

"""simple docstring"""


import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


a					:Any    = logging.getLogger(__name__)

@dataclass
class      __a							:






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:str       = field(
         metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default=UpperCamelCase_   ,	metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default=UpperCamelCase_   ,	metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default=UpperCamelCase_   ,	metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}   ,	)
     _SCREAMING_SNAKE_CASE						:bool       = field(default=UpperCamelCase_   ,	metadata={"""help""": """Whether tp freeze the encoder."""})
     _SCREAMING_SNAKE_CASE						:bool       = field(default=UpperCamelCase_   ,	metadata={"""help""": """Whether  to freeze the embeddings."""})





@dataclass
class      __a							:






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:str       = field(
         metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(
         default="""summarization"""   ,	metadata={"""help""": """Task name, summarization (or summarization_{dataset} for pegasus) or translation"""}   ,	)
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(
         default=10_24   ,	metadata={
             """help""": (
                 """The maximum total input sequence length after tokenization. Sequences longer """
                 """than this will be truncated, sequences shorter will be padded."""
             )
         }   ,	)
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(
         default=1_28   ,	metadata={
             """help""": (
                 """The maximum total sequence length for target text after tokenization. Sequences longer """
                 """than this will be truncated, sequences shorter will be padded."""
             )
         }   ,	)
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(
         default=1_42   ,	metadata={
             """help""": (
                 """The maximum total sequence length for validation target text after tokenization. Sequences longer """
                 """than this will be truncated, sequences shorter will be padded. """
                 """This argument is also used to override the ``max_length`` param of ``model.generate``, which is used """
                 """during ``evaluate`` and ``predict``."""
             )
         }   ,	)
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(
         default=1_42   ,	metadata={
             """help""": (
                 """The maximum total sequence length for test target text after tokenization. Sequences longer """
                 """than this will be truncated, sequences shorter will be padded."""
             )
         }   ,	)
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=-1   ,	metadata={"""help""": """# training examples. -1 means use all."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=-1   ,	metadata={"""help""": """# validation examples. -1 means use all."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=-1   ,	metadata={"""help""": """# test examples. -1 means use all."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(default=UpperCamelCase_   ,	metadata={"""help""": """Source language id for translation."""})
     _SCREAMING_SNAKE_CASE						:Optional[str]       = field(default=UpperCamelCase_   ,	metadata={"""help""": """Target language id for translation."""})
     _SCREAMING_SNAKE_CASE						:Optional[int]       = field(default=UpperCamelCase_   ,	metadata={"""help""": """# num_beams to use for evaluation."""})
     _SCREAMING_SNAKE_CASE						:bool       = field(
         default=UpperCamelCase_   ,	metadata={"""help""": """If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."""}   ,	)





def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Dict:
      logger.info(F'''***** {split} metrics *****'''							)
      for key in sorted(metrics.keys()							):
            logger.info(F'''  {key} = {metrics[key]}'''							)
      save_json(__lowerCAmelCase  ,					os.path.join(__lowerCAmelCase  ,					F'''{split}_results.json'''							)							)




def  _lowercase					(    )       ->				Dict:
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.

      SCREAMING_SNAKE_CASE__   :      Dict      		=	HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)							)

      if len(sys.argv							) == 2 and sys.argv[1].endswith(""".json"""							):
            # If we pass only one argument to the script and it's the path to a json file,
            # let's parse it to get our arguments.
            SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      int      		=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
      else:
            SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      str      		=	parser.parse_args_into_dataclasses()

      check_output_dir(__lowerCAmelCase							)

      # Setup logging
      logging.basicConfig(
          format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""  ,					datefmt="""%m/%d/%Y %H:%M:%S"""  ,					level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN  ,					)
      logger.warning(
          """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""  ,					training_args.local_rank  ,					training_args.device  ,					training_args.n_gpu  ,					bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED							)  ,					training_args.fpaa  ,					)
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()
      # Set the verbosity to info of the Transformers logger (on main process only):
      if is_main_process(training_args.local_rank							):
            transformers.utils.logging.set_verbosity_info()
      logger.info("""Training/evaluation parameters %s"""  ,					__lowerCAmelCase							)

      # Set seed
      set_seed(training_args.seed							)

      # Load pretrained model and tokenizer
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.

      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,					cache_dir=model_args.cache_dir  ,					)

      SCREAMING_SNAKE_CASE__   :      Dict      		=	("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
      for p in extra_model_params:
            if getattr(__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							):
                  assert hasattr(__lowerCAmelCase  ,					__lowerCAmelCase							), F'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
                  setattr(__lowerCAmelCase  ,					__lowerCAmelCase  ,					getattr(__lowerCAmelCase  ,					__lowerCAmelCase							)							)

