



"""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


__SCREAMING_SNAKE_CASE     :  Optional[Any]										=					'''1.5.0'''

__SCREAMING_SNAKE_CASE     :  Optional[Any]										=					['''vision-encoder-decoder''']



def 				lowerCAmelCase_(      lowercase_   :							Optional[Any]							)		->				Union[str, Any]:
				if is_optimum_available():
								from optimum.version import __version__ as optimum_version

								_lowerCamelCase					     = 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]'''							)
				_lowerCamelCase					     = [
				    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}""",
				]
				_lowerCamelCase					     = 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 				lowerCAmelCase_(      lowercase_   :							Union[str, Any]							)		->				Optional[int]:
				_lowerCamelCase					     = 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
				_lowerCamelCase					     = FeaturesManager.get_model_from_feature(
				    args.feature  ,      args.model  ,      framework=args.framework  ,      cache_dir=args.cache_dir							)

				_lowerCamelCase     ,   _lowerCamelCase					     = FeaturesManager.check_supported_model_or_raise(lowercase_  ,      feature=args.feature							)
				_lowerCamelCase					     = model_onnx_config(model.config							)

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

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

								if args.opset is None:
												_lowerCamelCase					     = 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."""							)

								_lowerCamelCase					     = AutoFeatureExtractor.from_pretrained(args.model							)

								_lowerCamelCase     ,   _lowerCamelCase					     = 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  ,      )

								_lowerCamelCase					     = AutoTokenizer.from_pretrained(args.model							)

								_lowerCamelCase     ,   _lowerCamelCase					     = 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":
												_lowerCamelCase					     = get_preprocessor(args.model							)
								elif args.preprocessor == "tokenizer":
												_lowerCamelCase					     = AutoTokenizer.from_pretrained(args.model							)
								elif args.preprocessor == "image_processor":
												_lowerCamelCase					     = AutoImageProcessor.from_pretrained(args.model							)
								elif args.preprocessor == "feature_extractor":
												_lowerCamelCase					     = AutoFeatureExtractor.from_pretrained(args.model							)
								elif args.preprocessor == "processor":
												_lowerCamelCase					     = 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:
												_lowerCamelCase					     = 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."""							)

								_lowerCamelCase     ,   _lowerCamelCase					     = export(
								    lowercase_  ,      lowercase_  ,      lowercase_  ,      args.opset  ,      args.output  ,      )

								if args.atol is None:
												_lowerCamelCase					     = 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 				lowerCAmelCase_(      )		->				int:
				_lowerCamelCase					     = 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.'''
				    )  ,      )

				_lowerCamelCase					     = 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__":
			__SCREAMING_SNAKE_CASE     :  int										=					logging.get_logger('''transformers.onnx''')  # pylint: disable=invalid-name
			logger.setLevel(logging.INFO)
			main()





"""simple docstring"""
import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE     :  Tuple										=					logging.get_logger(__name__)


@dataclass
class 							lowerCamelCase_:





					'''simple docstring'''



					lowercase__      :				nn.Module
					lowercase__      :				List[nn.Module]        =       field(default_factory=A__			)
					lowercase__      :				list        =       field(default_factory=A__			)

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = len(list(m.modules()      )      ) == 1 or isinstance(lowerCamelCase__ ,				nn.Convad      ) or isinstance(lowerCamelCase__ ,				nn.BatchNormad      )
									if has_not_submodules:
													if not isinstance(lowerCamelCase__ ,				lowerCamelCase__      ):
																	self.traced.append(lowerCamelCase__      )

					def __call__(			self ,				lowerCamelCase__      ):
									for m in self.module.modules():
													self.handles.append(m.register_forward_hook(self._forward_hook      )      )
									self.module(lowerCamelCase__      )
									[x.remove() for x in self.handles]
									return self





					@property
					def 	snake_case__					(			self      ):
									# check the len of the state_dict keys to see if we have learnable params
									return list(filter(lambda lowerCamelCase__      : len(list(x.state_dict().keys()      )      ) > 0 ,				self.traced      )      )


@dataclass
class 							lowerCamelCase_:





