
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							=1_6
__A							=3_2





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__ = 1_6      ,					lowerCamelCase__ = "bert-base-cased"       ):
				lowerCamelCase_         =     AutoTokenizer.from_pretrained(lowerCamelCase__       )
				lowerCamelCase_         =     load_dataset("glue"      ,					"mrpc"       )

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

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

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

				def collate_fn(lowerCamelCase__       ):
								# 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(lowerCamelCase__      ,					padding="max_length"      ,					max_length=1_2_8      ,					return_tensors="pt"       )
								return tokenizer.pad(lowerCamelCase__      ,					padding="longest"      ,					return_tensors="pt"       )

				# Instantiate dataloaders.
				lowerCamelCase_         =     DataLoader(
				    tokenized_datasets["train"]      ,					shuffle=lowerCamelCase__      ,					collate_fn=lowerCamelCase__      ,					batch_size=lowerCamelCase__       )
				lowerCamelCase_         =     DataLoader(
				    tokenized_datasets["validation"]      ,					shuffle=lowerCamelCase__      ,					collate_fn=lowerCamelCase__      ,					batch_size=lowerCamelCase__       )

				return train_dataloader, eval_dataloader





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				# Initialize accelerator
				lowerCamelCase_         =     Accelerator()

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				lowerCamelCase_         =     config["lr"]
				lowerCamelCase_         =     int(config["num_epochs"]       )
				lowerCamelCase_         =     int(config["seed"]       )
				lowerCamelCase_         =     int(config["batch_size"]       )
				lowerCamelCase_         =     args.model_name_or_path

				set_seed(lowerCamelCase__       )
				lowerCamelCase_      ,       lowerCamelCase_         =     get_dataloaders(lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       )

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				lowerCamelCase_         =     AutoModelForSequenceClassification.from_pretrained(lowerCamelCase__      ,					return_dict=lowerCamelCase__       )

				# Instantiate optimizer
				lowerCamelCase_         =     (
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				lowerCamelCase_         =     optimizer_cls(params=model.parameters()      ,					lr=lowerCamelCase__       )

				if accelerator.state.deepspeed_plugin is not None:
								lowerCamelCase_         =     accelerator.state.deepspeed_plugin.deepspeed_config[
								    "gradient_accumulation_steps"
								]
				else:
								lowerCamelCase_         =     1
				lowerCamelCase_         =     (len(lowerCamelCase__       ) * num_epochs) // gradient_accumulation_steps

				# Instantiate scheduler
				if (
				    accelerator.state.deepspeed_plugin is None
				    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
				):
								lowerCamelCase_         =     get_linear_schedule_with_warmup(
								    optimizer=lowerCamelCase__      ,					num_warmup_steps=0      ,					num_training_steps=lowerCamelCase__      ,					)
				else:
								lowerCamelCase_         =     DummyScheduler(lowerCamelCase__      ,					total_num_steps=lowerCamelCase__      ,					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.
				lowerCamelCase_      ,       lowerCamelCase_      ,       lowerCamelCase_      ,       lowerCamelCase_      ,       lowerCamelCase_         =     accelerator.prepare(
				    lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       )

				# We need to keep track of how many total steps we have iterated over
				lowerCamelCase_         =     0
				# We also need to keep track of the stating epoch so files are named properly
				lowerCamelCase_         =     0

				# Now we train the model
				lowerCamelCase_         =     evaluate.load("glue"      ,					"mrpc"       )
				lowerCamelCase_         =     0
				lowerCamelCase_         =     {}
				for epoch in range(lowerCamelCase__      ,					lowerCamelCase__       ):
								model.train()
								for step, batch in enumerate(lowerCamelCase__       ):
												lowerCamelCase_         =     model(**lowerCamelCase__       )
												lowerCamelCase_         =     outputs.loss
												lowerCamelCase_         =     loss / gradient_accumulation_steps
												accelerator.backward(lowerCamelCase__       )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1

