





'''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__      :   Any      	=			"1.5.0"

lowerCAmelCase__      :   Any      	=			["vision-encoder-decoder"]


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

				__UpperCAmelCase	:							Optional[int]     =     version.parse(_UpperCAmelCase  )
				if parsed_optimum_version < version.parse(_UpperCAmelCase  ):
						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	:							Optional[Any]     =     [
		    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	:							List[str]     =     subprocess.Popen(" ".join(_UpperCAmelCase  ),       stdout=subprocess.PIPE,       shell=_UpperCAmelCase  )
		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 						__UpperCamelCase						(			_UpperCAmelCase  ):
		__UpperCAmelCase	:							Optional[Any]     =     args.output if args.output.is_file() else args.output.joinpath("model.onnx"  )
		if not args.output.parent.exists():
				args.output.parent.mkdir(parents=_UpperCAmelCase  )

		# Allocate the model
		__UpperCAmelCase	:							Optional[Any]     =     FeaturesManager.get_model_from_feature(
		    args.feature,       args.model,       framework=args.framework,       cache_dir=args.cache_dir  )

		__UpperCAmelCase  ,			__UpperCAmelCase	:							List[Any]     =     FeaturesManager.check_supported_model_or_raise(_UpperCAmelCase,       feature=args.feature  )
		__UpperCAmelCase	:							int     =     model_onnx_config(model.config  )

		if model_kind in ENCODER_DECODER_MODELS:
				__UpperCAmelCase	:							Tuple     =     model.get_encoder()
				__UpperCAmelCase	:							Optional[Any]     =     model.get_decoder()

				__UpperCAmelCase	:							Union[str, Any]     =     onnx_config.get_encoder_config(encoder_model.config  )
				__UpperCAmelCase	:							Dict     =     onnx_config.get_decoder_config(
				    encoder_model.config,       decoder_model.config,       feature=args.feature  )

				if args.opset is None:
						__UpperCAmelCase	:							str     =     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	:							str     =     AutoFeatureExtractor.from_pretrained(args.model  )

				__UpperCAmelCase  ,			__UpperCAmelCase	:							List[str]     =     export(
				    _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       args.opset,       args.output.parent.joinpath("encoder_model.onnx"  ),       )

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

				__UpperCAmelCase	:							Optional[Any]     =     AutoTokenizer.from_pretrained(args.model  )

				__UpperCAmelCase  ,			__UpperCAmelCase	:							Optional[int]     =     export(
				    _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       args.opset,       args.output.parent.joinpath("decoder_model.onnx"  ),       )

				validate_model_outputs(
				    _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       args.output.parent.joinpath("decoder_model.onnx"  ),       _UpperCAmelCase,       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	:							Dict     =     get_preprocessor(args.model  )
				elif args.preprocessor == "tokenizer":
						__UpperCAmelCase	:							Optional[Any]     =     AutoTokenizer.from_pretrained(args.model  )
				elif args.preprocessor == "image_processor":
						__UpperCAmelCase	:							Union[str, Any]     =     AutoImageProcessor.from_pretrained(args.model  )
				elif args.preprocessor == "feature_extractor":
						__UpperCAmelCase	:							List[str]     =     AutoFeatureExtractor.from_pretrained(args.model  )
				elif args.preprocessor == "processor":
						__UpperCAmelCase	:							Dict     =     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	:							Optional[int]     =     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	:							str     =     export(
				    _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       args.opset,       args.output,       )

				if args.atol is None:
						__UpperCAmelCase	:							Tuple     =     onnx_config.atol_for_validation

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


def 						__UpperCamelCase						(			):
		__UpperCAmelCase	:							Dict     =     ArgumentParser("Hugging Face Transformers ONNX exporter"  )
		parser.add_argument(
		    "-m",       "--model",       type=_UpperCAmelCase,       required=_UpperCAmelCase,       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=_UpperCAmelCase,       default=_UpperCAmelCase,       help="ONNX opset version to export the model with."  )
		parser.add_argument(
		    "--atol",       type=_UpperCAmelCase,       default=_UpperCAmelCase,       help="Absolute difference tolerance when validating the model."  )
		parser.add_argument(
		    "--framework",       type=_UpperCAmelCase,       choices=["pt", "tf"],       default=_UpperCAmelCase,       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=_UpperCAmelCase,       help="Path indicating where to store generated ONNX model."  )
		parser.add_argument("--cache_dir",       type=_UpperCAmelCase,       default=_UpperCAmelCase,       help="Path indicating where to store cache."  )
		parser.add_argument(
		    "--preprocessor",       type=_UpperCAmelCase,       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	:							int     =     parser.parse_args()
		if args.export_with_transformers or not is_optimum_available():
				export_with_transformers(_UpperCAmelCase  )
		else:
				export_with_optimum(_UpperCAmelCase  )


