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''': 1_6_0_0, '''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''': 1_6_0_0, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
    ]    )
class  __magic_name__  (		unittest.TestCase    ):





   """simple docstring"""




   def   lowerCAmelCase						(	self					:  List[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=_lowercase		,  )
    assert hasattr(self		,  '''env'''							)



   def   lowerCAmelCase						(	self					:  Dict		,  _lowercase					:  Any							):



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= {
        '''enabled''': True,
        '''processes_per_host''': 8,
    }
    _UpperCamelCase:     List[Any]							= {
        '''enabled''': True,
        '''parameters''': {
            '''microbatches''': 4,
            '''placement_strategy''': '''spread''',
            '''pipeline''': '''interleaved''',
            '''optimize''': '''speed''',
            '''partitions''': 4,
            '''ddp''': True,
        },
    }

    _UpperCamelCase:     Optional[int]							= {'''smdistributed''': {'''modelparallel''': smp_options}, '''mpi''': mpi_options}

    _UpperCamelCase:     str							= '''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=_lowercase		,  instance_type=self.instance_type		,  debugger_hook_config=_lowercase		,  hyperparameters={
            **self.env.hyperparameters,
            '''model_name_or_path''': self.model_name_or_path,
            '''max_steps''': 500,
        }		,  metric_definitions=self.env.metric_definitions		,  distribution=_lowercase		,  py_version='''py36'''		,  )



   def   lowerCAmelCase						(	self					:  Optional[Any]		,  _lowercase					:  Any							):



    """simple docstring"""



    TrainingJobAnalytics(_lowercase							).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""							)



   @parameterized.expand([(1,)]							)
   def   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  Union[str, Any]							):



    """simple docstring"""



    _UpperCamelCase:     str							= self.create_estimator(_lowercase							)

    # run training
    estimator.fit()

    # result dataframe
    _UpperCamelCase:     Any							= TrainingJobAnalytics(estimator.latest_training_job.name							).dataframe()

    # extract kpis
    _UpperCamelCase:     Dict							= list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']							)
    _UpperCamelCase:     Optional[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:     Any							= (
        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}		,  _lowercase							)
# 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  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    Optional[Any]  			=   '''philschmid/bart-large-cnn-samsum'''
   lowerCAmelCase      :    Any  			=   (
       '''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.'''
   )
   lowerCAmelCase      :    Any  			=   '''summarizer'''
   lowerCAmelCase      :    Tuple  			=   AutoTokenizer
   lowerCAmelCase      :    Optional[Any]  			=   AutoModelForSeqaSeqLM

   lowerCAmelCase      :    Union[str, Any]  			=   ['''text''']
   lowerCAmelCase      :    Dict  			=   ['''text''']



   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  Union[str, Any]							):



    """simple docstring"""



    return self.pre_processor(_lowercase		,  return_tensors='''pt'''		,  truncation=_lowercase							)



   def   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  Optional[Any]							):



    """simple docstring"""



    return self.model.generate(**_lowercase							)[0]



   def   lowerCAmelCase						(	self					:  int		,  _lowercase					:  List[str]							):



    """simple docstring"""



    return self.pre_processor.decode(_lowercase		,  skip_special_tokens=_lowercase		,  clean_up_tokenization_spaces=_lowercase							)