




import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM



def       UpperCAmelCase__  (     lowerCamelCase   ):
  lowercase    :Any      	=						[
      "decoder.version",
      "decoder.output_projection.weight",
      "_float_tensor",
      "decoder.embed_positions._float_tensor",
  ]
  for k in ignore_keys:
    state_dict.pop(_A,			_A   )



def       UpperCAmelCase__  (     lowerCamelCase   ):
  lowercase	,    lowercase    :Union[str, Any]      	=						emb.weight.shape
  lowercase    :str      	=						nn.Linear(_A,			_A,			bias=_A   )
  lowercase    :Tuple      	=						emb.weight.data
  return lin_layer



def       UpperCAmelCase__  (     lowerCamelCase   ):
  lowercase    :Optional[int]      	=						torch.load(_A,			map_location="cpu"   )
  lowercase    :Optional[Any]      	=						Namespace(**checkpoint["cfg"]["model"]   )
  lowercase    :Union[str, Any]      	=						checkpoint["model"]
  remove_ignore_keys_(_A   )
  lowercase    :Optional[int]      	=						state_dict["decoder.embed_tokens.weight"].shape[0]

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

  lowercase    :Union[str, Any]      	=						XGLMConfig(
      vocab_size=_A,			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,			)

  lowercase    :Optional[Any]      	=						XGLMForCausalLM(_A   )
  lowercase    :Dict      	=						model.load_state_dict(_A,			strict=_A   )
  print(_A   )
  lowercase    :int      	=						make_linear_from_emb(model.model.embed_tokens   )

  return model


if __name__ == "__main__":
  _UpperCAmelCase	:						List[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	:						int			=     parser.parse_args()
  _UpperCAmelCase	:						Optional[Any]			=     convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
  model.save_pretrained(args.pytorch_dump_folder_path)









from collections.abc import Generator
from math import sin



def       UpperCAmelCase__  (     lowerCamelCase   ):
  if len(lowerCamelCase   ) != 32:
    raise ValueError("Input must be of length 32"   )

  lowercase    :Any      	=						B""
  for i in [3, 2, 1, 0]:
    little_endian += string_aa[8 * i : 8 * i + 8]
  return little_endian



def       UpperCAmelCase__  (     lowerCamelCase   ):
  if i < 0:
    raise ValueError("Input must be non-negative"   )

  lowercase    :List[Any]      	=						format(lowerCamelCase,			"08x"   )[-8:]
  lowercase    :Tuple      	=						B""
  for i in [3, 2, 1, 0]:
    little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("utf-8"   )
  return little_endian_hex



def       UpperCAmelCase__  (     lowerCamelCase   ):
  lowercase    :Union[str, Any]      	=						B""
  for char in message:
    bit_string += format(lowerCamelCase,			"08b"   ).encode("utf-8"   )
  lowercase    :str      	=						format(len(lowerCamelCase   ),			"064b"   ).encode("utf-8"   )

  # Pad bit_string to a multiple of 512 chars
  bit_string += b"1"
  while len(lowerCamelCase   ) % 512 != 448:
    bit_string += b"0"
  bit_string += to_little_endian(start_len[32:]   ) + to_little_endian(start_len[:32]   )

  return bit_string



def       UpperCAmelCase__  (     lowerCamelCase   ):
  if len(lowerCamelCase   ) % 512 != 0:
    raise ValueError("Input must have length that's a multiple of 512"   )

  for pos in range(0,			len(lowerCamelCase   ),			512   ):
    lowercase    :List[str]      	=						bit_string[pos : pos + 512]
    lowercase    :int      	=						[]
    for i in range(0,			512,			32   ):
      block_words.append(int(to_little_endian(block[i : i + 32]   ),			2   )   )
    yield block_words



def       UpperCAmelCase__  (     lowerCamelCase   ):
  if i < 0:
    raise ValueError("Input must be non-negative"   )

  lowercase    :List[str]      	=						format(lowerCamelCase,			"032b"   )
  lowercase    :Optional[Any]      	=						""
  for c in i_str:
    new_str += "1" if c == "0" else "0"
  return int(lowerCamelCase,			2   )



