


"""simple docstring"""





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 		snake_case_(       unittest.TestCase			):






     def      lowerCamelCase__     (	self  :					List[str]				):
           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      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					Dict=1				):
           # 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}-single'''					,				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}					,				metric_definitions=self.env.metric_definitions					,				py_version='''py36'''					,				)






     def      lowerCamelCase__     (	self  :					List[Any]					,				UpperCamelCase_  :					List[str]				):
           TrainingJobAnalytics(UpperCamelCase_				).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''				)






     def      lowerCamelCase__     (	self  :					List[Any]				):
           # create estimator
           lowerCAmelCase					:	int       =   self.create_estimator()

           # run training
           estimator.fit()

           # result dataframe
           lowerCAmelCase					:	int       =   TrainingJobAnalytics(estimator.latest_training_job.name				).dataframe()

           # extract kpis
           lowerCAmelCase					:	Tuple       =   list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']				)
           lowerCAmelCase					:	Dict       =   list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']				)
           # get train time from SageMaker job, this includes starting, preprocessing, stopping
           lowerCAmelCase					:	Optional[int]       =   (
               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_				)






"""simple docstring"""





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool



class 		snake_case_(       a__			):
     __UpperCamelCase  =					'''philschmid/bart-large-cnn-samsum'''
     __UpperCamelCase  =					(
         '''This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '''
         '''and returns a summary of the text.'''
     )
     __UpperCamelCase  =					'''summarizer'''
     __UpperCamelCase  =					AutoTokenizer
     __UpperCamelCase  =					AutoModelForSeqaSeqLM

     __UpperCamelCase  =					['''text''']
     __UpperCamelCase  =					['''text''']






     def      lowerCamelCase__     (	self  :					Dict					,				UpperCamelCase_  :					int				):
           return self.pre_processor(UpperCamelCase_					,				return_tensors='''pt'''					,				truncation=UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					str				):
           return self.model.generate(**UpperCamelCase_				)[0]






     def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					Tuple				):
           return self.pre_processor.decode(UpperCamelCase_					,				skip_special_tokens=UpperCamelCase_					,				clean_up_tokenization_spaces=UpperCamelCase_				)


