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_50, '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_00, '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_00, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
        },
    ]					)
class 					A							(    unittest.TestCase					):


   def       lowercase_				(self					:						Tuple		)						->    Union[str, 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					:						str	,  __UpperCAmelCase					:						List[Any]		)						->    Union[str, Any]:
         """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=__UpperCAmelCase	,  instance_count=__UpperCAmelCase	,  instance_type=self.instance_type	,  debugger_hook_config=__UpperCAmelCase	,  hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}	,  metric_definitions=self.env.metric_definitions	,  distribution=__UpperCAmelCase	,  py_version="py36"	,  )


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



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





   @parameterized.expand([(2,)]		)
   def       lowercase_				(self					:						Optional[int]	,  __UpperCAmelCase					:						Tuple		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.create_estimator(__UpperCAmelCase		)

         # 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		)




from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__    							= logging.get_logger(__name__)

UpperCamelCase__    							= {
    'microsoft/xprophetnet-large-wiki100-cased': (
        'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'
    ),
}





class 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			Optional[int]              =      'xlm-prophetnet'
   __UpperCAmelCase			:			int              =      ['past_key_values']
   __UpperCAmelCase			:			Optional[int]              =      {
       'num_attention_heads': 'num_encoder_attention_heads',
   }


   def __init__(self					:						str	,  __UpperCAmelCase					:						Optional[float] = 0.1	,  __UpperCAmelCase					:						Optional[Union[str, Callable]] = "gelu"	,  __UpperCAmelCase					:						Optional[int] = 3_0_5_2_2	,  __UpperCAmelCase					:						Optional[int] = 1_0_2_4	,  __UpperCAmelCase					:						Optional[int] = 4_0_9_6	,  __UpperCAmelCase					:						Optional[int] = 1_2	,  __UpperCAmelCase					:						Optional[int] = 1_6	,  __UpperCAmelCase					:						Optional[int] = 4_0_9_6	,  __UpperCAmelCase					:						Optional[int] = 1_2	,  __UpperCAmelCase					:						Optional[int] = 1_6	,  __UpperCAmelCase					:						Optional[float] = 0.1	,  __UpperCAmelCase					:						Optional[float] = 0.1	,  __UpperCAmelCase					:						Optional[int] = 5_1_2	,  __UpperCAmelCase					:						Optional[float] = 0.02	,  __UpperCAmelCase					:						Optional[bool] = True	,  __UpperCAmelCase					:						Optional[bool] = True	,  __UpperCAmelCase					:						Optional[int] = 0	,  __UpperCAmelCase					:						Optional[int] = 2	,  __UpperCAmelCase					:						Optional[int] = 3_2	,  __UpperCAmelCase					:						Optional[int] = 1_2_8	,  __UpperCAmelCase					:						Optional[bool] = False	,  __UpperCAmelCase					:						Optional[float] = 0.0	,  __UpperCAmelCase					:						Optional[bool] = True	,  __UpperCAmelCase					:						Optional[int] = 0	,  __UpperCAmelCase					:						Optional[int] = 1	,  __UpperCAmelCase					:						Optional[int] = 2	,  **__UpperCAmelCase					:						Optional[Any]	,  )						->    List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  vocab_size
         UpperCAmelCase__								=  hidden_size
         UpperCAmelCase__								=  encoder_ffn_dim
         UpperCAmelCase__								=  num_encoder_layers
         UpperCAmelCase__								=  num_encoder_attention_heads
         UpperCAmelCase__								=  decoder_ffn_dim
         UpperCAmelCase__								=  num_decoder_layers
         UpperCAmelCase__								=  num_decoder_attention_heads
         UpperCAmelCase__								=  max_position_embeddings
         UpperCAmelCase__								=  init_std  # Normal(0, this parameter)
         UpperCAmelCase__								=  activation_function

         # parameters for xlmprophetnet
         UpperCAmelCase__								=  ngram
         UpperCAmelCase__								=  num_buckets
         UpperCAmelCase__								=  relative_max_distance
         UpperCAmelCase__								=  disable_ngram_loss
         UpperCAmelCase__								=  eps

         # 3 Types of Dropout
         UpperCAmelCase__								=  attention_dropout
         UpperCAmelCase__								=  activation_dropout
         UpperCAmelCase__								=  dropout

         UpperCAmelCase__								=  use_cache

         super().__init__(
             pad_token_id=__UpperCAmelCase	,  bos_token_id=__UpperCAmelCase	,  eos_token_id=__UpperCAmelCase	,  is_encoder_decoder=__UpperCAmelCase	,  add_cross_attention=__UpperCAmelCase	,  decoder_start_token_id=__UpperCAmelCase	,  **__UpperCAmelCase	,  )


   @property
   def       lowercase_				(self					:						int		)						->    int:
         """simple docstring"""



         return self.num_encoder_layers + self.num_decoder_layers





   @num_hidden_layers.setter
   def       lowercase_				(self					:						Any	,  __UpperCAmelCase					:						List[Any]		)						->    Union[str, Any]:
         """simple docstring"""



         raise NotImplementedError(
             "This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"
             " `num_decoder_layers`."		)



