# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


__UpperCamelCase	: Dict    		=  logging.get_logger(__name__)

__UpperCamelCase	: Dict[Optional[str], Type[Formatter]]    		=  {}
__UpperCamelCase	: Dict[Optional[str], str]    		=  {}
__UpperCamelCase	: Dict[Optional[str], Exception]    		=  {}
def    A			(					_lowercase	,						_lowercase	,						_lowercase = None	,						):
						SCREAMING_SNAKE_CASE							:					List[Any]           =							aliases if aliases is not None else []
						if format_type in _FORMAT_TYPES:
												logger.warning(
												    f"""Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"""		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							formatter_cls
						for alias in set(aliases + [format_type]		):
												if alias in _FORMAT_TYPES_ALIASES:
																		logger.warning(
																		    f"""Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"""		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							format_type
def    A			(					_lowercase	,						_lowercase	,						_lowercase = None		):
						SCREAMING_SNAKE_CASE							:					Dict           =							aliases if aliases is not None else []
						for alias in set(aliases + [format_type]		):
												SCREAMING_SNAKE_CASE							:					Tuple           =							unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['python'])
_register_formatter(ArrowFormatter, 'arrow', aliases=['pa', 'pyarrow'])
_register_formatter(NumpyFormatter, 'numpy', aliases=['np'])
_register_formatter(PandasFormatter, 'pandas', aliases=['pd'])
_register_formatter(CustomFormatter, 'custom')

if config.TORCH_AVAILABLE:
					from .torch_formatter import TorchFormatter

					_register_formatter(TorchFormatter, 'torch', aliases=['pt', 'pytorch'])
else:
					__UpperCamelCase	: int    		=  ValueError('PyTorch needs to be installed to be able to return PyTorch tensors.')
					_register_unavailable_formatter(_torch_error, 'torch', aliases=['pt', 'pytorch'])

if config.TF_AVAILABLE:
					from .tf_formatter import TFFormatter

					_register_formatter(TFFormatter, 'tensorflow', aliases=['tf'])
else:
					__UpperCamelCase	: Union[str, Any]    		=  ValueError('Tensorflow needs to be installed to be able to return Tensorflow tensors.')
					_register_unavailable_formatter(_tf_error, 'tensorflow', aliases=['tf'])

if config.JAX_AVAILABLE:
					from .jax_formatter import JaxFormatter

					_register_formatter(JaxFormatter, 'jax', aliases=[])
else:
					__UpperCamelCase	: str    		=  ValueError('JAX needs to be installed to be able to return JAX arrays.')
					_register_unavailable_formatter(_jax_error, 'jax', aliases=[])
def    A			(					_lowercase		):
						if format_type in _FORMAT_TYPES_ALIASES:
												return _FORMAT_TYPES_ALIASES[format_type]
						else:
												return format_type



def    A			(					_lowercase	,						**_lowercase		):
						SCREAMING_SNAKE_CASE							:					Dict           =							get_format_type_from_alias(_lowercase		)
						if format_type in _FORMAT_TYPES:
												return _FORMAT_TYPES[format_type](**_lowercase		)
						if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
												raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
						else:
												raise ValueError(
												    f"""Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None		)}, but got '{format_type}'"""		)






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.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 650, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_ddp.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf_dist.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.6, """eval_loss""": 0.7},
        },
    ])
class       lowercase__		(     unittest.TestCase):


						def  __A ( self			:       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=UpperCamelCase__					,	)
												assert hasattr(self					,	'''env'''		)


						def  __A ( self			:       str					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							f"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
												# distributed data settings
												SCREAMING_SNAKE_CASE							:					Any           =							{'''smdistributed''': {'''dataparallel''': {'''enabled''': True}}} if self.script != '''run_ddp.py''' else None

												# 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=UpperCamelCase__					,	instance_count=UpperCamelCase__					,	instance_type=self.instance_type					,	debugger_hook_config=UpperCamelCase__					,	hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path}					,	metric_definitions=self.env.metric_definitions					,	distribution=UpperCamelCase__					,	py_version='''py36'''					,	)


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       List[str]		):





												'''simple docstring'''





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



						@parameterized.expand([(2,)]		)
						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       Optional[int]		):





												'''simple docstring'''





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

												# run training
												estimator.fit()

												# result dataframe
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							TrainingJobAnalytics(estimator.latest_training_job.name		).dataframe()

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





