




import subprocess
import sys
import warnings
from argparse import ArgumentParser
from pathlib import Path

from packaging import version

from .. import AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer
from ..utils import logging
from ..utils.import_utils import is_optimum_available
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import get_preprocessor


lowercase_			:       Tuple						       =				'1.5.0'

lowercase_			:       int						       =				['vision-encoder-decoder']


def 							__SCREAMING_SNAKE_CASE						(    snake_case_		):

						'''simple docstring'''


						if is_optimum_available():
												from optimum.version import __version__ as optimum_version

												_UpperCAmelCase									=  version.parse(snake_case_		)
												if parsed_optimum_version < version.parse(snake_case_		):
																		raise RuntimeError(
																		    f"""transformers.onnx requires optimum >= {MIN_OPTIMUM_VERSION} but {optimum_version} is installed. You """
																		    "can upgrade optimum by running: pip install -U optimum[exporters]"		)
						else:
												raise RuntimeError(
												    "transformers.onnx requires optimum to run, you can install the library by running: pip install "
												    "optimum[exporters]"		)
						_UpperCAmelCase									=  [
						    sys.executable,
						    "-m",
						    "optimum.exporters.onnx",
						    f"""--model {args.model}""",
						    f"""--task {args.feature}""",
						    f"""--framework {args.framework}""" if args.framework is not None else "",
						    f"""{args.output}""",
						]
						_UpperCAmelCase									=  subprocess.Popen(" ".join(snake_case_		)			,					stdout=subprocess.PIPE			,					shell=snake_case_		)
						proc.wait()

						logger.info(
						    "The export was done by optimum.exporters.onnx. We recommend using to use this package directly in future, as "
						    "transformers.onnx is deprecated, and will be removed in v5. You can find more information here: "
						    "https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."		)


def 							__SCREAMING_SNAKE_CASE						(    snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase									=  args.output if args.output.is_file() else args.output.joinpath("model.onnx"		)
						if not args.output.parent.exists():
												args.output.parent.mkdir(parents=snake_case_		)

						# Allocate the model
						_UpperCAmelCase									=  FeaturesManager.get_model_from_feature(
						    args.feature			,					args.model			,					framework=args.framework			,					cache_dir=args.cache_dir		)

						_UpperCAmelCase							,				_UpperCAmelCase									=  FeaturesManager.check_supported_model_or_raise(snake_case_			,					feature=args.feature		)
						_UpperCAmelCase									=  model_onnx_config(model.config		)

						if model_kind in ENCODER_DECODER_MODELS:
												_UpperCAmelCase									=  model.get_encoder()
												_UpperCAmelCase									=  model.get_decoder()

												_UpperCAmelCase									=  onnx_config.get_encoder_config(encoder_model.config		)
												_UpperCAmelCase									=  onnx_config.get_decoder_config(
												    encoder_model.config			,					decoder_model.config			,					feature=args.feature		)

												if args.opset is None:
																		_UpperCAmelCase									=  max(encoder_onnx_config.default_onnx_opset			,					decoder_onnx_config.default_onnx_opset		)

												if args.opset < min(encoder_onnx_config.default_onnx_opset			,					decoder_onnx_config.default_onnx_opset		):
																		raise ValueError(
																		    f"""Opset {args.opset} is not sufficient to export {model_kind}. At least """
																		    f""" {min(encoder_onnx_config.default_onnx_opset			,					decoder_onnx_config.default_onnx_opset		)} is required."""		)

												_UpperCAmelCase									=  AutoFeatureExtractor.from_pretrained(args.model		)

												_UpperCAmelCase							,				_UpperCAmelCase									=  export(
												    snake_case_			,					snake_case_			,					snake_case_			,					args.opset			,					args.output.parent.joinpath("encoder_model.onnx"		)			,					)

												validate_model_outputs(
												    snake_case_			,					snake_case_			,					snake_case_			,					args.output.parent.joinpath("encoder_model.onnx"		)			,					snake_case_			,					args.atol if args.atol else encoder_onnx_config.atol_for_validation			,					)

