


"""simple docstring"""






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


_lowerCamelCase  =	"1.5.0"

_lowerCamelCase  =	["vision-encoder-decoder"]


def __lowercase  (   lowerCamelCase_	:   int	):
				if is_optimum_available():
								from optimum.version import __version__ as optimum_version

								SCREAMING_SNAKE_CASE__  				=					version.parse(lowerCamelCase_	)
								if parsed_optimum_version < version.parse(lowerCamelCase_	):
												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]"	)
				SCREAMING_SNAKE_CASE__  				=					[
				    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}''',
				]
				SCREAMING_SNAKE_CASE__  				=					subprocess.Popen(" ".join(lowerCamelCase_	)    ,       stdout=subprocess.PIPE    ,       shell=lowerCamelCase_	)
				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 __lowercase  (   lowerCamelCase_	:   Dict	):
				SCREAMING_SNAKE_CASE__  				=					args.output if args.output.is_file() else args.output.joinpath("model.onnx"	)
				if not args.output.parent.exists():
								args.output.parent.mkdir(parents=lowerCamelCase_	)

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

				SCREAMING_SNAKE_CASE__  				=					FeaturesManager.check_supported_model_or_raise(lowerCamelCase_    ,       feature=args.feature	)
				SCREAMING_SNAKE_CASE__  				=					model_onnx_config(model.config	)

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

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

								if args.opset is None:
												SCREAMING_SNAKE_CASE__  				=					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.'''	)

								SCREAMING_SNAKE_CASE__  				=					AutoFeatureExtractor.from_pretrained(args.model	)

								SCREAMING_SNAKE_CASE__  				=					export(
								    lowerCamelCase_    ,       lowerCamelCase_    ,       lowerCamelCase_    ,       args.opset    ,       args.output.parent.joinpath("encoder_model.onnx"	)    ,       )

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

								SCREAMING_SNAKE_CASE__  				=					AutoTokenizer.from_pretrained(args.model	)

								SCREAMING_SNAKE_CASE__  				=					export(
								    lowerCamelCase_    ,       lowerCamelCase_    ,       lowerCamelCase_    ,       args.opset    ,       args.output.parent.joinpath("decoder_model.onnx"	)    ,       )

								validate_model_outputs(
								    lowerCamelCase_    ,       lowerCamelCase_    ,       lowerCamelCase_    ,       args.output.parent.joinpath("decoder_model.onnx"	)    ,       lowerCamelCase_    ,       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":
												SCREAMING_SNAKE_CASE__  				=					get_preprocessor(args.model	)
								elif args.preprocessor == "tokenizer":
												SCREAMING_SNAKE_CASE__  				=					AutoTokenizer.from_pretrained(args.model	)
								elif args.preprocessor == "image_processor":
												SCREAMING_SNAKE_CASE__  				=					AutoImageProcessor.from_pretrained(args.model	)
								elif args.preprocessor == "feature_extractor":
												SCREAMING_SNAKE_CASE__  				=					AutoFeatureExtractor.from_pretrained(args.model	)
								elif args.preprocessor == "processor":
												SCREAMING_SNAKE_CASE__  				=					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:
												SCREAMING_SNAKE_CASE__  				=					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.'''	)

								SCREAMING_SNAKE_CASE__  				=					export(
								    lowerCamelCase_    ,       lowerCamelCase_    ,       lowerCamelCase_    ,       args.opset    ,       args.output    ,       )

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

								validate_model_outputs(lowerCamelCase_    ,       lowerCamelCase_    ,       lowerCamelCase_    ,       args.output    ,       lowerCamelCase_    ,       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."    ,       lowerCamelCase_    ,       )


def __lowercase  (   ):
				SCREAMING_SNAKE_CASE__  				=					ArgumentParser("Hugging Face Transformers ONNX exporter"	)
				parser.add_argument(
				    "-m"    ,       "--model"    ,       type=lowerCamelCase_    ,       required=lowerCamelCase_    ,       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=lowerCamelCase_    ,       default=lowerCamelCase_    ,       help="ONNX opset version to export the model with."	)
				parser.add_argument(
				    "--atol"    ,       type=lowerCamelCase_    ,       default=lowerCamelCase_    ,       help="Absolute difference tolerance when validating the model."	)
				parser.add_argument(
				    "--framework"    ,       type=lowerCamelCase_    ,       choices=["pt", "tf"]    ,       default=lowerCamelCase_    ,       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=lowerCamelCase_    ,       help="Path indicating where to store generated ONNX model."	)
				parser.add_argument("--cache_dir"    ,       type=lowerCamelCase_    ,       default=lowerCamelCase_    ,       help="Path indicating where to store cache."	)
				parser.add_argument(
				    "--preprocessor"    ,       type=lowerCamelCase_    ,       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."
				    )    ,       )

