




"""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     _A					( __lowercase    ):





       """simple docstring"""



       lowerCamelCase__          =						[
           """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(lowercase__							,     lowercase__    )




def     _A					( __lowercase    ):





       """simple docstring"""



       lowerCamelCase__     ,	lowerCamelCase__          =						emb.weight.shape
       lowerCamelCase__          =						nn.Linear(lowercase__							,     lowercase__							,     bias=lowercase__    )
       lowerCamelCase__          =						emb.weight.data
       return lin_layer




def     _A					( __lowercase							,     __lowercase=None    ):





       """simple docstring"""



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




def     _A					( __lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase = WEIGHTS_NAME    ):





       """simple docstring"""



       lowerCamelCase__          =						[]
       lowerCamelCase__          =						0
       os.makedirs(lowercase__							,     exist_ok=lowercase__    )

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

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

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

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

       return metadata, index


if __name__ == "__main__":
  __magic_name__				     =     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.""",
  )
  __magic_name__				     =     parser.parse_args()
  __magic_name__				     =     shard_on_the_fly(
      args.nllb_moe_checkpoint_path,
      args.pytorch_dump_folder_path,
      1_28,
      args.dtype,
  )

  __magic_name__				     =     NllbMoeConfig.from_pretrained(
      """facebook/nllb-200-3.3B""", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_28
  )
  config.save_pretrained(args.pytorch_dump_folder_path)
  __magic_name__				     =     NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
  print("""Done""")
  model.save_pretrained(args.pytorch_dump_folder_path)










"""simple docstring"""




import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from requests.exceptions import HTTPError

from transformers.utils import (
    CONFIG_NAME,
    FLAX_WEIGHTS_NAME,
    TF2_WEIGHTS_NAME,
    TRANSFORMERS_CACHE,
    WEIGHTS_NAME,
    cached_file,
    get_file_from_repo,
    has_file,
)


__magic_name__						: Tuple    =				'hf-internal-testing/tiny-random-bert'
__magic_name__						: Dict    =				os.path.join(TRANSFORMERS_CACHE, 'models--hf-internal-testing--tiny-random-bert')
__magic_name__						: Optional[Any]    =				'9b8c223d42b2188cb49d29af482996f9d0f3e5a6'




class 			__snake_case   (unittest.TestCase  ):




							def   __a   (     self:    List[Any]   ):
										__lowerCamelCase          =      cached_file(A_     ,							A_   )
										# Should have downloaded the file in here
										self.assertTrue(os.path.isdir(A_   )   )
										# Cache should contain at least those three subfolders:
										for subfolder in ["blobs", "refs", "snapshots"]:
													self.assertTrue(os.path.isdir(os.path.join(A_     ,							A_   )   )   )
										with open(os.path.join(A_     ,							"""refs"""     ,							"""main"""   )   ) as f:
													__lowerCamelCase          =      f.read()
										self.assertEqual(A_     ,							os.path.join(A_     ,							"""snapshots"""     ,							A_     ,							A_   )   )
										self.assertTrue(os.path.isfile(A_   )   )

										# File is cached at the same place the second time.
										__lowerCamelCase          =      cached_file(A_     ,							A_   )
										self.assertEqual(A_     ,							A_   )

										# Using a specific revision to test the full commit hash.
										__lowerCamelCase          =      cached_file(A_     ,							A_     ,							revision="""9b8c223"""   )
										self.assertEqual(A_     ,							os.path.join(A_     ,							"""snapshots"""     ,							A_     ,							A_   )   )




							def   __a   (     self:    int   ):
										with self.assertRaisesRegex(A_     ,							"""is not a valid model identifier"""   ):
													__lowerCamelCase          =      cached_file("""tiny-random-bert"""     ,							A_   )

										with self.assertRaisesRegex(A_     ,							"""is not a valid git identifier"""   ):
													__lowerCamelCase          =      cached_file(A_     ,							A_     ,							revision="""aaaa"""   )

										with self.assertRaisesRegex(A_     ,							"""does not appear to have a file named"""   ):
													__lowerCamelCase          =      cached_file(A_     ,							"""conf"""   )




							def   __a   (     self:    Optional[int]   ):
										with self.assertRaisesRegex(A_     ,							"""does not appear to have a file named"""   ):
													__lowerCamelCase          =      cached_file(A_     ,							"""conf"""   )

										with open(os.path.join(A_     ,							"""refs"""     ,							"""main"""   )   ) as f:
													__lowerCamelCase          =      f.read()
										self.assertTrue(os.path.isfile(os.path.join(A_     ,							""".no_exist"""     ,							A_     ,							"""conf"""   )   )   )

										__lowerCamelCase          =      cached_file(A_     ,							"""conf"""     ,							_raise_exceptions_for_missing_entries=A_   )
										self.assertIsNone(A_   )

										__lowerCamelCase          =      cached_file(A_     ,							"""conf"""     ,							local_files_only=A_     ,							_raise_exceptions_for_missing_entries=A_   )
										self.assertIsNone(A_   )

										__lowerCamelCase          =      mock.Mock()
										__lowerCamelCase          =      5_00
										__lowerCamelCase          =      {}
										__lowerCamelCase          =      HTTPError
										__lowerCamelCase          =      {}

										# Under the mock environment we get a 500 error when trying to reach the tokenizer.
										with mock.patch("""requests.Session.request"""     ,							return_value=A_   ) as mock_head:
													__lowerCamelCase          =      cached_file(A_     ,							"""conf"""     ,							_raise_exceptions_for_connection_errors=A_   )
													self.assertIsNone(A_   )
													# This check we did call the fake head request
													mock_head.assert_called()




							def   __a   (     self:    str   ):
										self.assertTrue(has_file("""hf-internal-testing/tiny-bert-pt-only"""     ,							A_   )   )
										self.assertFalse(has_file("""hf-internal-testing/tiny-bert-pt-only"""     ,							A_   )   )
										self.assertFalse(has_file("""hf-internal-testing/tiny-bert-pt-only"""     ,							A_   )   )




							def   __a   (     self:    str   ):
										# `get_file_from_repo` returns None if the file does not exist
										self.assertIsNone(get_file_from_repo("""bert-base-cased"""     ,							"""ahah.txt"""   )   )

										# The function raises if the repository does not exist.
										with self.assertRaisesRegex(A_     ,							"""is not a valid model identifier"""   ):
													get_file_from_repo("""bert-base-case"""     ,							A_   )

										# The function raises if the revision does not exist.
										with self.assertRaisesRegex(A_     ,							"""is not a valid git identifier"""   ):
													get_file_from_repo("""bert-base-cased"""     ,							A_     ,							revision="""ahaha"""   )

										__lowerCamelCase          =      get_file_from_repo("""bert-base-cased"""     ,							A_   )
										# The name is the cached name which is not very easy to test, so instead we load the content.
										__lowerCamelCase          =      json.loads(open(A_     ,							"""r"""   ).read()   )
										self.assertEqual(config["""hidden_size"""]     ,							7_68   )




							def   __a   (     self:    Union[str, Any]   ):
										with tempfile.TemporaryDirectory() as tmp_dir:
													__lowerCamelCase          =      Path(A_   ) / """a.txt"""
													filename.touch()
													self.assertEqual(get_file_from_repo(A_     ,							"""a.txt"""   )     ,							str(A_   )   )

													self.assertIsNone(get_file_from_repo(A_     ,							"""b.txt"""   )   )




