





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int     ):
		SCREAMING_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(UpperCamelCase__       , UpperCamelCase__     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[Any]     ):
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   emb.weight.shape
		SCREAMING_SNAKE_CASE__								=   nn.Linear(UpperCamelCase__       , UpperCamelCase__       , bias=UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   emb.weight.data
		return lin_layer






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]     ):
		SCREAMING_SNAKE_CASE__								=   torch.load(UpperCamelCase__       , map_location="""cpu"""     )
		SCREAMING_SNAKE_CASE__								=   mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
		SCREAMING_SNAKE_CASE__								=   mam_aaa["""model"""]
		remove_ignore_keys_(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   state_dict["""encoder.embed_tokens.weight"""].shape[0]

		SCREAMING_SNAKE_CASE__								=   MaMaaaConfig(
		    vocab_size=UpperCamelCase__       , max_position_embeddings=1_024       , 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"""       , )

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

		return model


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


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 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    42
  _a		     =    42


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


