





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  Optional[Any]:
    lowerCamelCase__					:   str      				=	[
        'decoder.version',
        'decoder.output_projection.weight',
        '_float_tensor',
        'decoder.embed_positions._float_tensor',
    ]
    for k in ignore_keys:
        state_dict.pop(_UpperCAmelCase		,			_UpperCAmelCase				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  Optional[int]:
    lowerCamelCase__				,							lowerCamelCase__					:   Tuple      				=	emb.weight.shape
    lowerCamelCase__					:   Dict      				=	nn.Linear(_UpperCAmelCase		,			_UpperCAmelCase		,			bias=_UpperCAmelCase				)
    lowerCamelCase__					:   int      				=	emb.weight.data
    return lin_layer


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

    lowerCamelCase__					:   Union[str, Any]      				=	{key.replace('decoder'		,			'model'				): val for key, val in state_dict.items()}

    lowerCamelCase__					:   Dict      				=	XGLMConfig(
        vocab_size=_UpperCAmelCase		,			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		,			)

    lowerCamelCase__					:   Optional[Any]      				=	XGLMForCausalLM(_UpperCAmelCase				)
    lowerCamelCase__					:   Union[str, Any]      				=	model.load_state_dict(_UpperCAmelCase		,			strict=_UpperCAmelCase				)
    print(_UpperCAmelCase				)
    lowerCamelCase__					:   Union[str, Any]      				=	make_linear_from_emb(model.model.embed_tokens				)

    return model


if __name__ == "__main__":
    _UpperCAmelCase       : Any			     =       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       : Tuple			     =       parser.parse_args()
    _UpperCAmelCase       : Dict			     =       convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
    model.save_pretrained(args.pytorch_dump_folder_path)












_UpperCAmelCase       : List[Any]			     =       {
    """meter""": """m""",
    """kilometer""": """km""",
    """megametre""": """Mm""",
    """gigametre""": """Gm""",
    """terametre""": """Tm""",
    """petametre""": """Pm""",
    """exametre""": """Em""",
    """zettametre""": """Zm""",
    """yottametre""": """Ym""",
}
# Exponent of the factor(meter)
_UpperCAmelCase       : Dict			     =       {
    """m""": 0,
    """km""": 3,
    """Mm""": 6,
    """Gm""": 9,
    """Tm""": 12,
    """Pm""": 15,
    """Em""": 18,
    """Zm""": 21,
    """Ym""": 24,
}


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  float:
    lowerCamelCase__					:   Dict      				=	from_type.lower().strip('s'				)
    lowerCamelCase__					:   Dict      				=	to_type.lower().strip('s'				)

    lowerCamelCase__					:   Dict      				=	UNIT_SYMBOL.get(_UpperCAmelCase		,			_UpperCAmelCase				)
    lowerCamelCase__					:   str      				=	UNIT_SYMBOL.get(_UpperCAmelCase		,			_UpperCAmelCase				)

    if from_sanitized not in METRIC_CONVERSION:
        lowerCamelCase__					:   List[Any]      				=	(
            F"""Invalid 'from_type' value: {from_type!r}.\n"""
            F"""Conversion abbreviations are: {", ".join(_UpperCAmelCase				)}"""
        )
        raise ValueError(_UpperCAmelCase				)
    if to_sanitized not in METRIC_CONVERSION:
        lowerCamelCase__					:   Optional[Any]      				=	(
            F"""Invalid 'to_type' value: {to_type!r}.\n"""
            F"""Conversion abbreviations are: {", ".join(_UpperCAmelCase				)}"""
        )
        raise ValueError(_UpperCAmelCase				)
    lowerCamelCase__					:   Any      				=	METRIC_CONVERSION[from_sanitized]
    lowerCamelCase__					:   Optional[int]      				=	METRIC_CONVERSION[to_sanitized]
    lowerCamelCase__					:   List[str]      				=	1

    if from_exponent > to_exponent:
        lowerCamelCase__					:   Dict      				=	from_exponent - to_exponent
    else:
        lowerCamelCase__					:   Dict      				=	-(to_exponent - from_exponent)

    return value * pow(10		,			_UpperCAmelCase				)


if __name__ == "__main__":
    from doctest import testmod

    testmod()