												_UpperCAmelCase									=  AutoTokenizer.from_pretrained(args.model		)

												_UpperCAmelCase							,				_UpperCAmelCase									=  export(
												    snake_case_			,					snake_case_			,					snake_case_			,					args.opset			,					args.output.parent.joinpath("decoder_model.onnx"		)			,					)

												validate_model_outputs(
												    snake_case_			,					snake_case_			,					snake_case_			,					args.output.parent.joinpath("decoder_model.onnx"		)			,					snake_case_			,					args.atol if args.atol else decoder_onnx_config.atol_for_validation			,					)
												logger.info(
												    f"""All good, model saved at: {args.output.parent.joinpath("encoder_model.onnx"		).as_posix()},"""
												    f""" {args.output.parent.joinpath("decoder_model.onnx"		).as_posix()}"""		)

						else:
												# Instantiate the appropriate preprocessor
												if args.preprocessor == "auto":
																		_UpperCAmelCase									=  get_preprocessor(args.model		)
												elif args.preprocessor == "tokenizer":
																		_UpperCAmelCase									=  AutoTokenizer.from_pretrained(args.model		)
												elif args.preprocessor == "image_processor":
																		_UpperCAmelCase									=  AutoImageProcessor.from_pretrained(args.model		)
												elif args.preprocessor == "feature_extractor":
																		_UpperCAmelCase									=  AutoFeatureExtractor.from_pretrained(args.model		)
												elif args.preprocessor == "processor":
																		_UpperCAmelCase									=  AutoProcessor.from_pretrained(args.model		)
												else:
																		raise ValueError(f"""Unknown preprocessor type '{args.preprocessor}'"""		)

												# Ensure the requested opset is sufficient
												if args.opset is None:
																		_UpperCAmelCase									=  onnx_config.default_onnx_opset

												if args.opset < onnx_config.default_onnx_opset:
																		raise ValueError(
																		    f"""Opset {args.opset} is not sufficient to export {model_kind}. """
																		    f"""At least  {onnx_config.default_onnx_opset} is required."""		)

												_UpperCAmelCase							,				_UpperCAmelCase									=  export(
												    snake_case_			,					snake_case_			,					snake_case_			,					args.opset			,					args.output			,					)

												if args.atol is None:
																		_UpperCAmelCase									=  onnx_config.atol_for_validation

												validate_model_outputs(snake_case_			,					snake_case_			,					snake_case_			,					args.output			,					snake_case_			,					args.atol		)
												logger.info(f"""All good, model saved at: {args.output.as_posix()}"""		)
												warnings.warn(
												    "The export was done by transformers.onnx which is deprecated and will be removed in v5. We recommend"
												    " using optimum.exporters.onnx in future. You can find more information here:"
												    " https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."			,					snake_case_			,					)


def 							__SCREAMING_SNAKE_CASE						(    ):

						'''simple docstring'''


						_UpperCAmelCase									=  ArgumentParser("Hugging Face Transformers ONNX exporter"		)
						parser.add_argument(
						    "-m"			,					"--model"			,					type=snake_case_			,					required=snake_case_			,					help="Model ID on huggingface.co or path on disk to load model from."		)
						parser.add_argument(
						    "--feature"			,					default="default"			,					help="The type of features to export the model with."			,					)
						parser.add_argument("--opset"			,					type=snake_case_			,					default=snake_case_			,					help="ONNX opset version to export the model with."		)
						parser.add_argument(
						    "--atol"			,					type=snake_case_			,					default=snake_case_			,					help="Absolute difference tolerance when validating the model."		)
						parser.add_argument(
						    "--framework"			,					type=snake_case_			,					choices=["pt", "tf"]			,					default=snake_case_			,					help=(
						        "The framework to use for the ONNX export."
						        " If not provided, will attempt to use the local checkpoint's original framework"
						        " or what is available in the environment."
						    )			,					)
						parser.add_argument("output"			,					type=snake_case_			,					help="Path indicating where to store generated ONNX model."		)
						parser.add_argument("--cache_dir"			,					type=snake_case_			,					default=snake_case_			,					help="Path indicating where to store cache."		)
						parser.add_argument(
						    "--preprocessor"			,					type=snake_case_			,					choices=["auto", "tokenizer", "feature_extractor", "image_processor", "processor"]			,					default="auto"			,					help="Which type of preprocessor to use. 'auto' tries to automatically detect it."			,					)
						parser.add_argument(
						    "--export_with_transformers"			,					action="store_true"			,					help=(
						        "Whether to use transformers.onnx instead of optimum.exporters.onnx to perform the ONNX export. It can be "
						        "useful when exporting a model supported in transformers but not in optimum, otherwise it is not "
						        "recommended."
						    )			,					)