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

								lowerCamelCase_         =     metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F'epoch {epoch}:'      ,					lowerCamelCase__       )
								lowerCamelCase_         =     eval_metric["accuracy"]

								if best_performance < eval_metric["accuracy"]:
												lowerCamelCase_         =     eval_metric["accuracy"]

				if args.performance_lower_bound is not None:
								assert (
								    args.performance_lower_bound <= best_performance
								), F'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
								with open(os.path.join(args.output_dir      ,					"all_results.json"       )      ,					"w"       ) as f:
												json.dump(lowerCamelCase__      ,					lowerCamelCase__       )





def      lowerCamelCase_					(							):
				lowerCamelCase_         =     argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."       )
				parser.add_argument(
				    "--model_name_or_path"      ,					type=lowerCamelCase__      ,					default="bert-base-cased"      ,					help="Path to pretrained model or model identifier from huggingface.co/models."      ,					required=lowerCamelCase__      ,					)
				parser.add_argument(
				    "--output_dir"      ,					type=lowerCamelCase__      ,					default="."      ,					help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."      ,					)
				parser.add_argument(
				    "--performance_lower_bound"      ,					type=lowerCamelCase__      ,					default=lowerCamelCase__      ,					help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."      ,					)
				parser.add_argument(
				    "--num_epochs"      ,					type=lowerCamelCase__      ,					default=3      ,					help="Number of train epochs."      ,					)
				lowerCamelCase_         =     parser.parse_args()
				lowerCamelCase_         =     {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 4_2, "batch_size": 1_6}
				training_function(lowerCamelCase__      ,					lowerCamelCase__       )


if __name__ == "__main__":
			main()




import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


__A							='''\
@inproceedings{snover-etal-2006-study,
    title = "A Study of Translation Edit Rate with Targeted Human Annotation",
    author = "Snover, Matthew  and
      Dorr, Bonnie  and
      Schwartz, Rich  and
      Micciulla, Linnea  and
      Makhoul, John",
    booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",
    month = aug # " 8-12",
    year = "2006",
    address = "Cambridge, Massachusetts, USA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://aclanthology.org/2006.amta-papers.25",
    pages = "223--231",
}
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

__A							='''\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
'''

__A							='''
Produces TER scores alongside the number of edits and reference length.

Args:
    predictions (list of str): The system stream (a sequence of segments).
    references (list of list of str): A list of one or more reference streams (each a sequence of segments).
    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
                                    Only applies if `normalized = True`. Defaults to `False`.
    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.

Returns:
    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)
    \'num_edits\' (int): The cumulative number of edits
    \'ref_length\' (float): The cumulative average reference length

Examples:
    Example 1:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?",
        ...                     "What did the TER metric user say to the developer?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
        ...             ["Your jokes are...", "...TERrible"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}

    Example 2:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}

    Example 3:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         normalized=True,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}

    Example 4:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}

    Example 5:
        >>> predictions = ["does this sentence match??",
        ...                    "what about this sentence?",
        ...                    "What did the TER metric user say to the developer?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
        ...             ["Your jokes are...", "...TERrible"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}
'''




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



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Dict:
					if version.parse(scb.__version__					) < version.parse("1.4.12"					):
									raise ImportWarning(
									    "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
									    "You can install it with `pip install \"sacrebleu>=1.4.12\"`."					)
					return datasets.MetricInfo(
					    description=_DESCRIPTION		,    citation=_CITATION		,    homepage="http://www.cs.umd.edu/~snover/tercom/"		,    inputs_description=_KWARGS_DESCRIPTION		,    features=datasets.Features(
					        {
					            "predictions": datasets.Value("string"		,    id="sequence"					),
					            "references": datasets.Sequence(datasets.Value("string"		,    id="sequence"					)		,    id="references"					),
					        }					)		,    codebase_urls=["https://github.com/mjpost/sacreBLEU#ter"]		,    reference_urls=[
					        "https://github.com/jhclark/tercom",
					    ]		,    )



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    lowercase = False		,    lowercase = False		,    lowercase = False		,    lowercase = False		,    )							->			Tuple:
					lowerCamelCase_         =     len(references[0]					)
					if any(len(lowercase					) != references_per_prediction for refs in references					):
									raise ValueError("Sacrebleu requires the same number of references for each prediction"					)
					lowerCamelCase_         =     [[refs[i] for refs in references] for i in range(lowercase					)]

					lowerCamelCase_         =     TER(
					    normalized=lowercase		,    no_punct=lowercase		,    asian_support=lowercase		,    case_sensitive=lowercase		,    )
					lowerCamelCase_         =     sb_ter.corpus_score(lowercase		,    lowercase					)

					return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}


