


'''simple docstring'''
import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def    a__			(   lowerCAmelCase__		)       ->      Any:
    UpperCAmelCase__     :						Union[str, Any]						=							[
        '''decoder.version''',
        '''decoder.output_projection.weight''',
        '''_float_tensor''',
        '''decoder.embed_positions._float_tensor''',
    ]
    for k in ignore_keys:
        state_dict.pop(lowerCAmelCase__    ,   lowerCAmelCase__		)





def    a__			(   lowerCAmelCase__		)       ->      int:
    UpperCAmelCase__		,		UpperCAmelCase__     :						Optional[Any]						=							emb.weight.shape
    UpperCAmelCase__     :						Tuple						=							nn.Linear(lowerCAmelCase__    ,   lowerCAmelCase__    ,   bias=lowerCAmelCase__		)
    UpperCAmelCase__     :						Dict						=							emb.weight.data
    return lin_layer





def    a__			(   lowerCAmelCase__		)       ->      List[str]:
    UpperCAmelCase__     :						str						=							torch.load(lowerCAmelCase__    ,   map_location='''cpu'''		)
    UpperCAmelCase__     :						int						=							Namespace(**checkpoint['''cfg''']['''model''']		)
    UpperCAmelCase__     :						Optional[int]						=							checkpoint['''model''']
    remove_ignore_keys_(lowerCAmelCase__		)
    UpperCAmelCase__     :						Union[str, Any]						=							state_dict['''decoder.embed_tokens.weight'''].shape[0]

    UpperCAmelCase__     :						int						=							{key.replace('''decoder'''    ,   '''model'''		): val for key, val in state_dict.items()}

    UpperCAmelCase__     :						List[Any]						=							XGLMConfig(
        vocab_size=lowerCAmelCase__    ,   max_position_embeddings=args.max_target_positions    ,   num_layers=args.decoder_layers    ,   attention_heads=args.decoder_attention_heads    ,   ffn_dim=args.decoder_ffn_embed_dim    ,   d_model=args.decoder_embed_dim    ,   layerdrop=args.decoder_layerdrop    ,   dropout=args.dropout    ,   attention_dropout=args.attention_dropout    ,   activation_dropout=args.activation_dropout    ,   activation_function='''gelu'''    ,   scale_embedding=not args.no_scale_embedding    ,   tie_word_embeddings=args.share_decoder_input_output_embed    ,   )

    UpperCAmelCase__     :						List[str]						=							XGLMForCausalLM(lowerCAmelCase__		)
    UpperCAmelCase__     :						str						=							model.load_state_dict(lowerCAmelCase__    ,   strict=lowerCAmelCase__		)
    print(lowerCAmelCase__		)
    UpperCAmelCase__     :						str						=							make_linear_from_emb(model.model.embed_tokens		)

    return model


if __name__ == "__main__":
  UpperCamelCase__          = 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.''')
  UpperCamelCase__          = parser.parse_args()
  UpperCamelCase__          = convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
  model.save_pretrained(args.pytorch_dump_folder_path)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


lowerCamelCase		       =      {
    """configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
    """processing_trocr""": ["""TrOCRProcessor"""],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      [
						    """TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TrOCRForCausalLM""",
						    """TrOCRPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
						from .processing_trocr import TrOCRProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
						import sys

						lowerCamelCase		       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


