




'''simple docstring'''


import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

UpperCAmelCase_ 		=      2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
UpperCAmelCase_ 		=      {
    # fairseq:
    "wmt19-ru-en": {"length_penalty": 1.1},
    "wmt19-en-ru": {"length_penalty": 1.15},
    "wmt19-en-de": {"length_penalty": 1.0},
    "wmt19-de-en": {"length_penalty": 1.1},
    # allenai:
    "wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
    "wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
    "wmt16-en-de-12-1": {"length_penalty": 0.8},
    "wmt19-de-en-6-6-base": {"length_penalty": 0.6},
    "wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}

# this remaps the different models to their organization names
UpperCAmelCase_ 		=      {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
  UpperCAmelCase_ 		=      "facebook"
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
  UpperCAmelCase_ 		=      "allenai"


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[Any]			):
  # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
  # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
  __a			=					dict((re.sub(R'@@$'				,					''				,					lowerCamelCase__			), v) if k.endswith('@@'			) else (re.sub(R'$'				,					'</w>'				,					lowerCamelCase__			), v) for k, v in d.items()			)
  __a			=					'<s> <pad> </s> <unk>'.split()
  # restore the special tokens
  for k in keep_keys:
    del da[f"{k}</w>"]
    __a			=					d[k]  # restore
  return da


def 						SCREAMING_SNAKE_CASE       (      a_	:							Tuple				,					a_	:							Optional[Any]			):
  # prep
  assert os.path.exists(lowerCamelCase__			)
  os.makedirs(lowerCamelCase__				,					exist_ok=lowerCamelCase__			)
  print(f"Writing results to {pytorch_dump_folder_path}"			)

  # handle various types of models

  __a			=					basename(lowerCamelCase__			)
  __a			=					dirname(lowerCamelCase__			)

  __a			=					fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
  __a			=					cls.hub_models()
  __a			=					{'bpe': 'fastbpe', 'tokenizer': 'moses'}
  __a			=					'.'
  # note: since the model dump is old, fairseq has upgraded its model some
  # time later, and it does a whole lot of rewrites and splits on the saved
  # weights, therefore we can't use torch.load() directly on the model file.
  # see: upgrade_state_dict(state_dict) in fairseq_model.py
  print(f"using checkpoint {checkpoint_file}"			)
  __a			=					hub_utils.from_pretrained(
      lowerCamelCase__				,					lowerCamelCase__				,					lowerCamelCase__				,					archive_map=lowerCamelCase__				,					**lowerCamelCase__			)

  __a			=					vars(chkpt['args']['model']			)

  __a			=					args['source_lang']
  __a			=					args['target_lang']

  __a			=					dirname(lowerCamelCase__			)
  __a			=					basename(lowerCamelCase__			)

  # dicts
  __a			=					os.path.join(lowerCamelCase__				,					f"dict.{src_lang}.txt"			)
  __a			=					os.path.join(lowerCamelCase__				,					f"dict.{tgt_lang}.txt"			)

  __a			=					Dictionary.load(lowerCamelCase__			)
  __a			=					rewrite_dict_keys(src_dict.indices			)
  __a			=					len(lowerCamelCase__			)
  __a			=					os.path.join(lowerCamelCase__				,					'vocab-src.json'			)
  print(f"Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records"			)
  with open(lowerCamelCase__				,					'w'				,					encoding='utf-8'			) as f:
    f.write(json.dumps(lowerCamelCase__				,					ensure_ascii=lowerCamelCase__				,					indent=lowerCamelCase__			)			)

  # detect whether this is a do_lower_case situation, which can be derived by checking whether we
  # have at least one uppercase letter in the source vocab
  __a			=					True
  for k in src_vocab.keys():
    if not k.islower():
      __a			=					False
      break

  __a			=					Dictionary.load(lowerCamelCase__			)
  __a			=					rewrite_dict_keys(tgt_dict.indices			)
  __a			=					len(lowerCamelCase__			)
  __a			=					os.path.join(lowerCamelCase__				,					'vocab-tgt.json'			)
  print(f"Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records"			)
  with open(lowerCamelCase__				,					'w'				,					encoding='utf-8'			) as f:
    f.write(json.dumps(lowerCamelCase__				,					ensure_ascii=lowerCamelCase__				,					indent=lowerCamelCase__			)			)

  # merges_file (bpecodes)
  __a			=					os.path.join(lowerCamelCase__				,					VOCAB_FILES_NAMES['merges_file']			)
  for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
    __a			=					os.path.join(lowerCamelCase__				,					lowerCamelCase__			)
    if os.path.exists(lowerCamelCase__			):
      break
  with open(lowerCamelCase__				,					encoding='utf-8'			) as fin:
    __a			=					fin.read()
  __a			=					re.sub(R' \d+$'				,					''				,					lowerCamelCase__				,					0				,					re.M			)  # remove frequency number
  print(f"Generating {merges_file}"			)
  with open(lowerCamelCase__				,					'w'				,					encoding='utf-8'			) as fout:
    fout.write(lowerCamelCase__			)

