pretrained(lowercase_ )
	A__							:   Union[str, Any]					       =		load_dataset("""glue"""   ,				"""mrpc""" )

	def tokenize_function(lowercase_:			Tuple ):
		# max_length=None => use the model max length (it's actually the default)
		A__							:   Optional[Any]					       =		tokenizer(examples["""sentence1"""]   ,				examples["""sentence2"""]   ,				truncation=lowercase_   ,				max_length=lowercase_ )
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	A__							:   int					       =		datasets.map(
	    lowercase_   ,				batched=lowercase_   ,				remove_columns=["""idx""", """sentence1""", """sentence2"""]   ,				load_from_cache_file=lowercase_ )

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	A__							:   int					       =		tokenized_datasets.rename_column("""label"""   ,				"""labels""" )

	def collate_fn(lowercase_:			Optional[int] ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		if accelerator.distributed_type == DistributedType.TPU:
			return tokenizer.pad(lowercase_   ,				padding="""max_length"""   ,				max_length=128   ,				return_tensors="""pt""" )
		return tokenizer.pad(lowercase_   ,				padding="""longest"""   ,				return_tensors="""pt""" )

	# Instantiate dataloaders.
	A__							:   int					       =		DataLoader(
	    tokenized_datasets["""train"""]   ,				shuffle=lowercase_   ,				collate_fn=lowercase_   ,				batch_size=lowercase_ )
	A__							:   Optional[Any]					       =		DataLoader(
	    tokenized_datasets["""validation"""]   ,				shuffle=lowercase_   ,				collate_fn=lowercase_   ,				batch_size=lowercase_ )

	return train_dataloader, eval_dataloader

def 						UpperCamelCase (lowercase_:			Dict   ,				lowercase_:			Dict   ,				lowercase_:			Tuple   ,				lowercase_:			Optional[int] )					->				int:
	model.eval()
	A__							:   str					       =		0
	for step, batch in enumerate(lowercase_ ):
		# We could avoid this line since we set the accelerator with `device_placement=True`.
		batch.to(accelerator.device )
		with torch.no_grad():
			A__							:   Any					       =		model(**lowercase_ )
		A__							:   List[Any]					       =		outputs.logits.argmax(dim=-1 )
		# It is slightly faster to call this once, than multiple times
		A__       ,       A__							:   str					       =		accelerator.gather(
		    (predictions, batch["""labels"""]) )  # If we are in a multiprocess environment, the last batch has duplicates
		if accelerator.use_distributed:
			if step == len(lowercase_ ) - 1:
				A__							:   List[str]					       =		predictions[: len(eval_dataloader.dataset ) - samples_seen]
				A__							:   Dict					       =		references[: len(eval_dataloader.dataset ) - samples_seen]
			else:
				samples_seen += references.shape[0]
		metric.add_batch(
		    predictions=lowercase_   ,				references=lowercase_   ,				)

	A__							:   int					       =		metric.compute()
	return eval_metric["accuracy"]

def 						UpperCamelCase (lowercase_:			List[Any]   ,				lowercase_:			str )					->				List[str]:
	# Initialize accelerator
	A__							:   str					       =		Accelerator()

	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	A__							:   List[Any]					       =		config["""lr"""]
	A__							:   Union[str, Any]					       =		int(config["""num_epochs"""] )
	A__							:   List[Any]					       =		int(config["""seed"""] )
	A__							:   Optional[Any]					       =		int(config["""batch_size"""] )
	A__							:   Tuple					       =		args.model_name_or_path

	set_seed(lowercase_ )
	A__       ,       A__							:   Optional[Any]					       =		get_dataloaders(lowercase_   ,				lowercase_   ,				lowercase_ )

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	A__							:   Tuple					       =		AutoModelForSequenceClassification.from_pretrained(lowercase_   ,				return_dict=lowercase_ )

	# Instantiate optimizer
	A__							:   Dict					       =		(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	A__							:   Union[str, Any]					       =		optimizer_cls(params=model.parameters()   ,				lr=lowercase_ )

	if accelerator.state.deepspeed_plugin is not None:
		A__							:   List[Any]					       =		accelerator.state.deepspeed_plugin.deepspeed_config[
		    """gradient_accumulation_steps"""
		]
	else:
		A__							:   Optional[int]					       =		1
	A__							:   Optional[int]					       =		(len(lowercase_ ) * num_epochs) // gradient_accumulation_steps

	# Instantiate scheduler
	if (
	    accelerator.state.deepspeed_plugin is None
	    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
	):
		A__							:   Optional[int]					       =		get_linear_schedule_with_warmup(
		    optimizer=lowercase_   ,				num_warmup_steps=0   ,				num_training_steps=lowercase_   ,				)
	else:
		A__							:   int					       =		DummyScheduler(lowercase_   ,				total_num_steps=lowercase_   ,				warmup_num_steps=0 )

