





"""simple docstring"""




from __future__ import annotations

from typing import TypedDict


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					str
							lowerCamelCase   :					int






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str					) ->list[str]:






	'''simple docstring'''






	if not isinstance(_lowercase    ,       _lowercase					):
		raise TypeError("The parameter s type must be str."					)

	return [s[i:] + s[:i] for i in range(len(_lowercase					)					)]






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str					) ->BWTTransformDict:






	'''simple docstring'''






	if not isinstance(_lowercase    ,       _lowercase					):
		raise TypeError("The parameter s type must be str."					)
	if not s:
		raise ValueError("The parameter s must not be empty."					)

	a      :		Optional[int]	=				all_rotations(_lowercase					)
	rotations.sort()  # sort the list of rotations in alphabetically order
	# make a string composed of the last char of each rotation
	a      :		BWTTransformDict	=				{
	    "bwt_string": "".join([word[-1] for word in rotations]					),
	    "idx_original_string": rotations.index(_lowercase					),
	}
	return response






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str    ,       _lowercase						:    int					) ->str:






	'''simple docstring'''






	if not isinstance(_lowercase    ,       _lowercase					):
		raise TypeError("The parameter bwt_string type must be str."					)
	if not bwt_string:
		raise ValueError("The parameter bwt_string must not be empty."					)
	try:
		a      :		Tuple	=				int(_lowercase					)
	except ValueError:
		raise TypeError(
		    "The parameter idx_original_string type must be int or passive"
		    " of cast to int."					)
	if idx_original_string < 0:
		raise ValueError("The parameter idx_original_string must not be lower than 0."					)
	if idx_original_string >= len(_lowercase					):
		raise ValueError(
		    "The parameter idx_original_string must be lower than" " len(bwt_string)."					)

	a      :		Any	=				[""] * len(_lowercase					)
	for _ in range(len(_lowercase					)					):
		for i in range(len(_lowercase					)					):
			a      :		Tuple	=				bwt_string[i] + ordered_rotations[i]
		ordered_rotations.sort()
	return ordered_rotations[idx_original_string]


if __name__ == "__main__":
		a						: Dict     	=			'''Provide a string that I will generate its BWT transform: '''
		a						: Any     	=			input(entry_msg).strip()
		a						: str     	=			bwt_transform(s)
		print(
		    F'''Burrows Wheeler transform for string \'{s}\' results '''
		    F'''in \'{result["bwt_string"]}\''''
		)
		a						: int     	=			reverse_bwt(result['''bwt_string'''], result['''idx_original_string'''])
		print(
		    F'''Reversing Burrows Wheeler transform for entry \'{result["bwt_string"]}\' '''
		    F'''we get original string \'{original_string}\''''
		)




import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM

def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       [
      """decoder.version""",
      """decoder.output_projection.weight""",
      """_float_tensor""",
      """decoder.embed_positions._float_tensor""",
  ]
  for k in ignore_keys:
    state_dict.pop(A_,     A_					)

def a__    (       A_					):






  '''simple docstring'''





  __magic_name__     ,    __magic_name__				     =       emb.weight.shape
  __magic_name__				     =       nn.Linear(A_,     A_,     bias=A_					)
  __magic_name__				     =       emb.weight.data
  return lin_layer

def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       torch.load(A_,     map_location="""cpu"""					)
  __magic_name__				     =       Namespace(**checkpoint["""cfg"""]["""model"""]					)
  __magic_name__				     =       checkpoint["""model"""]
  remove_ignore_keys_(A_					)
  __magic_name__				     =       state_dict["""decoder.embed_tokens.weight"""].shape[0]

  __magic_name__				     =       {key.replace("""decoder""",     """model"""					): val for key, val in state_dict.items()}

  __magic_name__				     =       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,     )

  __magic_name__				     =       XGLMForCausalLM(A_					)
  __magic_name__				     =       model.load_state_dict(A_,     strict=A_					)
  print(A_					)
  __magic_name__				     =       make_linear_from_emb(model.model.embed_tokens					)

  return model


if __name__ == "__main__":
    __lowerCAmelCase			:     Dict           =   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			:     List[str]           =   parser.parse_args()
    __lowerCAmelCase			:     str           =   convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
    model.save_pretrained(args.pytorch_dump_folder_path)

