
import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						[
					    "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 							A_     (__a				):

					'''simple docstring'''





					A_ ,    A_				 =						emb.weight.shape
					A_				 =						nn.Linear(UpperCAmelCase__				,   UpperCAmelCase__				,   bias=UpperCAmelCase__				)
					A_				 =						emb.weight.data
					return lin_layer
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						torch.load(UpperCAmelCase__				,   map_location="cpu"				)
					A_				 =						mam_aaa["args"] or mam_aaa["cfg"]["model"]
					A_				 =						mam_aaa["model"]
					remove_ignore_keys_(UpperCAmelCase__				)
					A_				 =						state_dict["encoder.embed_tokens.weight"].shape[0]

					A_				 =						MaMaaaConfig(
					    vocab_size=UpperCAmelCase__				,   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"				,   )

					A_				 =						state_dict["decoder.embed_tokens.weight"]
					A_				 =						MaMaaaForConditionalGeneration(UpperCAmelCase__				)
					model.model.load_state_dict(UpperCAmelCase__				,   strict=UpperCAmelCase__				)
					A_				 =						make_linear_from_emb(model.model.shared				)

					return model


if __name__ == "__main__":
		UpperCamelCase_					:						int				= 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_					:						Dict				= parser.parse_args()
		UpperCamelCase_					:						Optional[int]				= convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
		model.save_pretrained(args.pytorch_dump_folder_path)






"""simple docstring"""





from __future__ import annotations

from collections import Counter
from random import random



class __lowerCAmelCase				:




			"""simple docstring"""







			def __init__(				self						:					int	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						{}




			def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					str	)      -> None:







								"""simple docstring"""
								A_				 =						{}




			def      lowerCamelCase__     (				self						:					Optional[int]       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					float	)      -> None:







								"""simple docstring"""
								if nodea not in self.connections:
													self.add_node(_snake_case	)
								if nodea not in self.connections:
													self.add_node(_snake_case	)
								A_				 =						probability




			def      lowerCamelCase__     (				self						:					Tuple	)      -> list[str]:







								"""simple docstring"""
								return list(self.connections	)






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







								"""simple docstring"""
								A_				 =						0
								A_				 =						random()

								for dest in self.connections[node]:
													current_probability += self.connections[node][dest]
													if current_probability > random_value:
																		return dest
								return ""





def 							A_     (__a				,   __a				,   __a				):

					'''simple docstring'''





					A_				 =						MarkovChainGraphUndirectedUnweighted()

					for nodea, nodea, probability in transitions:
										graph.add_transition_probability(__a				,   __a				,   __a				)

					A_				 =						Counter(graph.get_nodes()				)
					A_				 =						start

					for _ in range(__a				):
										A_				 =						graph.transition(__a				)
										visited[node] += 1

					return visited


if __name__ == "__main__":
		import doctest

		doctest.testmod()




