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   :	List[str]     )							-> Tuple:







    """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(__a			,					__a     )



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	str     )							-> List[Any]:







    """simple docstring"""





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



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Any			,					__UpperCamelCase   :	str=None     )							-> int:







    """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   :	Dict			,					__UpperCamelCase   :	Optional[Any]			,					__UpperCamelCase   :	str			,					__UpperCamelCase   :	Dict			,					__UpperCamelCase   :	str = WEIGHTS_NAME     )							-> Optional[Any]:







    """simple docstring"""





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

    for expert in range(__a     ):
        SCREAMING_SNAKE_CASE__      							=				switch_checkpoint_path + f"""-rank-{expert}.pt"""
        if os.path.isfile(__a     ):
            SCREAMING_SNAKE_CASE__      							=				torch.load(__a     )['''model''']
            remove_ignore_keys_(__a     )
            SCREAMING_SNAKE_CASE__      							=				rename_fairseq_keys(__a			,					__a     )
            SCREAMING_SNAKE_CASE__      							=				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
    SCREAMING_SNAKE_CASE__      							=				os.path.join(__a			,					weights_name.replace(""".bin"""			,					f"""-{len(__a     )+1:05d}-of-???.bin"""     )     )
    SCREAMING_SNAKE_CASE__      							=				torch.load(switch_checkpoint_path + """-shared.pt"""     )['''model''']
    remove_ignore_keys_(__a     )
    SCREAMING_SNAKE_CASE__      							=				rename_fairseq_keys(__a			,					__a     )
    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(__a     ) == 1:
        SCREAMING_SNAKE_CASE__      							=				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
    SCREAMING_SNAKE_CASE__      							=				{}
    for idx, shard in enumerate(__a     ):
        SCREAMING_SNAKE_CASE__      							=				weights_name.replace(""".bin"""			,					f"""-{idx+1:05d}-of-{len(__a     ):05d}.bin"""     )
        SCREAMING_SNAKE_CASE__      							=				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:
            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(__a			,					__a     )			,					"""w"""			,					encoding="""utf-8"""     ) as f:
        SCREAMING_SNAKE_CASE__      							=				json.dumps(__a			,					indent=2			,					sort_keys=__a     ) + '''\n'''
        f.write(__a     )

    return metadata, index


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

   __lowerCamelCase      :			List[str]							       =		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      :			Dict							       =		NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
   print('''Done''')
   model.save_pretrained(args.pytorch_dump_folder_path)

from __future__ import annotations

__lowerCamelCase      :			Dict							       =		[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__lowerCamelCase      :			Union[str, Any]							       =		[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list[float]     )							-> list[float]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[]
  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     )

  for i in range(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				-1
    for j in range(i + 1			,					__UpperCamelCase     ):
      if arr[i] < arr[j]:
        SCREAMING_SNAKE_CASE__      							=				arr[j]
        break
    result.append(__UpperCamelCase     )
  return result



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list[float]     )							-> list[float]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[]
  for i, outer in enumerate(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				-1
    for inner in arr[i + 1 :]:
      if outer < inner:
        SCREAMING_SNAKE_CASE__      							=				inner
        break
    result.append(__UpperCamelCase     )
  return result



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list[float]     )							-> list[float]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				[]
  SCREAMING_SNAKE_CASE__      							=				[-1] * arr_size

  for index in reversed(range(__UpperCamelCase     )     ):
    if stack:
      while stack[-1] <= arr[index]:
        stack.pop()
        if not stack:
          break
    if stack:
      SCREAMING_SNAKE_CASE__      							=				stack[-1]
    stack.append(arr[index]     )
  return result


if __name__ == "__main__":
   from doctest import testmod
   from timeit import timeit

   testmod()
   print(next_greatest_element_slow(arr))
   print(next_greatest_element_fast(arr))
   print(next_greatest_element(arr))

   __lowerCamelCase      :			List[Any]							       =		(
       '''from __main__ import arr, next_greatest_element_slow, '''
       '''next_greatest_element_fast, next_greatest_element'''
   )
   print(
       '''next_greatest_element_slow():''',
       timeit('''next_greatest_element_slow(arr)''', setup=setup),
   )
   print(
       '''next_greatest_element_fast():''',
       timeit('''next_greatest_element_fast(arr)''', setup=setup),
   )
   print(
       '''     next_greatest_element():''',
       timeit('''next_greatest_element(arr)''', setup=setup),
   )
