
import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get('''SDP_ENABLED''') or is_sagemaker_dp_enabled():
     __magic_name__           =					True
     __magic_name__           =					'''p3dn.24xlarge'''
     import smdistributed.dataparallel.tensorflow as sdp
else:
     __magic_name__           =					False





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase=None							):
    snake_case__  =			tqdm(__lowerCAmelCase							)
    for i, batch in enumerate(__lowerCAmelCase							):
        with tf.GradientTape() as tape:
            snake_case__  ,   snake_case__  =			batch
            snake_case__  =			model(__lowerCAmelCase							)
            snake_case__  =			loss(__lowerCAmelCase      ,						outputs.logits							)

        if SDP_ENABLED:
            snake_case__  =			sdp.DistributedGradientTape(__lowerCAmelCase      ,						sparse_as_dense=__lowerCAmelCase							)

        snake_case__  =			tape.gradient(__lowerCAmelCase      ,						model.trainable_variables							)
        opt.apply_gradients(zip(__lowerCAmelCase      ,						model.trainable_variables							)							)

        pbar.set_description(F"""Loss: {loss_value:.4f}"""							)

        if SDP_ENABLED and i == 0:
            sdp.broadcast_variables(model.variables      ,						root_rank=0							)
            sdp.broadcast_variables(opt.variables()      ,						root_rank=0							)

        if max_steps and i >= max_steps:
            break

    snake_case__  =			{"loss": loss_value.numpy()}
    return train_results





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    # Load dataset
    snake_case__  ,   snake_case__  =			load_dataset("imdb"      ,						split=["train", "test"]							)

    # Preprocess train dataset
    snake_case__  =			train_dataset.map(
        lambda __lowerCAmelCase							: tokenizer(e["text"]      ,						truncation=__lowerCAmelCase      ,						padding="max_length"							)      ,						batched=__lowerCAmelCase							)
    train_dataset.set_format(type="tensorflow"      ,						columns=["input_ids", "attention_mask", "label"]							)

    snake_case__  =			{
        x: train_dataset[x].to_tensor(default_value=0      ,						shape=[None, tokenizer.model_max_length]							)
        for x in ["input_ids", "attention_mask"]
    }
    snake_case__  =			tf.data.Dataset.from_tensor_slices((train_features, train_dataset["label"])							)

    # Preprocess test dataset
    snake_case__  =			test_dataset.map(
        lambda __lowerCAmelCase							: tokenizer(e["text"]      ,						truncation=__lowerCAmelCase      ,						padding="max_length"							)      ,						batched=__lowerCAmelCase							)
    test_dataset.set_format(type="tensorflow"      ,						columns=["input_ids", "attention_mask", "label"]							)

    snake_case__  =			{
        x: test_dataset[x].to_tensor(default_value=0      ,						shape=[None, tokenizer.model_max_length]							)
        for x in ["input_ids", "attention_mask"]
    }
    snake_case__  =			tf.data.Dataset.from_tensor_slices((test_features, test_dataset["label"])							)

    if SDP_ENABLED:
        snake_case__  =			tf_train_dataset.shard(sdp.size()      ,						sdp.rank()							)
        snake_case__  =			tf_test_dataset.shard(sdp.size()      ,						sdp.rank()							)
    snake_case__  =			tf_train_dataset.batch(__lowerCAmelCase      ,						drop_remainder=__lowerCAmelCase							)
    snake_case__  =			tf_test_dataset.batch(__lowerCAmelCase      ,						drop_remainder=__lowerCAmelCase							)

    return tf_train_dataset, tf_test_dataset


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

     # Hyperparameters sent by the client are passed as command-line arguments to the script.
     parser.add_argument('''--epochs''', type=int, default=3)
     parser.add_argument('''--per_device_train_batch_size''', type=int, default=16)
     parser.add_argument('''--per_device_eval_batch_size''', type=int, default=8)
     parser.add_argument('''--model_name_or_path''', type=str)
     parser.add_argument('''--learning_rate''', type=str, default=5e-5)
     parser.add_argument('''--do_train''', type=bool, default=True)
     parser.add_argument('''--do_eval''', type=bool, default=True)
     parser.add_argument('''--output_dir''', type=str)
     parser.add_argument('''--max_steps''', type=int, default=None)

