




"""simple docstring"""




import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def     a__ (				snake_case__				)						->					Optional[Any]:
			lowerCamelCase			      =					[
			    """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     a__ (				snake_case__				)						->					Optional[Any]:
			lowerCamelCase      ,   lowerCamelCase			      =					emb.weight.shape
			lowerCamelCase			      =					nn.Linear(snake_case__       , snake_case__       , bias=snake_case__				)
			lowerCamelCase			      =					emb.weight.data
			return lin_layer



def     a__ (				snake_case__				)						->					List[Any]:
			lowerCamelCase			      =					torch.load(snake_case__       , map_location="""cpu"""				)
			lowerCamelCase			      =					mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
			lowerCamelCase			      =					mam_aaa["""model"""]
			remove_ignore_keys_(snake_case__				)
			lowerCamelCase			      =					state_dict["""encoder.embed_tokens.weight"""].shape[0]

			lowerCamelCase			      =					MaMaaaConfig(
			    vocab_size=snake_case__       , max_position_embeddings=10_24       , 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			      =					state_dict["""decoder.embed_tokens.weight"""]
			lowerCamelCase			      =					MaMaaaForConditionalGeneration(snake_case__				)
			model.model.load_state_dict(snake_case__       , strict=snake_case__				)
			lowerCamelCase			      =					make_linear_from_emb(model.model.shared				)

			return model


if __name__ == "__main__":
					lowerCAmelCase							:			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.""")
					lowerCAmelCase							:			List[str]     							=							parser.parse_args()
					lowerCAmelCase							:			Union[str, Any]     							=							convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
					model.save_pretrained(args.pytorch_dump_folder_path)









"""simple docstring"""




from math import ceil



def     a__ (				snake_case__       , snake_case__				)						->					Optional[int]:
			lowerCamelCase			      =					list(range(0       , snake_case__				)				)

			lowerCamelCase			      =					[item for sublist in list(device_map.values()				) for item in sublist]

			# Duplicate check
			lowerCamelCase			      =					[]
			for i in device_map_blocks:
						if device_map_blocks.count(snake_case__				) > 1 and i not in duplicate_blocks:
									duplicate_blocks.append(snake_case__				)
    # Missing blocks
			lowerCamelCase			      =					[i for i in blocks if i not in device_map_blocks]
			lowerCamelCase			      =					[i for i in device_map_blocks if i not in blocks]

			if len(snake_case__				) != 0:
						raise ValueError(
						    """Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."""
						    """ These attention blocks were specified more than once: """ + str(snake_case__				)				)
			if len(snake_case__				) != 0:
						raise ValueError(
						    """There are attention blocks for this model that are not specified in the device_map. Add these attention """
						    """blocks to a device on the device_map: """ + str(snake_case__				)				)
			if len(snake_case__				) != 0:
						raise ValueError(
						    """The device_map contains more attention blocks than this model has. Remove these from the device_map:"""
						    + str(snake_case__				)				)



def     a__ (				snake_case__       , snake_case__				)						->					List[Any]:
			lowerCamelCase			      =					list(range(snake_case__				)				)
			lowerCamelCase			      =					int(ceil(n_layers / len(snake_case__				)				)				)
			lowerCamelCase			      =					[layers[i : i + n_blocks] for i in range(0       , snake_case__       , snake_case__				)]

			return dict(zip(snake_case__       , snake_case__				)				)



