
"""simple docstring"""



import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME


def  _UpperCamelCase	(						_A      )    ->  List[str]:





    """simple docstring"""

    _UpperCAmelCase          =   [
        """encoder.version""",
        """decoder.version""",
        """model.encoder.version""",
        """model.decoder.version""",
        """decoder.output_projection.weight""",
        """_float_tensor""",
        """encoder.embed_positions._float_tensor""",
        """decoder.embed_positions._float_tensor""",
    ]
    for k in ignore_keys:
        state_dict.pop(_A					,      _A      )


def  _UpperCamelCase	(						_A      )    ->  int:





    """simple docstring"""

    _UpperCAmelCase    ,_UpperCAmelCase          =   emb.weight.shape
    _UpperCAmelCase          =   nn.Linear(_A					,      _A					,      bias=_A      )
    _UpperCAmelCase          =   emb.weight.data
    return lin_layer


def  _UpperCamelCase	(						_A					,      _A=None      )    ->  Optional[int]:





    """simple docstring"""

    _UpperCAmelCase          =   {}
    for old_key in state_dict.keys():
        _UpperCAmelCase          =   old_key
        if "moe_layer.experts." in key:
            if expert_idx is not None:
                _UpperCAmelCase          =   key.replace("""moe_layer.experts.0"""					,      F"""ffn.experts.expert_{expert_idx}"""      )
            else:
                _UpperCAmelCase          =   key.replace("""moe_layer.experts."""					,      """ffn.experts.expert_"""      )
        if "gate" in key:
            _UpperCAmelCase          =   key.replace(""".moe_layer.gate.wg"""					,      """.ffn.router.classifier"""      )
        if "fc2" and "experts" not in key:
            _UpperCAmelCase          =   key.replace(""".fc2."""					,      """.ffn.fc2."""      )
        if "fc1" and "experts" not in key:
            _UpperCAmelCase          =   key.replace(""".fc1."""					,      """.ffn.fc1."""      )
        if ".encoder_attn." in key:
            _UpperCAmelCase          =   key.replace(""".encoder_attn."""					,      """.cross_attention."""      )
        if "encoder_attn_layer_norm" in key:
            _UpperCAmelCase          =   key.replace("""encoder_attn_layer_norm"""					,      """cross_attention_layer_norm"""      )
        if "final_layer_norm" in key:
            _UpperCAmelCase          =   key.replace("""final_layer_norm"""					,      """ff_layer_norm"""      )
        _UpperCAmelCase          =   state_dict[old_key]
    return new_dict


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A					,      _A = WEIGHTS_NAME      )    ->  str:





    """simple docstring"""

    _UpperCAmelCase          =   []
    _UpperCAmelCase          =   0
    os.makedirs(_A					,      exist_ok=_A      )

    for expert in range(_A      ):
        _UpperCAmelCase          =   switch_checkpoint_path + F"""-rank-{expert}.pt"""
        if os.path.isfile(_A      ):
            _UpperCAmelCase          =   torch.load(_A      )["""model"""]
            remove_ignore_keys_(_A      )
            _UpperCAmelCase          =   rename_fairseq_keys(_A					,      _A      )
            _UpperCAmelCase          =   os.path.join(
                _A					,      weights_name.replace(""".bin"""					,      F"""-{len(_A      )+1:05d}-of-???.bin"""      )      )
            torch.save(_A					,      _A      )
            sharded_state_dicts.append(expert_state.keys()      )
            total_size += sum([value.numel() for key, value in expert_state.items()]      ) * dtype_byte_size(
                expert_state[list(_A      )[0]].dtype      )

    # Add the last block
    _UpperCAmelCase          =   os.path.join(_A					,      weights_name.replace(""".bin"""					,      F"""-{len(_A      )+1:05d}-of-???.bin"""      )      )
    _UpperCAmelCase          =   torch.load(switch_checkpoint_path + """-shared.pt"""      )["""model"""]
    remove_ignore_keys_(_A      )
    _UpperCAmelCase          =   rename_fairseq_keys(_A					,      _A      )
    _UpperCAmelCase          =   shared_weights["""decoder.embed_tokens.weight"""]
    sharded_state_dicts.append(shared_weights.keys()      )