     # Data, model, and output directories
     parser.add_argument('''--output_data_dir''', type=str, default=os.environ['''SM_OUTPUT_DATA_DIR'''])
     parser.add_argument('''--model_dir''', type=str, default=os.environ['''SM_MODEL_DIR'''])
     parser.add_argument('''--n_gpus''', type=str, default=os.environ['''SM_NUM_GPUS'''])

     __magic_name__    , __magic_name__           =					parser.parse_known_args()

     # Set up logging
     __magic_name__           =					logging.getLogger(__name__)

     logging.basicConfig(
         level=logging.getLevelName('''INFO'''),
         handlers=[logging.StreamHandler(sys.stdout)],
         format='''%(asctime)s - %(name)s - %(levelname)s - %(message)s''',
     )

     if SDP_ENABLED:
          sdp.init()

          __magic_name__           =					tf.config.experimental.list_physical_devices('''GPU''')
          for gpu in gpus:
               tf.config.experimental.set_memory_growth(gpu, True)
          if gpus:
               tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], '''GPU''')

    # Load model and tokenizer
     __magic_name__           =					TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
     __magic_name__           =					AutoTokenizer.from_pretrained(args.model_name_or_path)

     # get datasets
     __magic_name__    , __magic_name__           =					get_datasets(
         tokenizer=tokenizer,
         train_batch_size=args.per_device_train_batch_size,
         eval_batch_size=args.per_device_eval_batch_size,
     )

     # fine optimizer and loss
     __magic_name__           =					tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
     __magic_name__           =					tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
     __magic_name__           =					[tf.keras.metrics.SparseCategoricalAccuracy()]
     model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

     # Training
     if args.do_train:
          # train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
          __magic_name__           =					time.time()
          __magic_name__           =					fit(
              model,
              loss,
              optimizer,
              tf_train_dataset,
              args.epochs,
              args.per_device_train_batch_size,
              max_steps=args.max_steps,
          )
          __magic_name__           =					time.time() - start_train_time
          logger.info('''*** Train ***''')
          logger.info(F'''train_runtime = {end_train_time}''')

          __magic_name__           =					os.path.join(args.output_dir, '''train_results.txt''')

          if not SDP_ENABLED or sdp.rank() == 0:
               with open(output_eval_file, '''w''') as writer:
                    logger.info('''***** Train results *****''')
                    logger.info(train_results)
                    for key, value in train_results.items():
                         logger.info(F'''  {key} = {value}''')
                         writer.write(F'''{key} = {value}\n''')

    # Evaluation
     if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
          __magic_name__           =					model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
          logger.info('''*** Evaluate ***''')

          __magic_name__           =					os.path.join(args.output_dir, '''eval_results.txt''')

          with open(output_eval_file, '''w''') as writer:
               logger.info('''***** Eval results *****''')
               logger.info(result)
               for key, value in result.items():
                    logger.info(F'''  {key} = {value}''')
                    writer.write(F'''{key} = {value}\n''')

    # Save result
     if SDP_ENABLED:
          if sdp.rank() == 0:
               model.save_pretrained(args.output_dir)
               tokenizer.save_pretrained(args.output_dir)
     else:
          model.save_pretrained(args.output_dir)
          tokenizer.save_pretrained(args.output_dir)





import sacrebleu as scb
from packaging import version

import datasets


__magic_name__           =					'''\
@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",
}
'''

__magic_name__           =					'''\
SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.
Inspired by Rico Sennrich\'s `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.
It also knows all the standard test sets and handles downloading, processing, and tokenization for you.

