





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM




def  __UpperCAmelCase(   lowercase_   ):
       _lowerCamelCase				:  Any						=		[
           '''decoder.version''',
           '''decoder.output_projection.weight''',
           '''_float_tensor''',
           '''decoder.embed_positions._float_tensor''',
       ]
       for k in ignore_keys:
              state_dict.pop(lowercase_			,     lowercase_   )




def  __UpperCAmelCase(   lowercase_   ):
       _lowerCamelCase,	_lowerCamelCase				:  List[str]						=		emb.weight.shape
       _lowerCamelCase				:  List[Any]						=		nn.Linear(lowercase_			,     lowercase_			,     bias=lowercase_   )
       _lowerCamelCase				:  List[Any]						=		emb.weight.data
       return lin_layer




def  __UpperCAmelCase(   lowercase_   ):
       _lowerCamelCase				:  Optional[int]						=		torch.load(lowercase_			,     map_location='''cpu'''   )
       _lowerCamelCase				:  Dict						=		Namespace(**checkpoint['''cfg''']['''model''']   )
       _lowerCamelCase				:  int						=		checkpoint['''model''']
       remove_ignore_keys_(lowercase_   )
       _lowerCamelCase				:  Tuple						=		state_dict['''decoder.embed_tokens.weight'''].shape[0]

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

       _lowerCamelCase				:  Optional[int]						=		XGLMConfig(
           vocab_size=lowercase_			,     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				:  Dict						=		XGLMForCausalLM(lowercase_   )
       _lowerCamelCase				:  Dict						=		model.load_state_dict(lowercase_			,     strict=lowercase_   )
       print(lowercase_   )
       _lowerCamelCase				:  Dict						=		make_linear_from_emb(model.model.embed_tokens   )

       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_xglm_checkpoint_from_disk(args.fairseq_path)
       model.save_pretrained(args.pytorch_dump_folder_path)








import mpmath  # for roots of unity
import numpy as np







class       __A							:




      """simple docstring"""







      def __init__(		self   ,				a__=None   ,				a__=None):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		list(poly_a or [0])[:]
             _lowerCamelCase				:  Union[str, Any]						=		list(poly_b or [0])[:]

             # Remove leading zero coefficients
             while self.polyA[-1] == 0:
                    self.polyA.pop()
             _lowerCamelCase				:  Optional[int]						=		len(self.polyA)

             while self.polyB[-1] == 0:
                    self.polyB.pop()
             _lowerCamelCase				:  Any						=		len(self.polyB)

             # Add 0 to make lengths equal a power of 2
             _lowerCamelCase				:  Dict						=		int(
                 2 ** np.ceil(np.loga(len(self.polyA) + len(self.polyB) - 1)))

             while len(self.polyA) < self.c_max_length:
                    self.polyA.append(0)
             while len(self.polyB) < self.c_max_length:
                    self.polyB.append(0)
             # A complex root used for the fourier transform
             _lowerCamelCase				:  int						=		complex(mpmath.root(x=1   ,				n=self.c_max_length   ,				k=1))

             # The product
             _lowerCamelCase				:  Optional[int]						=		self.__multiply()


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		[[x] for x in self.polyA] if which == '''A''' else [[x] for x in self.polyB]
             # Corner case
             if len(a__) <= 1:
                    return dft[0]
             #
             _lowerCamelCase				:  Optional[Any]						=		self.c_max_length // 2
             while next_ncol > 0:
                    _lowerCamelCase				:  Optional[Any]						=		[[] for i in range(a__)]
                    _lowerCamelCase				:  List[str]						=		self.root**next_ncol

                    # First half of next step
                    _lowerCamelCase				:  Tuple						=		1
                    for j in range(self.c_max_length // (next_ncol * 2)):
                           for i in range(a__):
                                  new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j])
                           current_root *= root
                    # Second half of next step
                    _lowerCamelCase				:  Tuple						=		1
                    for j in range(self.c_max_length // (next_ncol * 2)):
                           for i in range(a__):
                                  new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j])
                           current_root *= root
                    # Update
                    _lowerCamelCase				:  Tuple						=		new_dft
                    _lowerCamelCase				:  Union[str, Any]						=		next_ncol // 2
             return dft[0]


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		self.__dft('''A''')
             _lowerCamelCase				:  Optional[Any]						=		self.__dft('''B''')
             _lowerCamelCase				:  List[str]						=		[[dft_a[i] * dft_b[i] for i in range(self.c_max_length)]]
             del dft_a
             del dft_b

             # Corner Case
             if len(inverce_c[0]) <= 1:
                    return inverce_c[0]
             # Inverse DFT
             _lowerCamelCase				:  str						=		2
             while next_ncol <= self.c_max_length:
                    _lowerCamelCase				:  Optional[int]						=		[[] for i in range(a__)]
                    _lowerCamelCase				:  Dict						=		self.root ** (next_ncol // 2)
                    _lowerCamelCase				:  List[str]						=		1
                    # First half of next step
                    for j in range(self.c_max_length // next_ncol):
                           for i in range(next_ncol // 2):
                                  # Even positions
                                  new_inverse_c[i].append(
                                      (
                                          inverce_c[i][j]
                                          + inverce_c[i][j + self.c_max_length // next_ncol]
                                      )
                                      / 2)
                                  # Odd positions
                                  new_inverse_c[i + next_ncol // 2].append(
                                      (
                                          inverce_c[i][j]
                                          - inverce_c[i][j + self.c_max_length // next_ncol]
                                      )
                                      / (2 * current_root))
                           current_root *= root
                    # Update
                    _lowerCamelCase				:  Any						=		new_inverse_c
                    next_ncol *= 2
             # Unpack
             _lowerCamelCase				:  Optional[int]						=		[round(x[0].real   ,				8) + round(x[0].imag   ,				8) * 1j for x in inverce_c]

             # Remove leading 0's
             while inverce_c[-1] == 0:
                    inverce_c.pop()
             return inverce_c



      def __str__(		self):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		'''A = ''' + ''' + '''.join(
                 F"""{coef}*x^{i}""" for coef, i in enumerate(self.polyA[: self.len_A]))
             _lowerCamelCase				:  List[str]						=		'''B = ''' + ''' + '''.join(
                 F"""{coef}*x^{i}""" for coef, i in enumerate(self.polyB[: self.len_B]))
             _lowerCamelCase				:  Tuple						=		'''A*B = ''' + ''' + '''.join(
                 F"""{coef}*x^{i}""" for coef, i in enumerate(self.product))

             return F"""{a}\n{b}\n{c}"""


# Unit tests
if __name__ == "__main__":
       import doctest

       doctest.testmod()