      SCREAMING_SNAKE_CASE__   :      int      		=	AutoTokenizer.from_pretrained(
          model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path  ,					cache_dir=model_args.cache_dir  ,					)
      SCREAMING_SNAKE_CASE__   :      int      		=	AutoModelForSeqaSeqLM.from_pretrained(
          model_args.model_name_or_path  ,					from_tf=""".ckpt""" in model_args.model_name_or_path  ,					config=__lowerCAmelCase  ,					cache_dir=model_args.cache_dir  ,					)

      # use task specific params
      use_task_specific_params(__lowerCAmelCase  ,					data_args.task							)

      # set num_beams for evaluation
      if data_args.eval_beams is None:
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	model.config.num_beams

      # set decoder_start_token_id for MBart
      if model.config.decoder_start_token_id is None and isinstance(__lowerCAmelCase  ,					(MBartTokenizer, MBartTokenizerFast)							):
            assert (
                data_args.tgt_lang is not None and data_args.src_lang is not None
            ), "mBart requires --tgt_lang and --src_lang"
            if isinstance(__lowerCAmelCase  ,					__lowerCAmelCase							):
                  SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	tokenizer.lang_code_to_id[data_args.tgt_lang]
            else:
                  SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	tokenizer.convert_tokens_to_ids(data_args.tgt_lang							)

      if model_args.freeze_embeds:
            freeze_embeds(__lowerCAmelCase							)
      if model_args.freeze_encoder:
            freeze_params(model.get_encoder()							)
            assert_all_frozen(model.get_encoder()							)

      SCREAMING_SNAKE_CASE__   :      List[str]      		=	SeqaSeqDataset

      # Get datasets
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	(
          dataset_class(
              __lowerCAmelCase  ,					type_path="""train"""  ,					data_dir=data_args.data_dir  ,					n_obs=data_args.n_train  ,					max_target_length=data_args.max_target_length  ,					max_source_length=data_args.max_source_length  ,					prefix=model.config.prefix or """"""  ,					)
          if training_args.do_train
          else None
      )
      SCREAMING_SNAKE_CASE__   :      int      		=	(
          dataset_class(
              __lowerCAmelCase  ,					type_path="""val"""  ,					data_dir=data_args.data_dir  ,					n_obs=data_args.n_val  ,					max_target_length=data_args.val_max_target_length  ,					max_source_length=data_args.max_source_length  ,					prefix=model.config.prefix or """"""  ,					)
          if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
          else None
      )
      SCREAMING_SNAKE_CASE__   :      Any      		=	(
          dataset_class(
              __lowerCAmelCase  ,					type_path="""test"""  ,					data_dir=data_args.data_dir  ,					n_obs=data_args.n_test  ,					max_target_length=data_args.test_max_target_length  ,					max_source_length=data_args.max_source_length  ,					prefix=model.config.prefix or """"""  ,					)
          if training_args.do_predict
          else None
      )

      # Initialize our Trainer
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	(
          build_compute_metrics_fn(data_args.task  ,					__lowerCAmelCase							) if training_args.predict_with_generate else None
      )
      SCREAMING_SNAKE_CASE__   :      Dict      		=	SeqaSeqTrainer(
          model=__lowerCAmelCase  ,					args=__lowerCAmelCase  ,					data_args=__lowerCAmelCase  ,					train_dataset=__lowerCAmelCase  ,					eval_dataset=__lowerCAmelCase  ,					data_collator=SeqaSeqDataCollator(
              __lowerCAmelCase  ,					__lowerCAmelCase  ,					model.config.decoder_start_token_id  ,					training_args.tpu_num_cores							)  ,					compute_metrics=__lowerCAmelCase  ,					tokenizer=__lowerCAmelCase  ,					)

      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	{}
      # Training
      if training_args.do_train:
            logger.info("""*** Train ***"""							)

            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	trainer.train(
                model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path							) else None							)
            SCREAMING_SNAKE_CASE__   :      Any      		=	train_result.metrics
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	data_args.n_train

            trainer.save_model()  # this also saves the tokenizer

            if trainer.is_world_process_zero():
                  handle_metrics("""train"""  ,					__lowerCAmelCase  ,					training_args.output_dir							)
                  all_metrics.update(__lowerCAmelCase							)

                  # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
                  trainer.state.save_to_json(os.path.join(training_args.output_dir  ,					"""trainer_state.json"""							)							)

                  # For convenience, we also re-save the tokenizer to the same directory,
                  # so that you can share your model easily on huggingface.co/models =)
                  tokenizer.save_pretrained(training_args.output_dir							)

    # Evaluation
      if training_args.do_eval:
            logger.info("""*** Evaluate ***"""							)

            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	trainer.evaluate(metric_key_prefix="""val"""							)
            SCREAMING_SNAKE_CASE__   :      List[str]      		=	data_args.n_val
            SCREAMING_SNAKE_CASE__   :      Dict      		=	round(metrics["""val_loss"""]  ,					4							)

            if trainer.is_world_process_zero():
                  handle_metrics("""val"""  ,					__lowerCAmelCase  ,					training_args.output_dir							)
                  all_metrics.update(__lowerCAmelCase							)

      if training_args.do_predict:
            logger.info("""*** Predict ***"""							)

