from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase__      :    Dict          =     {
    '''configuration_sam''': [
        '''SAM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SamConfig''',
        '''SamMaskDecoderConfig''',
        '''SamPromptEncoderConfig''',
        '''SamVisionConfig''',
    ],
    '''processing_sam''': ['''SamProcessor'''],
}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase__      :    Dict          =     [
	    '''SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''SamModel''',
	    '''SamPreTrainedModel''',
	]
try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase__      :    str          =     [
	    '''TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFSamModel''',
	    '''TFSamPreTrainedModel''',
	]
try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase__      :    Dict          =     ['''SamImageProcessor''']


if TYPE_CHECKING:
	from .configuration_sam import (
	    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    SamConfig,
	    SamMaskDecoderConfig,
	    SamPromptEncoderConfig,
	    SamVisionConfig,
	)
	from .processing_sam import SamProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_sam import SamImageProcessor

else:
	import sys

	lowercase__      :    Dict          =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json




class        lowercase_  (  UpperCamelCase_						):



	"""simple docstring"""





	@classmethod
	def       SCREAMING_SNAKE_CASE_    (							cls    )						->Union[str, Any]:
								return cls




	@slow
	@require_torch_gpu
	def       SCREAMING_SNAKE_CASE_    (							self    )						->int:
								lowerCAmelCase     		=		self.get_auto_remove_tmp_dir()
								lowerCAmelCase     		=		F"\n            --model_name Helsinki-NLP/opus-mt-en-ro\n            --save_dir {output_dir}\n            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro\n            --num_beams 2\n            --task translation\n        ".split()

								# we want this test to run even if there is only one GPU, but if there are more we use them all
								lowerCAmelCase     		=		get_gpu_count()
								lowerCAmelCase     		=		F"\n            -m torch.distributed.launch\n            --nproc_per_node={n_gpu}\n            {self.test_file_dir}/run_distributed_eval.py\n        ".split()
								lowerCAmelCase     		=		[sys.executable] + distributed_args + args
								execute_subprocess_async(__SCREAMING_SNAKE_CASE		,      env=self.get_env()    )

								lowerCAmelCase     		=		os.path.join(__SCREAMING_SNAKE_CASE		,      '''test_bleu.json'''    )
								lowerCAmelCase     		=		load_json(__SCREAMING_SNAKE_CASE    )
								# print(metrics)
								self.assertGreaterEqual(metrics['''bleu''']		,      25    )