						_UpperCAmelCase									=  parser.parse_args()
						if args.export_with_transformers or not is_optimum_available():
												export_with_transformers(snake_case_		)
						else:
												export_with_optimum(snake_case_		)


if __name__ == "__main__":
				lowercase_			:       Union[str, Any]						       =				logging.get_logger('transformers.onnx')  # pylint: disable=invalid-name
				logger.setLevel(logging.INFO)
				main()










import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_			:       Tuple						       =				logging.get_logger(__name__)

lowercase_			:       List[Any]						       =				{
    'facebook/sam-vit-huge': 'https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json',
    'facebook/sam-vit-large': 'https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json',
    'facebook/sam-vit-base': 'https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json',
}






class 					__lowerCAmelCase				( UpperCAmelCase__				):







			def __init__( self					:    Optional[int]					,					snake_case__					:    int=256					,					snake_case__					:    Dict=1_024					,					snake_case__					:    Dict=16					,					snake_case__					:    Union[str, Any]=16					,					snake_case__					:    Any=4					,					snake_case__					:    Tuple="gelu"					,					snake_case__					:    Tuple=1e-6					,					**snake_case__					:    str					,					):




									"""simple docstring"""


									super().__init__(**snake_case__   )
									_UpperCAmelCase									=  hidden_size
									_UpperCAmelCase									=  image_size
									_UpperCAmelCase									=  patch_size
									_UpperCAmelCase									=  image_size // patch_size
									_UpperCAmelCase									=  mask_input_channels
									_UpperCAmelCase									=  num_point_embeddings
									_UpperCAmelCase									=  hidden_act
									_UpperCAmelCase									=  layer_norm_eps






class 					__lowerCAmelCase				( UpperCAmelCase__				):







			def __init__( self					:    str					,					snake_case__					:    List[str]=256					,					snake_case__					:    Tuple="relu"					,					snake_case__					:    int=2_048					,					snake_case__					:    Optional[int]=2					,					snake_case__					:    Dict=8					,					snake_case__					:    List[Any]=2					,					snake_case__					:    List[Any]=3					,					snake_case__					:    List[Any]=3					,					snake_case__					:    Optional[int]=256					,					snake_case__					:    Tuple=1e-6					,					**snake_case__					:    Any					,					):




									"""simple docstring"""


									super().__init__(**snake_case__   )
									_UpperCAmelCase									=  hidden_size
									_UpperCAmelCase									=  hidden_act
									_UpperCAmelCase									=  mlp_dim
									_UpperCAmelCase									=  num_hidden_layers
									_UpperCAmelCase									=  num_attention_heads
									_UpperCAmelCase									=  attention_downsample_rate
									_UpperCAmelCase									=  num_multimask_outputs
									_UpperCAmelCase									=  iou_head_depth
									_UpperCAmelCase									=  iou_head_hidden_dim
									_UpperCAmelCase									=  layer_norm_eps






class 					__lowerCAmelCase				( UpperCAmelCase__				):







