"""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__    (			_UpperCamelCase       :				str					)					->				Any:





      """simple docstring"""






      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(_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE					)





def    lowerCAmelCase__    (			_UpperCamelCase       :				Any					)					->				Optional[Any]:





      """simple docstring"""






      snake_case					,snake_case								=			emb.weight.shape
      snake_case								=			nn.Linear(_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE ,			bias=_SCREAMING_SNAKE_CASE					)
      snake_case								=			emb.weight.data
      return lin_layer





def    lowerCAmelCase__    (			_UpperCamelCase       :				Any ,			_UpperCamelCase       :				str=None					)					->				int:





      """simple docstring"""






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





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[int] ,			_UpperCamelCase       :				Optional[int] ,			_UpperCamelCase       :				Optional[Any] ,			_UpperCamelCase       :				List[Any] ,			_UpperCamelCase       :				Tuple = WEIGHTS_NAME					)					->				List[Any]:





      """simple docstring"""






      snake_case								=			[]
      snake_case								=			0
      os.makedirs(_SCREAMING_SNAKE_CASE ,			exist_ok=_SCREAMING_SNAKE_CASE					)

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

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

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

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

      return metadata, index


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

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



"""simple docstring"""
import math





def    lowerCAmelCase__    (			_UpperCamelCase       :				float ,			_UpperCamelCase       :				float					)					->				float:





						"""simple docstring"""






						if initial_intensity < 0:
												raise ValueError('The value of intensity cannot be negative'					)
												# handling of negative values of initial intensity
						if angle < 0 or angle > 3_6_0:
												raise ValueError('In Malus Law, the angle is in the range 0-360 degrees'					)
												# handling of values out of allowed range
						return initial_intensity * (math.cos(math.radians(_UpperCamelCase					)					) ** 2)


if __name__ == "__main__":
					import doctest

					doctest.testmod(name="malus_law")


