





'''simple docstring'''

from __future__ import annotations

lowercase__		:     Dict			=							[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
lowercase__		:     Optional[int]			=							[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]







def    a__							(							lowercase   :	list[float] )						->						list[float]:





     """simple docstring"""

     _UpperCamelCase							       =			[]
     _UpperCamelCase							       =			len(lowercase )

     for i in range(lowercase ):
          _UpperCamelCase							       =			-1
          for j in range(i + 1,   lowercase ):
               if arr[i] < arr[j]:
                    _UpperCamelCase							       =			arr[j]
                    break
          result.append(lowercase )
     return result







def    a__							(							lowercase   :	list[float] )						->						list[float]:





     """simple docstring"""

     _UpperCamelCase							       =			[]
     for i, outer in enumerate(lowercase ):
          _UpperCamelCase							       =			-1
          for inner in arr[i + 1 :]:
               if outer < inner:
                    _UpperCamelCase							       =			inner
                    break
          result.append(lowercase )
     return result







def    a__							(							lowercase   :	list[float] )						->						list[float]:





     """simple docstring"""

     _UpperCamelCase							       =			len(lowercase )
     _UpperCamelCase							       =			[]
     _UpperCamelCase							       =			[-1] * arr_size

     for index in reversed(range(lowercase ) ):
          if stack:
               while stack[-1] <= arr[index]:
                    stack.pop()
                    if not stack:
                         break
          if stack:
               _UpperCamelCase							       =			stack[-1]
          stack.append(arr[index] )
     return result


if __name__ == "__main__":
   from doctest import testmod
   from timeit import timeit

   testmod()
   print(next_greatest_element_slow(arr))
   print(next_greatest_element_fast(arr))
   print(next_greatest_element(arr))

   lowercase__		:     Dict			=							(
       'from __main__ import arr, next_greatest_element_slow, '
       'next_greatest_element_fast, next_greatest_element'
   )
   print(
       'next_greatest_element_slow():',
       timeit('next_greatest_element_slow(arr)', setup=setup),
   )
   print(
       'next_greatest_element_fast():',
       timeit('next_greatest_element_fast(arr)', setup=setup),
   )
   print(
       '     next_greatest_element():',
       timeit('next_greatest_element(arr)', setup=setup),
   )




'''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__  :						Optional[Any]					=      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__  :						str					=      {
    # 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__  :						Tuple					=      {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
  UpperCamelCase__  :						Tuple					=      '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__  :						Tuple					=      'allenai'







def __UpperCamelCase( _A       :					int     ):
   '''simple docstring'''
   # (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}
   UpperCAmelCase__				:					int 					=	dict((re.sub(R'''@@$'''	,      ''''''	,      _A     ), v) if k.endswith('''@@'''     ) else (re.sub(R'''$'''	,      '''</w>'''	,      _A     ), v) for k, v in d.items()     )
   UpperCAmelCase__				:					List[str] 					=	'''<s> <pad> </s> <unk>'''.split()
   # restore the special tokens
   for k in keep_keys:
      del da[F'''{k}</w>''']
      UpperCAmelCase__				:					Optional[Any] 					=	d[k]  # restore
   return da







def __UpperCamelCase( _A       :					Tuple	,      _A       :					List[Any]     ):
   '''simple docstring'''
   # prep
   assert os.path.exists(_A     )
   os.makedirs(_A	,      exist_ok=_A     )
   print(F'''Writing results to {pytorch_dump_folder_path}'''     )

   # handle various types of models

   UpperCAmelCase__				:					Dict 					=	basename(_A     )
   UpperCAmelCase__				:					List[str] 					=	dirname(_A     )

   UpperCAmelCase__				:					Union[str, Any] 					=	fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
   UpperCAmelCase__				:					Dict 					=	cls.hub_models()
   UpperCAmelCase__				:					Dict 					=	{'''bpe''': '''fastbpe''', '''tokenizer''': '''moses'''}
   UpperCAmelCase__				:					int 					=	'''.'''
   # 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}'''     )
   UpperCAmelCase__				:					Union[str, Any] 					=	hub_utils.from_pretrained(
       _A	,      _A	,      _A	,      archive_map=_A	,      **_A     )

