



from __future__ import annotations

import math

a_   :Any        =      """2020.9.26"""
a_   :Optional[int]        =      """xcodz-dot, cclaus, dhruvmanila"""
def        lowercase_							(A : str		,					A : Union[str, Any]		,					A : Any		,					A : Optional[Any]		,					A : Optional[int]      ):
       if not all(isinstance(_UpperCAmelCase		,					(float, int)      ) for val in locals().values()      ):
              snake_case__							:	Any   	=	F'''Input values must either be float or int: {list(locals().values()      )}'''
              raise TypeError(_UpperCAmelCase      )
       snake_case__							:	str   	=	((x * distance) / (z + distance)) * scale
       snake_case__							:	Union[str, Any]   	=	((y * distance) / (z + distance)) * scale
       return projected_x, projected_y
def        lowercase_							(A : str		,					A : str		,					A : int		,					A : Any		,					A : int      ):
       if not isinstance(_UpperCAmelCase		,					_UpperCAmelCase      ):
              raise TypeError('Axis must be a str'      )
       snake_case__							:	List[str]   	=	locals()
       del input_variables["axis"]
       if not all(isinstance(_UpperCAmelCase		,					(float, int)      ) for val in input_variables.values()      ):
              snake_case__							:	str   	=	(
                  'Input values except axis must either be float or int: '
                  F'''{list(input_variables.values()      )}'''
              )
              raise TypeError(_UpperCAmelCase      )
       snake_case__							:	Any   	=	(angle % 3_6_0) / 4_5_0 * 1_8_0 / math.pi
       if axis == "z":
              snake_case__							:	Dict   	=	x * math.cos(_UpperCAmelCase      ) - y * math.sin(_UpperCAmelCase      )
              snake_case__							:	Dict   	=	y * math.cos(_UpperCAmelCase      ) + x * math.sin(_UpperCAmelCase      )
              snake_case__							:	int   	=	z
       elif axis == "x":
              snake_case__							:	Dict   	=	y * math.cos(_UpperCAmelCase      ) - z * math.sin(_UpperCAmelCase      )
              snake_case__							:	int   	=	z * math.cos(_UpperCAmelCase      ) + y * math.sin(_UpperCAmelCase      )
              snake_case__							:	str   	=	x
       elif axis == "y":
              snake_case__							:	List[str]   	=	x * math.cos(_UpperCAmelCase      ) - z * math.sin(_UpperCAmelCase      )
              snake_case__							:	int   	=	z * math.cos(_UpperCAmelCase      ) + x * math.sin(_UpperCAmelCase      )
              snake_case__							:	Any   	=	y
       else:
              raise ValueError('not a valid axis, choose one of \'x\', \'y\', \'z\''      )

       return new_x, new_y, new_z


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(F"""{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }""")
 print(F"""{rotate(1.0, 2.0, 3.0, "y", 90.0) = }""")













import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


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


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     str:
    lowerCamelCase__    :      Tuple					 =		torch.load(_UpperCAmelCase						,	map_location='cpu' )
    lowerCamelCase__    :      List[str]					 =		mam_aaa['args'] or mam_aaa['cfg']['model']
    lowerCamelCase__    :      Optional[int]					 =		mam_aaa['model']
    remove_ignore_keys_(_UpperCAmelCase )
    lowerCamelCase__    :      str					 =		state_dict['encoder.embed_tokens.weight'].shape[0]

    lowerCamelCase__    :      Union[str, 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'						,	)

    lowerCamelCase__    :      Optional[Any]					 =		state_dict['decoder.embed_tokens.weight']
    lowerCamelCase__    :      Union[str, Any]					 =		MaMaaaForConditionalGeneration(_UpperCAmelCase )
    model.model.load_state_dict(_UpperCAmelCase						,	strict=_UpperCAmelCase )
    lowerCamelCase__    :      List[str]					 =		make_linear_from_emb(model.model.shared )

    return model


if __name__ == "__main__":
    _UpperCAmelCase				:       Tuple	   =				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				:       str	   =				parser.parse_args()
    _UpperCAmelCase				:       Optional[Any]	   =				convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)





