



"""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


__UpperCAmelCase      :      Optional[Any]						=					'1.5.0'

__UpperCAmelCase      :      Any						=					['vision-encoder-decoder']
def      A      (			_A							):




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

										snake_case_						:List[str]								=						version.parse(_A							)
										if parsed_optimum_version < version.parse(_A							):
															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]"							)
					snake_case_						: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}''',
					]
					snake_case_						:List[str]								=						subprocess.Popen(" ".join(_A							),     stdout=subprocess.PIPE,     shell=_A							)
					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      (			_A							):




					"""simple docstring"""
					snake_case_						:List[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=_A							)

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

					snake_case_						:Dict								=						FeaturesManager.check_supported_model_or_raise(_A,     feature=args.feature							)
					snake_case_						:Union[str, Any]								=						model_onnx_config(model.config							)

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

										snake_case_						:List[Any]								=						onnx_config.get_encoder_config(encoder_model.config							)
										snake_case_						:str								=						onnx_config.get_decoder_config(
										    encoder_model.config,     decoder_model.config,     feature=args.feature							)

										if args.opset is None:
															snake_case_						: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.'''							)

										snake_case_						:Union[str, Any]								=						AutoFeatureExtractor.from_pretrained(args.model							)

										snake_case_						:int								=						export(
										    _A,     _A,     _A,     args.opset,     args.output.parent.joinpath("encoder_model.onnx"							),     )

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

										snake_case_						:Union[str, Any]								=						AutoTokenizer.from_pretrained(args.model							)

										snake_case_						:Union[str, Any]								=						export(
										    _A,     _A,     _A,     args.opset,     args.output.parent.joinpath("decoder_model.onnx"							),     )

										validate_model_outputs(
										    _A,     _A,     _A,     args.output.parent.joinpath("decoder_model.onnx"							),     _A,     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":
															snake_case_						:Optional[Any]								=						get_preprocessor(args.model							)
										elif args.preprocessor == "tokenizer":
															snake_case_						:List[str]								=						AutoTokenizer.from_pretrained(args.model							)
										elif args.preprocessor == "image_processor":
															snake_case_						:List[str]								=						AutoImageProcessor.from_pretrained(args.model							)
										elif args.preprocessor == "feature_extractor":
															snake_case_						:Optional[Any]								=						AutoFeatureExtractor.from_pretrained(args.model							)
										elif args.preprocessor == "processor":
															snake_case_						:List[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:
															snake_case_						: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.'''							)

										snake_case_						:List[str]								=						export(
										    _A,     _A,     _A,     args.opset,     args.output,     )

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

										validate_model_outputs(_A,     _A,     _A,     args.output,     _A,     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.",     _A,     )
def      A      (			):




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

					snake_case_						:Optional[int]								=						parser.parse_args()
					if args.export_with_transformers or not is_optimum_available():
										export_with_transformers(_A							)
					else:
										export_with_optimum(_A							)


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






"""simple docstring"""






from typing import Any

import numpy as np

class 	__lowerCAmelCase							:


					'''simple docstring'''


					def __init__(      self    ,       a    ,       a   ):




										"""simple docstring"""





										if np.any(tableau[:, -1]    ,       where=tableau[:, -1] < 0   ):
															raise ValueError("RHS must be > 0"   )

										snake_case_						:List[Any]								=						tableau
										snake_case_	,  snake_case_						:Dict								=						tableau.shape

										# Number of decision variables x1, x2, x3...
										snake_case_						:Union[str, Any]								=						n_vars

										# Number of artificial variables to be minimised
										snake_case_						:Tuple								=						len(np.where(tableau[self.n_vars : -1] == -1   )[0]   )

										# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
										snake_case_						:List[str]								=						(self.n_art_vars > 0) + 1

										# Number of slack variables added to make inequalities into equalities
										snake_case_						:Optional[int]								=						self.n_rows - self.n_stages

										# Objectives for each stage
										snake_case_						:Union[str, Any]								=						["max"]

										# In two stage simplex, first minimise then maximise
										if self.n_art_vars:
															self.objectives.append("min"   )

										snake_case_						:Dict								=						[""]

										# Index of current pivot row and column
										snake_case_						:List[str]								=						None
										snake_case_						:List[Any]								=						None

										# Does objective row only contain (non)-negative values?
										snake_case_						:Union[str, Any]								=						False







					@staticmethod
					def 		_a (      *a   ):




										"""simple docstring"""





										if len(a   ) != 3:
															raise ValueError("Must provide n_vars, n_slack, and n_art_vars"   )

										if not all(x >= 0 and isinstance(a    ,       a   ) for x in args   ):
															raise ValueError("All arguments must be non-negative integers"   )

										# decision | slack | artificial
										snake_case_						:Tuple								=						["x", "s", "a"]
										snake_case_						:Dict								=						[]
										for i in range(3   ):
															for j in range(args[i]   ):
																				titles.append(string_starts[i] + str(j + 1   )   )
										titles.append("RHS"   )
										return titles







					def 		_a (      self    ,       a   ):