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

__magic_name__           =					'''
Produces BLEU scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.
    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).
    smooth_method (`str`): The smoothing method to use, defaults to `\'exp\'`. Possible values are:
        - `\'none\'`: no smoothing
        - `\'floor\'`: increment zero counts
        - `\'add-k\'`: increment num/denom by k for n>1
        - `\'exp\'`: exponential decay
    smooth_value (`float`): The smoothing value. Only valid when `smooth_method=\'floor\'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method=\'add-k\'` (in which case `smooth_value` defaults to `1`).
    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `\'zh\'` for Chinese, `\'ja-mecab\'` for Japanese and `\'13a\'` (mteval) otherwise. Possible values are:
        - `\'none\'`: No tokenization.
        - `\'zh\'`: Chinese tokenization.
        - `\'13a\'`: mimics the `mteval-v13a` script from Moses.
        - `\'intl\'`: International tokenization, mimics the `mteval-v14` script from Moses
        - `\'char\'`: Language-agnostic character-level tokenization.
        - `\'ja-mecab\'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).
    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.
    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.
    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.

Returns:
    \'score\': BLEU score,
    \'counts\': Counts,
    \'totals\': Totals,
    \'precisions\': Precisions,
    \'bp\': Brevity penalty,
    \'sys_len\': predictions length,
    \'ref_len\': reference length,

Examples:

    Example 1:
        >>> predictions = ["hello there general kenobi", "foo bar foobar"]
        >>> references = [["hello there general kenobi", "hello there !"], ["foo bar foobar", "foo bar foobar"]]
        >>> sacrebleu = datasets.load_metric("sacrebleu")
        >>> results = sacrebleu.compute(predictions=predictions, references=references)
        >>> print(list(results.keys()))
        [\'score\', \'counts\', \'totals\', \'precisions\', \'bp\', \'sys_len\', \'ref_len\']
        >>> print(round(results["score"], 1))
        100.0

    Example 2:
        >>> predictions = ["hello there general kenobi",
        ...                 "on our way to ankh morpork"]
        >>> references = [["hello there general kenobi", "hello there !"],
        ...                 ["goodbye ankh morpork", "ankh morpork"]]
        >>> sacrebleu = datasets.load_metric("sacrebleu")
        >>> results = sacrebleu.compute(predictions=predictions,
        ...                             references=references)
        >>> print(list(results.keys()))
        [\'score\', \'counts\', \'totals\', \'precisions\', \'bp\', \'sys_len\', \'ref_len\']
        >>> print(round(results["score"], 1))
        39.8
'''




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,   _KWARGS_DESCRIPTION	)
class    _SCREAMING_SNAKE_CASE					( datasets.Metric	):
      def A_     (     self	):
          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="https://github.com/mjpost/sacreBLEU"						,		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"]						,		reference_urls=[
                  "https://github.com/mjpost/sacreBLEU",
                  "https://en.wikipedia.org/wiki/BLEU",
                  "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
              ]						,		)



      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase="exp"						,		lowerCamelCase=None						,		lowerCamelCase=False						,		lowerCamelCase=False						,		lowerCamelCase=None						,		lowerCamelCase=False						,		):
          snake_case__  =			len(references[0]	)
          if any(len(lowerCamelCase	) != references_per_prediction for refs in references	):
              raise ValueError("Sacrebleu requires the same number of references for each prediction"	)
          snake_case__  =			[[refs[i] for refs in references] for i in range(lowerCamelCase	)]
          snake_case__  =			scb.corpus_bleu(
              lowerCamelCase						,		lowerCamelCase						,		smooth_method=lowerCamelCase						,		smooth_value=lowerCamelCase						,		force=lowerCamelCase						,		lowercase=lowerCamelCase						,		use_effective_order=lowerCamelCase						,		**({"tokenize": tokenize} if tokenize else {})						,		)
          snake_case__  =			{
              "score": output.score,
              "counts": output.counts,
              "totals": output.totals,
              "precisions": output.precisions,
              "bp": output.bp,
              "sys_len": output.sys_len,
              "ref_len": output.ref_len,
          }
          return output_dict