					'''simple docstring'''



					lowercase__      :				nn.Module
					lowercase__      :				nn.Module
					lowercase__      :				int        =       0
					lowercase__      :				List        =       field(default_factory=A__			)
					lowercase__      :				List        =       field(default_factory=A__			)

					def __call__(			self ,				lowerCamelCase__      ):
									_lowerCamelCase					     = Tracker(self.dest      )(lowerCamelCase__      ).parametrized
									_lowerCamelCase					     = Tracker(self.src      )(lowerCamelCase__      ).parametrized

									_lowerCamelCase					     = list(filter(lambda lowerCamelCase__      : type(lowerCamelCase__      ) not in self.src_skip ,				lowerCamelCase__      )      )
									_lowerCamelCase					     = list(filter(lambda lowerCamelCase__      : type(lowerCamelCase__      ) not in self.dest_skip ,				lowerCamelCase__      )      )

									if len(lowerCamelCase__      ) != len(lowerCamelCase__      ):
													raise Exception(
													    F"""Numbers of operations are different. Source module has {len(lowerCamelCase__      )} operations while"""
													    F""" destination module has {len(lowerCamelCase__      )}."""      )

									for dest_m, src_m in zip(lowerCamelCase__ ,				lowerCamelCase__      ):
													dest_m.load_state_dict(src_m.state_dict()      )
													if self.verbose == 1:
																	print(F"""Transfered from={src_m} to={dest_m}"""      )



def 				lowerCAmelCase_(      lowercase_   :							nn.Module  ,      lowercase_   :							nn.Module							)		->				nn.Module:
				# nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
				_lowerCamelCase					     = from_model.state_dict()
				_lowerCamelCase					     = our_model.state_dict()
				_lowerCamelCase					     = our_model.config
				_lowerCamelCase					     = []
				for stage_idx in range(len(config.hidden_sizes							)							):
								for block_id in range(config.depths[stage_idx]							):
												_lowerCamelCase					     = F"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
												_lowerCamelCase					     = F"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

												all_keys.append((from_key, to_key)							)
												_lowerCamelCase					     = F"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
												_lowerCamelCase					     = F"""van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"""

												all_keys.append((from_key, to_key)							)

				for from_key, to_key in all_keys:
								_lowerCamelCase					     = from_state_dict.pop(lowercase_							)

				our_model.load_state_dict(lowercase_							)
				return our_model



def 				lowerCAmelCase_(      lowercase_   :							str  ,      lowercase_   :							VanConfig  ,      lowercase_   :							str  ,      lowercase_   :							nn.Module  ,      lowercase_   :							Path  ,      lowercase_   :							bool = True  ,      )		->				Union[str, Any]:
				print(F"""Downloading weights for {name}..."""							)
				_lowerCamelCase					     = cached_download(lowercase_							)
				print(F"""Converting {name}..."""							)
				_lowerCamelCase					     = torch.load(lowercase_							)['''state_dict''']
				from_model.load_state_dict(lowercase_							)
				from_model.eval()
				with torch.no_grad():
								_lowerCamelCase					     = VanForImageClassification(lowercase_							).eval()
								_lowerCamelCase					     = ModuleTransfer(src=lowercase_  ,      dest=lowercase_							)
								_lowerCamelCase					     = torch.randn((1, 3, 2_24, 2_24)							)
								module_transfer(lowercase_							)
								_lowerCamelCase					     = copy_parameters(lowercase_  ,      lowercase_							)

				if not torch.allclose(from_model(lowercase_							)  ,      our_model(lowercase_							).logits							):
								raise ValueError('''The model logits don\'t match the original one.'''							)

				_lowerCamelCase					     = name
				print(lowercase_							)

				if push_to_hub:
								our_model.push_to_hub(
								    repo_path_or_name=save_directory / checkpoint_name  ,      commit_message='''Add model'''  ,      use_temp_dir=lowercase_  ,      )

								# we can use the convnext one
								_lowerCamelCase					     = AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''							)
								image_processor.push_to_hub(
								    repo_path_or_name=save_directory / checkpoint_name  ,      commit_message='''Add image processor'''  ,      use_temp_dir=lowercase_  ,      )

								print(F"""Pushed {checkpoint_name}"""							)



def 				lowerCAmelCase_(      lowercase_   :							Path  ,      lowercase_   :							str = None  ,      lowercase_   :							bool = True							)		->				List[str]:
				_lowerCamelCase					     = '''imagenet-1k-id2label.json'''
				_lowerCamelCase					     = 10_00