            SCREAMING_SNAKE_CASE__   :      int      		=	trainer.predict(test_dataset=__lowerCAmelCase  ,					metric_key_prefix="""test"""							)
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	test_output.metrics
            SCREAMING_SNAKE_CASE__   :      int      		=	data_args.n_test

            if trainer.is_world_process_zero():
                  SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	round(metrics["""test_loss"""]  ,					4							)
                  handle_metrics("""test"""  ,					__lowerCAmelCase  ,					training_args.output_dir							)
                  all_metrics.update(__lowerCAmelCase							)

                  if training_args.predict_with_generate:
                        SCREAMING_SNAKE_CASE__   :      int      		=	tokenizer.batch_decode(
                            test_output.predictions  ,					skip_special_tokens=__lowerCAmelCase  ,					clean_up_tokenization_spaces=__lowerCAmelCase							)
                        SCREAMING_SNAKE_CASE__   :      str      		=	lmap(str.strip  ,					__lowerCAmelCase							)
                        write_txt_file(__lowerCAmelCase  ,					os.path.join(training_args.output_dir  ,					"""test_generations.txt"""							)							)

      if trainer.is_world_process_zero():
            save_json(__lowerCAmelCase  ,					os.path.join(training_args.output_dir  ,					"""all_results.json"""							)							)

      return all_metrics




def  _lowercase					(    __lowerCAmelCase							)       ->				List[Any]:
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
  main()







"""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					:str    = 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					:int    = {
    # 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					:Dict    = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
  a					:List[Any]    = "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					:str    = "allenai"




def  _lowercase					(    __lowerCAmelCase							)       ->				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}
      SCREAMING_SNAKE_CASE__   :      str      		=	dict((re.sub(r"""@@$"""  ,					""""""  ,					__lowerCAmelCase							), v) if k.endswith("""@@"""							) else (re.sub(r"""$"""  ,					"""</w>"""  ,					__lowerCAmelCase							), v) for k, v in d.items()							)
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	"""<s> <pad> </s> <unk>""".split()
      # restore the special tokens
      for k in keep_keys:
            del da[F'''{k}</w>''']
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	d[k]  # restore
      return da




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Optional[int]:
      # 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

      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	basename(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	dirname(__lowerCAmelCase							)

      SCREAMING_SNAKE_CASE__   :      int      		=	fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	cls.hub_models()
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	{"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	"""."""
      # 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__   :      Union[str, Any]      		=	hub_utils.from_pretrained(
          __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					archive_map=__lowerCAmelCase  ,					**__lowerCAmelCase							)

      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	vars(chkpt["""args"""]["""model"""]							)

      SCREAMING_SNAKE_CASE__   :      Any      		=	args["""source_lang"""]
      SCREAMING_SNAKE_CASE__   :      Any      		=	args["""target_lang"""]

      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	dirname(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	basename(__lowerCAmelCase							)

      # dicts
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	os.path.join(__lowerCAmelCase  ,					F'''dict.{src_lang}.txt'''							)
      SCREAMING_SNAKE_CASE__   :      Any      		=	os.path.join(__lowerCAmelCase  ,					F'''dict.{tgt_lang}.txt'''							)

      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	Dictionary.load(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	rewrite_dict_keys(src_dict.indices							)
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	len(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      int      		=	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
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	True
      for k in src_vocab.keys():
            if not k.islower():
                  SCREAMING_SNAKE_CASE__   :      Tuple      		=	False
                  break

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

      SCREAMING_SNAKE_CASE__   :      str      		=	{
          """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
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	5
      SCREAMING_SNAKE_CASE__   :      str      		=	False
      if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	best_score_hparams[model_dir]["""length_penalty"""]
      else:
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	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
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	os.path.join(__lowerCAmelCase  ,					__lowerCAmelCase							)

      SCREAMING_SNAKE_CASE__   :      Tuple      		=	{
          """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
      SCREAMING_SNAKE_CASE__   :      Dict      		=	chkpt["""models"""][0]
      SCREAMING_SNAKE_CASE__   :      int      		=	model.state_dict()

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

      # remove unneeded keys
      SCREAMING_SNAKE_CASE__   :      str      		=	[
          """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							)

      SCREAMING_SNAKE_CASE__   :      Dict      		=	FSMTConfig.from_pretrained(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	FSMTForConditionalGeneration(__lowerCAmelCase							)

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

      # save
      SCREAMING_SNAKE_CASE__   :      int      		=	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__":
  a					:Optional[Any]    = 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					:List[str]    = parser.parse_args()
  convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)