if __name__ == "__main__":
			lowerCAmelCase__      :   List[str]      	=			logging.get_logger("transformers.onnx")  # pylint: disable=invalid-name
			logger.setLevel(logging.INFO)
			main()











'''simple docstring'''

class      SCREAMING_SNAKE_CASE__      :







					"""simple docstring"""






					def __init__(   self       :      Tuple		,	UpperCAmelCase_       :      Dict		):







							"""simple docstring"""
							__UpperCAmelCase	:							Any     =     val
							# Number of nodes in left subtree
							__UpperCAmelCase	:							Dict     =     0
							__UpperCAmelCase	:							int     =     None
							__UpperCAmelCase	:							Optional[int]     =     None
							__UpperCAmelCase	:							List[str]     =     None






					def 		lowerCamelCase_   (   self       :      int		,	UpperCAmelCase_       :      Optional[Any]		):







							"""simple docstring"""
							assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

							if self.val < other.val:
									__UpperCAmelCase	:							str     =     self.right
									__UpperCAmelCase	:							Tuple     =     None
									if self.right:
											__UpperCAmelCase	:							int     =     other
									__UpperCAmelCase	:							List[str]     =     other
									__UpperCAmelCase	:							Any     =     self.left_tree_size * 2 + 1
									return self
							else:
									__UpperCAmelCase	:							List[Any]     =     other.right
									__UpperCAmelCase	:							Union[str, Any]     =     None
									if other.right:
											__UpperCAmelCase	:							List[str]     =     self
									__UpperCAmelCase	:							Optional[Any]     =     self
									__UpperCAmelCase	:							Any     =     other.left_tree_size * 2 + 1
									return other






class      SCREAMING_SNAKE_CASE__      :







					"""simple docstring"""






					def __init__(   self       :      Optional[int]		,	UpperCAmelCase_       :      Optional[int]=None		,	UpperCAmelCase_       :      Optional[int]=None		,	UpperCAmelCase_       :      Optional[int]=0		):







							"""simple docstring"""
							__UpperCAmelCase	:							Any     =     heap_size
							__UpperCAmelCase	:							Any     =     bottom_root
							__UpperCAmelCase	:							List[str]     =     min_node
					def 		lowerCamelCase_   (   self       :      List[str]		,	UpperCAmelCase_       :      Dict		):







							"""simple docstring"""

							# Empty heaps corner cases
							if other.size == 0:
									return None
							if self.size == 0:
									__UpperCAmelCase	:							Dict     =     other.size
									__UpperCAmelCase	:							Optional[Any]     =     other.bottom_root
									__UpperCAmelCase	:							str     =     other.min_node
									return None
							# Update size
							__UpperCAmelCase	:							str     =     self.size + other.size

							# Update min.node
							if self.min_node.val > other.min_node.val:
									__UpperCAmelCase	:							Tuple     =     other.min_node
							# Merge

							# Order roots by left_subtree_size
							__UpperCAmelCase	:							int     =     []
							__UpperCAmelCase  ,			__UpperCAmelCase	:							List[str]     =     self.bottom_root, other.bottom_root
							while i or j:
									if i and ((not j) or i.left_tree_size < j.left_tree_size):
											combined_roots_list.append((i, True)		)
											__UpperCAmelCase	:							Optional[Any]     =     i.parent
									else:
											combined_roots_list.append((j, False)		)
											__UpperCAmelCase	:							Tuple     =     j.parent
        # Insert links between them
							for i in range(len(UpperCAmelCase_		) - 1		):
									if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
											__UpperCAmelCase	:							Optional[Any]     =     combined_roots_list[i + 1][0]
											__UpperCAmelCase	:							Any     =     combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
							__UpperCAmelCase	:							List[str]     =     combined_roots_list[0][0]
							while i.parent:
									if (
									    (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
									) or (
									    i.left_tree_size == i.parent.left_tree_size
									    and i.left_tree_size != i.parent.parent.left_tree_size
									):
											# Neighbouring Nodes
											__UpperCAmelCase	:							List[str]     =     i.left
											__UpperCAmelCase	:							Dict     =     i.parent.parent

