
"""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  lowerCamelCase__						(     __snake_case      )				->						int:


					"""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(__snake_case,	__snake_case      )



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


					"""simple docstring"""





					_UpperCamelCase					,    _UpperCamelCase									=    emb.weight.shape
					_UpperCamelCase									=    nn.Linear(__snake_case,	__snake_case,	bias=__snake_case      )
					_UpperCamelCase									=    emb.weight.data
					return lin_layer



def  lowerCamelCase__						(     __snake_case,	__snake_case=None      )				->						Optional[Any]:


					"""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  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case = WEIGHTS_NAME      )				->						Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    []
					_UpperCamelCase									=    0
					os.makedirs(__snake_case,	exist_ok=__snake_case      )

					for expert in range(__snake_case      ):
										_UpperCamelCase									=    switch_checkpoint_path + F'''-rank-{expert}.pt'''
										if os.path.isfile(__snake_case      ):
															_UpperCamelCase									=    torch.load(__snake_case      )['''model''']
															remove_ignore_keys_(__snake_case      )
															_UpperCamelCase									=    rename_fairseq_keys(__snake_case,	__snake_case      )
															_UpperCamelCase									=    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
					_UpperCamelCase									=    os.path.join(__snake_case,	weights_name.replace('''.bin''',	F'''-{len(__snake_case      )+1:05d}-of-???.bin'''      )      )
					_UpperCamelCase									=    torch.load(switch_checkpoint_path + '''-shared.pt'''      )['''model''']
					remove_ignore_keys_(__snake_case      )
					_UpperCamelCase									=    rename_fairseq_keys(__snake_case,	__snake_case      )
					_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(__snake_case      ) == 1:
										_UpperCamelCase									=    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
					_UpperCamelCase									=    {}
					for idx, shard in enumerate(__snake_case      ):
										_UpperCamelCase									=    weights_name.replace('''.bin''',	F'''-{idx+1:05d}-of-{len(__snake_case      ):05d}.bin'''      )
										_UpperCamelCase									=    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:
															_UpperCamelCase									=    shard_file

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

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

					return metadata, index


if __name__ == "__main__":
				_a								=						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								=						parser.parse_args()
				_a				,				_a								=						shard_on_the_fly(
				    args.nllb_moe_checkpoint_path,
				    args.pytorch_dump_folder_path,
				    128,
				    args.dtype,
				)

				_a								=						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)
				_a								=						NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
				print("""Done""")
				model.save_pretrained(args.pytorch_dump_folder_path)











import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a_      :      Dict    =     logging.get_logger(__name__)


a_      :      Any    =     {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

a_      :      Union[str, Any]    =     {
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

a_      :      List[Any]    =     {'facebook/blenderbot_small-90M': 5_12}






def     __a					(		__UpperCAmelCase       ):
 a__												= set()
 a__												= word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)       )
  a__												= char

 a__												= set(__UpperCAmelCase       )
 return pairs

class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 _lowercase   :    Union[str, Any]      						=					VOCAB_FILES_NAMES
 _lowercase   :    List[Any]      						=					PRETRAINED_VOCAB_FILES_MAP
 _lowercase   :    Tuple      						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowercase   :    int      						=					['''input_ids''', '''attention_mask''']

 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE="__start__"		,				SCREAMING_SNAKE_CASE="__end__"		,				SCREAMING_SNAKE_CASE="__unk__"		,				SCREAMING_SNAKE_CASE="__null__"		,				**SCREAMING_SNAKE_CASE		,				) ->			List[str]:
  super().__init__(unk_token=SCREAMING_SNAKE_CASE		,				bos_token=SCREAMING_SNAKE_CASE		,				eos_token=SCREAMING_SNAKE_CASE		,				pad_token=SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

  with open(SCREAMING_SNAKE_CASE		,				encoding='''utf-8'''      ) as vocab_handle:
   a__												= json.load(SCREAMING_SNAKE_CASE      )
  a__												= {v: k for k, v in self.encoder.items()}
  with open(SCREAMING_SNAKE_CASE		,				encoding='''utf-8'''      ) as merges_handle:
   a__												= merges_handle.read().split('''\n'''      )[1:-1]
  a__												= [tuple(merge.split()      ) for merge in merges]
  a__												= dict(zip(SCREAMING_SNAKE_CASE		,				range(len(SCREAMING_SNAKE_CASE      )      )      )      )
  a__												= {}