	# Prepare everything
	# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
	# prepare method.
	A__       ,       A__       ,       A__       ,       A__       ,       A__							:   str					       =		accelerator.prepare(
	    lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_ )

	# We need to keep track of how many total steps we have iterated over
	A__							:   Dict					       =		0
	# We also need to keep track of the stating epoch so files are named properly
	A__							:   Any					       =		0
	A__							:   Optional[Any]					       =		evaluate.load("""glue"""   ,				"""mrpc""" )
	A__							:   Optional[Any]					       =		num_epochs

	if args.partial_train_epoch is not None:
		A__							:   Tuple					       =		args.partial_train_epoch

	if args.resume_from_checkpoint:
		accelerator.load_state(args.resume_from_checkpoint )
		A__							:   Dict					       =		args.resume_from_checkpoint.split("""epoch_""" )[1]
		A__							:   int					       =		""""""
		for char in epoch_string:
			if char.isdigit():
				state_epoch_num += char
			else:
				break
		A__							:   Any					       =		int(lowercase_ ) + 1
		A__							:   Any					       =		evaluation_loop(lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_ )
		accelerator.print("""resumed checkpoint performance:"""   ,				lowercase_ )
		accelerator.print("""resumed checkpoint's scheduler's lr:"""   ,				lr_scheduler.get_lr()[0] )
		accelerator.print("""resumed optimizers's lr:"""   ,				optimizer.param_groups[0]["""lr"""] )
		with open(os.path.join(args.output_dir   ,				f"""state_{starting_epoch-1}.json""" )   ,				"""r""" ) as f:
			A__							:   int					       =		json.load(lowercase_ )
			assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
			assert (
			    resumed_state["lr"] == lr_scheduler.get_lr()[0]
			), "Scheduler learning rate mismatch, loading from checkpoint failed"
			assert (
			    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
			), "Optimizer learning rate mismatch, loading from checkpoint failed"
			assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
			return

    # Now we train the model
	A__							:   Optional[Any]					       =		{}
	for epoch in range(lowercase_   ,				lowercase_ ):
		model.train()
		for step, batch in enumerate(lowercase_ ):
			A__							:   int					       =		model(**lowercase_ )
			A__							:   int					       =		outputs.loss
			A__							:   int					       =		loss / gradient_accumulation_steps
			accelerator.backward(lowercase_ )
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1
		A__							:   Any					       =		f"""epoch_{epoch}"""
		A__							:   int					       =		os.path.join(args.output_dir   ,				lowercase_ )
		accelerator.save_state(lowercase_ )
		A__							:   List[Any]					       =		evaluation_loop(lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_ )
		A__							:   Tuple					       =		accuracy
		A__							:   Optional[Any]					       =		lr_scheduler.get_lr()[0]
		A__							:   Tuple					       =		optimizer.param_groups[0]["""lr"""]
		A__							:   int					       =		epoch
		A__							:   int					       =		overall_step
		accelerator.print(f"""epoch {epoch}:"""   ,				lowercase_ )

		accelerator.wait_for_everyone()
		if accelerator.is_main_process:
			with open(os.path.join(args.output_dir   ,				f"""state_{epoch}.json""" )   ,				"""w""" ) as f:
				json.dump(lowercase_   ,				lowercase_ )

def 						UpperCamelCase ()					->				int:
	A__							:   Optional[int]					       =		argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
	parser.add_argument(
	    """--model_name_or_path"""   ,				type=lowercase_   ,				default="""bert-base-cased"""   ,				help="""Path to pretrained model or model identifier from huggingface.co/models."""   ,				required=lowercase_   ,				)
	parser.add_argument(
	    """--output_dir"""   ,				type=lowercase_   ,				default="""."""   ,				help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""   ,				)
	parser.add_argument(
	    """--resume_from_checkpoint"""   ,				type=lowercase_   ,				default=lowercase_   ,				help="""If the training should continue from a checkpoint folder."""   ,				)
	parser.add_argument(
	    """--partial_train_epoch"""   ,				type=lowercase_   ,				default=lowercase_   ,				help="""If passed, the training will stop after this number of epochs."""   ,				)
	parser.add_argument(
	    """--num_epochs"""   ,				type=lowercase_   ,				default=2   ,				help="""Number of train epochs."""   ,				)
	A__							:   List[str]					       =		parser.parse_args()
	A__							:   List[str]					       =		{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

	training_function(lowercase_   ,				lowercase_ )


if __name__ == "__main__":
	main()



