


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_mbart import MBartTokenizer
else:
    lowerCAmelCase__      		= None


lowerCAmelCase__      		= logging.get_logger(__name__)


lowerCAmelCase__      		= {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase__      		= {
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase__      		= {
    'facebook/mbart-large-en-ro': 10_24,
    'facebook/mbart-large-cc25': 10_24,
}

# fmt: off
lowerCAmelCase__      		= ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE  			= ["input_ids", "attention_mask"]
     __SCREAMING_SNAKE_CASE  			= MBartTokenizer

     __SCREAMING_SNAKE_CASE  			= []
     __SCREAMING_SNAKE_CASE  			= []


     def __init__(						self ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase="<s>" ,						__lowerCamelCase="</s>" ,						__lowerCamelCase="</s>" ,						__lowerCamelCase="<s>" ,						__lowerCamelCase="<unk>" ,						__lowerCamelCase="<pad>" ,						__lowerCamelCase="<mask>" ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						**__lowerCamelCase ,						)			->				Optional[int]:
            # Mask token behave like a normal word, i.e. include the space before it
            _A      :       List[str]				    = AddedToken(__lowerCamelCase ,						lstrip=__lowerCamelCase ,						rstrip=__lowerCamelCase) if isinstance(__lowerCamelCase ,						__lowerCamelCase) else mask_token

            super().__init__(
                vocab_file=__lowerCamelCase ,						tokenizer_file=__lowerCamelCase ,						bos_token=__lowerCamelCase ,						eos_token=__lowerCamelCase ,						sep_token=__lowerCamelCase ,						cls_token=__lowerCamelCase ,						unk_token=__lowerCamelCase ,						pad_token=__lowerCamelCase ,						mask_token=__lowerCamelCase ,						src_lang=__lowerCamelCase ,						tgt_lang=__lowerCamelCase ,						additional_special_tokens=__lowerCamelCase ,						**__lowerCamelCase ,						)

            _A      :       Union[str, Any]				    = vocab_file
            _A      :       int				    = False if not self.vocab_file else True

            _A      :       Optional[int]				    = FAIRSEQ_LANGUAGE_CODES.copy()

            if additional_special_tokens is not None:
                   # Only add those special tokens if they are not already there.
                   _additional_special_tokens.extend(
                       [t for t in additional_special_tokens if t not in _additional_special_tokens])

            self.add_special_tokens({"additional_special_tokens": _additional_special_tokens})
            _A      :       Union[str, Any]				    = {
                lang_code: self.convert_tokens_to_ids(__lowerCamelCase) for lang_code in FAIRSEQ_LANGUAGE_CODES
            }

            _A      :       Optional[int]				    = src_lang if src_lang is not None else "en_XX"
            _A      :       Union[str, Any]				    = self.convert_tokens_to_ids(self._src_lang)
            _A      :       int				    = tgt_lang
            self.set_src_lang_special_tokens(self._src_lang)


     @property
     def  _lowerCamelCase (						self)			->				str:
            return self._src_lang


     @src_lang.setter
     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				None:
            _A      :       Dict				    = new_src_lang
            self.set_src_lang_special_tokens(self._src_lang)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				List[int]:
            if token_ids_a is None:
                   return self.prefix_tokens + token_ids_a + self.suffix_tokens
            # We don't expect to process pairs, but leave the pair logic for API consistency
            return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				List[int]:
            _A      :       List[str]				    = [self.sep_token_id]
            _A      :       List[str]				    = [self.cls_token_id]

            if token_ids_a is None:
                   return len(cls + token_ids_a + sep) * [0]
            return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase)			->				Dict:
            if src_lang is None or tgt_lang is None:
                   raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
            _A      :       str				    = src_lang
            _A      :       Any				    = self(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase)
            _A      :       Tuple				    = self.convert_tokens_to_ids(__lowerCamelCase)
            _A      :       Dict				    = tgt_lang_id
            return inputs


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = "en_XX" ,						__lowerCamelCase = None ,						__lowerCamelCase = "ro_RO" ,						**__lowerCamelCase ,						)			->				BatchEncoding:
            _A      :       Any				    = src_lang
            _A      :       int				    = tgt_lang
            return super().prepare_seqaseq_batch(__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				List[str]:
            return self.set_src_lang_special_tokens(self.src_lang)


     def  _lowerCamelCase (						self)			->				List[Any]:
            return self.set_tgt_lang_special_tokens(self.tgt_lang)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				None:
            _A      :       int				    = self.convert_tokens_to_ids(__lowerCamelCase)
            _A      :       int				    = []
            _A      :       List[str]				    = [self.eos_token_id, self.cur_lang_code]