  # model config
  __a			=					os.path.join(lowerCamelCase__				,					'config.json'			)

  # validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
  # may have to modify the tokenizer if a different type is used by a future model
  assert args["bpe"] == "fastbpe", f"need to extend tokenizer to support bpe={args['bpe']}"
  assert args["tokenizer"] == "moses", f"need to extend tokenizer to support bpe={args['tokenizer']}"

  __a			=					{
      'architectures': ['FSMTForConditionalGeneration'],
      'model_type': 'fsmt',
      'activation_dropout': args['activation_dropout'],
      'activation_function': 'relu',
      'attention_dropout': args['attention_dropout'],
      'd_model': args['decoder_embed_dim'],
      'dropout': args['dropout'],
      'init_std': 0.02,
      'max_position_embeddings': args['max_source_positions'],
      'num_hidden_layers': args['encoder_layers'],
      'src_vocab_size': src_vocab_size,
      'tgt_vocab_size': tgt_vocab_size,
      'langs': [src_lang, tgt_lang],
      'encoder_attention_heads': args['encoder_attention_heads'],
      'encoder_ffn_dim': args['encoder_ffn_embed_dim'],
      'encoder_layerdrop': args['encoder_layerdrop'],
      'encoder_layers': args['encoder_layers'],
      'decoder_attention_heads': args['decoder_attention_heads'],
      'decoder_ffn_dim': args['decoder_ffn_embed_dim'],
      'decoder_layerdrop': args['decoder_layerdrop'],
      'decoder_layers': args['decoder_layers'],
      'bos_token_id': 0,
      'pad_token_id': 1,
      'eos_token_id': 2,
      'is_encoder_decoder': True,
      'scale_embedding': not args['no_scale_embedding'],
      'tie_word_embeddings': args['share_all_embeddings'],
  }

  # good hparam defaults to start with
  __a			=					5
  __a			=					False
  if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
    __a			=					best_score_hparams[model_dir]['length_penalty']
  else:
    __a			=					1.0

  print(f"Generating {fsmt_model_config_file}"			)
  with open(lowerCamelCase__				,					'w'				,					encoding='utf-8'			) as f:
    f.write(json.dumps(lowerCamelCase__				,					ensure_ascii=lowerCamelCase__				,					indent=lowerCamelCase__			)			)

  # tokenizer config
  __a			=					os.path.join(lowerCamelCase__				,					lowerCamelCase__			)

  __a			=					{
      'langs': [src_lang, tgt_lang],
      'model_max_length': 1024,
      'do_lower_case': do_lower_case,
  }

  print(f"Generating {fsmt_tokenizer_config_file}"			)
  with open(lowerCamelCase__				,					'w'				,					encoding='utf-8'			) as f:
    f.write(json.dumps(lowerCamelCase__				,					ensure_ascii=lowerCamelCase__				,					indent=lowerCamelCase__			)			)

  # model
  __a			=					chkpt['models'][0]
  __a			=					model.state_dict()

  # rename keys to start with 'model.'
  __a			=					OrderedDict(('model.' + k, v) for k, v in model_state_dict.items()			)

  # remove unneeded keys
  __a			=					[
      'model.model',
      'model.encoder.version',
      'model.decoder.version',
      'model.encoder_embed_tokens.weight',
      'model.decoder_embed_tokens.weight',
      'model.encoder.embed_positions._float_tensor',
      'model.decoder.embed_positions._float_tensor',
  ]
  for k in ignore_keys:
    model_state_dict.pop(lowerCamelCase__				,					lowerCamelCase__			)

  __a			=					FSMTConfig.from_pretrained(lowerCamelCase__			)
  __a			=					FSMTForConditionalGeneration(lowerCamelCase__			)

  # check that it loads ok
  model_new.load_state_dict(lowerCamelCase__				,					strict=lowerCamelCase__			)

  # save
  __a			=					os.path.join(lowerCamelCase__				,					lowerCamelCase__			)
  print(f"Generating {pytorch_weights_dump_path}"			)
  torch.save(lowerCamelCase__				,					lowerCamelCase__			)

  print('Conversion is done!'			)
  print('\nLast step is to upload the files to s3'			)
  print(f"cd {data_root}"			)
  print(f"transformers-cli upload {model_dir}"			)


if __name__ == "__main__":
  UpperCAmelCase_ 		=      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--fsmt_checkpoint_path",
      default=None,
      type=str,
      required=True,
      help=(
          "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
          " bpecodes, etc."
      ),
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  UpperCAmelCase_ 		=      parser.parse_args()
  convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)









'''simple docstring'''


def 						SCREAMING_SNAKE_CASE       (      a_	:							int				,					a_	:							int			):
  return int((input_a, input_a).count(0			) != 0			)


def 						SCREAMING_SNAKE_CASE       (      ):
  assert nand_gate(0				,					0			) == 1
  assert nand_gate(0				,					1			) == 1
  assert nand_gate(1				,					0			) == 1
  assert nand_gate(1				,					1			) == 0


if __name__ == "__main__":
  print(nand_gate(0, 0))
  print(nand_gate(0, 1))
  print(nand_gate(1, 0))
  print(nand_gate(1, 1))



