




"""simple docstring"""

import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def 					a			(    __snake_case  :					str	):



				'''simple docstring'''





				UpperCAmelCase_       :Optional[int]         =   [
				    "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			(    __snake_case  :					Optional[Any]	):



				'''simple docstring'''





				UpperCAmelCase_       :str         =   emb.weight.shape
				UpperCAmelCase_       :Dict         =   nn.Linear(_UpperCAmelCase,     _UpperCAmelCase,     bias=_UpperCAmelCase	)
				UpperCAmelCase_       :Optional[Any]         =   emb.weight.data
				return lin_layer



def 					a			(    __snake_case  :					Dict	):



				'''simple docstring'''





				UpperCAmelCase_       :Dict         =   torch.load(_UpperCAmelCase,     map_location='''cpu'''	)
				UpperCAmelCase_       :Tuple         =   mam_aaa["args"] or mam_aaa["cfg"]["model"]
				UpperCAmelCase_       :Union[str, Any]         =   mam_aaa["model"]
				remove_ignore_keys_(_UpperCAmelCase	)
				UpperCAmelCase_       :List[str]         =   state_dict["encoder.embed_tokens.weight"].shape[0]

				UpperCAmelCase_       :Any         =   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''',     )

				UpperCAmelCase_       :Optional[Any]         =   state_dict["decoder.embed_tokens.weight"]
				UpperCAmelCase_       :Optional[Any]         =   MaMaaaForConditionalGeneration(_UpperCAmelCase	)
				model.model.load_state_dict(_UpperCAmelCase,     strict=_UpperCAmelCase	)
				UpperCAmelCase_       :Optional[int]         =   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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCamelCase_						:     int	       =							{
    """configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
    """tokenization_transfo_xl""": ["""TransfoXLCorpus""", """TransfoXLTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_						:     Any	       =							[
							    """TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """AdaptiveEmbedding""",
							    """TransfoXLForSequenceClassification""",
							    """TransfoXLLMHeadModel""",
							    """TransfoXLModel""",
							    """TransfoXLPreTrainedModel""",
							    """load_tf_weights_in_transfo_xl""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_						:     Tuple	       =							[
							    """TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFAdaptiveEmbedding""",
							    """TFTransfoXLForSequenceClassification""",
							    """TFTransfoXLLMHeadModel""",
							    """TFTransfoXLMainLayer""",
							    """TFTransfoXLModel""",
							    """TFTransfoXLPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
							from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_transfo_xl import (
														    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    AdaptiveEmbedding,
														    TransfoXLForSequenceClassification,
														    TransfoXLLMHeadModel,
														    TransfoXLModel,
														    TransfoXLPreTrainedModel,
														    load_tf_weights_in_transfo_xl,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_transfo_xl import (
														    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFAdaptiveEmbedding,
														    TFTransfoXLForSequenceClassification,
														    TFTransfoXLLMHeadModel,
														    TFTransfoXLMainLayer,
														    TFTransfoXLModel,
														    TFTransfoXLPreTrainedModel,
														)

else:
							import sys

							UpperCamelCase_						:     Optional[int]	       =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




