





'''simple docstring'''


import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def 	__a(SCREAMING_SNAKE_CASE_  :					Optional[int]    ):



    '''simple docstring'''





    _lowerCAmelCase				=      [
        "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(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    )


def 	__a(SCREAMING_SNAKE_CASE_  :					Dict    ):



    '''simple docstring'''





    _lowerCAmelCase			,			_lowerCAmelCase				=      emb.weight.shape
    _lowerCAmelCase				=      nn.Linear(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_		,       bias=SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      emb.weight.data
    return lin_layer


def 	__a(SCREAMING_SNAKE_CASE_  :					Optional[int]    ):



    '''simple docstring'''





    _lowerCAmelCase				=      torch.load(SCREAMING_SNAKE_CASE_		,       map_location="cpu"    )
    _lowerCAmelCase				=      mam_aaa["args"] or mam_aaa["cfg"]["model"]
    _lowerCAmelCase				=      mam_aaa["model"]
    remove_ignore_keys_(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      state_dict["encoder.embed_tokens.weight"].shape[0]

    _lowerCAmelCase				=      MaMaaaConfig(
        vocab_size=SCREAMING_SNAKE_CASE_		,       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"		,       )

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

    return model


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









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE	 =							{"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							["XGLMTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							["XGLMTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "XGLMForCausalLM",
           "XGLMModel",
           "XGLMPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "FlaxXGLMForCausalLM",
           "FlaxXGLMModel",
           "FlaxXGLMPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFXGLMForCausalLM",
           "TFXGLMModel",
           "TFXGLMPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xglm import XGLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xglm_fast import XGLMTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_xglm import (
                  TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFXGLMForCausalLM,
                  TFXGLMModel,
                  TFXGLMPreTrainedModel,
              )


else:
       import sys

       _SCREAMING_SNAKE_CASE	 =							_LazyModule(__name__, globals()["__file__"], _import_structure)


