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      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Dict     )							-> str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[
      '''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(__lowerCAmelCase			,					__lowerCAmelCase     )



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Dict     )							-> Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				emb.weight.shape
  SCREAMING_SNAKE_CASE__      							=				nn.Linear(__lowerCAmelCase			,					__lowerCAmelCase			,					bias=__lowerCAmelCase     )
  SCREAMING_SNAKE_CASE__      							=				emb.weight.data
  return lin_layer



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[Any]			,					__UpperCamelCase   :	Dict=None     )							-> Optional[Any]:







  """simple docstring"""





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



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Any			,					__UpperCamelCase   :	Dict			,					__UpperCamelCase   :	List[str]			,					__UpperCamelCase   :	str			,					__UpperCamelCase   :	List[str] = WEIGHTS_NAME     )							-> Union[str, Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[]
  SCREAMING_SNAKE_CASE__      							=				0
  os.makedirs(__lowerCAmelCase			,					exist_ok=__lowerCAmelCase     )

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

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

    # Add the metadata
  SCREAMING_SNAKE_CASE__      							=				{'''total_size''': total_size}
  SCREAMING_SNAKE_CASE__      							=				{'''metadata''': metadata, '''weight_map''': weight_map}

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

  return metadata, index


if __name__ == "__main__":
   __lowerCamelCase      :			List[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.''',
   )
   __lowerCamelCase      :			Optional[int]							       =		parser.parse_args()
   __lowerCamelCase       ,						__lowerCamelCase      :			Dict							       =		shard_on_the_fly(
       args.nllb_moe_checkpoint_path,
       args.pytorch_dump_folder_path,
       128,
       args.dtype,
   )

   __lowerCamelCase      :			Tuple							       =		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)
   __lowerCamelCase      :			Optional[Any]							       =		NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
   print('''Done''')
   model.save_pretrained(args.pytorch_dump_folder_path)




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer







@require_torch
@require_sentencepiece
@require_tokenizers
class __snake_case    (						unittest.TestCase		):




   @slow
   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""	,     return_dict=_lowercase				).to(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				AutoTokenizer.from_pretrained("""google/mt5-small"""				)

     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hello there"""	,     return_tensors="""pt"""				).input_ids
     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hi I am"""	,     return_tensors="""pt"""				).input_ids

     SCREAMING_SNAKE_CASE__      							=				model(input_ids.to(_lowercase				)	,     labels=labels.to(_lowercase				)				).loss
     SCREAMING_SNAKE_CASE__      							=				-(labels.shape[-1] * loss.item())

     SCREAMING_SNAKE_CASE__      							=				-84.91_27
     self.assertTrue(abs(mtf_score - EXPECTED_SCORE				) < 1E-4				)
