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			)							->      Union[str, Any]:







  """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(__UpperCamelCase				,  __UpperCamelCase			)



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







  """simple docstring"""





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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: Union[str, Any]=None			)							->      Dict:







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







  """simple docstring"""





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

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

    # Add the last block
  SCREAMING_SNAKE_CASE__    							=   os.path.join(__UpperCamelCase				,  weights_name.replace(""".bin"""				,  f"""-{len(__UpperCamelCase			)+1:05d}-of-???.bin"""			)			)
  SCREAMING_SNAKE_CASE__    							=   torch.load(switch_checkpoint_path + """-shared.pt"""			)["""model"""]
  remove_ignore_keys_(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   rename_fairseq_keys(__UpperCamelCase				,  __UpperCamelCase			)
  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(__UpperCamelCase			) == 1:
    SCREAMING_SNAKE_CASE__    							=   os.path.join(__UpperCamelCase				,  __UpperCamelCase			)
    torch.save(__UpperCamelCase				,  __UpperCamelCase			)
    return {weights_name: sharded_state_dicts[0]}, None
  else:
    torch.save(__UpperCamelCase				,  __UpperCamelCase			)
  # Otherwise, let's build the index
  SCREAMING_SNAKE_CASE__    							=   {}
  for idx, shard in enumerate(__UpperCamelCase			):
    SCREAMING_SNAKE_CASE__    							=   weights_name.replace(""".bin"""				,  f"""-{idx+1:05d}-of-{len(__UpperCamelCase			):05d}.bin"""			)
    SCREAMING_SNAKE_CASE__    							=   os.path.join(__UpperCamelCase				,  weights_name.replace(""".bin"""				,  f"""-{idx+1:05d}-of-???.bin"""			)			)
    os.rename(__UpperCamelCase				,  os.path.join(__UpperCamelCase				,  __UpperCamelCase			)			)
    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(__UpperCamelCase				,  __UpperCamelCase			)				,  """w"""				,  encoding="""utf-8"""			) as f:
    SCREAMING_SNAKE_CASE__    							=   json.dumps(__UpperCamelCase				,  indent=2				,  sort_keys=__UpperCamelCase			) + """\n"""
    f.write(__UpperCamelCase			)

  return metadata, index


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

   __lowerCamelCase    :				Union[str, 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)
   __lowerCamelCase    :				str							     =	NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
   print('''Done''')
   model.save_pretrained(args.pytorch_dump_folder_path)

# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


__lowerCamelCase    :				Tuple							     =	'''3'''

print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)

try:
   import torch

   print('''Torch version:''', torch.__version__)
   print('''Cuda available:''', torch.cuda.is_available())
   print('''Cuda version:''', torch.version.cuda)
   print('''CuDNN version:''', torch.backends.cudnn.version())
   print('''Number of GPUs available:''', torch.cuda.device_count())
   print('''NCCL version:''', torch.cuda.nccl.version())
except ImportError:
   print('''Torch version:''', None)

try:
   import deepspeed

   print('''DeepSpeed version:''', deepspeed.__version__)
except ImportError:
   print('''DeepSpeed version:''', None)

try:
   import tensorflow as tf

   print('''TensorFlow version:''', tf.__version__)
   print('''TF GPUs available:''', bool(tf.config.list_physical_devices('''GPU''')))
   print('''Number of TF GPUs available:''', len(tf.config.list_physical_devices('''GPU''')))
except ImportError:
   print('''TensorFlow version:''', None)
