
import argparse
import logging
import os
import re

import tensorflow as tf

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


__A							=logging.getLogger(__name__)

__A							=tf.data.AUTOTUNE





def      lowerCamelCase_					(							):
				lowerCamelCase_         =     argparse.ArgumentParser(description="Train a masked language model on TPU."       )
				parser.add_argument(
				    "--pretrained_model_config"      ,					type=lowerCamelCase__      ,					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=lowerCamelCase__      ,					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=lowerCamelCase__      ,					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=lowerCamelCase__      ,					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=lowerCamelCase__      ,					help="Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes."      ,					)

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

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

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

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

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

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

				lowerCamelCase_         =     parser.parse_args()
				return args





def      lowerCamelCase_					(							lowerCamelCase__       ):
				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(lowerCamelCase__       )
				tf.tpu.experimental.initialize_tpu_system(lowerCamelCase__       )

				return tpu





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

				return num_samples





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__=None       ):
				lowerCamelCase_         =     count_samples(lowerCamelCase__       )
				lowerCamelCase_         =     tf.data.Dataset.from_tensor_slices(lowerCamelCase__       )
				if shuffle:
								lowerCamelCase_         =     dataset.shuffle(len(lowerCamelCase__       )       )
				lowerCamelCase_         =     tf.data.TFRecordDataset(lowerCamelCase__      ,					num_parallel_reads=lowerCamelCase__       )
				# 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(lowerCamelCase__       )       )
				lowerCamelCase_         =     dataset.map(lowerCamelCase__      ,					num_parallel_calls=lowerCamelCase__       )
				if shuffle:
								assert shuffle_buffer_size is not None
								lowerCamelCase_         =     dataset.shuffle(args.shuffle_buffer_size       )
				lowerCamelCase_         =     dataset.batch(lowerCamelCase__      ,					drop_remainder=lowerCamelCase__       )
				lowerCamelCase_         =     dataset.map(lowerCamelCase__      ,					num_parallel_calls=lowerCamelCase__       )
				lowerCamelCase_         =     dataset.prefetch(lowerCamelCase__       )
				return dataset





def      lowerCamelCase_					(							lowerCamelCase__       ):
				if not args.no_tpu:
								lowerCamelCase_         =     initialize_tpu(lowerCamelCase__       )
								lowerCamelCase_         =     tf.distribute.TPUStrategy(lowerCamelCase__       )
				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(lowerCamelCase__       )

				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(lowerCamelCase__       )
								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=lowerCamelCase__      ,					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=lowerCamelCase__      ,					metrics=["accuracy"]       )

				def decode_fn(lowerCamelCase__       ):
								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(lowerCamelCase__      ,					lowerCamelCase__       )

				# 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=lowerCamelCase__      ,					mlm_probability=args.mlm_probability      ,					mlm=lowerCamelCase__      ,					return_tensors="tf"       )

				def mask_with_collator(lowerCamelCase__       ):
								# 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(lowerCamelCase__       )      ,					mask_token_id=tokenizer.mask_token_id      ,					special_tokens_mask=lowerCamelCase__      ,					)
								return batch

				lowerCamelCase_         =     args.per_replica_batch_size * strategy.num_replicas_in_sync

				lowerCamelCase_         =     prepare_dataset(
				    lowerCamelCase__      ,					decode_fn=lowerCamelCase__      ,					mask_fn=lowerCamelCase__      ,					batch_size=lowerCamelCase__      ,					shuffle=lowerCamelCase__      ,					shuffle_buffer_size=args.shuffle_buffer_size      ,					)

				lowerCamelCase_         =     prepare_dataset(
				    lowerCamelCase__      ,					decode_fn=lowerCamelCase__      ,					mask_fn=lowerCamelCase__      ,					batch_size=lowerCamelCase__      ,					shuffle=lowerCamelCase__      ,					)

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

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

				model.save_pretrained(args.output_dir       )


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




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}


