



# 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


lowerCamelCase__			= logging.get_logger(__name__)

lowerCamelCase__			= {}
lowerCamelCase__			= {}
lowerCamelCase__			= {}

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			type				,     _SCREAMING_SNAKE_CASE       :			Optional[str]				,     _SCREAMING_SNAKE_CASE       :			Optional[List[str]] = None				,     ):
    """simple docstring"""







    __a       =		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__})"    )
    __a       =		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})"    )
        __a       =		format_type

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Exception				,     _SCREAMING_SNAKE_CASE       :			Optional[str]				,     _SCREAMING_SNAKE_CASE       :			Optional[List[str]] = None    ):
    """simple docstring"""







    __a       =		aliases if aliases is not None else []
    for alias in set(aliases + [format_type]    ):
        __a       =		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:
      lowerCamelCase__			= 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:
      lowerCamelCase__			= 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:
      lowerCamelCase__			= ValueError("""JAX needs to be installed to be able to return JAX arrays.""")
      _register_unavailable_formatter(_jax_error, """jax""", aliases=[])

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Optional[str]    ):
    """simple docstring"""







    if format_type in _FORMAT_TYPES_ALIASES:
        return _FORMAT_TYPES_ALIASES[format_type]
    else:
        return format_type

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Optional[str]				,     **_SCREAMING_SNAKE_CASE       :			Union[str, Any]    ):
    """simple docstring"""







    __a       =		get_format_type_from_alias(_SCREAMING_SNAKE_CASE    )
    if format_type in _FORMAT_TYPES:
        return _FORMAT_TYPES[format_type](**_SCREAMING_SNAKE_CASE    )
    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 unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict






@dataclass
class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	List[Any]    =ComputeEnvironment.AMAZON_SAGEMAKER
							__lowerCamelCase	:	Optional[int]    =True
							__lowerCamelCase	:	List[Any]    ='ml.p3.2xlarge'
							__lowerCamelCase	:	Optional[int]    ='accelerate_sagemaker_execution_role'
							__lowerCamelCase	:	Optional[Any]    ='hf-sm'
							__lowerCamelCase	:	Any    ='us-east-1'
							__lowerCamelCase	:	Any    =1
							__lowerCamelCase	:	List[str]    ='accelerate-sagemaker-1'
							__lowerCamelCase	:	Union[str, Any]    ='1.6'
							__lowerCamelCase	:	str    ='4.4'
							__lowerCamelCase	:	List[str]    ='train.py'
							__lowerCamelCase	:	List[Any]    =[
							    '--model_name_or_path',
							    'bert',
							    '--do_train',
							    'False',
							    '--epochs',
							    '3',
							    '--learning_rate',
							    '5e-5',
							    '--max_steps',
							    '50.5',
							]
							__lowerCamelCase	:	Tuple    =[
							    '--model_name_or_path',
							    'bert',
							    '--do_train',
							    '--do_test',
							    'False',
							    '--do_predict',
							    '--epochs',
							    '3',
							    '--learning_rate',
							    '5e-5',
							    '--max_steps',
							    '50.5',
							]






class    SCREAMING_SNAKE_CASE			(							unittest.TestCase					):





							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											# If no defaults are changed, `to_kwargs` returns an empty dict.
											__a       =		_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args	)
											assert isinstance(converted_args["""model_name_or_path"""]      ,				__lowercase	)
											assert isinstance(converted_args["""do_train"""]      ,				__lowercase	)
											assert isinstance(converted_args["""epochs"""]      ,				__lowercase	)
											assert isinstance(converted_args["""learning_rate"""]      ,				__lowercase	)
											assert isinstance(converted_args["""max_steps"""]      ,				__lowercase	)

											with pytest.raises(__lowercase	):
															_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args	)
