


"""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 lowerCAmelCase_      (				UpperCamelCase__	: Optional[int]      ):






 """simple docstring"""







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


def lowerCAmelCase_      (				UpperCamelCase__	: Union[str, Any]				,   UpperCamelCase__	: int      ):






 """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

 __lowercase									=	basename(UpperCamelCase__      )
 __lowercase									=	dirname(UpperCamelCase__      )

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

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

 __lowercase									=	args["""source_lang"""]
 __lowercase									=	args["""target_lang"""]

 __lowercase									=	dirname(UpperCamelCase__      )
 __lowercase									=	basename(UpperCamelCase__      )

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

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

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

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

 __lowercase									=	{
     """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
 __lowercase									=	chkpt["""models"""][0]
 __lowercase									=	model.state_dict()

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

 # remove unneeded keys
 __lowercase									=	[
     """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__      )

 __lowercase									=	FSMTConfig.from_pretrained(UpperCamelCase__      )
 __lowercase									=	FSMTForConditionalGeneration(UpperCamelCase__      )

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

 # save
 __lowercase									=	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__":
       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"""

import argparse
from copy import deepcopy

import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    set_seed,
)


def lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	argparse.ArgumentParser()
 parser.add_argument("""--model_ckpt"""				,   type=UpperCamelCase__				,   default="""microsoft/unixcoder-base-nine""" )
 parser.add_argument("""--num_epochs"""				,   type=UpperCamelCase__				,   default=5 )
 parser.add_argument("""--batch_size"""				,   type=UpperCamelCase__				,   default=6 )
 parser.add_argument("""--gradient_accumulation_steps"""				,   type=UpperCamelCase__				,   default=1 )
 parser.add_argument("""--freeze"""				,   type=UpperCamelCase__				,   default=UpperCamelCase__ )
 parser.add_argument("""--learning_rate"""				,   type=UpperCamelCase__				,   default=5E-4 )
 parser.add_argument("""--seed"""				,   type=UpperCamelCase__				,   default=0 )
 parser.add_argument("""--lr_scheduler_type"""				,   type=UpperCamelCase__				,   default="""cosine""" )
 parser.add_argument("""--num_warmup_steps"""				,   type=UpperCamelCase__				,   default=10 )
 parser.add_argument("""--weight_decay"""				,   type=UpperCamelCase__				,   default=0.01 )
 parser.add_argument("""--output_dir"""				,   type=UpperCamelCase__				,   default="""./results""" )
 return parser.parse_args()


UpperCAmelCase__  							=load("accuracy")


def lowerCAmelCase_      (				UpperCamelCase__	: List[Any] ):






 """simple docstring"""







 __lowercase							,		__lowercase									=	eval_pred
 __lowercase									=	np.argmax(UpperCamelCase__				,   axis=1 )
 return metric.compute(predictions=UpperCamelCase__				,   references=UpperCamelCase__ )





class    lowerCamelCase__						(		_a    ):
      def __init__(		self	:	Any      ,       A_	:	int							):






       '''simple docstring'''


       super().__init__()
       __lowercase									=	trainer






      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]      ,       A_	:	int      ,       A_	:	str      ,       A_	:	Any      ,       **A_	:	Optional[int]							):






       '''simple docstring'''


       if control.should_evaluate:
        __lowercase									=	deepcopy(A_							)
        self._trainer.evaluate(eval_dataset=self._trainer.train_dataset      ,       metric_key_prefix="""train"""							)
        return control_copy






def lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	get_args()
 set_seed(args.seed )

 __lowercase									=	load_dataset("""codeparrot/codecomplex"""				,   split="""train""" )
 __lowercase									=	dataset.train_test_split(test_size=0.2 )
 __lowercase									=	train_test["""test"""].train_test_split(test_size=0.5 )
 __lowercase									=	DatasetDict(
     {
         """train""": train_test["""train"""],
         """test""": test_validation["""train"""],
         """valid""": test_validation["""test"""],
     } )

 print("""Loading tokenizer and model""" )
 __lowercase									=	AutoTokenizer.from_pretrained(args.model_ckpt )
 __lowercase									=	tokenizer.eos_token
 __lowercase									=	AutoModelForSequenceClassification.from_pretrained(args.model_ckpt				,   num_labels=7 )
 __lowercase									=	model.config.eos_token_id

 if args.freeze:
  for param in model.roberta.parameters():
   __lowercase									=	False

 __lowercase									=	ClassLabel(num_classes=7				,   names=list(set(train_test_validation["""train"""]["""complexity"""] ) ) )

 def tokenize(UpperCamelCase__	: Optional[Any] ):
  __lowercase									=	tokenizer(example["""src"""]				,   truncation=UpperCamelCase__				,   max_length=1024 )
  __lowercase									=	labels.straint(example["""complexity"""] )
  return {
      "input_ids": inputs["input_ids"],
      "attention_mask": inputs["attention_mask"],
      "label": label,
  }

 __lowercase									=	train_test_validation.map(
     UpperCamelCase__				,   batched=UpperCamelCase__				,   remove_columns=train_test_validation["""train"""].column_names				,   )
 __lowercase									=	DataCollatorWithPadding(tokenizer=UpperCamelCase__ )

 __lowercase									=	TrainingArguments(
     output_dir=args.output_dir				,   learning_rate=args.learning_rate				,   lr_scheduler_type=args.lr_scheduler_type				,   evaluation_strategy="""epoch"""				,   save_strategy="""epoch"""				,   logging_strategy="""epoch"""				,   per_device_train_batch_size=args.batch_size				,   per_device_eval_batch_size=args.batch_size				,   num_train_epochs=args.num_epochs				,   gradient_accumulation_steps=args.gradient_accumulation_steps				,   weight_decay=0.01				,   metric_for_best_model="""accuracy"""				,   run_name="""complexity-java"""				,   report_to="""wandb"""				,   )

 __lowercase									=	Trainer(
     model=UpperCamelCase__				,   args=UpperCamelCase__				,   train_dataset=tokenized_datasets["""train"""]				,   eval_dataset=tokenized_datasets["""valid"""]				,   tokenizer=UpperCamelCase__				,   data_collator=UpperCamelCase__				,   compute_metrics=UpperCamelCase__				,   )

 print("""Training...""" )
 trainer.add_callback(CustomCallback(UpperCamelCase__ ) )
 trainer.train()


if __name__ == "__main__":
       main()
