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__":
		SCREAMING_SNAKE_CASE :	Tuple							     =		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.""")
		SCREAMING_SNAKE_CASE :	List[Any]							     =		parser.parse_args()
		SCREAMING_SNAKE_CASE :	List[Any]							     =		convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
		model.save_pretrained(args.pytorch_dump_folder_path)



from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
  from .pipeline_unclip import UnCLIPPipeline
  from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
  from .text_proj import UnCLIPTextProjModel


