





'''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()

a   	=    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
a   	=    {
    # 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
a   	=    {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
    a   	=    "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",
]:
    a   	=    "allenai"

def     __magic_name__							(				__UpperCAmelCase   )    ->       int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							dict((re.sub(R"""@@$"""				,						""""""				,						__UpperCAmelCase   ), v) if k.endswith("""@@"""   ) else (re.sub(R"""$"""				,						"""</w>"""				,						__UpperCAmelCase   ), v) for k, v in d.items()   )
   __SCREAMING_SNAKE_CASE   				=							"""<s> <pad> </s> <unk>""".split()
   # restore the special tokens
   for k in keep_keys:
      del da[f"""{k}</w>"""]
      __SCREAMING_SNAKE_CASE   				=							d[k]  # restore
   return da

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       List[str]:

   '''simple docstring'''







   assert os.path.exists(__UpperCAmelCase   )
   os.makedirs(__UpperCAmelCase				,						exist_ok=__UpperCAmelCase   )
   print(f"""Writing results to {pytorch_dump_folder_path}"""   )

   # handle various types of models

   __SCREAMING_SNAKE_CASE   				=							basename(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							dirname(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
   __SCREAMING_SNAKE_CASE   				=							cls.hub_models()
   __SCREAMING_SNAKE_CASE   				=							{"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
   __SCREAMING_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}"""   )
   __SCREAMING_SNAKE_CASE   				=							hub_utils.from_pretrained(
       __UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						archive_map=__UpperCAmelCase				,						**__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							vars(chkpt["""args"""]["""model"""]   )

   __SCREAMING_SNAKE_CASE   				=							args["""source_lang"""]
   __SCREAMING_SNAKE_CASE   				=							args["""target_lang"""]

   __SCREAMING_SNAKE_CASE   				=							dirname(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							basename(__UpperCAmelCase   )

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

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

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

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

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

   # model config
   __SCREAMING_SNAKE_CASE   				=							os.path.join(__UpperCAmelCase				,						"""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']}"""

   __SCREAMING_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""": 0.0_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
   __SCREAMING_SNAKE_CASE   				=							5
   __SCREAMING_SNAKE_CASE   				=							False
   if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
      __SCREAMING_SNAKE_CASE   				=							best_score_hparams[model_dir]["""length_penalty"""]
   else:
      __SCREAMING_SNAKE_CASE   				=							1.0

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

   # tokenizer config
   __SCREAMING_SNAKE_CASE   				=							os.path.join(__UpperCAmelCase				,						__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							{
       """langs""": [src_lang, tgt_lang],
       """model_max_length""": 1024,
       """do_lower_case""": do_lower_case,
   }

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

   # model
   __SCREAMING_SNAKE_CASE   				=							chkpt["""models"""][0]
   __SCREAMING_SNAKE_CASE   				=							model.state_dict()

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

   # remove unneeded keys
   __SCREAMING_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(__UpperCAmelCase				,						__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							FSMTConfig.from_pretrained(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							FSMTForConditionalGeneration(__UpperCAmelCase   )

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

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

   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__":
    a   	=    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."
    )
    a   	=    parser.parse_args()
    convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)








import gc
import threading
import time

import psutil
import torch





class        lowerCAmelCase__  :



       '''simple docstring'''







       def __init__(							self     ):







             '''simple docstring'''



             __A    =psutil.Process()
             __A    =False

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =-1

             while True:
                   __A    =max(self.process.memory_info().rss ,   self.cpu_memory_peak     )

                   # can't sleep or will not catch the peak right (this comment is here on purpose)
                   if not self.peak_monitoring:
                         break

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =True
             __A    =threading.Thread(target=self.peak_monitor     )
             __A    =True
             self.thread.start()


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =False
             self.thread.join()
             return self.cpu_memory_peak

_lowerCamelCase      :					Union[str, Any]					    =      PeakCPUMemory()





def      A__					(			)					->Dict:
      # Time
      __A    ={'''time''': time.time()}

      gc.collect()
      torch.cuda.empty_cache()

      # CPU mem
      __A    =psutil.Process().memory_info().rss
      cpu_peak_tracker.start()

      # GPU mem
      for i in range(torch.cuda.device_count()			):
            __A    =torch.cuda.memory_allocated(__A			)
      torch.cuda.reset_peak_memory_stats()

      return measures





def      A__					(			__A  :						Any			)					->Any:
      # Time
      __A    ={'''time''': time.time() - start_measures['''time''']}

      gc.collect()
      torch.cuda.empty_cache()

      # CPU mem
      __A    =(psutil.Process().memory_info().rss - start_measures['''cpu''']) / 2**20
      __A    =(cpu_peak_tracker.stop() - start_measures['''cpu''']) / 2**20

      # GPU mem
      for i in range(torch.cuda.device_count()			):
            __A    =(torch.cuda.memory_allocated(__A			) - start_measures[str(__A			)]) / 2**20
            __A    =(torch.cuda.max_memory_allocated(__A			) - start_measures[str(__A			)]) / 2**20

      return measures





def      A__					(			__A  :						Union[str, Any]     ,				__A  :						Any			)					->Union[str, Any]:
      print(F'''{description}:'''			)
      print(F'''- Time: {measures['time']:.2f}s'''			)
      for i in range(torch.cuda.device_count()			):
            print(F'''- GPU {i} allocated: {measures[str(__A			)]:.2f}MiB'''			)
            __A    =measures[F'''{i}-peak''']
            print(F'''- GPU {i} peak: {peak:.2f}MiB'''			)
      print(F'''- CPU RAM allocated: {measures['cpu']:.2f}MiB'''			)
      print(F'''- CPU RAM peak: {measures['cpu-peak']:.2f}MiB'''			)