											# Merging trees
											__UpperCAmelCase	:							Optional[int]     =     i.merge_trees(i.parent		)

											# Updating links
											__UpperCAmelCase	:							Dict     =     previous_node
											__UpperCAmelCase	:							Union[str, Any]     =     next_node
											if previous_node:
													__UpperCAmelCase	:							List[str]     =     i
											if next_node:
													__UpperCAmelCase	:							Dict     =     i
									else:
											__UpperCAmelCase	:							Optional[int]     =     i.parent
        # Updating self.bottom_root
							while i.left:
									__UpperCAmelCase	:							str     =     i.left
							__UpperCAmelCase	:							List[Any]     =     i

							# Update other
							__UpperCAmelCase	:							int     =     self.size
							__UpperCAmelCase	:							Dict     =     self.bottom_root
							__UpperCAmelCase	:							Union[str, Any]     =     self.min_node

							# Return the merged heap
							return self
					def 		lowerCamelCase_   (   self       :      Tuple		,	UpperCAmelCase_       :      Optional[int]		):







							"""simple docstring"""
							if self.size == 0:
									__UpperCAmelCase	:							Optional[Any]     =     Node(UpperCAmelCase_		)
									__UpperCAmelCase	:							Optional[Any]     =     1
									__UpperCAmelCase	:							Optional[Any]     =     self.bottom_root
							else:
									# Create new node
									__UpperCAmelCase	:							Optional[Any]     =     Node(UpperCAmelCase_		)

									# Update size
									self.size += 1

									# update min_node
									if val < self.min_node.val:
											__UpperCAmelCase	:							Optional[int]     =     new_node
									# Put new_node as a bottom_root in heap
									__UpperCAmelCase	:							Union[str, Any]     =     new_node
									__UpperCAmelCase	:							Optional[Any]     =     self.bottom_root
									__UpperCAmelCase	:							List[str]     =     new_node

									# Consecutively merge roots with same left_tree_size
									while (
									    self.bottom_root.parent
									    and self.bottom_root.left_tree_size
									    == self.bottom_root.parent.left_tree_size
									):
											# Next node
											__UpperCAmelCase	:							Optional[Any]     =     self.bottom_root.parent.parent

											# Merge
											__UpperCAmelCase	:							str     =     self.bottom_root.merge_trees(self.bottom_root.parent		)

											# Update Links
											__UpperCAmelCase	:							int     =     next_node
											__UpperCAmelCase	:							Any     =     None
											if next_node:
													__UpperCAmelCase	:							Any     =     self.bottom_root
					def 		lowerCamelCase_   (   self       :      Any		):







							"""simple docstring"""
							return self.min_node.val
					def 		lowerCamelCase_   (   self       :      str		):







							"""simple docstring"""
							return self.size == 0
					def 		lowerCamelCase_   (   self       :      int		):







							"""simple docstring"""
							__UpperCAmelCase	:							str     =     self.min_node.val

							# Last element in heap corner case
							if self.size == 1:
									# Update size
									__UpperCAmelCase	:							Optional[int]     =     0

									# Update bottom root
									__UpperCAmelCase	:							List[str]     =     None

									# Update min_node
									__UpperCAmelCase	:							int     =     None

									return min_value
							# No right subtree corner case
							# The structure of the tree implies that this should be the bottom root
							# and there is at least one other root
							if self.min_node.right is None:
									# Update size
									self.size -= 1

									# Update bottom root
									__UpperCAmelCase	:							Union[str, Any]     =     self.bottom_root.parent
									__UpperCAmelCase	:							str     =     None

									# Update min_node
									__UpperCAmelCase	:							int     =     self.bottom_root
									__UpperCAmelCase	:							int     =     self.bottom_root.parent
									while i:
											if i.val < self.min_node.val:
													__UpperCAmelCase	:							Union[str, Any]     =     i
											__UpperCAmelCase	:							Optional[int]     =     i.parent
									return min_value
							# General case
							# Find the BinomialHeap of the right subtree of min_node
							__UpperCAmelCase	:							Optional[int]     =     self.min_node.right
							__UpperCAmelCase	:							List[Any]     =     None
							__UpperCAmelCase	:							Optional[int]     =     bottom_of_new
							__UpperCAmelCase	:							Union[str, Any]     =     1

