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': 1600, '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': 1600, 'eval_accuracy': 0.3, 'eval_loss': 1.2},
        },
    ]					)
class 			UpperCAmelCase__    (		unittest.TestCase					):



  """simple docstring"""






  def 	_UpperCAmelCase				(				self:			Union[str, Any]   ) -> int:




    '''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 	_UpperCAmelCase				(				self:			Union[str, Any]				,				__lowerCAmelCase:			Optional[int]   ) -> List[str]:




    '''simple docstring'''




    __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=UpperCAmelCase_				,				instance_type=self.instance_type				,				debugger_hook_config=UpperCAmelCase_				,				hyperparameters={
            **self.env.hyperparameters,
            "model_name_or_path": self.model_name_or_path,
            "max_steps": 500,
        }				,				metric_definitions=self.env.metric_definitions				,				distribution=UpperCAmelCase_				,				py_version="py36"				,				)






  def 	_UpperCAmelCase				(				self:			Union[str, Any]				,				__lowerCAmelCase:			Optional[int]   ) -> Optional[Any]:




    '''simple docstring'''




    TrainingJobAnalytics(UpperCAmelCase_   ).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''   )






  @parameterized.expand([(1,)]   )
  def 	_UpperCAmelCase				(				self:			Optional[Any]				,				__lowerCAmelCase:			Optional[int]   ) -> 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"				,				999_999   )
    )

    # 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 warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






  lowerCAmelCase__		:	Optional[int]    		= ['image_processor', 'tokenizer']
  lowerCAmelCase__		:	Any    		= 'CLIPImageProcessor'
  lowerCAmelCase__		:	Any    		= ('CLIPTokenizer', 'CLIPTokenizerFast')






  def __init__(				self:			str				,				__lowerCAmelCase:			List[Any]=None				,				__lowerCAmelCase:			Any=None				,				**__lowerCAmelCase:			str   ) -> Optional[int]:




    '''simple docstring'''




    __UpperCAmelCase						    =       None
    if "feature_extractor" in kwargs:
      warnings.warn(
          "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
          " instead."				,				__lowerCAmelCase				,				)
      __UpperCAmelCase						    =       kwargs.pop("feature_extractor"   )

    __UpperCAmelCase						    =       image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
      raise ValueError("You need to specify an `image_processor`."   )
    if tokenizer is None:
      raise ValueError("You need to specify a `tokenizer`."   )

    super().__init__(__lowerCAmelCase				,				__lowerCAmelCase   )






  def __call__(				self:			Optional[int]				,				__lowerCAmelCase:			Union[str, Any]=None				,				__lowerCAmelCase:			Dict=None				,				__lowerCAmelCase:			Optional[int]=None				,				**__lowerCAmelCase:			Optional[int]   ) -> Dict:




    '''simple docstring'''




    if text is None and images is None:
      raise ValueError("You have to specify either text or images. Both cannot be none."   )

    if text is not None:
      __UpperCAmelCase						    =       self.tokenizer(__lowerCAmelCase				,				return_tensors=__lowerCAmelCase				,				**__lowerCAmelCase   )

    if images is not None:
      __UpperCAmelCase						    =       self.image_processor(__lowerCAmelCase				,				return_tensors=__lowerCAmelCase				,				**__lowerCAmelCase   )

    if text is not None and images is not None:
      __UpperCAmelCase						    =       image_features.pixel_values
      return encoding
    elif text is not None:
      return encoding
    else:
      return BatchEncoding(data=dict(**__lowerCAmelCase   )				,				tensor_type=__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			str				,				*__lowerCAmelCase:			str				,				**__lowerCAmelCase:			List[Any]   ) -> Optional[Any]:




    '''simple docstring'''




    return self.tokenizer.batch_decode(*__lowerCAmelCase				,				**__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			Any				,				*__lowerCAmelCase:			List[Any]				,				**__lowerCAmelCase:			Dict   ) -> Union[str, Any]:




    '''simple docstring'''




    return self.tokenizer.decode(*__lowerCAmelCase				,				**__lowerCAmelCase   )






  @property
  def 	_UpperCAmelCase				(				self:			Tuple   ) -> List[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer.model_input_names
    __UpperCAmelCase						    =       self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )






  @property
  def 	_UpperCAmelCase				(				self:			Optional[int]   ) -> str:




    '''simple docstring'''




    warnings.warn(
        "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,				__lowerCAmelCase				,				)
    return self.image_processor_class






  @property
  def 	_UpperCAmelCase				(				self:			List[Any]   ) -> List[str]:




    '''simple docstring'''




    warnings.warn(
        "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,				__lowerCAmelCase				,				)
    return self.image_processor



