





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_UpperCAmelCase        =				{
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

_UpperCAmelCase        =				{
    'junnyu/roformer_chinese_small': 1_5_3_6,
    'junnyu/roformer_chinese_base': 1_5_3_6,
    'junnyu/roformer_chinese_char_small': 5_1_2,
    'junnyu/roformer_chinese_char_base': 5_1_2,
    'junnyu/roformer_small_discriminator': 1_2_8,
    'junnyu/roformer_small_generator': 1_2_8,
}


_UpperCAmelCase        =				{
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}



class 							_UpperCamelCase					(	_snake_case     ):
							_UpperCamelCase				:					int         =   VOCAB_FILES_NAMES
							_UpperCamelCase				:					str         =   PRETRAINED_VOCAB_FILES_MAP
							_UpperCamelCase				:					List[Any]         =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_UpperCamelCase				:					Union[str, Any]         =   PRETRAINED_INIT_CONFIGURATION
							_UpperCamelCase				:					Any         =   RoFormerTokenizer

							def __init__(		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: Optional[int]=None					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=None					,   _SCREAMING_SNAKE_CASE: Tuple=True					,   _SCREAMING_SNAKE_CASE: List[str]="[UNK]"					,   _SCREAMING_SNAKE_CASE: Optional[int]="[SEP]"					,   _SCREAMING_SNAKE_CASE: Union[str, Any]="[PAD]"					,   _SCREAMING_SNAKE_CASE: List[str]="[CLS]"					,   _SCREAMING_SNAKE_CASE: Optional[Any]="[MASK]"					,   _SCREAMING_SNAKE_CASE: int=True					,   _SCREAMING_SNAKE_CASE: Dict=None					,   **_SCREAMING_SNAKE_CASE: List[Any]					,   )       -> Optional[Any]:




														"""simple docstring"""


														super().__init__(
														    _SCREAMING_SNAKE_CASE					,   tokenizer_file=_SCREAMING_SNAKE_CASE					,   do_lower_case=_SCREAMING_SNAKE_CASE					,   unk_token=_SCREAMING_SNAKE_CASE					,   sep_token=_SCREAMING_SNAKE_CASE					,   pad_token=_SCREAMING_SNAKE_CASE					,   cls_token=_SCREAMING_SNAKE_CASE					,   mask_token=_SCREAMING_SNAKE_CASE					,   tokenize_chinese_chars=_SCREAMING_SNAKE_CASE					,   strip_accents=_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE					,   )

														UpperCamelCase_          =  json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
														if (
														    pre_tok_state.get("lowercase"					,   _SCREAMING_SNAKE_CASE		) != do_lower_case
														    or pre_tok_state.get("strip_accents"					,   _SCREAMING_SNAKE_CASE		) != strip_accents
														):
																					UpperCamelCase_          =  getattr(_SCREAMING_SNAKE_CASE					,   pre_tok_state.pop("type"		)		)
																					UpperCamelCase_          =  do_lower_case
																					UpperCamelCase_          =  strip_accents
																					UpperCamelCase_          =  pre_tok_class(**_SCREAMING_SNAKE_CASE		)

														UpperCamelCase_          =  do_lower_case

							def __getstate__(		self: Dict		)       -> Optional[int]:




														"""simple docstring"""


														UpperCamelCase_          =  self.__dict__.copy()
														UpperCamelCase_          =  BertPreTokenizer()
														return state

							def __setstate__(		self: List[Any]					,   _SCREAMING_SNAKE_CASE: Optional[int]		)       -> Dict:




														"""simple docstring"""


														UpperCamelCase_          =  d
														UpperCamelCase_          =  self.__dict__["_tokenizer"].get_vocab()
														UpperCamelCase_          =  PreTokenizer.custom(JiebaPreTokenizer(_SCREAMING_SNAKE_CASE		)		)

							def lowercase (		self: List[str]					,   _SCREAMING_SNAKE_CASE: int					,   _SCREAMING_SNAKE_CASE: List[Any]=None		)       -> Dict:




														"""simple docstring"""


														UpperCamelCase_          =  [self.cls_token_id] + token_ids_a + [self.sep_token_id]

														if token_ids_a:
																					output += token_ids_a + [self.sep_token_id]

														return output

							def lowercase (		self: int					,   _SCREAMING_SNAKE_CASE: List[int]					,   _SCREAMING_SNAKE_CASE: Optional[List[int]] = None		)       -> Any:




														"""simple docstring"""


														UpperCamelCase_          =  [self.sep_token_id]
														UpperCamelCase_          =  [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 lowercase (		self: Tuple					,   _SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: Optional[str] = None		)       -> Optional[Any]:




														"""simple docstring"""


														UpperCamelCase_          =  self._tokenizer.model.save(_SCREAMING_SNAKE_CASE					,   name=_SCREAMING_SNAKE_CASE		)
														return tuple(_SCREAMING_SNAKE_CASE		)



							def lowercase (		self: int					,   _SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: Dict=None					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=None					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=False					,   **_SCREAMING_SNAKE_CASE: str					,   )       -> Tuple:




														"""simple docstring"""


														UpperCamelCase_          =  BertPreTokenizer()
														return super().save_pretrained(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE		)








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''': 6_5_0, '''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''': 6_0_0, '''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''': 6_0_0, '''eval_accuracy''': 0.6, '''eval_loss''': 0.7},
        },
    ]     )
class 							_UpperCamelCase					(	unittest.TestCase     ):

       def lowercase (		self: Union[str, Any]		)       -> List[str]:




              """simple docstring"""


              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=_SCREAMING_SNAKE_CASE					,   )
              assert hasattr(self					,   "env"		)

       def lowercase (		self: List[str]					,   _SCREAMING_SNAKE_CASE: Dict		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  f'''{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'''
              # distributed data settings
              UpperCamelCase_          =  {"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=_SCREAMING_SNAKE_CASE					,   instance_count=_SCREAMING_SNAKE_CASE					,   instance_type=self.instance_type					,   debugger_hook_config=_SCREAMING_SNAKE_CASE					,   hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}					,   metric_definitions=self.env.metric_definitions					,   distribution=_SCREAMING_SNAKE_CASE					,   py_version="py36"					,   )

       def lowercase (		self: List[str]					,   _SCREAMING_SNAKE_CASE: Any		)       -> List[Any]:




              """simple docstring"""


              TrainingJobAnalytics(_SCREAMING_SNAKE_CASE		).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''		)



       @parameterized.expand([(2,)]		)
       def lowercase (		self: str					,   _SCREAMING_SNAKE_CASE: Tuple		)       -> Optional[Any]:




              """simple docstring"""


              UpperCamelCase_          =  self.create_estimator(_SCREAMING_SNAKE_CASE		)

              # run training
              estimator.fit()

              # result dataframe
              UpperCamelCase_          =  TrainingJobAnalytics(estimator.latest_training_job.name		).dataframe()

              # extract kpis
              UpperCamelCase_          =  list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]		)
              UpperCamelCase_          =  list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]		)
              # get train time from SageMaker job, this includes starting, preprocessing, stopping
              UpperCamelCase_          =  (
                  Session().describe_training_job(estimator.latest_training_job.name		).get("TrainingTimeInSeconds"					,   999999		)
              )

              # 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}					,   _SCREAMING_SNAKE_CASE		)

