


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_(      lowerCamelCase_ )    ->						str:
    _lowercase       :							List[str]      		=		[
        "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(__snake_case							, __snake_case )



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						Dict:
    _lowercase       :							Dict      		=		emb.weight.shape
    _lowercase       :							Union[str, Any]      		=		nn.Linear(__snake_case							, __snake_case							, bias=__snake_case )
    _lowercase       :							Tuple      		=		emb.weight.data
    return lin_layer



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_=None )    ->						Optional[Any]:
    _lowercase       :							Dict      		=		{}
    for old_key in state_dict.keys():
        _lowercase       :							List[Any]      		=		old_key
        if "moe_layer.experts." in key:
            if expert_idx is not None:
                _lowercase       :							Optional[int]      		=		key.replace('moe_layer.experts.0'							, F'''ffn.experts.expert_{expert_idx}''' )
            else:
                _lowercase       :							Tuple      		=		key.replace('moe_layer.experts.'							, 'ffn.experts.expert_' )
        if "gate" in key:
            _lowercase       :							Tuple      		=		key.replace('.moe_layer.gate.wg'							, '.ffn.router.classifier' )
        if "fc2" and "experts" not in key:
            _lowercase       :							int      		=		key.replace('.fc2.'							, '.ffn.fc2.' )
        if "fc1" and "experts" not in key:
            _lowercase       :							str      		=		key.replace('.fc1.'							, '.ffn.fc1.' )
        if ".encoder_attn." in key:
            _lowercase       :							Dict      		=		key.replace('.encoder_attn.'							, '.cross_attention.' )
        if "encoder_attn_layer_norm" in key:
            _lowercase       :							Union[str, Any]      		=		key.replace('encoder_attn_layer_norm'							, 'cross_attention_layer_norm' )
        if "final_layer_norm" in key:
            _lowercase       :							List[Any]      		=		key.replace('final_layer_norm'							, 'ff_layer_norm' )
        _lowercase       :							str      		=		state_dict[old_key]
    return new_dict



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ = WEIGHTS_NAME )    ->						int:
    _lowercase       :							List[Any]      		=		[]
    _lowercase       :							Dict      		=		0
    os.makedirs(__snake_case							, exist_ok=__snake_case )

    for expert in range(__snake_case ):
        _lowercase       :							List[Any]      		=		switch_checkpoint_path + F'''-rank-{expert}.pt'''
        if os.path.isfile(__snake_case ):
            _lowercase       :							List[str]      		=		torch.load(__snake_case )["model"]
            remove_ignore_keys_(__snake_case )
            _lowercase       :							Optional[Any]      		=		rename_fairseq_keys(__snake_case							, __snake_case )
            _lowercase       :							List[Any]      		=		os.path.join(
                __snake_case							, weights_name.replace('.bin'							, F'''-{len(__snake_case )+1:05d}-of-???.bin''' ) )
            torch.save(__snake_case							, __snake_case )
            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(__snake_case )[0]].dtype )

    # Add the last block
    _lowercase       :							Optional[int]      		=		os.path.join(__snake_case							, weights_name.replace('.bin'							, F'''-{len(__snake_case )+1:05d}-of-???.bin''' ) )
    _lowercase       :							List[Any]      		=		torch.load(switch_checkpoint_path + '-shared.pt' )["model"]
    remove_ignore_keys_(__snake_case )
    _lowercase       :							int      		=		rename_fairseq_keys(__snake_case							, __snake_case )
    _lowercase       :							Any      		=		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(__snake_case ) == 1:
        _lowercase       :							Tuple      		=		os.path.join(__snake_case							, __snake_case )
        torch.save(__snake_case							, __snake_case )
        return {weights_name: sharded_state_dicts[0]}, None
    else:
        torch.save(__snake_case							, __snake_case )
    # Otherwise, let's build the index
    _lowercase       :							List[Any]      		=		{}
    for idx, shard in enumerate(__snake_case ):
        _lowercase       :							Union[str, Any]      		=		weights_name.replace('.bin'							, F'''-{idx+1:05d}-of-{len(__snake_case ):05d}.bin''' )
        _lowercase       :							Dict      		=		os.path.join(__snake_case							, weights_name.replace('.bin'							, F'''-{idx+1:05d}-of-???.bin''' ) )
        os.rename(__snake_case							, os.path.join(__snake_case							, __snake_case ) )
        for key in shard:
            _lowercase       :							List[Any]      		=		shard_file