										"""simple docstring"""





										snake_case_						:str								=						self.objectives[-1]

										# Find entries of highest magnitude in objective rows
										snake_case_						:Dict								=						(objective == "min") - (objective == "max")
										snake_case_						:Optional[Any]								=						np.argmax(sign * tableau[0, : self.n_vars]   )

										# Choice is only valid if below 0 for maximise, and above for minimise
										if sign * self.tableau[0, col_idx] <= 0:
															snake_case_						:Dict								=						True
															return 0, 0

										# Pivot row is chosen as having the lowest quotient when elements of
										# the pivot column divide the right-hand side

										# Slice excluding the objective rows
										snake_case_						:str								=						slice(self.n_stages    ,       self.n_rows   )

										# RHS
										snake_case_						:Optional[Any]								=						tableau[s, -1]

										# Elements of pivot column within slice
										snake_case_						:Union[str, Any]								=						tableau[s, col_idx]

										# Array filled with nans
										snake_case_						:Optional[Any]								=						np.full(self.n_rows - self.n_stages    ,       np.nan   )

										# If element in pivot column is greater than zeron_stages, return
										# quotient or nan otherwise
										snake_case_						:Dict								=						np.divide(a    ,       a    ,       out=a    ,       where=divisor > 0   )

										# Arg of minimum quotient excluding the nan values. n_stages is added
										# to compensate for earlier exclusion of objective columns
										snake_case_						:List[Any]								=						np.nanargmin(a   ) + self.n_stages
										return row_idx, col_idx







					def 		_a (      self    ,       a    ,       a    ,       a   ):




										"""simple docstring"""





										snake_case_						:Tuple								=						tableau[row_idx].copy()

										snake_case_						:Any								=						piv_row[col_idx]

										# Entry becomes 1
										piv_row *= 1 / piv_val

										# Variable in pivot column becomes basic, ie the only non-zero entry
										for idx, coeff in enumerate(tableau[:, col_idx]   ):
															tableau[idx] += -coeff * piv_row
										snake_case_						:str								=						piv_row
										return tableau







					def 		_a (      self    ,       a   ):




										"""simple docstring"""





										self.objectives.pop()

										if not self.objectives:
															return tableau

										# Slice containing ids for artificial columns
										snake_case_						:str								=						slice(-self.n_art_vars - 1    ,       -1   )

										# Delete the artificial variable columns
										snake_case_						:Optional[int]								=						np.delete(a    ,       a    ,       axis=1   )

										# Delete the objective row of the first stage
										snake_case_						:List[str]								=						np.delete(a    ,       0    ,       axis=0   )

										snake_case_						:Optional[Any]								=						1
										self.n_rows -= 1
										snake_case_						:List[Any]								=						0
										snake_case_						:Dict								=						False
										return tableau







					def 		_a (      self   ):




										"""simple docstring"""





										for _ in range(1_00   ):
															# Completion of each stage removes an objective. If both stages
															# are complete, then no objectives are left
															if not self.objectives:
																				snake_case_						:str								=						self.generate_col_titles(
																				    self.n_vars    ,       self.n_slack    ,       self.n_art_vars   )

																				# Find the values of each variable at optimal solution
																				return self.interpret_tableau(self.tableau    ,       self.col_titles   )

															snake_case_	,  snake_case_						:Union[str, Any]								=						self.find_pivot(self.tableau   )

															# If there are no more negative values in objective row
															if self.stop_iter:
																				# Delete artificial variable columns and rows. Update attributes
																				snake_case_						:str								=						self.change_stage(self.tableau   )
															else:
																				snake_case_						:str								=						self.pivot(self.tableau    ,       a    ,       a   )
										return {}







					def 		_a (      self    ,       a    ,       a   ):




										"""simple docstring"""





										snake_case_						:Any								=						{"P": abs(tableau[0, -1]   )}

										for i in range(self.n_vars   ):
															# Gives ids of nonzero entries in the ith column
															snake_case_						:Dict								=						np.nonzero(tableau[:, i]   )
															snake_case_						:Tuple								=						len(nonzero[0]   )

															# First entry in the nonzero ids
															snake_case_						:Any								=						nonzero[0][0]
															snake_case_						:int								=						tableau[nonzero_rowidx, i]

															# If there is only one nonzero value in column, which is one
															if n_nonzero == nonzero_val == 1:
																				snake_case_						:Tuple								=						tableau[nonzero_rowidx, -1]
																				snake_case_						:Tuple								=						rhs_val

        # Check for basic variables
										for title in col_titles:
															# Don't add RHS or slack variables to output dict
															if title[0] not in "R-s-a":
																				output_dict.setdefault(a    ,       0   )
										return output_dict


if __name__ == "__main__":
				import doctest

				doctest.testmod()

