





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


_A							= """1.5.0"""

_A							= ["""vision-encoder-decoder"""]


def        __snake_case       (						lowerCAmelCase_  )							->	Dict:
    if is_optimum_available():
        from optimum.version import __version__ as optimum_version

        SCREAMING_SNAKE_CASE__       							= version.parse(lowerCAmelCase_  )
        if parsed_optimum_version < version.parse(lowerCAmelCase_  ):
            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]'''  )
    SCREAMING_SNAKE_CASE__       							= [
        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}''',
    ]
    SCREAMING_SNAKE_CASE__       							= subprocess.Popen(''' '''.join(lowerCAmelCase_  )   ,       stdout=subprocess.PIPE   ,       shell=lowerCAmelCase_  )
    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        __snake_case       (						lowerCAmelCase_  )							->	Optional[Any]:
    SCREAMING_SNAKE_CASE__       							= args.output if args.output.is_file() else args.output.joinpath('''model.onnx'''  )
    if not args.output.parent.exists():
        args.output.parent.mkdir(parents=lowerCAmelCase_  )

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

    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= FeaturesManager.check_supported_model_or_raise(lowerCAmelCase_   ,       feature=args.feature  )
    SCREAMING_SNAKE_CASE__       							= model_onnx_config(model.config  )

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

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

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

        SCREAMING_SNAKE_CASE__       							= AutoFeatureExtractor.from_pretrained(args.model  )

        SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= export(
            lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_   ,       args.opset   ,       args.output.parent.joinpath('''encoder_model.onnx'''  )   ,       )

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

        SCREAMING_SNAKE_CASE__       							= AutoTokenizer.from_pretrained(args.model  )

        SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= export(
            lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_   ,       args.opset   ,       args.output.parent.joinpath('''decoder_model.onnx'''  )   ,       )

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

        SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= export(
            lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_   ,       args.opset   ,       args.output   ,       )

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

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


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

    SCREAMING_SNAKE_CASE__       							= parser.parse_args()
    if args.export_with_transformers or not is_optimum_available():
        export_with_transformers(lowerCAmelCase_  )
    else:
        export_with_optimum(lowerCAmelCase_  )


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







import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	str:
    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.in_proj_weight'''].chunk(3   ,       dim=0  )
    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.in_proj_bias'''].chunk(3   ,       dim=0  )

    SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.out_proj.weight''']
    SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.out_proj.bias''']

    SCREAMING_SNAKE_CASE__       							= q_proj
    SCREAMING_SNAKE_CASE__       							= q_proj_bias

    SCREAMING_SNAKE_CASE__       							= k_proj
    SCREAMING_SNAKE_CASE__       							= k_proj_bias

    SCREAMING_SNAKE_CASE__       							= v_proj
    SCREAMING_SNAKE_CASE__       							= v_proj_bias

    SCREAMING_SNAKE_CASE__       							= out_proj_weights
    SCREAMING_SNAKE_CASE__       							= out_proj_bias


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Dict:
    copy_linear(hf_mlp.fca   ,       lowerCAmelCase_   ,       f'''{prefix}.c_fc'''  )
    copy_linear(hf_mlp.fca   ,       lowerCAmelCase_   ,       f'''{prefix}.c_proj'''  )


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	str:
    SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.weight'''].data
    SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.bias'''].data


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Dict:
    # copy layer norms
    copy_linear(hf_layer.layer_norma   ,       lowerCAmelCase_   ,       f'''{prefix}.ln_1'''  )
    copy_linear(hf_layer.layer_norma   ,       lowerCAmelCase_   ,       f'''{prefix}.ln_2'''  )

    # copy MLP
    copy_mlp(hf_layer.mlp   ,       lowerCAmelCase_   ,       f'''{prefix}.mlp'''  )

    # copy attn
    copy_attn_layer(hf_layer.self_attn   ,       lowerCAmelCase_   ,       f'''{prefix}.attn'''  )


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Optional[int]:
    for layer_id, hf_layer in enumerate(lowerCAmelCase_  ):
        copy_layer(lowerCAmelCase_   ,       lowerCAmelCase_   ,       f'''{prefix}.{layer_id}'''  )


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Any:
    # copy projection
    SCREAMING_SNAKE_CASE__       							= pt_weights['''text_projection'''].data.T

    # copy text encoder
    for name, param in hf_model.text_model.named_parameters():
        SCREAMING_SNAKE_CASE__       							= pt_weights[f'''bert.{name}'''].data


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	List[str]:
    # copy projection
    SCREAMING_SNAKE_CASE__       							= pt_weights['''visual.proj'''].data.T

    # copy layer norms
    copy_linear(hf_model.vision_model.pre_layrnorm   ,       lowerCAmelCase_   ,       '''visual.ln_pre'''  )
    copy_linear(hf_model.vision_model.post_layernorm   ,       lowerCAmelCase_   ,       '''visual.ln_post'''  )

    # copy embeddings
    SCREAMING_SNAKE_CASE__       							= pt_weights['''visual.conv1.weight'''].data
    SCREAMING_SNAKE_CASE__       							= pt_weights['''visual.class_embedding'''].data
    SCREAMING_SNAKE_CASE__       							= pt_weights['''visual.positional_embedding'''].data

    # copy encoder
    copy_layers(hf_model.vision_model.encoder.layers   ,       lowerCAmelCase_   ,       '''visual.transformer.resblocks'''  )


@torch.no_grad()
def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_=None  )							->	str:
    assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
    SCREAMING_SNAKE_CASE__       							= ChineseCLIPConfig.from_pretrained(lowerCAmelCase_  )

    SCREAMING_SNAKE_CASE__       							= ChineseCLIPModel(lowerCAmelCase_  ).eval()

    SCREAMING_SNAKE_CASE__       							= torch.load(lowerCAmelCase_   ,       map_location='''cpu'''  )['''state_dict''']
    SCREAMING_SNAKE_CASE__       							= {(name[7:] if name.startswith('''module.'''  ) else name): value for name, value in pt_weights.items()}

    copy_text_model_and_projection(lowerCAmelCase_   ,       lowerCAmelCase_  )
    copy_vision_model_and_projection(lowerCAmelCase_   ,       lowerCAmelCase_  )
    SCREAMING_SNAKE_CASE__       							= pt_weights['''logit_scale'''].data

    hf_model.save_pretrained(lowerCAmelCase_  )


if __name__ == "__main__":
  _A				:							Union[str, Any]							= argparse.ArgumentParser()
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default=None,
      type=str,
      help="""Path to the output folder storing converted hf PyTorch model.""",
  )
  parser.add_argument(
      """--checkpoint_path""", default=None, type=str, help="""Path to original github format ChineseCLIP checkpoint."""
  )
  parser.add_argument(
      """--config_path""", default=None, required=True, type=str, help="""Path to hf config.json of model to convert."""
  )
  _A				:							Union[str, Any]							= parser.parse_args()

  convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
  print("""The conversion is finished!""")