    # Add the metadata
    _lowercase       :							Any      		=		{"total_size": total_size}
    _lowercase       :							List[str]      		=		{"metadata": metadata, "weight_map": weight_map}

    with open(os.path.join(__snake_case							, __snake_case )							, 'w'							, encoding='utf-8' ) as f:
        _lowercase       :							Optional[int]      		=		json.dumps(__snake_case							, indent=2							, sort_keys=__snake_case ) + "\n"
        f.write(__snake_case )

    return metadata, index


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE       :      int												= 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.",
 )
 SCREAMING_SNAKE_CASE       :      List[str]												= parser.parse_args()
 SCREAMING_SNAKE_CASE       :      Optional[Any]												= shard_on_the_fly(
     args.nllb_moe_checkpoint_path,
     args.pytorch_dump_folder_path,
     128,
     args.dtype,
 )

 SCREAMING_SNAKE_CASE       :      List[Any]												= NllbMoeConfig.from_pretrained(
     "facebook/nllb-200-3.3B", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
 )
 config.save_pretrained(args.pytorch_dump_folder_path)
 SCREAMING_SNAKE_CASE       :      List[str]												= NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
 print("Done")
 model.save_pretrained(args.pytorch_dump_folder_path)




"""simple docstring"""




import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list

@require_torchaudio
@require_sentencepiece
class 		a__						(      unittest.TestCase						):
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							"laion/clap-htsat-unfused"
          lowercase    :				Optional[int]       		=							tempfile.mkdtemp()
   def 			__magic_name__			(			self	,    **_a				):
          return RobertaTokenizer.from_pretrained(self.checkpoint	,    **_a				)
   def 			__magic_name__			(			self	,    **_a				):
          return ClapFeatureExtractor.from_pretrained(self.checkpoint	,    **_a				)
   def 			__magic_name__			(			self				):
          shutil.rmtree(self.tmpdirname				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							self.get_tokenizer()
          lowercase    :				List[Any]       		=							self.get_feature_extractor()

          lowercase    :				Dict       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          processor.save_pretrained(self.tmpdirname				)
          lowercase    :				int       		=							ClapProcessor.from_pretrained(self.tmpdirname				)

          self.assertEqual(processor.tokenizer.get_vocab()	,    tokenizer.get_vocab()				)
          self.assertIsInstance(processor.tokenizer	,    _a				)

          self.assertEqual(processor.feature_extractor.to_json_string()	,    feature_extractor.to_json_string()				)
          self.assertIsInstance(processor.feature_extractor	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				Tuple       		=							ClapProcessor(tokenizer=self.get_tokenizer()	,    feature_extractor=self.get_feature_extractor()				)
          processor.save_pretrained(self.tmpdirname				)

          lowercase    :				Union[str, Any]       		=							self.get_tokenizer(bos_token="(BOS)"	,    eos_token="(EOS)"				)
          lowercase    :				Optional[int]       		=							self.get_feature_extractor(do_normalize=_a	,    padding_value=1.0				)

          lowercase    :				Dict       		=							ClapProcessor.from_pretrained(
              self.tmpdirname	,    bos_token="(BOS)"	,    eos_token="(EOS)"	,    do_normalize=_a	,    padding_value=1.0				)

          self.assertEqual(processor.tokenizer.get_vocab()	,    tokenizer_add_kwargs.get_vocab()				)
          self.assertIsInstance(processor.tokenizer	,    _a				)

          self.assertEqual(processor.feature_extractor.to_json_string()	,    feature_extractor_add_kwargs.to_json_string()				)
          self.assertIsInstance(processor.feature_extractor	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							self.get_feature_extractor()
          lowercase    :				List[str]       		=							self.get_tokenizer()

          lowercase    :				int       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          lowercase    :				Dict       		=							floats_list((3, 1_000)				)

          lowercase    :				str       		=							feature_extractor(_a	,    return_tensors="np"				)
          lowercase    :				Dict       		=							processor(audios=_a	,    return_tensors="np"				)

          for key in input_feat_extract.keys():
                 self.assertAlmostEqual(input_feat_extract[key].sum()	,    input_processor[key].sum()	,    delta=1E-2				)
   def 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							self.get_feature_extractor()
          lowercase    :				int       		=							self.get_tokenizer()

          lowercase    :				Dict       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          lowercase    :				Optional[Any]       		=							"This is a test string"

          lowercase    :				Any       		=							processor(text=_a				)

          lowercase    :				List[Any]       		=							tokenizer(_a				)

          for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]	,    encoded_processor[key]				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							self.get_feature_extractor()
          lowercase    :				Any       		=							self.get_tokenizer()

          lowercase    :				Union[str, Any]       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          lowercase    :				Union[str, Any]       		=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          lowercase    :				str       		=							processor.batch_decode(_a				)
          lowercase    :				Optional[int]       		=							tokenizer.batch_decode(_a				)

          self.assertListEqual(_a	,    _a				)

   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							self.get_feature_extractor()
          lowercase    :				Union[str, Any]       		=							self.get_tokenizer()

          lowercase    :				Any       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          self.assertListEqual(
              processor.model_input_names[2:]	,    feature_extractor.model_input_names	,    msg="`processor` and `feature_extractor` model input names do not match"	,    )


