




"""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 				a (						__snake_case     :				Any  ):



				'''simple docstring'''





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

								UpperCAmelCase_					:Optional[int]      			=      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_					:List[str]      			=      [
				    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_					:Optional[int]      			=      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 				a (						__snake_case     :				int  ):



				'''simple docstring'''





				UpperCAmelCase_					:Dict      			=      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_					:int      			=      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(__snake_case,    feature=args.feature  )
				UpperCAmelCase_					:Union[str, Any]      			=      model_onnx_config(model.config  )

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

								UpperCAmelCase_					:Any      			=      onnx_config.get_encoder_config(encoder_model.config  )
								UpperCAmelCase_					:Tuple      			=      onnx_config.get_decoder_config(
								    encoder_model.config,    decoder_model.config,    feature=args.feature  )

								if args.opset is None:
												UpperCAmelCase_					:Union[str, Any]      			=      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_					:Dict      			=      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_					:Optional[Any]      			=      AutoTokenizer.from_pretrained(args.model  )

								UpperCAmelCase_			,UpperCAmelCase_					:int      			=      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_					:Any      			=      get_preprocessor(args.model  )
								elif args.preprocessor == "tokenizer":
												UpperCAmelCase_					:List[Any]      			=      AutoTokenizer.from_pretrained(args.model  )
								elif args.preprocessor == "image_processor":
												UpperCAmelCase_					:List[Any]      			=      AutoImageProcessor.from_pretrained(args.model  )
								elif args.preprocessor == "feature_extractor":
												UpperCAmelCase_					:Union[str, Any]      			=      AutoFeatureExtractor.from_pretrained(args.model  )
								elif args.preprocessor == "processor":
												UpperCAmelCase_					:Optional[Any]      			=      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_					:Union[str, Any]      			=      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_					:Optional[Any]      			=      export(
								    __snake_case,    __snake_case,    __snake_case,    args.opset,    args.output,    )

								if args.atol is None:
												UpperCAmelCase_					:Dict      			=      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 				a (						):



				'''simple docstring'''





				UpperCAmelCase_					:Optional[int]      			=      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_					:Optional[int]      			=      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__":
						__lowerCamelCase								=						logging.get_logger("transformers.onnx")  # pylint: disable=invalid-name
						logger.setLevel(logging.INFO)
						main()








"""simple docstring"""

# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint



class  _snake_case				:

				'''simple docstring'''




				def __init__(							self      :  str					,							snake_case      :  Optional[Any]					,							snake_case      :  str     ):
								UpperCAmelCase_					:Union[str, Any]      			=      key
								UpperCAmelCase_					:str      			=      freq





				def __str__(							self      :  Optional[Any]     ):
								return f'Node(key={self.key}, freq={self.freq})'



def 				a (						__snake_case     :				int,    __snake_case     :				Optional[int],    __snake_case     :				Tuple,    __snake_case     :				Any,    __snake_case     :				Dict,    __snake_case     :				List[str]  ):



				'''simple docstring'''





				if i > j or i < 0 or j > len(__snake_case  ) - 1:
								return

				UpperCAmelCase_					:List[str]      			=      root[i][j]
				if parent == -1:  # root does not have a parent
								print(f'{key[node]} is the root of the binary search tree.'  )
				elif is_left:
								print(f'{key[node]} is the left child of key {parent}.'  )
				else:
								print(f'{key[node]} is the right child of key {parent}.'  )

				print_binary_search_tree(__snake_case,    __snake_case,    __snake_case,    node - 1,    key[node],    __snake_case  )
				print_binary_search_tree(__snake_case,    __snake_case,    node + 1,    __snake_case,    key[node],    __snake_case  )



def 				a (						__snake_case     :				Optional[Any]  ):



				'''simple docstring'''





				nodes.sort(key=lambda __snake_case  : node.key  )

				UpperCAmelCase_					:List[str]      			=      len(__snake_case  )

				UpperCAmelCase_					:Any      			=      [nodes[i].key for i in range(__snake_case  )]
				UpperCAmelCase_					:int      			=      [nodes[i].freq for i in range(__snake_case  )]

				# This 2D array stores the overall tree cost (which's as minimized as possible);
				# for a single key, cost is equal to frequency of the key.
				UpperCAmelCase_					:Dict      			=      [[freqs[i] if i == j else 0 for j in range(__snake_case  )] for i in range(__snake_case  )]
				# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
				# array
				UpperCAmelCase_					:Optional[Any]      			=      [[freqs[i] if i == j else 0 for j in range(__snake_case  )] for i in range(__snake_case  )]
				# stores tree roots that will be used later for constructing binary search tree
				UpperCAmelCase_					:int      			=      [[i if i == j else 0 for j in range(__snake_case  )] for i in range(__snake_case  )]

				for interval_length in range(2,    n + 1  ):
								for i in range(n - interval_length + 1  ):
												UpperCAmelCase_					:Union[str, Any]      			=      i + interval_length - 1

												UpperCAmelCase_					:Optional[int]      			=      sys.maxsize  # set the value to "infinity"
												UpperCAmelCase_					:List[Any]      			=      total[i][j - 1] + freqs[j]

												# Apply Knuth's optimization
												# Loop without optimization: for r in range(i, j + 1):
												for r in range(root[i][j - 1],    root[i + 1][j] + 1  ):  # r is a temporal root
																UpperCAmelCase_					:Any      			=      dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
																UpperCAmelCase_					:Optional[int]      			=      dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
																UpperCAmelCase_					:Union[str, Any]      			=      left + total[i][j] + right

																if dp[i][j] > cost:
																				UpperCAmelCase_					:Optional[Any]      			=      cost
																				UpperCAmelCase_					:Optional[int]      			=      r

				print('''Binary search tree nodes:'''  )
				for node in nodes:
								print(__snake_case  )

				print(f'\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}.'  )
				print_binary_search_tree(__snake_case,    __snake_case,    0,    n - 1,    -1,    __snake_case  )



def 				a (						):



				'''simple docstring'''





				UpperCAmelCase_					:Union[str, Any]      			=      [Node(__snake_case,    randint(1,    50  )  ) for i in range(10,    0,    -1  )]
				find_optimal_binary_search_tree(__snake_case  )


if __name__ == "__main__":
						main()