 @property
 def 						_UpperCAmelCase       (	self      ) ->			int:
  return len(self.encoder      )

 def 						_UpperCAmelCase       (	self      ) ->			Dict:
  return dict(self.encoder		,				**self.added_tokens_encoder      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			str:
  if token in self.cache:
   return self.cache[token]
  a__												= re.sub('''([.,!?()])'''		,				R''' \1'''		,				SCREAMING_SNAKE_CASE      )
  a__												= re.sub('''(\')'''		,				R''' \1 '''		,				SCREAMING_SNAKE_CASE      )
  a__												= re.sub(R'''\s{2,}'''		,				''' '''		,				SCREAMING_SNAKE_CASE      )
  if "\n" in token:
   a__												= token.replace('''\n'''		,				''' __newln__'''      )

  a__												= token.split(''' '''      )
  a__												= []
  for token in tokens:
   if not len(SCREAMING_SNAKE_CASE      ):
    continue

   a__												= token.lower()
   a__												= tuple(SCREAMING_SNAKE_CASE      )
   a__												= tuple(list(word[:-1]      ) + [word[-1] + '''</w>''']      )
   a__												= get_pairs(SCREAMING_SNAKE_CASE      )

   if not pairs:
    words.append(SCREAMING_SNAKE_CASE      )
    continue

   while True:
    a__												= min(SCREAMING_SNAKE_CASE		,				key=lambda SCREAMING_SNAKE_CASE      : self.bpe_ranks.get(SCREAMING_SNAKE_CASE		,				float('''inf'''      )      )      )
    if bigram not in self.bpe_ranks:
     break
    a__							,			a__												= bigram
    a__												= []
    a__												= 0

    while i < len(SCREAMING_SNAKE_CASE      ):
     try:
      a__												= word.index(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
      new_word.extend(word[i:j]      )
      a__												= j
     except ValueError:
      new_word.extend(word[i:]      )
      break

     if word[i] == first and i < len(SCREAMING_SNAKE_CASE      ) - 1 and word[i + 1] == second:
      new_word.append(first + second      )
      i += 2
     else:
      new_word.append(word[i]      )
      i += 1
    a__												= tuple(SCREAMING_SNAKE_CASE      )
    a__												= new_word
    if len(SCREAMING_SNAKE_CASE      ) == 1:
     break
    else:
     a__												= get_pairs(SCREAMING_SNAKE_CASE      )
   a__												= '''@@ '''.join(SCREAMING_SNAKE_CASE      )
   a__												= word[:-4]

   a__												= word
   words.append(SCREAMING_SNAKE_CASE      )
  return " ".join(SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			List[str]:
  a__												= []

  a__												= re.findall(R'''\S+\n?'''		,				SCREAMING_SNAKE_CASE      )

  for token in words:
   split_tokens.extend(list(self.bpe(SCREAMING_SNAKE_CASE      ).split(''' '''      )      )      )
  return split_tokens

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			int:
  a__												= token.lower()
  return self.encoder.get(SCREAMING_SNAKE_CASE		,				self.encoder.get(self.unk_token      )      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			str:
  return self.decoder.get(SCREAMING_SNAKE_CASE		,				self.unk_token      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			str:
  a__												= ''' '''.join(SCREAMING_SNAKE_CASE      ).replace('''@@ '''		,				''''''      ).strip()
  return out_string



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None      ) ->			Tuple[str]:
  if not os.path.isdir(SCREAMING_SNAKE_CASE      ):
   logger.error(f"Vocabulary path ({save_directory}) should be a directory"      )
   return
  a__												= os.path.join(
      SCREAMING_SNAKE_CASE		,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )
  a__												= os.path.join(
      SCREAMING_SNAKE_CASE		,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']      )

  with open(SCREAMING_SNAKE_CASE		,				'''w'''		,				encoding='''utf-8'''      ) as f:
   f.write(json.dumps(self.encoder		,				indent=2		,				sort_keys=SCREAMING_SNAKE_CASE		,				ensure_ascii=SCREAMING_SNAKE_CASE      ) + '''\n'''      )

  a__												= 0
  with open(SCREAMING_SNAKE_CASE		,				'''w'''		,				encoding='''utf-8'''      ) as writer:
   writer.write('''#version: 0.2\n'''      )
   for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,				key=lambda SCREAMING_SNAKE_CASE      : kv[1]      ):
    if index != token_index:
     logger.warning(
         f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
         ''' Please check that the tokenizer is not corrupted!'''      )
     a__												= token_index
    writer.write(''' '''.join(SCREAMING_SNAKE_CASE      ) + '''\n'''      )
    index += 1

  return vocab_file, merge_file