            _A      :       Union[str, Any]				    = self.convert_ids_to_tokens(self.prefix_tokens)
            _A      :       str				    = self.convert_ids_to_tokens(self.suffix_tokens)

            _A      :       List[Any]				    = processors.TemplateProcessing(
                single=prefix_tokens_str + ["$A"] + suffix_tokens_str ,						pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str ,						special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,						self.prefix_tokens + self.suffix_tokens)) ,						)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				None:
            _A      :       Optional[int]				    = self.convert_tokens_to_ids(__lowerCamelCase)
            _A      :       List[Any]				    = []
            _A      :       str				    = [self.eos_token_id, self.cur_lang_code]

            _A      :       Optional[int]				    = self.convert_ids_to_tokens(self.prefix_tokens)
            _A      :       int				    = self.convert_ids_to_tokens(self.suffix_tokens)

            _A      :       str				    = processors.TemplateProcessing(
                single=prefix_tokens_str + ["$A"] + suffix_tokens_str ,						pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str ,						special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,						self.prefix_tokens + self.suffix_tokens)) ,						)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				Tuple[str]:
            if not self.can_save_slow_tokenizer:
                   raise ValueError(
                       "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                       "tokenizer.")

            if not os.path.isdir(__lowerCamelCase):
                   logger.error(F"Vocabulary path ({save_directory}) should be a directory.")
                   return
            _A      :       int				    = os.path.join(
                __lowerCamelCase ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

            if os.path.abspath(self.vocab_file) != os.path.abspath(__lowerCamelCase):
                   copyfile(self.vocab_file ,						__lowerCamelCase)

            return (out_vocab_file,)




import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
    from sagemaker import Session, TrainingJobAnalytics
    from sagemaker.huggingface import HuggingFace







@pytest.mark.skipif(
    literal_eval(os.getenv("TEST_SAGEMAKER"     ,    "False")) is not True     ,    reason="Skipping test because should only be run when releasing minor transformers version"     ,    )
@pytest.mark.usefixtures("sm_env")
@parameterized_class(
    [
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 650, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "pytorch",
            "script": "run_ddp.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "tensorflow",
            "script": "run_tf_dist.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.6, "eval_loss": 0.7},
        },
    ])
class      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


     def  _lowerCamelCase (						self)			->				str:
            if self.framework == "pytorch":
                   subprocess.run(
                       F"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split() ,						encoding="utf-8" ,						check=__lowerCamelCase ,						)
            assert hasattr(self ,						"env")


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Tuple:
            _A      :       Dict				    = F"{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"
            # distributed data settings
            _A      :       Optional[Any]				    = {"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None

            # creates estimator
            return HuggingFace(
                entry_point=self.script ,						source_dir=self.env.test_path ,						role=self.env.role ,						image_uri=self.env.image_uri ,						base_job_name=__lowerCamelCase ,						instance_count=__lowerCamelCase ,						instance_type=self.instance_type ,						debugger_hook_config=__lowerCamelCase ,						hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path} ,						metric_definitions=self.env.metric_definitions ,						distribution=__lowerCamelCase ,						py_version="py36" ,						)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Optional[Any]:
            TrainingJobAnalytics(__lowerCamelCase).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv")


     @parameterized.expand([(2,)])
     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Any:
            # create estimator
            _A      :       Union[str, Any]				    = self.create_estimator(__lowerCamelCase)

            # run training
            estimator.fit()

            # result dataframe
            _A      :       Optional[Any]				    = TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

            # extract kpis
            _A      :       List[Any]				    = list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"])
            _A      :       Dict				    = list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"])
            # get train time from SageMaker job, this includes starting, preprocessing, stopping
            _A      :       Optional[Any]				    = (
                Session().describe_training_job(estimator.latest_training_job.name).get("TrainingTimeInSeconds" ,						9_9_9_9_9_9)
            )

            # assert kpis
            assert train_runtime <= self.results["train_runtime"]
            assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy)
            assert all(t <= self.results["eval_loss"] for t in eval_loss)

            # dump tests result into json file to share in PR
            with open(F"{estimator.latest_training_job.name}.json" ,						"w") as outfile:
                   json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss} ,						__lowerCamelCase)