				_lowerCamelCase					     = '''huggingface/label-files'''
				_lowerCamelCase					     = num_labels
				_lowerCamelCase					     = json.load(open(hf_hub_download(lowercase_  ,      lowercase_  ,      repo_type='''dataset'''							)  ,      '''r'''							)							)
				_lowerCamelCase					     = {int(lowercase_							): v for k, v in idalabel.items()}

				_lowerCamelCase					     = idalabel
				_lowerCamelCase					     = {v: k for k, v in idalabel.items()}

				_lowerCamelCase					     = partial(lowercase_  ,      num_labels=lowercase_  ,      idalabel=lowercase_  ,      labelaid=lowercase_							)

				_lowerCamelCase					     = {
				    '''van-tiny''': ImageNetPreTrainedConfig(
				        hidden_sizes=[32, 64, 1_60, 2_56]  ,      depths=[3, 3, 5, 2]  ,      mlp_ratios=[8, 8, 4, 4]  ,      ),
				    '''van-small''': ImageNetPreTrainedConfig(
				        hidden_sizes=[64, 1_28, 3_20, 5_12]  ,      depths=[2, 2, 4, 2]  ,      mlp_ratios=[8, 8, 4, 4]  ,      ),
				    '''van-base''': ImageNetPreTrainedConfig(
				        hidden_sizes=[64, 1_28, 3_20, 5_12]  ,      depths=[3, 3, 12, 3]  ,      mlp_ratios=[8, 8, 4, 4]  ,      ),
				    '''van-large''': ImageNetPreTrainedConfig(
				        hidden_sizes=[64, 1_28, 3_20, 5_12]  ,      depths=[3, 5, 27, 3]  ,      mlp_ratios=[8, 8, 4, 4]  ,      ),
				}

				_lowerCamelCase					     = {
				    '''van-tiny''': van_tiny,
				    '''van-small''': van_small,
				    '''van-base''': van_base,
				    '''van-large''': van_large,
				}

				_lowerCamelCase					     = {
				    '''van-tiny''': (
				        '''https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar'''
				    ),
				    '''van-small''': (
				        '''https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar'''
				    ),
				    '''van-base''': (
				        '''https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar'''
				    ),
				    '''van-large''': (
				        '''https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar'''
				    ),
				}

				if model_name:
								convert_weight_and_push(
								    lowercase_  ,      names_to_config[model_name]  ,      checkpoint=names_to_original_checkpoints[model_name]  ,      from_model=names_to_original_models[model_name]()  ,      save_directory=lowercase_  ,      push_to_hub=lowercase_  ,      )
				else:
								for model_name, config in names_to_config.items():
												convert_weight_and_push(
												    lowercase_  ,      lowercase_  ,      checkpoint=names_to_original_checkpoints[model_name]  ,      from_model=names_to_original_models[model_name]()  ,      save_directory=lowercase_  ,      push_to_hub=lowercase_  ,      )


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE     :  Any										=					argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--model-name''',
			    default=None,
			    type=str,
			    help=(
			        '''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
			        ''' currently: van-tiny/small/base/large. If `None`, all of them will the converted.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''',
			    default=None,
			    type=Path,
			    required=True,
			    help='''Path to the output PyTorch model directory.''',
			)
			parser.add_argument(
			    '''--van_dir''',
			    required=True,
			    type=Path,
			    help=(
			        '''A path to VAN\'s original implementation directory. You can download from here:'''
			        ''' https://github.com/Visual-Attention-Network/VAN-Classification'''
			    ),
			)
			parser.add_argument(
			    '''--push_to_hub''',
			    default=True,
			    type=bool,
			    required=False,
			    help='''If True, push model and image processor to the hub.''',
			)

			__SCREAMING_SNAKE_CASE     :  int										=					parser.parse_args()
			__SCREAMING_SNAKE_CASE     :  Path										=					args.pytorch_dump_folder_path
			pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
			__SCREAMING_SNAKE_CASE     :  List[str]										=					args.van_dir
			# append the path to the parents to maskformer dir
			sys.path.append(str(van_dir.parent))
			from van.models.van import van_base, van_large, van_small, van_tiny

			convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