    # If we only have the shared weights (dummy model/experts saved on the same file)
    if len(_A      ) == 1:
        _UpperCAmelCase          =   os.path.join(_A					,      _A      )
        torch.save(_A					,      _A      )
        return {weights_name: sharded_state_dicts[0]}, None
    else:
        torch.save(_A					,      _A      )
    # Otherwise, let's build the index
    _UpperCAmelCase          =   {}
    for idx, shard in enumerate(_A      ):
        _UpperCAmelCase          =   weights_name.replace(""".bin"""					,      F"""-{idx+1:05d}-of-{len(_A      ):05d}.bin"""      )
        _UpperCAmelCase          =   os.path.join(_A					,      weights_name.replace(""".bin"""					,      F"""-{idx+1:05d}-of-???.bin"""      )      )
        os.rename(_A					,      os.path.join(_A					,      _A      )      )
        for key in shard:
            _UpperCAmelCase          =   shard_file

    # Add the metadata
    _UpperCAmelCase          =   {"""total_size""": total_size}
    _UpperCAmelCase          =   {"""metadata""": metadata, """weight_map""": weight_map}

    with open(os.path.join(_A					,      _A      )					,      """w"""					,      encoding="""utf-8"""      ) as f:
        _UpperCAmelCase          =   json.dumps(_A					,      indent=2					,      sort_keys=_A      ) + """\n"""
        f.write(_A      )

    return metadata, index


if __name__ == "__main__":
 a					:	Optional[Any]							=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--nllb_moe_checkpoint_path''',
     default='''/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000''',
     type=str,
     required=False,
     help='''Path to a directory containing a folder per layer. Follows the original Google format.''',
 )
 parser.add_argument('''--dtype''', default='''float32''', type=str, required=False, help='''dtype of the saved model''')
 parser.add_argument(
     '''--pytorch_dump_folder_path''',
     default='''/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b''',
     type=str,
     required=False,
     help='''Path to the output pytorch model.''',
 )
 a					:	Optional[int]							=		parser.parse_args()
 a		, a					:	Optional[Any]							=		shard_on_the_fly(
     args.nllb_moe_checkpoint_path,
     args.pytorch_dump_folder_path,
     1_2_8,
     args.dtype,
 )

 a					:	Union[str, Any]							=		NllbMoeConfig.from_pretrained(
     '''facebook/nllb-200-3.3B''', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
 )
 config.save_pretrained(args.pytorch_dump_folder_path)
 a					:	Union[str, Any]							=		NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
 print('''Done''')
 model.save_pretrained(args.pytorch_dump_folder_path)

"""simple docstring"""



import argparse
import logging
import os
import sys

import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers

import transformers
from transformers import BartForConditionalGeneration, BartTokenizer


logging.basicConfig(
    format='''%(asctime)s | %(levelname)s | %(name)s |  [%(filename)s:%(lineno)d] %(message)s''',
    datefmt='''%Y-%m-%d %H:%M:%S''',
    level=os.environ.get('''LOGLEVEL''', '''INFO''').upper(),
    stream=sys.stdout,
)

a					:	List[str]							=		logging.getLogger(__name__)

a					:	int							=		{'''facebook/bart-base''': BartForConditionalGeneration}
a					:	Dict							=		{'''facebook/bart-base''': BartTokenizer}


def  _UpperCamelCase	(						)    ->  Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   argparse.ArgumentParser(description="""Export Bart model + Beam Search to ONNX graph."""      )
    parser.add_argument(
        """--validation_file"""					,      type=_A					,      default=_A					,      help="""A csv or a json file containing the validation data."""      )
    parser.add_argument(
        """--max_length"""					,      type=_A					,      default=5					,      help="""The maximum total input sequence length after tokenization."""					,      )
    parser.add_argument(
        """--num_beams"""					,      type=_A					,      default=_A					,      help=(
            """Number of beams to use for evaluation. This argument will be """
            """passed to ``model.generate``, which is used during ``evaluate`` and ``predict``."""
        )					,      )
    parser.add_argument(
        """--model_name_or_path"""					,      type=_A					,      help="""Path to pretrained model or model identifier from huggingface.co/models."""					,      required=_A					,      )
    parser.add_argument(
        """--config_name"""					,      type=_A					,      default=_A					,      help="""Pretrained config name or path if not the same as model_name"""					,      )
    parser.add_argument(
        """--device"""					,      type=_A					,      default="""cpu"""					,      help="""Device where the model will be run"""					,      )
    parser.add_argument("""--output_file_path"""					,      type=_A					,      default=_A					,      help="""Where to store the final ONNX file."""      )

