



"""simple docstring"""

import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration





def       __magic_name__    (   _lowerCamelCase				: Any					):
							__a		:		Tuple									=       [
							    """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(_A  ,       _A					)





def       __magic_name__    (   _lowerCamelCase				: Union[str, Any]					):
							__a							,     __a		:		Optional[Any]									=       emb.weight.shape
							__a		:		Optional[int]									=       nn.Linear(_A  ,       _A  ,       bias=_A					)
							__a		:		str									=       emb.weight.data
							return lin_layer





def       __magic_name__    (   _lowerCamelCase				: List[Any]					):
							__a		:		Tuple									=       torch.load(_A  ,       map_location="""cpu"""					)
							__a		:		Tuple									=       mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
							__a		:		Union[str, Any]									=       mam_aaa["""model"""]
							remove_ignore_keys_(_A					)
							__a		:		List[Any]									=       state_dict["""encoder.embed_tokens.weight"""].shape[0]

							__a		:		Optional[int]									=       MaMaaaConfig(
							    vocab_size=_A  ,       max_position_embeddings=1_0_2_4  ,       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		:		List[str]									=       state_dict["""decoder.embed_tokens.weight"""]
							__a		:		Any									=       MaMaaaForConditionalGeneration(_A					)
							model.model.load_state_dict(_A  ,       strict=_A					)
							__a		:		str									=       make_linear_from_emb(model.model.shared					)

							return model


if __name__ == "__main__":
		lowercase__			   =  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.")
		lowercase__			   =  parser.parse_args()
		lowercase__			   =  convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
		model.save_pretrained(args.pytorch_dump_folder_path)






"""simple docstring"""

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available




@dataclass
class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					42
						_lowerCAmelCase							=					42


if is_transformers_available() and is_torch_available():
	from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline

