
"""simple docstring"""



# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

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 					_UpperCAmelCase      ( __lowerCamelCase   :       int		)     ->					List[str]:
  # (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}
  _snake_case					     =       dict((re.sub(R'''@@$''' ,					'''''' ,					__lowerCamelCase		), v) if k.endswith('''@@'''		) else (re.sub(R'''$''' ,					'''</w>''' ,					__lowerCamelCase		), v) for k, v in d.items()		)
  _snake_case					     =       '''<s> <pad> </s> <unk>'''.split()
  # restore the special tokens
  for k in keep_keys:
    del da[f'''{k}</w>''']
    _snake_case					     =       d[k]  # restore
  return da
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int ,					__lowerCamelCase   :       Tuple		)     ->					str:
  # 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

  _snake_case					     =       basename(__lowerCamelCase		)
  _snake_case					     =       dirname(__lowerCamelCase		)

  _snake_case					     =       fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
  _snake_case					     =       cls.hub_models()
  _snake_case					     =       {'''bpe''': '''fastbpe''', '''tokenizer''': '''moses'''}
  _snake_case					     =       '''.'''
  # 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}'''		)
  _snake_case					     =       hub_utils.from_pretrained(
      __lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase ,					archive_map=__lowerCamelCase ,					**__lowerCamelCase		)

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

  _snake_case					     =       args['''source_lang''']
  _snake_case					     =       args['''target_lang''']

  _snake_case					     =       dirname(__lowerCamelCase		)
  _snake_case					     =       basename(__lowerCamelCase		)

  # dicts
  _snake_case					     =       os.path.join(__lowerCamelCase ,					f'''dict.{src_lang}.txt'''		)
  _snake_case					     =       os.path.join(__lowerCamelCase ,					f'''dict.{tgt_lang}.txt'''		)

  _snake_case					     =       Dictionary.load(__lowerCamelCase		)
  _snake_case					     =       rewrite_dict_keys(src_dict.indices		)
  _snake_case					     =       len(__lowerCamelCase		)
  _snake_case					     =       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
  _snake_case					     =       True
  for k in src_vocab.keys():
    if not k.islower():
      _snake_case					     =       False
      break

  _snake_case					     =       Dictionary.load(__lowerCamelCase		)
  _snake_case					     =       rewrite_dict_keys(tgt_dict.indices		)
  _snake_case					     =       len(__lowerCamelCase		)
  _snake_case					     =       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)
  _snake_case					     =       os.path.join(__lowerCamelCase ,					VOCAB_FILES_NAMES['''merges_file''']		)
  for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
    _snake_case					     =       os.path.join(__lowerCamelCase ,					__lowerCamelCase		)
    if os.path.exists(__lowerCamelCase		):
      break
  with open(__lowerCamelCase ,					encoding='''utf-8'''		) as fin:
    _snake_case					     =       fin.read()
  _snake_case					     =       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
  _snake_case					     =       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']}'''

  _snake_case					     =       {
      '''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
  _snake_case					     =       5
  _snake_case					     =       False
  if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
    _snake_case					     =       best_score_hparams[model_dir]['''length_penalty''']
  else:
    _snake_case					     =       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
  _snake_case					     =       os.path.join(__lowerCamelCase ,					__lowerCamelCase		)

  _snake_case					     =       {
      '''langs''': [src_lang, tgt_lang],
      '''model_max_length''': 10_24,
      '''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
  _snake_case					     =       chkpt['''models'''][0]
  _snake_case					     =       model.state_dict()

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

  # remove unneeded keys
  _snake_case					     =       [
      '''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		)

  _snake_case					     =       FSMTConfig.from_pretrained(__lowerCamelCase		)
  _snake_case					     =       FSMTForConditionalGeneration(__lowerCamelCase		)

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

  # save
  _snake_case					     =       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 					_UpperCAmelCase      ( __lowerCamelCase   :       int = 1_00_00_00		)     ->					int:
  _snake_case					     =       limit + 1
  _snake_case					     =       [0] * limit
  for first_term in range(1 ,					__lowerCamelCase		):
    for n in range(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		):
      _snake_case					     =       first_term + n / first_term
      if common_difference % 4:  # d must be divisble by 4
        continue
      else:
        common_difference /= 4
        if (
            first_term > common_difference
            and first_term < 4 * common_difference
        ):  # since x,y,z are positive integers
          frequency[n] += 1  # so z>0 and a>d ,also 4d<a

  _snake_case					     =       sum(1 for x in frequency[1:limit] if x == 10		)

  return count


if __name__ == "__main__":
 print(F"{solution() = }")



