





from ...processing_utils import ProcessorMixin




class  _lowerCamelCase       (			a_			):



      """simple docstring"""






      UpperCAmelCase_  :					Optional[int]      			="SpeechT5FeatureExtractor"
      UpperCAmelCase_  :					Tuple      			="SpeechT5Tokenizer"







      def __init__(       self ,  UpperCAmelCase ,  UpperCAmelCase      )				->      Optional[Any]:






        '''simple docstring'''






        super().__init__(_A ,  _A      )







      def __call__(       self ,  *UpperCAmelCase ,  **UpperCAmelCase      )				->      Optional[Any]:






        '''simple docstring'''






        __snake_case      : Union[str, Any]						=				kwargs.pop("audio" ,  _A      )
        __snake_case      : Tuple						=				kwargs.pop("text" ,  _A      )
        __snake_case      : Dict						=				kwargs.pop("text_target" ,  _A      )
        __snake_case      : List[Any]						=				kwargs.pop("audio_target" ,  _A      )
        __snake_case      : Optional[Any]						=				kwargs.pop("sampling_rate" ,  _A      )

        if audio is not None and text is not None:
          raise ValueError(
              "Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"      )
        if audio_target is not None and text_target is not None:
          raise ValueError(
              "Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"      )
        if audio is None and audio_target is None and text is None and text_target is None:
          raise ValueError(
              "You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."      )

        if audio is not None:
          __snake_case      : Tuple						=				self.feature_extractor(_A ,  *_A ,  sampling_rate=_A ,  **_A      )
        elif text is not None:
          __snake_case      : Any						=				self.tokenizer(_A ,  **_A      )
        else:
          __snake_case      : List[Any]						=				None

        if audio_target is not None:
          __snake_case      : Union[str, Any]						=				self.feature_extractor(audio_target=_A ,  *_A ,  sampling_rate=_A ,  **_A      )
          __snake_case      : Tuple						=				targets["input_values"]
        elif text_target is not None:
          __snake_case      : List[Any]						=				self.tokenizer(_A ,  **_A      )
          __snake_case      : List[Any]						=				targets["input_ids"]
        else:
          __snake_case      : str						=				None

        if inputs is None:
          return targets

        if targets is not None:
          __snake_case      : List[str]						=				labels

          __snake_case      : Optional[int]						=				targets.get("attention_mask"      )
          if decoder_attention_mask is not None:
            __snake_case      : List[str]						=				decoder_attention_mask

        return inputs







      def   UpperCAmelCase						(       self ,  *UpperCAmelCase ,  **UpperCAmelCase      )				->      Optional[Any]:






        '''simple docstring'''






        __snake_case      : List[Any]						=				kwargs.pop("input_values" ,  _A      )
        __snake_case      : Optional[int]						=				kwargs.pop("input_ids" ,  _A      )
        __snake_case      : Optional[int]						=				kwargs.pop("labels" ,  _A      )

        if input_values is not None and input_ids is not None:
          raise ValueError("Cannot process both `input_values` and `input_ids` inputs."      )
        if input_values is None and input_ids is None and labels is None:
          raise ValueError(
              "You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."      )

        if input_values is not None:
          __snake_case      : Optional[int]						=				self.feature_extractor.pad(_A ,  *_A ,  **_A      )
        elif input_ids is not None:
          __snake_case      : Optional[Any]						=				self.tokenizer.pad(_A ,  **_A      )
        else:
          __snake_case      : List[str]						=				None

        if labels is not None:
          if "input_ids" in labels or (isinstance(_A ,  _A      ) and "input_ids" in labels[0]):
            __snake_case      : Optional[int]						=				self.tokenizer.pad(_A ,  **_A      )
            __snake_case      : List[Any]						=				targets["input_ids"]
          else:
            __snake_case      : List[Any]						=				self.feature_extractor.feature_size
            __snake_case      : Optional[int]						=				self.feature_extractor.num_mel_bins
            __snake_case      : str						=				self.feature_extractor.pad(_A ,  *_A ,  **_A      )
            __snake_case      : Tuple						=				feature_size_hack
            __snake_case      : Optional[int]						=				targets["input_values"]
        else:
          __snake_case      : Optional[int]						=				None

        if inputs is None:
          return targets

        if targets is not None:
          __snake_case      : Dict						=				labels

          __snake_case      : Tuple						=				targets.get("attention_mask"      )
          if decoder_attention_mask is not None:
            __snake_case      : Any						=				decoder_attention_mask

        return inputs







      def   UpperCAmelCase						(       self ,  *UpperCAmelCase ,  **UpperCAmelCase      )				->      Dict:






        '''simple docstring'''






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







      def   UpperCAmelCase						(       self ,  *UpperCAmelCase ,  **UpperCAmelCase      )				->      Optional[Any]:






        '''simple docstring'''






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








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_5_0, '''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_0_0, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]			)
class 	A_       (unittest.TestCase			):
	def      _lowercase				(    self		):






				'''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=_A							,					)
				assert hasattr(self							,					'''env'''		)
	def      _lowercase				(    self							,					_A=1		):






				'''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=_A							,					instance_type=self.instance_type							,					debugger_hook_config=_A							,					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							,					_A		):






				'''simple docstring'''







				TrainingJobAnalytics(_A		).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""		)







	def      _lowercase				(    self		):






				'''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}							,					_A		)



