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

import pytest
from parameterized import 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.g4dn.xlarge',
            'results': {'train_runtime': 6_50, 'eval_accuracy': 0.6, 'eval_loss': 0.9},
        },
        {
            'framework': 'tensorflow',
            'script': 'run_tf.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.g4dn.xlarge',
            'results': {'train_runtime': 6_00, 'eval_accuracy': 0.3, 'eval_loss': 0.9},
        },
    ]     )
class      A							(				unittest.TestCase     ):


   def 			lowercase_      (self     :						int		)						->				Optional[Any]:
         """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=__UpperCAmelCase       ,  )
         assert hasattr(self       ,  "env"		)


   def 			lowercase_      (self     :						List[Any]       ,  __UpperCAmelCase     :						Optional[int]=1		)						->				Dict:
         """simple docstring"""



         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=f"""{self.env.base_job_name}-single"""       ,  instance_count=__UpperCAmelCase       ,  instance_type=self.instance_type       ,  debugger_hook_config=__UpperCAmelCase       ,  hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}       ,  metric_definitions=self.env.metric_definitions       ,  py_version="py36"       ,  )


   def 			lowercase_      (self     :						Optional[Any]       ,  __UpperCAmelCase     :						Tuple		)						->				Optional[int]:
         """simple docstring"""



         TrainingJobAnalytics(__UpperCAmelCase		).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""		)





   def 			lowercase_      (self     :						Any		)						->				Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.create_estimator()

         # 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"       ,  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}       ,  __UpperCAmelCase		)









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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase		:				str       			=		logging.get_logger(__name__)

__lowerCAmelCase		:				Dict       			=		{'vocab_file': 'spiece.model'}

__lowerCAmelCase		:				Optional[int]       			=		{
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

__lowerCAmelCase		:				Dict       			=		{'bert_for_seq_generation': 512}




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Any							 =					VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_       :							Union[str, Any]							 =					PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_       :							int							 =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_       :							List[int]							 =					[]
				SCREAMING_SNAKE_CASE_       :							Optional[Any]							 =					["""input_ids""", """attention_mask"""]





				def __init__(      self				:     List[Any]					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     str="<s>"					,      __lowerCamelCase				:     Optional[int]="</s>"					,      __lowerCamelCase				:     int="<unk>"					,      __lowerCamelCase				:     List[str]="<pad>"					,      __lowerCamelCase				:     Union[str, Any]="<::::>"					,      __lowerCamelCase				:     Optional[Dict[str, Any]] = None					,      **__lowerCamelCase				:     List[str]					,      )					->     None:
							a    =			{} if sp_model_kwargs is None else sp_model_kwargs

							# Add extra_ids to the special token list
							super().__init__(
							    bos_token=__lowerCamelCase					,      eos_token=__lowerCamelCase					,      unk_token=__lowerCamelCase					,      pad_token=__lowerCamelCase					,      sep_token=__lowerCamelCase					,      sp_model_kwargs=self.sp_model_kwargs					,      **__lowerCamelCase					,      )

							a    =			vocab_file

							a    =			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
							self.sp_model.Load(__lowerCamelCase							)





				@property
				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Dict:
							return self.sp_model.get_piece_size()





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Optional[int]:
							a    =			{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				:     Dict							)					->     Optional[Any]:
							a    =			self.__dict__.copy()
							a    =			None
							return state





				def __setstate__(      self				:     Optional[Any]					,      __lowerCamelCase				:     Dict							)					->     Optional[Any]:
							a    =			d

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

							a    =			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
							self.sp_model.Load(self.vocab_file							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]					,      __lowerCamelCase				:     str							)					->     List[str]:
							return self.sp_model.encode(__lowerCamelCase					,      out_type=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     str					,      __lowerCamelCase				:     Union[str, Any]							)					->     int:
							return self.sp_model.piece_to_id(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Tuple					,      __lowerCamelCase				:     List[str]							)					->     Any:
							a    =			self.sp_model.IdToPiece(__lowerCamelCase							)
							return token





				def 							__UpperCAmelCase							(      self				:     Any					,      __lowerCamelCase				:     Dict							)					->     Any:
							a    =			[]
							a    =			""
							for token in tokens:
										# make sure that special tokens are not decoded using sentencepiece model
										if token in self.all_special_tokens:
													out_string += self.sp_model.decode(__lowerCamelCase							) + token
													a    =			[]
										else:
													current_sub_tokens.append(__lowerCamelCase							)
							out_string += self.sp_model.decode(__lowerCamelCase							)
							return out_string.strip()





				def 							__UpperCAmelCase							(      self				:     Dict					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Optional[str] = None							)					->     Tuple[str]:
							if not os.path.isdir(__lowerCamelCase							):
										logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
										return
							a    =			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    =			self.sp_model.serialized_model_proto()
													fi.write(__lowerCamelCase							)

							return (out_vocab_file,)






