






'''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	(   _UpperCAmelCase )				->				List[str]:






					"""simple docstring"""







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

										a_									=   version.parse(lowercase__ )
										if parsed_optimum_version < version.parse(lowercase__ ):
															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]' )
					a_									=   [
					    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}''',
					]
					a_									=   subprocess.Popen(' '.join(lowercase__ )					,  stdout=subprocess.PIPE					,  shell=lowercase__ )
					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	(   _UpperCAmelCase )				->				Dict:






					"""simple docstring"""







					a_									=   args.output if args.output.is_file() else args.output.joinpath('model.onnx' )
					if not args.output.parent.exists():
										args.output.parent.mkdir(parents=lowercase__ )

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

					a_							,					a_									=   FeaturesManager.check_supported_model_or_raise(lowercase__					,  feature=args.feature )
					a_									=   model_onnx_config(model.config )

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

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

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

										a_									=   AutoFeatureExtractor.from_pretrained(args.model )

										a_							,					a_									=   export(
										    lowercase__					,  lowercase__					,  lowercase__					,  args.opset					,  args.output.parent.joinpath('encoder_model.onnx' )					,  )

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

										a_									=   AutoTokenizer.from_pretrained(args.model )

										a_							,					a_									=   export(
										    lowercase__					,  lowercase__					,  lowercase__					,  args.opset					,  args.output.parent.joinpath('decoder_model.onnx' )					,  )

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

										a_							,					a_									=   export(
										    lowercase__					,  lowercase__					,  lowercase__					,  args.opset					,  args.output					,  )

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

										validate_model_outputs(lowercase__					,  lowercase__					,  lowercase__					,  args.output					,  lowercase__					,  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.'					,  lowercase__					,  )

def 							a	(   )				->				Union[str, Any]:






					"""simple docstring"""







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

					a_									=   parser.parse_args()
					if args.export_with_transformers or not is_optimum_available():
										export_with_transformers(lowercase__ )
					else:
										export_with_optimum(lowercase__ )


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









'''simple docstring'''
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = False )				->				List[str]:






					"""simple docstring"""







					if device_specific:
										seed += AcceleratorState().process_index
					random.seed(_UpperCAmelCase )
					np.random.seed(_UpperCAmelCase )
					torch.manual_seed(_UpperCAmelCase )
					if is_xpu_available():
										torch.xpu.manual_seed_all(_UpperCAmelCase )
					elif is_npu_available():
										torch.npu.manual_seed_all(_UpperCAmelCase )
					else:
										torch.cuda.manual_seed_all(_UpperCAmelCase )
					# ^^ safe to call this function even if cuda is not available
					if is_tpu_available():
										xm.set_rng_state(_UpperCAmelCase )

def 							a	(   _UpperCAmelCase = None					,  _UpperCAmelCase = None )				->				Tuple:






					"""simple docstring"""







					if rng_type == RNGType.TORCH:
										a_									=   torch.get_rng_state()
					elif rng_type == RNGType.CUDA:
										a_									=   torch.cuda.get_rng_state()
					elif rng_type == RNGType.XLA:
										assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
										a_									=   torch.tensor(xm.get_rng_state() )
					elif rng_type == RNGType.NPU:
										assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
										a_									=   torch.npu.get_rng_state()
					elif rng_type == RNGType.XPU:
										assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
										a_									=   torch.xpu.get_rng_state()
					elif rng_type == RNGType.GENERATOR:
										assert generator is not None, "Need a generator to synchronize its seed."
										a_									=   generator.get_state()

					# Broadcast the rng state from device 0 to other devices
					a_									=   AcceleratorState()
					if state.distributed_type == DistributedType.TPU:
										a_									=   rng_state.to(xm.xla_device() )
										xm.collective_broadcast([rng_state] )
										xm.mark_step()
										a_									=   rng_state.cpu()
					elif (
					    state.distributed_type in CUDA_DISTRIBUTED_TYPES
					    or state.distributed_type == DistributedType.MULTI_NPU
					    or state.distributed_type == DistributedType.MULTI_XPU
					):
										a_									=   rng_state.to(state.device )
										torch.distributed.broadcast(_UpperCAmelCase					,  0 )
										a_									=   rng_state.cpu()
					elif state.distributed_type == DistributedType.MULTI_CPU:
										torch.distributed.broadcast(_UpperCAmelCase					,  0 )

					# Set the broadcast rng state
					if rng_type == RNGType.TORCH:
										torch.set_rng_state(_UpperCAmelCase )
					elif rng_type == RNGType.CUDA:
										torch.cuda.set_rng_state(_UpperCAmelCase )
					elif rng_type == RNGType.NPU:
										torch.npu.set_rng_state(_UpperCAmelCase )
					elif rng_type == RNGType.XPU:
										torch.xpu.set_rng_state(_UpperCAmelCase )
					elif rng_type == RNGType.XLA:
										xm.set_rng_state(rng_state.item() )
					elif rng_type == RNGType.GENERATOR:
										generator.set_state(_UpperCAmelCase )





def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = None )				->				Any:






					"""simple docstring"""







					for rng_type in rng_types:
										synchronize_rng_state(RNGType(_UpperCAmelCase )					,  generator=_UpperCAmelCase )