    _UpperCAmelCase          =   parser.parse_args()

    return args


def  _UpperCamelCase	(						_A					,      _A="cpu"      )    ->  Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   model_dict[model_name].from_pretrained(_A      ).to(_A      )
    _UpperCAmelCase          =   tokenizer_dict[model_name].from_pretrained(_A      )

    if model_name in ["facebook/bart-base"]:
        _UpperCAmelCase          =   0
        _UpperCAmelCase          =   None
        _UpperCAmelCase          =   0

    return huggingface_model, tokenizer


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A					,      _A      )    ->  Optional[int]:





    """simple docstring"""

    model.eval()

    _UpperCAmelCase          =   None
    _UpperCAmelCase          =   torch.jit.script(BARTBeamSearchGenerator(_A      )      )

    with torch.no_grad():
        _UpperCAmelCase          =   """My friends are cool but they eat too many carbs."""
        _UpperCAmelCase          =   tokenizer([ARTICLE_TO_SUMMARIZE]					,      max_length=1_0_2_4					,      return_tensors="""pt"""      ).to(model.device      )

        _UpperCAmelCase          =   model.generate(
            inputs["""input_ids"""]					,      attention_mask=inputs["""attention_mask"""]					,      num_beams=_A					,      max_length=_A					,      early_stopping=_A					,      decoder_start_token_id=model.config.decoder_start_token_id					,      )

        torch.onnx.export(
            _A					,      (
                inputs["""input_ids"""],
                inputs["""attention_mask"""],
                num_beams,
                max_length,
                model.config.decoder_start_token_id,
            )					,      _A					,      opset_version=1_4					,      input_names=["""input_ids""", """attention_mask""", """num_beams""", """max_length""", """decoder_start_token_id"""]					,      output_names=["""output_ids"""]					,      dynamic_axes={
                """input_ids""": {0: """batch""", 1: """seq"""},
                """output_ids""": {0: """batch""", 1: """seq_out"""},
            }					,      example_outputs=_A					,      )

        logger.info("""Model exported to {}""".format(_A      )      )

        _UpperCAmelCase          =   remove_dup_initializers(os.path.abspath(_A      )      )

        logger.info("""Deduplicated and optimized model written to {}""".format(_A      )      )

        _UpperCAmelCase          =   onnxruntime.InferenceSession(_A      )
        _UpperCAmelCase          =   ort_sess.run(
            _A					,      {
                """input_ids""": inputs["""input_ids"""].cpu().numpy(),
                """attention_mask""": inputs["""attention_mask"""].cpu().numpy(),
                """num_beams""": np.array(_A      ),
                """max_length""": np.array(_A      ),
                """decoder_start_token_id""": np.array(model.config.decoder_start_token_id      ),
            }					,      )

        np.testing.assert_allclose(summary_ids.cpu().numpy()					,      ort_out[0]					,      rtol=1e-3					,      atol=1e-3      )

        logger.info("""Model outputs from torch and ONNX Runtime are similar."""      )
        logger.info("""Success."""      )


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





    """simple docstring"""

    _UpperCAmelCase          =   parse_args()
    _UpperCAmelCase          =   5
    _UpperCAmelCase          =   4

    # Make one log on every process with the configuration for debugging.
    logging.basicConfig(
        format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""					,      datefmt="""%m/%d/%Y %H:%M:%S"""					,      level=logging.INFO					,      )

    logger.setLevel(logging.INFO      )
    transformers.utils.logging.set_verbosity_error()

    _UpperCAmelCase          =   torch.device(args.device      )

    _UpperCAmelCase    ,_UpperCAmelCase          =   load_model_tokenizer(args.model_name_or_path					,      _A      )

    if model.config.decoder_start_token_id is None:
        raise ValueError("""Make sure that `config.decoder_start_token_id` is correctly defined"""      )

    model.to(_A      )

    if args.max_length:
        _UpperCAmelCase          =   args.max_length

    if args.num_beams:
        _UpperCAmelCase          =   args.num_beams

    if args.output_file_path:
        _UpperCAmelCase          =   args.output_file_path
    else:
        _UpperCAmelCase          =   """BART.onnx"""

    logger.info("""Exporting model to ONNX"""      )
    export_and_validate_model(_A					,      _A					,      _A					,      _A					,      _A      )


if __name__ == "__main__":
 main()