				SCREAMING_SNAKE_CASE__  				=					parser.parse_args()
				if args.export_with_transformers or not is_optimum_available():
								export_with_transformers(lowerCamelCase_	)
				else:
								export_with_optimum(lowerCamelCase_	)


if __name__ == "__main__":
	_lowerCamelCase  =	logging.get_logger('transformers.onnx')  # pylint: disable=invalid-name
	logger.setLevel(logging.INFO)
	main()








'''simple docstring'''

import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


A:   str						=					get_logger(__name__)

# Datasets
A:   Optional[Any]						=					"https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets"
A:   Any						=					"https://cdn-datasets.huggingface.co/datasets/datasets"
A:   Dict						=					"https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}"

# Metrics
A:   List[str]						=					"https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics"
A:   Any						=					"https://cdn-datasets.huggingface.co/datasets/metric"
A:   List[str]						=					"https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}"

# Hub
A:   str						=					os.environ.get("HF_ENDPOINT", "https://huggingface.co")
A:   List[Any]						=					HF_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
A:   List[Any]						=					"main"

A:   Any						=					version.parse(platform.python_version())

if PY_VERSION < version.parse("3.8"):
						import importlib_metadata
else:
						import importlib.metadata as importlib_metadata

# General environment variables accepted values for booleans
A:   Optional[Any]						=					{"1", "ON", "YES", "TRUE"}
A:   Optional[int]						=					ENV_VARS_TRUE_VALUES.union({"AUTO"})


# Imports
A:   Any						=					version.parse(importlib_metadata.version("dill"))
A:   str						=					version.parse(importlib_metadata.version("pandas"))
A:   Tuple						=					version.parse(importlib_metadata.version("pyarrow"))

A:   str						=					os.environ.get("USE_TF", "AUTO").upper()
A:   List[Any]						=					os.environ.get("USE_TORCH", "AUTO").upper()
A:   Union[str, Any]						=					os.environ.get("USE_JAX", "AUTO").upper()

A:   Dict						=					"N/A"
A:   Optional[int]						=					False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
						A:   List[str]						=					importlib.util.find_spec("torch") is not None
						if TORCH_AVAILABLE:
												try:
																		A:   List[str]						=					version.parse(importlib_metadata.version("torch"))
																		logger.info(f"""PyTorch version {TORCH_VERSION} available.""")
												except importlib_metadata.PackageNotFoundError:
																		pass
else:
						logger.info("Disabling PyTorch because USE_TF is set")

A:   Union[str, Any]						=					"N/A"
A:   Dict						=					False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
						A:   Optional[Any]						=					importlib.util.find_spec("tensorflow") is not None
						if TF_AVAILABLE:
												# For the metadata, we have to look for both tensorflow and tensorflow-cpu
												for package in [
												    "tensorflow",
												    "tensorflow-cpu",
												    "tensorflow-gpu",
												    "tf-nightly",
												    "tf-nightly-cpu",
												    "tf-nightly-gpu",
												    "intel-tensorflow",
												    "tensorflow-rocm",
												    "tensorflow-macos",
												]:
																		try:
																								A:   Any						=					version.parse(importlib_metadata.version(package))
																		except importlib_metadata.PackageNotFoundError:
																								continue
																		else:
																								break
												else:
																		A:   Any						=					False
						if TF_AVAILABLE:
												if TF_VERSION.major < 2:
																		logger.info(f"""TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.""")
																		A:   Tuple						=					False
												else:
																		logger.info(f"""TensorFlow version {TF_VERSION} available.""")
else:
						logger.info("Disabling Tensorflow because USE_TORCH is set")


A:   Any						=					"N/A"
A:   int						=					False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
						A:   Dict						=					importlib.util.find_spec("jax") is not None and importlib.util.find_spec("jaxlib") is not None
						if JAX_AVAILABLE:
												try:
																		A:   List[str]						=					version.parse(importlib_metadata.version("jax"))
																		logger.info(f"""JAX version {JAX_VERSION} available.""")
												except importlib_metadata.PackageNotFoundError:
																		pass
else:
						logger.info("Disabling JAX because USE_JAX is set to False")


