


import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__      		= logging.get_logger(__name__)
lowerCAmelCase__      		= {'vocab_file': 'spiece.model'}

lowerCAmelCase__      		= {
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    }
}

lowerCAmelCase__      		= {
    'albert-base-v1': 5_12,
    'albert-large-v1': 5_12,
    'albert-xlarge-v1': 5_12,
    'albert-xxlarge-v1': 5_12,
    'albert-base-v2': 5_12,
    'albert-large-v2': 5_12,
    'albert-xlarge-v2': 5_12,
    'albert-xxlarge-v2': 5_12,
}

lowerCAmelCase__      		= '▁'







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase=True ,						__lowerCamelCase=True ,						__lowerCamelCase=False ,						__lowerCamelCase="[CLS]" ,						__lowerCamelCase="[SEP]" ,						__lowerCamelCase="<unk>" ,						__lowerCamelCase="[SEP]" ,						__lowerCamelCase="<pad>" ,						__lowerCamelCase="[CLS]" ,						__lowerCamelCase="[MASK]" ,						__lowerCamelCase = None ,						**__lowerCamelCase ,						)			->				None:
            # Mask token behave like a normal word, i.e. include the space before it and
            # is included in the raw text, there should be a match in a non-normalized sentence.
            _A      :       Tuple				    = (
                AddedToken(__lowerCamelCase ,						lstrip=__lowerCamelCase ,						rstrip=__lowerCamelCase ,						normalized=__lowerCamelCase)
                if isinstance(__lowerCamelCase ,						__lowerCamelCase)
                else mask_token
            )

            _A      :       Union[str, Any]				    = {} if sp_model_kwargs is None else sp_model_kwargs

            super().__init__(
                do_lower_case=__lowerCamelCase ,						remove_space=__lowerCamelCase ,						keep_accents=__lowerCamelCase ,						bos_token=__lowerCamelCase ,						eos_token=__lowerCamelCase ,						unk_token=__lowerCamelCase ,						sep_token=__lowerCamelCase ,						pad_token=__lowerCamelCase ,						cls_token=__lowerCamelCase ,						mask_token=__lowerCamelCase ,						sp_model_kwargs=self.sp_model_kwargs ,						**__lowerCamelCase ,						)

            _A      :       Union[str, Any]				    = do_lower_case
            _A      :       List[str]				    = remove_space
            _A      :       str				    = keep_accents
            _A      :       List[str]				    = vocab_file

            _A      :       Optional[Any]				    = spm.SentencePieceProcessor(**self.sp_model_kwargs)
            self.sp_model.Load(__lowerCamelCase)


     @property
     def  _lowerCamelCase (						self)			->				Tuple:
            return len(self.sp_model)


     def  _lowerCamelCase (						self)			->				List[Any]:
            _A      :       Optional[Any]				    = {self.convert_ids_to_tokens(__lowerCamelCase): i for i in range(self.vocab_size)}
            vocab.update(self.added_tokens_encoder)
            return vocab


     def __getstate__(						self)			->				Union[str, Any]:
            _A      :       int				    = self.__dict__.copy()
            _A      :       Optional[int]				    = None
            return state


     def __setstate__(						self ,						__lowerCamelCase)			->				int:
            _A      :       List[Any]				    = d

            # for backward compatibility
            if not hasattr(self ,						"sp_model_kwargs"):
                   _A      :       Tuple				    = {}

            _A      :       Tuple				    = spm.SentencePieceProcessor(**self.sp_model_kwargs)
            self.sp_model.Load(self.vocab_file)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				int:
            if self.remove_space:
                   _A      :       Optional[Any]				    = " ".join(inputs.strip().split())
            else:
                   _A      :       List[str]				    = inputs
            _A      :       int				    = outputs.replace("``" ,						"\"").replace("''" ,						"\"")

            if not self.keep_accents:
                   _A      :       Dict				    = unicodedata.normalize("NFKD" ,						__lowerCamelCase)
                   _A      :       Union[str, Any]				    = "".join([c for c in outputs if not unicodedata.combining(__lowerCamelCase)])
            if self.do_lower_case:
                   _A      :       Optional[Any]				    = outputs.lower()

            return outputs


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				List[str]:
            _A      :       Union[str, Any]				    = self.preprocess_text(__lowerCamelCase)
            _A      :       int				    = self.sp_model.encode(__lowerCamelCase ,						out_type=__lowerCamelCase)
            _A      :       Any				    = []
            for piece in pieces:
                   if len(__lowerCamelCase) > 1 and piece[-1] == str(",") and piece[-2].isdigit():
                          _A      :       Optional[Any]				    = self.sp_model.EncodeAsPieces(piece[:-1].replace(__lowerCamelCase ,						""))
                          if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                                 if len(cur_pieces[0]) == 1:
                                        _A      :       List[str]				    = cur_pieces[1:]
                                 else:
                                        _A      :       Tuple				    = cur_pieces[0][1:]
                          cur_pieces.append(piece[-1])
                          new_pieces.extend(__lowerCamelCase)
                   else:
                          new_pieces.append(__lowerCamelCase)

            return new_pieces


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Any:
            return self.sp_model.PieceToId(__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Dict:
            return self.sp_model.IdToPiece(__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Tuple:
            _A      :       int				    = []
            _A      :       str				    = ""
            _A      :       List[Any]				    = False
            for token in tokens:
                   # make sure that special tokens are not decoded using sentencepiece model
                   if token in self.all_special_tokens:
                          if not prev_is_special:
                                 out_string += " "
                          out_string += self.sp_model.decode(__lowerCamelCase) + token
                          _A      :       Any				    = True
                          _A      :       List[str]				    = []
                   else:
                          current_sub_tokens.append(__lowerCamelCase)
                          _A      :       Union[str, Any]				    = False
            out_string += self.sp_model.decode(__lowerCamelCase)
            return out_string.strip()


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				List[int]:
            _A      :       List[str]				    = [self.sep_token_id]
            _A      :       List[Any]				    = [self.cls_token_id]
            if token_ids_a is None:
                   return cls + token_ids_a + sep
            return cls + token_ids_a + sep + token_ids_a + sep


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None ,						__lowerCamelCase = False)			->				List[int]:

            if already_has_special_tokens:
                   return super().get_special_tokens_mask(
                       token_ids_a=__lowerCamelCase ,						token_ids_a=__lowerCamelCase ,						already_has_special_tokens=__lowerCamelCase)

            if token_ids_a is not None:
                   return [1] + ([0] * len(__lowerCamelCase)) + [1] + ([0] * len(__lowerCamelCase)) + [1]
            return [1] + ([0] * len(__lowerCamelCase)) + [1]


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				List[int]:
            _A      :       List[str]				    = [self.sep_token_id]
            _A      :       Optional[int]				    = [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) * [0] + len(token_ids_a + sep) * [1]


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				Tuple[str]:
            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) and os.path.isfile(self.vocab_file):
                   copyfile(self.vocab_file ,						__lowerCamelCase)
            elif not os.path.isfile(self.vocab_file):
                   with open(__lowerCamelCase ,						"wb") as fi:
                          _A      :       List[str]				    = self.sp_model.serialized_model_proto()
                          fi.write(__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)