							# Size, min_node and bottom_root
							while bottom_of_new.left:
									__UpperCAmelCase	:							str     =     size_of_new * 2 + 1
									__UpperCAmelCase	:							List[str]     =     bottom_of_new.left
									if bottom_of_new.val < min_of_new.val:
											__UpperCAmelCase	:							Union[str, Any]     =     bottom_of_new
        # Corner case of single root on top left path
							if (not self.min_node.left) and (not self.min_node.parent):
									__UpperCAmelCase	:							Optional[Any]     =     size_of_new
									__UpperCAmelCase	:							List[str]     =     bottom_of_new
									__UpperCAmelCase	:							List[Any]     =     min_of_new
									# print("Single root, multiple nodes case")
									return min_value
							# Remaining cases
							# Construct heap of right subtree
							__UpperCAmelCase	:							Union[str, Any]     =     BinomialHeap(
							    bottom_root=UpperCAmelCase_		,	min_node=UpperCAmelCase_		,	heap_size=UpperCAmelCase_		)

							# Update size
							__UpperCAmelCase	:							Any     =     self.size - 1 - size_of_new

							# Neighbour nodes
							__UpperCAmelCase	:							Union[str, Any]     =     self.min_node.left
							__UpperCAmelCase	:							List[str]     =     self.min_node.parent

							# Initialize new bottom_root and min_node
							__UpperCAmelCase	:							Optional[Any]     =     previous_node or next_node
							__UpperCAmelCase	:							Optional[Any]     =     next_node

							# Update links of previous_node and search below for new min_node and
							# bottom_root
							if previous_node:
									__UpperCAmelCase	:							List[str]     =     next_node

									# Update bottom_root and search for min_node below
									__UpperCAmelCase	:							List[str]     =     previous_node
									__UpperCAmelCase	:							int     =     previous_node
									while self.bottom_root.left:
											__UpperCAmelCase	:							Dict     =     self.bottom_root.left
											if self.bottom_root.val < self.min_node.val:
													__UpperCAmelCase	:							Union[str, Any]     =     self.bottom_root
							if next_node:
									__UpperCAmelCase	:							Tuple     =     previous_node

									# Search for new min_node above min_node
									__UpperCAmelCase	:							Optional[int]     =     next_node
									while i:
											if i.val < self.min_node.val:
													__UpperCAmelCase	:							List[Any]     =     i
											__UpperCAmelCase	:							List[Any]     =     i.parent
        # Merge heaps
							self.merge_heaps(UpperCAmelCase_		)

							return min_value
					def 		lowerCamelCase_   (   self       :      Union[str, Any]		):







							"""simple docstring"""
							__UpperCAmelCase	:							Tuple     =     self.bottom_root
							while top_root.parent:
									__UpperCAmelCase	:							Optional[int]     =     top_root.parent
							# preorder
							__UpperCAmelCase	:							Union[str, Any]     =     []
							self.__traversal(UpperCAmelCase_		,	UpperCAmelCase_		)
							return heap_pre_order
					def 		lowerCamelCase_   (   self       :      Optional[int]		,	UpperCAmelCase_       :      Optional[Any]		,	UpperCAmelCase_       :      int		,	UpperCAmelCase_       :      Dict=0		):







							"""simple docstring"""
							if curr_node:
									preorder.append((curr_node.val, level)		)
									self.__traversal(curr_node.left		,	UpperCAmelCase_		,	level + 1		)
									self.__traversal(curr_node.right		,	UpperCAmelCase_		,	level + 1		)
							else:
									preorder.append(("#", level)		)






					def __str__(   self       :      Optional[int]		):







							"""simple docstring"""
							if self.is_empty():
									return ""
							__UpperCAmelCase	:							Optional[Any]     =     self.pre_order()

							return "\n".join(("-" * level + str(UpperCAmelCase_		)) for value, level in preorder_heap		)


# Unit Tests
if __name__ == "__main__":
			import doctest

			doctest.testmod()




