




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 						lowercase							(   unittest.TestCase		):

     def 					a__	(  self				)						->							Optional[int]:
           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 					a__	(  self		,     _a				)						->							List[Any]:
           # configuration for running training on smdistributed Model Parallel
           _A							:     Optional[Any]			=						{
               """enabled""": True,
               """processes_per_host""": 8,
           }
           _A							:     List[Any]			=						{
               """enabled""": True,
               """parameters""": {
                   """microbatches""": 4,
                   """placement_strategy""": """spread""",
                   """pipeline""": """interleaved""",
                   """optimize""": """speed""",
                   """partitions""": 4,
                   """ddp""": True,
               },
           }

           _A							:     Optional[Any]			=						{"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

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

     def 					a__	(  self		,     _a				)						->							Optional[int]:
           TrainingJobAnalytics(_a				).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''				)



     @parameterized.expand([(1,)]				)
     def 					a__	(  self		,     _a				)						->							Union[str, Any]:
           # create estimator
           _A							:     str			=						self.create_estimator(_a				)

           # run training
           estimator.fit()

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

           # extract kpis
           _A							:     Any			=						list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]				)
           _A							:     List[Any]			=						list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]				)
           # get train time from SageMaker job, this includes starting, preprocessing, stopping
           _A							:     Tuple			=						(
               Session().describe_training_job(estimator.latest_training_job.name				).get("""TrainingTimeInSeconds"""		,     99_9999				)
           )

           # 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				)







import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
  import torch







@is_pipeline_test
class 						lowercase							(   unittest.TestCase		):
     _a							 =						MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     _a							 =						TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

     def 					a__	(  self		,     _a		,     _a		,     _a				)						->							int:
           _A							:     str			=						TextaTextGenerationPipeline(model=_a		,     tokenizer=_a				)
           return generator, ["Something to write", "Something else"]

     def 					a__	(  self		,     _a		,     _a				)						->							Dict:
           _A							:     Any			=						generator("""Something there"""				)
           self.assertEqual(_a		,     [{"""generated_text""": ANY(_a				)}]				)
           # These are encoder decoder, they don't just append to incoming string
           self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there"""				)				)

           _A							:     List[Any]			=						generator(["""This is great !""", """Something else"""]		,     num_return_sequences=2		,     do_sample=_a				)
           self.assertEqual(
               _a		,     [
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
               ]		,     )

           _A							:     Optional[int]			=						generator(
               ["""This is great !""", """Something else"""]		,     num_return_sequences=2		,     batch_size=2		,     do_sample=_a				)
           self.assertEqual(
               _a		,     [
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
               ]		,     )

           with self.assertRaises(_a				):
                 generator(4				)

     @require_torch
     def 					a__	(  self				)						->							List[str]:
           _A							:     Any			=						pipeline("""text2text-generation"""		,     model="""patrickvonplaten/t5-tiny-random"""		,     framework="""pt"""				)
           # do_sample=False necessary for reproducibility
           _A							:     Dict			=						generator("""Something there"""		,     do_sample=_a				)
           self.assertEqual(_a		,     [{"""generated_text""": """"""}]				)

           _A							:     Any			=						3
           _A							:     Any			=						generator(
               """Something there"""		,     num_return_sequences=_a		,     num_beams=_a		,     )
           _A							:     Optional[int]			=						[
               {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
               {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
               {"""generated_text""": """"""},
           ]
           self.assertEqual(_a		,     _a				)

           _A							:     Dict			=						generator("""This is a test"""		,     do_sample=_a		,     num_return_sequences=2		,     return_tensors=_a				)
           self.assertEqual(
               _a		,     [
                   {"""generated_token_ids""": ANY(torch.Tensor				)},
                   {"""generated_token_ids""": ANY(torch.Tensor				)},
               ]		,     )
           _A							:     Dict			=						generator.model.config.eos_token_id
           _A							:     List[str]			=						"""<pad>"""
           _A							:     Dict			=						generator(
               ["""This is a test""", """This is a second test"""]		,     do_sample=_a		,     num_return_sequences=2		,     batch_size=2		,     return_tensors=_a		,     )
           self.assertEqual(
               _a		,     [
                   [
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                   ],
                   [
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                   ],
               ]		,     )



     @require_tf
     def 					a__	(  self				)						->							int:
           _A							:     Optional[Any]			=						pipeline("""text2text-generation"""		,     model="""patrickvonplaten/t5-tiny-random"""		,     framework="""tf"""				)
           # do_sample=False necessary for reproducibility
           _A							:     str			=						generator("""Something there"""		,     do_sample=_a				)
           self.assertEqual(_a		,     [{"""generated_text""": """"""}]				)