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase   ):
  return (a + b) % 2**32



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase   ):
  if i < 0:
    raise ValueError("Input must be non-negative"   )
  if shift < 0:
    raise ValueError("Shift must be non-negative"   )
  return ((i << shift) ^ (i >> (32 - shift))) % 2**32



def       UpperCAmelCase__  (     lowerCamelCase   ):
  lowercase    :Tuple      	=						preprocess(lowerCamelCase   )

  lowercase    :Tuple      	=						[int(2**32 * abs(sin(i + 1   )   )   ) for i in range(64   )]

  # Starting states
  lowercase    :Optional[Any]      	=						0X6745_2301
  lowercase    :Union[str, Any]      	=						0XEFCD_AB89
  lowercase    :Optional[int]      	=						0X98BA_DCFE
  lowercase    :str      	=						0X1032_5476

  lowercase    :str      	=						[
      7,
      12,
      17,
      22,
      7,
      12,
      17,
      22,
      7,
      12,
      17,
      22,
      7,
      12,
      17,
      22,
      5,
      9,
      14,
      20,
      5,
      9,
      14,
      20,
      5,
      9,
      14,
      20,
      5,
      9,
      14,
      20,
      4,
      11,
      16,
      23,
      4,
      11,
      16,
      23,
      4,
      11,
      16,
      23,
      4,
      11,
      16,
      23,
      6,
      10,
      15,
      21,
      6,
      10,
      15,
      21,
      6,
      10,
      15,
      21,
      6,
      10,
      15,
      21,
  ]

  # Process bit string in chunks, each with 16 32-char words
  for block_words in get_block_words(lowerCamelCase   ):
    lowercase    :str      	=						aa
    lowercase    :int      	=						ba
    lowercase    :int      	=						ca
    lowercase    :List[str]      	=						da

    # Hash current chunk
    for i in range(64   ):
      if i <= 15:
        # f = (b & c) | (not_32(b) & d)     # Alternate definition for f
        lowercase    :Optional[int]      	=						d ^ (b & (c ^ d))
        lowercase    :Optional[Any]      	=						i
      elif i <= 31:
        # f = (d & b) | (not_32(d) & c)     # Alternate definition for f
        lowercase    :Any      	=						c ^ (d & (b ^ c))
        lowercase    :Union[str, Any]      	=						(5 * i + 1) % 16
      elif i <= 47:
        lowercase    :Any      	=						b ^ c ^ d
        lowercase    :List[Any]      	=						(3 * i + 5) % 16
      else:
        lowercase    :List[Any]      	=						c ^ (b | not_aa(lowerCamelCase   ))
        lowercase    :Any      	=						(7 * i) % 16
      lowercase    :Dict      	=						(f + a + added_consts[i] + block_words[g]) % 2**32
      lowercase    :Any      	=						d
      lowercase    :str      	=						c
      lowercase    :Union[str, Any]      	=						b
      lowercase    :Union[str, Any]      	=						sum_aa(lowerCamelCase,			left_rotate_aa(lowerCamelCase,			shift_amounts[i]   )   )

    # Add hashed chunk to running total
    lowercase    :Optional[Any]      	=						sum_aa(lowerCamelCase,			lowerCamelCase   )
    lowercase    :List[Any]      	=						sum_aa(lowerCamelCase,			lowerCamelCase   )
    lowercase    :List[str]      	=						sum_aa(lowerCamelCase,			lowerCamelCase   )
    lowercase    :Union[str, Any]      	=						sum_aa(lowerCamelCase,			lowerCamelCase   )

  lowercase    :int      	=						reformat_hex(lowerCamelCase   ) + reformat_hex(lowerCamelCase   ) + reformat_hex(lowerCamelCase   ) + reformat_hex(lowerCamelCase   )
  return digest


if __name__ == "__main__":
     import doctest

     doctest.testmod()