   UpperCAmelCase__				:					List[Any] 					=	vars(chkpt['''args''']['''model''']     )

   UpperCAmelCase__				:					str 					=	args['''source_lang''']
   UpperCAmelCase__				:					Tuple 					=	args['''target_lang''']

   UpperCAmelCase__				:					Optional[int] 					=	dirname(_A     )
   UpperCAmelCase__				:					int 					=	basename(_A     )

   # dicts
   UpperCAmelCase__				:					List[Any] 					=	os.path.join(_A	,      F'''dict.{src_lang}.txt'''     )
   UpperCAmelCase__				:					Tuple 					=	os.path.join(_A	,      F'''dict.{tgt_lang}.txt'''     )

   UpperCAmelCase__				:					int 					=	Dictionary.load(_A     )
   UpperCAmelCase__				:					Optional[int] 					=	rewrite_dict_keys(src_dict.indices     )
   UpperCAmelCase__				:					List[Any] 					=	len(_A     )
   UpperCAmelCase__				:					str 					=	os.path.join(_A	,      '''vocab-src.json'''     )
   print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records'''     )
   with open(_A	,      '''w'''	,      encoding='''utf-8'''     ) as f:
      f.write(json.dumps(_A	,      ensure_ascii=_A	,      indent=_A     )     )

   # 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
   UpperCAmelCase__				:					Tuple 					=	True
   for k in src_vocab.keys():
      if not k.islower():
         UpperCAmelCase__				:					int 					=	False
         break

   UpperCAmelCase__				:					str 					=	Dictionary.load(_A     )
   UpperCAmelCase__				:					Tuple 					=	rewrite_dict_keys(tgt_dict.indices     )
   UpperCAmelCase__				:					List[str] 					=	len(_A     )
   UpperCAmelCase__				:					Dict 					=	os.path.join(_A	,      '''vocab-tgt.json'''     )
   print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records'''     )
   with open(_A	,      '''w'''	,      encoding='''utf-8'''     ) as f:
      f.write(json.dumps(_A	,      ensure_ascii=_A	,      indent=_A     )     )

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

   # model config
   UpperCAmelCase__				:					Optional[int] 					=	os.path.join(_A	,      '''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']}'''

   UpperCAmelCase__				:					Optional[Any] 					=	{
       '''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''': 0.0_2,
       '''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
   UpperCAmelCase__				:					Optional[int] 					=	5
   UpperCAmelCase__				:					Optional[Any] 					=	False
   if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
      UpperCAmelCase__				:					str 					=	best_score_hparams[model_dir]['''length_penalty''']
   else:
      UpperCAmelCase__				:					Dict 					=	1.0

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

   # tokenizer config
   UpperCAmelCase__				:					List[str] 					=	os.path.join(_A	,      _A     )

   UpperCAmelCase__				:					Any 					=	{
       '''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(_A	,      '''w'''	,      encoding='''utf-8'''     ) as f:
      f.write(json.dumps(_A	,      ensure_ascii=_A	,      indent=_A     )     )

   # model
   UpperCAmelCase__				:					Dict 					=	chkpt['''models'''][0]
   UpperCAmelCase__				:					Any 					=	model.state_dict()

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

   # remove unneeded keys
   UpperCAmelCase__				:					Union[str, Any] 					=	[
       '''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(_A	,      _A     )

   UpperCAmelCase__				:					List[str] 					=	FSMTConfig.from_pretrained(_A     )
   UpperCAmelCase__				:					int 					=	FSMTForConditionalGeneration(_A     )

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

   # save
   UpperCAmelCase__				:					List[str] 					=	os.path.join(_A	,      _A     )
   print(F'''Generating {pytorch_weights_dump_path}'''     )
   torch.save(_A	,      _A     )

   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__  :						int					=      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__  :						List[str]					=      parser.parse_args()
  convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)




