



'''simple docstring'''


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 	UpperCAmelCase	(		unittest.TestCase):


 '''simple docstring'''




 def       lowercase_  (   self) ->      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=lowerCAmelCase_							,  )
      assert hasattr(self							,  "env")




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      List[str]:



      """simple docstring"""







      a_       		=f"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
      # distributed data settings
      a_       		={"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       lowercase_  (   self							,  lowerCAmelCase_) ->      Optional[int]:



      """simple docstring"""







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




 @parameterized.expand([(2,)])
 def       lowercase_  (   self							,  lowerCAmelCase_) ->      int:



      """simple docstring"""







      a_       		=self.create_estimator(lowerCAmelCase_)

      # run training
      estimator.fit()

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

      # extract kpis
      a_       		=list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"])
      a_       		=list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"])
      # get train time from SageMaker job, this includes starting, preprocessing, stopping
      a_       		=(
          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_)







'''simple docstring'''


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__=5       ):


     '''simple docstring'''







     assert masked_input.count("<mask>"       ) == 1
     a_       		=torch.tensor(tokenizer.encode(lowercase__       ,	add_special_tokens=lowercase__       )       ).unsqueeze(0       )  # Batch size 1
     a_       		=model(lowercase__       )[0]  # The last hidden-state is the first element of the output tuple
     a_       		=(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
     a_       		=logits[0, masked_index, :]
     a_       		=logits.softmax(dim=0       )
     a_		,						a_       		=prob.topk(k=lowercase__       ,	dim=0       )
     a_       		=" ".join(
         [tokenizer.convert_ids_to_tokens(indices[i].item()       ) for i in range(len(lowercase__       )       )]       )
     a_       		=tokenizer.mask_token
     a_       		=[]
     for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "       )       ):
          a_       		=predicted_token_bpe.replace("\u2581"       ,	" "       )
          if " {0}".format(lowercase__       ) in masked_input:
               topk_filled_outputs.append(
                   (
                       masked_input.replace(" {0}".format(lowercase__       )       ,	lowercase__       ),
                       values[index].item(),
                       predicted_token,
                   )       )
          else:
               topk_filled_outputs.append(
                   (
                       masked_input.replace(lowercase__       ,	lowercase__       ),
                       values[index].item(),
                       predicted_token,
                   )       )
     return topk_filled_outputs


lowercase							       =			CamembertTokenizer.from_pretrained('''camembert-base''')
lowercase							       =			CamembertForMaskedLM.from_pretrained('''camembert-base''')
model.eval()

lowercase							       =			'''Le camembert est <mask> :)'''

print(fill_mask(masked_input, model, tokenizer, topk=3))


