





"""simple docstring"""
import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM



def 					__lowerCamelCase       (							a_					:		int							)      ->				Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  [
          '''decoder.version''',
          '''decoder.output_projection.weight''',
          '''_float_tensor''',
          '''decoder.embed_positions._float_tensor''',
      ]
      for k in ignore_keys:
            state_dict.pop(snake_case_			,       snake_case_							)



def 					__lowerCamelCase       (							a_					:		Dict							)      ->				Dict:
      __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Tuple		    =  emb.weight.shape
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  nn.Linear(snake_case_			,       snake_case_			,       bias=snake_case_							)
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  emb.weight.data
      return lin_layer



def 					__lowerCamelCase       (							a_					:		List[str]							)      ->				Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  torch.load(snake_case_			,       map_location='''cpu'''							)
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  Namespace(**checkpoint['''cfg''']['''model''']							)
      __SCREAMING_SNAKE_CASE       :Tuple		    =  checkpoint['''model''']
      remove_ignore_keys_(snake_case_							)
      __SCREAMING_SNAKE_CASE       :Dict		    =  state_dict['''decoder.embed_tokens.weight'''].shape[0]

      __SCREAMING_SNAKE_CASE       :str		    =  {key.replace('''decoder'''			,       '''model'''							): val for key, val in state_dict.items()}

      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  XGLMConfig(
          vocab_size=snake_case_			,       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			,       )

      __SCREAMING_SNAKE_CASE       :Any		    =  XGLMForCausalLM(snake_case_							)
      __SCREAMING_SNAKE_CASE       :str		    =  model.load_state_dict(snake_case_			,       strict=snake_case_							)
      print(snake_case_							)
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  make_linear_from_emb(model.model.embed_tokens							)

      return model


if __name__ == "__main__":
  __A       =		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.")
  __A       =		parser.parse_args()
  __A       =		convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
  model.save_pretrained(args.pytorch_dump_folder_path)






"""simple docstring"""
import math
import random



def 					__lowerCamelCase       (							a_					:		float			,       a_					:		bool = False							)      ->				float:
      if deriv:
            return value * (1 - value)
      return 1 / (1 + math.exp(-value							))


# Initial Value
lowerCamelCase_       =		0.02



def 					__lowerCamelCase       (							a_					:		int			,       a_					:		int							)      ->				float:
      __SCREAMING_SNAKE_CASE       :Any		    =  float(2 * (random.randint(1			,       1_00							)) - 1							)

      for _ in range(a_							):
            # Forward propagation
            __SCREAMING_SNAKE_CASE       :Any		    =  sigmoid_function(INITIAL_VALUE * weight							)
            # How much did we miss?
            __SCREAMING_SNAKE_CASE       :Tuple		    =  (expected / 1_00) - layer_a
            # Error delta
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  layer_1_error * sigmoid_function(a_			,       a_							)
            # Update weight
            weight += INITIAL_VALUE * layer_1_delta

      return layer_a * 1_00


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  lowerCamelCase_       =		int(input("Expected value: "))
  lowerCamelCase_       =		int(input("Number of propagations: "))
  print(forward_propagation(expected, number_propagations))