			def __init__( self					:    List[str]					,					snake_case__					:    str=768					,					snake_case__					:    Dict=256					,					snake_case__					:    Any=12					,					snake_case__					:    Union[str, Any]=12					,					snake_case__					:    Any=3					,					snake_case__					:    List[str]=1_024					,					snake_case__					:    Union[str, Any]=16					,					snake_case__					:    Any="gelu"					,					snake_case__					:    List[str]=1e-06					,					snake_case__					:    Union[str, Any]=0.0					,					snake_case__					:    Optional[int]=1e-10					,					snake_case__					:    int=True					,					snake_case__					:    Dict=4.0					,					snake_case__					:    Optional[Any]=True					,					snake_case__					:    Tuple=True					,					snake_case__					:    List[Any]=14					,					snake_case__					:    List[str]=[2, 5, 8, 11]					,					snake_case__					:    List[Any]=128					,					snake_case__					:    List[Any]=None					,					**snake_case__					:    Any					,					):




									"""simple docstring"""


									super().__init__(**snake_case__   )

									_UpperCAmelCase									=  hidden_size
									_UpperCAmelCase									=  output_channels
									_UpperCAmelCase									=  num_hidden_layers
									_UpperCAmelCase									=  num_attention_heads
									_UpperCAmelCase									=  num_channels
									_UpperCAmelCase									=  image_size
									_UpperCAmelCase									=  patch_size
									_UpperCAmelCase									=  hidden_act
									_UpperCAmelCase									=  layer_norm_eps
									_UpperCAmelCase									=  attention_dropout
									_UpperCAmelCase									=  initializer_range
									_UpperCAmelCase									=  qkv_bias
									_UpperCAmelCase									=  mlp_ratio
									_UpperCAmelCase									=  use_abs_pos
									_UpperCAmelCase									=  use_rel_pos
									_UpperCAmelCase									=  window_size
									_UpperCAmelCase									=  global_attn_indexes
									_UpperCAmelCase									=  num_pos_feats
									_UpperCAmelCase									=  int(hidden_size * mlp_ratio   ) if mlp_dim is None else mlp_dim






class 					__lowerCAmelCase				( UpperCAmelCase__				):
			snake_case_   :				Tuple				  =  "sam"
			snake_case_   :				Tuple				  =  True







			def __init__( self					:    Optional[int]					,					snake_case__					:    Optional[Any]=None					,					snake_case__					:    Any=None					,					snake_case__					:    int=None					,					snake_case__					:    List[Any]=0.02					,					**snake_case__					:    str					,					):




									"""simple docstring"""


									super().__init__(**snake_case__   )
									_UpperCAmelCase									=  vision_config if vision_config is not None else {}
									_UpperCAmelCase									=  prompt_encoder_config if prompt_encoder_config is not None else {}
									_UpperCAmelCase									=  mask_decoder_config if mask_decoder_config is not None else {}

									if isinstance(snake_case__					,					snake_case__   ):
															_UpperCAmelCase									=  vision_config.to_dict()
									if isinstance(snake_case__					,					snake_case__   ):
															_UpperCAmelCase									=  prompt_encoder_config.to_dict()
									if isinstance(snake_case__					,					snake_case__   ):
															_UpperCAmelCase									=  mask_decoder_config.to_dict()

									_UpperCAmelCase									=  SamVisionConfig(**snake_case__   )
									_UpperCAmelCase									=  SamPromptEncoderConfig(**snake_case__   )
									_UpperCAmelCase									=  SamMaskDecoderConfig(**snake_case__   )
									_UpperCAmelCase									=  initializer_range







			def UpperCamelCase    ( self					:    Any   ):




									"""simple docstring"""


									_UpperCAmelCase									=  copy.deepcopy(self.__dict__   )
									_UpperCAmelCase									=  self.vision_config.to_dict()
									_UpperCAmelCase									=  self.prompt_encoder_config.to_dict()
									_UpperCAmelCase									=  self.mask_decoder_config.to_dict()
									_UpperCAmelCase									=  self.__class__.model_type
									return output




