
from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar






class 							SCREAMING_SNAKE_CASE__     (				__snake_case     ):

							"""simple docstring"""






							@abstractmethod
							def __lt__(	self  ,						SCREAMING_SNAKE_CASE_      )-> bool:





													'''simple docstring'''






													pass



							@abstractmethod
							def __gt__(	self  ,						SCREAMING_SNAKE_CASE_      )-> bool:





													'''simple docstring'''






													pass



							@abstractmethod
							def __eq__(	self  ,						SCREAMING_SNAKE_CASE_      )-> bool:





													'''simple docstring'''






													pass





lowercase__				:				Tuple						=      TypeVar("T", bound=Comparable)






class 							SCREAMING_SNAKE_CASE__     (				Generic[T]     ):

							"""simple docstring"""






							def __init__(	self      )-> None:





													'''simple docstring'''






													__UpperCamelCase  	=      []
													__UpperCamelCase  	=      0



							def __repr__(	self      )-> str:





													'''simple docstring'''






													return str(self.h      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> int | None:





													'''simple docstring'''






													if child_idx > 0:
																			return (child_idx - 1) // 2
													return None



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> int | None:





													'''simple docstring'''






													__UpperCamelCase  	=      2 * parent_idx + 1
													if left_child_index < self.heap_size:
																			return left_child_index
													return None



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> int | None:





													'''simple docstring'''






													__UpperCamelCase  	=      2 * parent_idx + 2
													if right_child_index < self.heap_size:
																			return right_child_index
													return None



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> None:





													'''simple docstring'''






													if index < self.heap_size:
																			__UpperCamelCase  	=      index
																			__UpperCamelCase  	=      self.left_child_idx(lowerCAmelCase__      )
																			__UpperCamelCase  	=      self.right_child_idx(lowerCAmelCase__      )
																			# check which child is larger than its parent
																			if left_child is not None and self.h[left_child] > self.h[violation]:
																									__UpperCamelCase  	=      left_child
																			if right_child is not None and self.h[right_child] > self.h[violation]:
																									__UpperCamelCase  	=      right_child
																			# if violation indeed exists
																			if violation != index:
																									# swap to fix the violation
																									__UpperCamelCase  	=      self.h[index], self.h[violation]
																									# fix the subsequent violation recursively if any
																									self.max_heapify(lowerCAmelCase__      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> None:





													'''simple docstring'''






													__UpperCamelCase  	=      list(lowerCAmelCase__      )
													__UpperCamelCase  	=      len(self.h      )
													if self.heap_size > 1:
																			# max_heapify from right to left but exclude leaves (last level)
																			for i in range(self.heap_size // 2 - 1  ,						-1  ,						-1      ):
																									self.max_heapify(lowerCAmelCase__      )



							def      A__		(	self      )-> T:





													'''simple docstring'''






													if self.heap_size >= 2:
																			__UpperCamelCase  	=      self.h[0]
																			__UpperCamelCase  	=      self.h.pop(-1      )
																			self.heap_size -= 1
																			self.max_heapify(0      )
																			return me
													elif self.heap_size == 1:
																			self.heap_size -= 1
																			return self.h.pop(-1      )
													else:
																			raise Exception('''Empty heap'''      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> None:





													'''simple docstring'''






													self.h.append(lowerCAmelCase__      )
													__UpperCamelCase  	=      (self.heap_size - 1) // 2
													self.heap_size += 1
													while idx >= 0:
																			self.max_heapify(lowerCAmelCase__      )
																			__UpperCamelCase  	=      (idx - 1) // 2



							def      A__		(	self      )-> None:





													'''simple docstring'''






													__UpperCamelCase  	=      self.heap_size
													for j in range(size - 1  ,						0  ,						-1      ):
																			__UpperCamelCase  	=      self.h[j], self.h[0]
																			self.heap_size -= 1
																			self.max_heapify(0      )
													__UpperCamelCase  	=      size


if __name__ == "__main__":
				import doctest

				# run doc test
				doctest.testmod()

				# demo
				for unsorted in [
				    [0],
				    [2],
				    [3, 5],
				    [5, 3],
				    [5, 5],
				    [0, 0, 0, 0],
				    [1, 1, 1, 1],
				    [2, 2, 3, 5],
				    [0, 2, 2, 3, 5],
				    [2, 5, 3, 0, 2, 3, 0, 3],
				    [6, 1, 2, 7, 9, 3, 4, 5, 1_0, 8],
				    [1_0_3, 9, 1, 7, 1_1, 1_5, 2_5, 2_0_1, 2_0_9, 1_0_7, 5],
				    [-4_5, -2, -5],
				]:
								print(F"unsorted array: {unsorted}")

								lowercase__				:				Heap[int]						=      Heap()
								heap.build_max_heap(unsorted)
								print(F"after build heap: {heap}")

								print(F"max value: {heap.extract_max()}")
								print(F"after max value removed: {heap}")

								heap.insert(1_0_0)
								print(F"after new value 100 inserted: {heap}")

								heap.heap_sort()
								print(F"heap-sorted array: {heap}\n")





'''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 :				List[Any]														= """1.5.0"""

_lowerCamelCase :				List[Any]														= ["""vision-encoder-decoder"""]



def  _lowerCAmelCase				(				__a      )  ->      Any:
     '''simple docstring'''







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

          _UpperCamelCase  :Dict       =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]"""      )
     _UpperCamelCase  :Tuple       =[
         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  :Union[str, Any]       =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  _lowerCAmelCase				(				__a      )  ->      Optional[int]:
     '''simple docstring'''







     _UpperCamelCase  :Optional[int]       =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
     _UpperCamelCase  :Dict       =FeaturesManager.get_model_from_feature(
         args.feature			,      args.model			,      framework=args.framework			,      cache_dir=args.cache_dir      )

     _UpperCamelCase       ,      _UpperCamelCase  :List[str]       =FeaturesManager.check_supported_model_or_raise(__a			,      feature=args.feature      )
     _UpperCamelCase  :int       =model_onnx_config(model.config      )

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

          _UpperCamelCase  :Dict       =onnx_config.get_encoder_config(encoder_model.config      )
          _UpperCamelCase  :str       =onnx_config.get_decoder_config(
              encoder_model.config			,      decoder_model.config			,      feature=args.feature      )

          if args.opset is None:
               _UpperCamelCase  :int       =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  :Optional[Any]       =AutoFeatureExtractor.from_pretrained(args.model      )

          _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =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			,      )

          _UpperCamelCase  :str       =AutoTokenizer.from_pretrained(args.model      )

          _UpperCamelCase       ,      _UpperCamelCase  :str       =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":
               _UpperCamelCase  :Union[str, Any]       =get_preprocessor(args.model      )
          elif args.preprocessor == "tokenizer":
               _UpperCamelCase  :Dict       =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  :int       =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  :Optional[Any]       =export(
              __a			,      __a			,      __a			,      args.opset			,      args.output			,      )

          if args.atol is None:
               _UpperCamelCase  :List[Any]       =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  _lowerCAmelCase				(				)  ->      List[str]:
     '''simple docstring'''







     _UpperCamelCase  :Union[str, 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."""
         )			,      )

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