
'''simple docstring'''






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def 		A_							(						_lowerCAmelCase :     List[str]				):



      """simple docstring"""




      _lowerCamelCase					: List[Any]		    = [
          "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(_lowerCAmelCase					,						_lowerCAmelCase				)







def 		A_							(						_lowerCAmelCase :     Tuple				):



      """simple docstring"""




      _lowerCamelCase    ,		_lowerCamelCase					: Tuple		    = emb.weight.shape
      _lowerCamelCase					: List[str]		    = nn.Linear(_lowerCAmelCase					,						_lowerCAmelCase					,						bias=_lowerCAmelCase				)
      _lowerCamelCase					: int		    = emb.weight.data
      return lin_layer







def 		A_							(						_lowerCAmelCase :     str				):



      """simple docstring"""




      _lowerCamelCase					: Union[str, Any]		    = torch.load(_lowerCAmelCase					,						map_location="cpu"				)
      _lowerCamelCase					: List[Any]		    = mam_aaa["args"] or mam_aaa["cfg"]["model"]
      _lowerCamelCase					: Optional[Any]		    = mam_aaa["model"]
      remove_ignore_keys_(_lowerCAmelCase				)
      _lowerCamelCase					: int		    = state_dict["encoder.embed_tokens.weight"].shape[0]

      _lowerCamelCase					: str		    = MaMaaaConfig(
          vocab_size=_lowerCAmelCase					,						max_position_embeddings=1024					,						encoder_layers=args.encoder_layers					,						decoder_layers=args.decoder_layers					,						encoder_attention_heads=args.encoder_attention_heads					,						decoder_attention_heads=args.decoder_attention_heads					,						encoder_ffn_dim=args.encoder_ffn_embed_dim					,						decoder_ffn_dim=args.decoder_ffn_embed_dim					,						d_model=args.encoder_embed_dim					,						encoder_layerdrop=args.encoder_layerdrop					,						decoder_layerdrop=args.decoder_layerdrop					,						dropout=args.dropout					,						attention_dropout=args.attention_dropout					,						activation_dropout=args.activation_dropout					,						activation_function="relu"					,						)

      _lowerCamelCase					: Optional[int]		    = state_dict["decoder.embed_tokens.weight"]
      _lowerCamelCase					: List[Any]		    = MaMaaaForConditionalGeneration(_lowerCAmelCase				)
      model.model.load_state_dict(_lowerCAmelCase					,						strict=_lowerCAmelCase				)
      _lowerCamelCase					: Optional[int]		    = make_linear_from_emb(model.model.shared				)

      return model


if __name__ == "__main__":
 UpperCAmelCase_							: List[Any] =							argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
 parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 UpperCAmelCase_							: Optional[Any] =							parser.parse_args()
 UpperCAmelCase_							: List[str] =							convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
 model.save_pretrained(args.pytorch_dump_folder_path)






def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    bool:
		return not any(
		    neighbour == 1 and colored_vertices[i] == color
		    for i, neighbour in enumerate(snake_case							)							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case							)		->    bool:

		# Base Case
		if index == len(snake_case							):
				return True

		# Recursive Step
		for i in range(snake_case							):
				if valid_coloring(graph[index]						,   snake_case						,   snake_case							):
						# Color current vertex
						__lowercase											=     i
						# Validate coloring
						if util_color(snake_case						,   snake_case						,   snake_case						,   index + 1							):
								return True
						# Backtrack
						__lowercase											=     -1
		return False



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    list[int]:
		__lowercase											=     [-1] * len(snake_case							)

		if util_color(snake_case						,   snake_case						,   snake_case						,   0							):
				return colored_vertices

		return []




