


import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


lowerCAmelCase__					  =  get_logger(__name__)

class 				snake_case__(enum.Enum      ):

					"""simple docstring"""






					lowercase_								=		"""all_checks"""
					lowercase_								=		"""basic_checks"""
					lowercase_								=		"""no_checks"""






class 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""












class 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""












class 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""












class 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""












def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__=None						):

				"""simple docstring"""


				if expected_checksums is None:
								logger.info("Unable to verify checksums."						)
								return
				if len(set(lowerCamelCase__						) - set(lowerCamelCase__						)						) > 0:
								raise ExpectedMoreDownloadedFiles(str(set(lowerCamelCase__						) - set(lowerCamelCase__						)						)						)
				if len(set(lowerCamelCase__						) - set(lowerCamelCase__						)						) > 0:
								raise UnexpectedDownloadedFile(str(set(lowerCamelCase__						) - set(lowerCamelCase__						)						)						)
				lowercase__					:   int													=					[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
				lowercase__					:   str													=					" for " + verification_name if verification_name is not None else ""
				if len(lowerCamelCase__						) > 0:
								raise NonMatchingChecksumError(
								    F"""Checksums didn't match{for_verification_name}:\n"""
								    F"""{bad_urls}\n"""
								    "Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"						)
				logger.info("All the checksums matched successfully" + for_verification_name						)

class 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""












class 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""












class 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""












class 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""












def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				if expected_splits is None:
								logger.info("Unable to verify splits sizes."						)
								return
				if len(set(lowerCamelCase__						) - set(lowerCamelCase__						)						) > 0:
								raise ExpectedMoreSplits(str(set(lowerCamelCase__						) - set(lowerCamelCase__						)						)						)
				if len(set(lowerCamelCase__						) - set(lowerCamelCase__						)						) > 0:
								raise UnexpectedSplits(str(set(lowerCamelCase__						) - set(lowerCamelCase__						)						)						)
				lowercase__					:   Any													=					[
				    {"expected": expected_splits[name], "recorded": recorded_splits[name]}
				    for name in expected_splits
				    if expected_splits[name].num_examples != recorded_splits[name].num_examples
				]
				if len(lowerCamelCase__						) > 0:
								raise NonMatchingSplitsSizesError(str(lowerCamelCase__						)						)
				logger.info("All the splits matched successfully."						)


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__ = True						):

				"""simple docstring"""


				if record_checksum:
								lowercase__					:   List[str]													=					shaaaa()
								with open(lowerCamelCase__					,			"rb"						) as f:
												for chunk in iter(lambda: f.read(1 << 20						)					,			b""						):
																m.update(lowerCamelCase__						)
												lowercase__					:   str													=					m.hexdigest()
				else:
								lowercase__					:   Optional[int]													=					None
				return {"num_bytes": os.path.getsize(lowerCamelCase__						), "checksum": checksum}


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				if dataset_size and config.IN_MEMORY_MAX_SIZE:
								return dataset_size < config.IN_MEMORY_MAX_SIZE
				else:
								return False









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 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					def 			snake_case					(							self :     Union[str, Any]					):
									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 			snake_case					(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     str=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=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 			snake_case					(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     str					):
									TrainingJobAnalytics(SCREAMING_SNAKE_CASE					).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""					)







					def 			snake_case					(							self :     str					):
									# create estimator
									lowercase__					:   Optional[int]													=					self.create_estimator()

									# run training
									estimator.fit()

									# result dataframe
									lowercase__					:   Union[str, Any]													=					TrainingJobAnalytics(estimator.latest_training_job.name					).dataframe()

									# extract kpis
									lowercase__					:   str													=					list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]					)
									lowercase__					:   Tuple													=					list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]					)
									# get train time from SageMaker job, this includes starting, preprocessing, stopping
									lowercase__					:   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}				,       SCREAMING_SNAKE_CASE					)




