




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_model_parallelism.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 16_00, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 16_00, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
    ]		)
class 			_UpperCamelCase      (				unittest.TestCase		):
      def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			Optional[int]:
       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 	UpperCAmelCase_						(			self   :Tuple ,      lowerCamelCase   :Any      )     ->			int:
       # configuration for running training on smdistributed Model Parallel
       UpperCAmelCase__							 =						{
           "enabled": True,
           "processes_per_host": 8,
       }
       UpperCAmelCase__							 =						{
           "enabled": True,
           "parameters": {
               "microbatches": 4,
               "placement_strategy": "spread",
               "pipeline": "interleaved",
               "optimize": "speed",
               "partitions": 4,
               "ddp": True,
           },
       }

       UpperCAmelCase__							 =						{"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

       UpperCAmelCase__							 =						"trainer" if self.script == "run_glue.py" else "smtrainer"
       # 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=f'''{self.env.base_job_name}-{instance_count}-smp-{name_extension}''' ,      instance_count=lowerCamelCase ,      instance_type=self.instance_type ,      debugger_hook_config=lowerCamelCase ,      hyperparameters={
               **self.env.hyperparameters,
               "model_name_or_path": self.model_name_or_path,
               "max_steps": 500,
           } ,      metric_definitions=self.env.metric_definitions ,      distribution=lowerCamelCase ,      py_version="py36" ,      )
      def 	UpperCAmelCase_						(			self   :str ,      lowerCamelCase   :Tuple      )     ->			Union[str, Any]:
       TrainingJobAnalytics(lowerCamelCase      ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''      )







      @parameterized.expand([(1,)]      )
      def 	UpperCAmelCase_						(			self   :Tuple ,      lowerCamelCase   :Any      )     ->			Tuple:
       # create estimator
       UpperCAmelCase__							 =						self.create_estimator(lowerCamelCase      )

       # 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" ,      99_9999      )
       )

       # 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      )











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


_lowerCAmelCase						:   Union[str, Any]		=   logging.get_logger(__name__)

_lowerCAmelCase						:   Tuple		=   {
    "CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
        "https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}
class 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		"""trajectory_transformer"""
      UpperCAmelCase_        =		["""past_key_values"""]
      UpperCAmelCase_        =		{
          """hidden_size""": """n_embd""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }
      def __init__(			self   :Union[str, Any] ,      lowerCamelCase   :Optional[int]=100 ,      lowerCamelCase   :Optional[int]=5 ,      lowerCamelCase   :Optional[Any]=1 ,      lowerCamelCase   :Any=1 ,      lowerCamelCase   :int=249 ,      lowerCamelCase   :Optional[Any]=6 ,      lowerCamelCase   :Optional[Any]=17 ,      lowerCamelCase   :Optional[int]=25 ,      lowerCamelCase   :Union[str, Any]=4 ,      lowerCamelCase   :Union[str, Any]=4 ,      lowerCamelCase   :Optional[int]=128 ,      lowerCamelCase   :List[Any]=0.1 ,      lowerCamelCase   :List[str]=0.1 ,      lowerCamelCase   :Tuple=0.1 ,      lowerCamelCase   :Dict=0.00_06 ,      lowerCamelCase   :Dict=512 ,      lowerCamelCase   :Optional[Any]=0.02 ,      lowerCamelCase   :Any=1e-12 ,      lowerCamelCase   :Optional[Any]=1 ,      lowerCamelCase   :List[Any]=True ,      lowerCamelCase   :int=1 ,      lowerCamelCase   :Dict=5_0256 ,      lowerCamelCase   :Union[str, Any]=5_0256 ,      **lowerCamelCase   :int ,      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						vocab_size
       UpperCAmelCase__							 =						action_weight
       UpperCAmelCase__							 =						reward_weight
       UpperCAmelCase__							 =						value_weight
       UpperCAmelCase__							 =						max_position_embeddings
       UpperCAmelCase__							 =						block_size
       UpperCAmelCase__							 =						action_dim
       UpperCAmelCase__							 =						observation_dim
       UpperCAmelCase__							 =						transition_dim
       UpperCAmelCase__							 =						learning_rate
       UpperCAmelCase__							 =						n_layer
       UpperCAmelCase__							 =						n_head
       UpperCAmelCase__							 =						n_embd
       UpperCAmelCase__							 =						embd_pdrop
       UpperCAmelCase__							 =						attn_pdrop
       UpperCAmelCase__							 =						resid_pdrop
       UpperCAmelCase__							 =						initializer_range
       UpperCAmelCase__							 =						layer_norm_eps
       UpperCAmelCase__							 =						kaiming_initializer_range
       UpperCAmelCase__							 =						use_cache
       super().__init__(pad_token_id=lowerCamelCase ,      bos_token_id=lowerCamelCase ,      eos_token_id=lowerCamelCase ,      **lowerCamelCase      )





