
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase  :					Dict													=       {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase  :					List[str]													=       ['GLPNFeatureExtractor']
		lowercase  :					Any													=       ['GLPNImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase  :					int													=       [
		    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'GLPNForDepthEstimation',
		    'GLPNLayer',
		    'GLPNModel',
		    'GLPNPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_glpn import GLPNFeatureExtractor
				from .image_processing_glpn import GLPNImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_glpn import (
				    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GLPNForDepthEstimation,
				    GLPNLayer,
				    GLPNModel,
				    GLPNPreTrainedModel,
				)


else:
		import sys

		lowercase  :					Any													=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

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  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     ComputeEnvironment.AMAZON_SAGEMAKER
	_A								=     True
	_A								=     'ml.p3.2xlarge'
	_A								=     'accelerate_sagemaker_execution_role'
	_A								=     'hf-sm'
	_A								=     'us-east-1'
	_A								=     1
	_A								=     'accelerate-sagemaker-1'
	_A								=     '1.6'
	_A								=     '4.4'
	_A								=     'train.py'
	_A								=     [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    'False',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]
	_A								=     [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    '--do_test',
	    'False',
	    '--do_predict',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]




class  lowerCamelCase__							(				unittest.TestCase):




	'''simple docstring'''




	def    _lowerCamelCase				(					self		:Any						)		-> Optional[int]:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								__UpperCamelCase	:   Optional[int]			   =					_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args						)
								assert isinstance(converted_args["model_name_or_path"]     ,   a						)
								assert isinstance(converted_args["do_train"]     ,   a						)
								assert isinstance(converted_args["epochs"]     ,   a						)
								assert isinstance(converted_args["learning_rate"]     ,   a						)
								assert isinstance(converted_args["max_steps"]     ,   a						)

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