




import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				Any:
						_UpperCAmelCase      =			[
						    """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      (		snake_case       )						->				str:
						_UpperCAmelCase    ,					_UpperCAmelCase      =			emb.weight.shape
						_UpperCAmelCase      =			nn.Linear(__UpperCamelCase			,     __UpperCamelCase			,     bias=__UpperCamelCase       )
						_UpperCAmelCase      =			emb.weight.data
						return lin_layer




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				str:
						_UpperCAmelCase      =			torch.load(__UpperCamelCase			,     map_location="""cpu"""       )
						_UpperCAmelCase      =			mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
						_UpperCAmelCase      =			mam_aaa["""model"""]
						remove_ignore_keys_(__UpperCamelCase       )
						_UpperCAmelCase      =			state_dict["""encoder.embed_tokens.weight"""].shape[0]

						_UpperCAmelCase      =			MaMaaaConfig(
						    vocab_size=__UpperCamelCase			,     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"""			,     )

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

						return model


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





import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


a													=			logging.get_logger(__name__)

a													=			"T5Config"




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				jnp.ndarray:
						_UpperCAmelCase      =			jnp.zeros_like(snake_case       )
						_UpperCAmelCase      =			shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]       )
						_UpperCAmelCase      =			shifted_input_ids.at[:, 0].set(snake_case       )

						_UpperCAmelCase      =			jnp.where(shifted_input_ids == -1_0_0			,     snake_case			,     snake_case       )
						return shifted_input_ids




class       _A				(		__lowercase     ):
		__a					    =       """mt5"""
		__a					    =       MTaConfig




class       _A				(		__lowercase     ):
		__a					    =       """mt5"""
		__a					    =       MTaConfig




class       _A				(		__lowercase     ):
		__a					    =       """mt5"""
		__a					    =       MTaConfig