A:   Optional[Any]						=					os.environ.get("USE_BEAM", "AUTO").upper()
A:   Any						=					"N/A"
A:   List[Any]						=					False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
						try:
												A:   Tuple						=					version.parse(importlib_metadata.version("apache_beam"))
												A:   Dict						=					True
												logger.info(f"""Apache Beam version {BEAM_VERSION} available.""")
						except importlib_metadata.PackageNotFoundError:
												pass
else:
						logger.info("Disabling Apache Beam because USE_BEAM is set to False")


# Optional tools for data loading
A:   Union[str, Any]						=					importlib.util.find_spec("sqlalchemy") is not None

# Optional tools for feature decoding
A:   Optional[int]						=					importlib.util.find_spec("PIL") is not None
A:   Dict						=					importlib.util.find_spec("soundfile") is not None and version.parse(
    importlib.import_module("soundfile").__libsndfile_version__
) >= version.parse("1.0.31")
A:   Any						=					importlib.util.find_spec("soundfile") is not None and version.parse(
    importlib.import_module("soundfile").__libsndfile_version__
) >= version.parse("1.1.0")

# Optional compression tools
A:   Optional[Any]						=					importlib.util.find_spec("rarfile") is not None
A:   Any						=					importlib.util.find_spec("zstandard") is not None
A:   Union[str, Any]						=					importlib.util.find_spec("lz4") is not None
A:   str						=					importlib.util.find_spec("py7zr") is not None


# Cache location
A:   Any						=					"~/.cache"
A:   Tuple						=					os.getenv("XDG_CACHE_HOME", DEFAULT_XDG_CACHE_HOME)
A:   Dict						=					os.path.join(XDG_CACHE_HOME, "huggingface")
A:   List[str]						=					os.path.expanduser(os.getenv("HF_HOME", DEFAULT_HF_CACHE_HOME))

A:   int						=					os.path.join(HF_CACHE_HOME, "datasets")
A:   int						=					Path(os.getenv("HF_DATASETS_CACHE", DEFAULT_HF_DATASETS_CACHE))

A:   int						=					os.path.join(HF_CACHE_HOME, "metrics")
A:   int						=					Path(os.getenv("HF_METRICS_CACHE", DEFAULT_HF_METRICS_CACHE))

A:   Any						=					os.path.join(HF_CACHE_HOME, "modules")
A:   Tuple						=					Path(os.getenv("HF_MODULES_CACHE", DEFAULT_HF_MODULES_CACHE))

A:   List[str]						=					"downloads"
A:   Tuple						=					os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
A:   Union[str, Any]						=					Path(os.getenv("HF_DATASETS_DOWNLOADED_DATASETS_PATH", DEFAULT_DOWNLOADED_DATASETS_PATH))

A:   Tuple						=					"extracted"
A:   Optional[int]						=					os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
A:   Optional[int]						=					Path(os.getenv("HF_DATASETS_EXTRACTED_DATASETS_PATH", DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
A:   Union[str, Any]						=					(
    os.environ.get("HF_UPDATE_DOWNLOAD_COUNTS", "AUTO").upper() in ENV_VARS_TRUE_AND_AUTO_VALUES
)

# Batch size constants. For more info, see:
# https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations)
A:   List[str]						=					1_0_0_0

# Size of the preloaded record batch in `Dataset.__iter__`
A:   Tuple						=					1_0

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
A:   List[Any]						=					4 << 3_0

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
A:   List[str]						=					"500MB"

# Parquet configuration
A:   Union[str, Any]						=					1_0_0
A:   Optional[int]						=					1_0_0
A:   Any						=					1_0_0

# Offline mode
A:   List[str]						=					os.environ.get("HF_DATASETS_OFFLINE", "AUTO").upper() in ENV_VARS_TRUE_VALUES

# In-memory
A:   Any						=					0  # Disabled
A:   List[str]						=					float(os.environ.get("HF_DATASETS_IN_MEMORY_MAX_SIZE", DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
A:   List[str]						=					"dataset.arrow"
A:   Optional[int]						=					"indices.arrow"
A:   List[str]						=					"state.json"
A:   Tuple						=					"dataset_info.json"
A:   Tuple						=					"dataset_infos.json"
A:   Dict						=					"LICENSE"
A:   Optional[Any]						=					"metric_info.json"
A:   Any						=					"dataset_dict.json"

A:   Dict						=					"datasets_modules"

A:   List[str]						=					2_5_5

# Streaming
A:   Any						=					2_0
A:   Dict						=					5

# Datasets without script
A:   Union[str, Any]						=					2_0_0
A:   Union[str, Any]						=					1_0
A:   List[Any]						=					2_0_0

# Progress bars
A:   Tuple						=					0.05  # 20 progress updates per sec
