
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''': 650, '''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''': 600, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]			)
class 					_lowerCamelCase			(   unittest.TestCase			):





     """simple docstring"""







     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[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=__SCREAMING_SNAKE_CASE    ,    )
           assert hasattr(self    ,    '''env'''	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE=1	)	-> Union[str, Any]:




           """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=__SCREAMING_SNAKE_CASE    ,    instance_type=self.instance_type    ,    debugger_hook_config=__SCREAMING_SNAKE_CASE    ,    hyperparameters={**self.env.hyperparameters, '''model_name_or_path''': self.model_name_or_path}    ,    metric_definitions=self.env.metric_definitions    ,    py_version='''py36'''    ,    )






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> List[Any]:




           """simple docstring"""







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






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					self.create_estimator()

           # run training
           estimator.fit()

           # result dataframe
           UpperCamelCase__     : str											=					TrainingJobAnalytics(estimator.latest_training_job.name	).dataframe()

           # extract kpis
           UpperCamelCase__     : Optional[Any]											=					list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']	)
           UpperCamelCase__     : Union[str, Any]											=					list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']	)
           # get train time from SageMaker job, this includes starting, preprocessing, stopping
           UpperCamelCase__     : Optional[Any]											=					(
               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}    ,    __SCREAMING_SNAKE_CASE	)




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
    from transformers import TvltImageProcessor

if is_speech_available():
    from transformers import TvltFeatureExtractor

from transformers import TvltProcessor




@require_torch
class 					_lowerCamelCase			(   unittest.TestCase			):





     """simple docstring"""







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




           """simple docstring"""







           UpperCamelCase__     : int											=					'''ZinengTang/tvlt-base'''
           UpperCamelCase__     : Optional[Any]											=					tempfile.mkdtemp()






     def  __SCREAMING_SNAKE_CASE      (				self    ,    **__SCREAMING_SNAKE_CASE	)	-> Tuple:




           """simple docstring"""







           return TvltImageProcessor.from_pretrained(self.checkpoint    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    **__SCREAMING_SNAKE_CASE	)	-> str:




           """simple docstring"""







           return TvltFeatureExtractor.from_pretrained(self.checkpoint    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           shutil.rmtree(self.tmpdirname	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					self.get_image_processor()
           UpperCamelCase__     : Tuple											=					self.get_feature_extractor()

           UpperCamelCase__     : Dict											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)
           processor.save_pretrained(self.tmpdirname	)
           UpperCamelCase__     : Optional[Any]											=					TvltProcessor.from_pretrained(self.tmpdirname	)

           self.assertIsInstance(processor.feature_extractor    ,    __SCREAMING_SNAKE_CASE	)
           self.assertIsInstance(processor.image_processor    ,    __SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[str]:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					self.get_image_processor()
           UpperCamelCase__     : int											=					self.get_feature_extractor()

           UpperCamelCase__     : Union[str, Any]											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : str											=					np.ones([1_2_0_0_0]	)

           UpperCamelCase__     : Dict											=					feature_extractor(__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)
           UpperCamelCase__     : Dict											=					processor(audio=__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)

           for key in audio_dict.keys():
                 self.assertAlmostEqual(audio_dict[key].sum()    ,    input_processor[key].sum()    ,    delta=1e-2	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					self.get_image_processor()
           UpperCamelCase__     : Tuple											=					self.get_feature_extractor()

           UpperCamelCase__     : str											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					np.ones([3, 2_2_4, 2_2_4]	)

           UpperCamelCase__     : Optional[int]											=					image_processor(__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)
           UpperCamelCase__     : Tuple											=					processor(images=__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)

           for key in image_dict.keys():
                 self.assertAlmostEqual(image_dict[key].sum()    ,    input_processor[key].sum()    ,    delta=1e-2	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					self.get_image_processor()
           UpperCamelCase__     : Optional[Any]											=					self.get_feature_extractor()

           UpperCamelCase__     : Union[str, Any]											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Dict											=					np.ones([1_2_0_0_0]	)
           UpperCamelCase__     : str											=					np.ones([3, 2_2_4, 2_2_4]	)

           UpperCamelCase__     : List[Any]											=					processor(audio=__SCREAMING_SNAKE_CASE    ,    images=__SCREAMING_SNAKE_CASE	)

           self.assertListEqual(list(inputs.keys()	)    ,    ['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']	)

           # test if it raises when no input is passed
           with pytest.raises(__SCREAMING_SNAKE_CASE	):
                 processor()






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           UpperCamelCase__     : int											=					self.get_image_processor()
           UpperCamelCase__     : str											=					self.get_feature_extractor()

           UpperCamelCase__     : Dict											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           self.assertListEqual(
               processor.model_input_names    ,    image_processor.model_input_names + feature_extractor.model_input_names    ,    msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''    ,    )


