




'''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()

lowerCAmelCase_      :  Dict				     =  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
lowerCAmelCase_      :  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
lowerCAmelCase_      :  Optional[Any]				     =  {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
 lowerCAmelCase_      :  str				     =  '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",
]:
 lowerCAmelCase_      :  Optional[Any]				     =  'allenai'



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Tuple				):




     """simple docstring"""


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



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Optional[Any] ,    UpperCamelCase__    :					Any				):




     """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

     a_     :							List[str]     						=  basename(UpperCamelCase__				)
     a_     :							List[str]     						=  dirname(UpperCamelCase__				)

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

     a_     :							str     						=  vars(chkpt["""args"""]["""model"""]				)

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

     a_     :							Any     						=  dirname(UpperCamelCase__				)
     a_     :							Dict     						=  basename(UpperCamelCase__				)

     # dicts
     a_     :							Optional[Any]     						=  os.path.join(UpperCamelCase__ ,    F"dict.{src_lang}.txt"				)
     a_     :							Any     						=  os.path.join(UpperCamelCase__ ,    F"dict.{tgt_lang}.txt"				)

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

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

     a_     :							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
     a_     :							int     						=  5
     a_     :							Tuple     						=  False
     if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
          a_     :							int     						=  best_score_hparams[model_dir]["""length_penalty"""]
     else:
          a_     :							int     						=  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
     a_     :							List[Any]     						=  os.path.join(UpperCamelCase__ ,    UpperCamelCase__				)

     a_     :							List[str]     						=  {
         """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
     a_     :							Optional[Any]     						=  chkpt["""models"""][0]
     a_     :							Any     						=  model.state_dict()

     # rename keys to start with 'model.'
     a_     :							List[str]     						=  OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items()				)

     # remove unneeded keys
     a_     :							Optional[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(UpperCamelCase__ ,    UpperCamelCase__				)

     a_     :							Union[str, Any]     						=  FSMTConfig.from_pretrained(UpperCamelCase__				)
     a_     :							Optional[int]     						=  FSMTForConditionalGeneration(UpperCamelCase__				)

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

     # save
     a_     :							List[str]     						=  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__":
 lowerCAmelCase_      :  Union[str, 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.'
 )
 lowerCAmelCase_      :  List[str]				     =  parser.parse_args()
 convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)






'''simple docstring'''


import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


lowerCAmelCase_      :  List[str]				     =  'sshleifer/mar_enro_6_3_student'







class      SCREAMING_SNAKE_CASE    (						snake_case_				):






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           super().setUp()

           a_     :							Optional[int]     						=  cached_path(
               """https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"""						,						extract_compressed_file=lowercase__						,						)
           a_     :							List[Any]     						=  F"{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"






      @slow
      @require_torch_gpu
      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           MarianMTModel.from_pretrained(lowercase__       )






      @slow
      @require_torch_gpu
      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  {
               """$MAX_LEN""": 64,
               """$BS""": 64,
               """$GAS""": 1,
               """$ENRO_DIR""": self.data_dir,
               """facebook/mbart-large-cc25""": MARIAN_MODEL,
               # "val_check_interval=0.25": "val_check_interval=1.0",
               """--learning_rate=3e-5""": """--learning_rate 3e-4""",
               """--num_train_epochs 6""": """--num_train_epochs 1""",
           }

           # Clean up bash script
           a_     :							Optional[int]     						=  (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""       )[1].strip()
           a_     :							List[str]     						=  bash_script.replace("""\\\n"""						,						""""""       ).strip().replace("""\"$@\""""						,						""""""       )
           for k, v in env_vars_to_replace.items():
                a_     :							Optional[Any]     						=  bash_script.replace(lowercase__						,						str(lowercase__       )       )
           a_     :							Union[str, Any]     						=  self.get_auto_remove_tmp_dir()

           # bash_script = bash_script.replace("--fp16 ", "")
           a_     :							List[str]     						=  F"\n            --output_dir {output_dir}\n            --tokenizer_name Helsinki-NLP/opus-mt-en-ro\n            --sortish_sampler\n            --do_predict\n            --gpus 1\n            --freeze_encoder\n            --n_train 40000\n            --n_val 500\n            --n_test 500\n            --fp16_opt_level O1\n            --num_sanity_val_steps 0\n            --eval_beams 2\n        ".split()
           # XXX: args.gpus > 1 : handle multi_gpu in the future

           a_     :							Dict     						=  ["""finetune.py"""] + bash_script.split() + args
           with patch.object(lowercase__						,						"""argv"""						,						lowercase__       ):
                a_     :							List[Any]     						=  argparse.ArgumentParser()
                a_     :							int     						=  pl.Trainer.add_argparse_args(lowercase__       )
                a_     :							Dict     						=  SummarizationModule.add_model_specific_args(lowercase__						,						os.getcwd()       )
                a_     :							List[str]     						=  parser.parse_args()
                a_     :							List[Any]     						=  main(lowercase__       )

           # Check metrics
           a_     :							List[str]     						=  load_json(model.metrics_save_path       )
           a_     :							Any     						=  metrics["""val"""][0]
           a_     :							int     						=  metrics["""val"""][-1]
           self.assertEqual(len(metrics["""val"""]       )						,						(args.max_epochs / args.val_check_interval)       )
           assert isinstance(last_step_stats[F"val_avg_{model.val_metric}"]						,						lowercase__       )

           self.assertGreater(last_step_stats["""val_avg_gen_time"""]						,						0.01       )
           # model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
           self.assertLessEqual(last_step_stats["""val_avg_gen_time"""]						,						1.0       )

           # test learning requirements:

           # 1. BLEU improves over the course of training by more than 2 pts
           self.assertGreater(last_step_stats["""val_avg_bleu"""] - first_step_stats["""val_avg_bleu"""]						,						2       )

           # 2. BLEU finishes above 17
           self.assertGreater(last_step_stats["""val_avg_bleu"""]						,						17       )

           # 3. test BLEU and val BLEU within ~1.1 pt.
           self.assertLess(abs(metrics["""val"""][-1]["""val_avg_bleu"""] - metrics["""test"""][-1]["""test_avg_bleu"""]       )						,						1.1       )

           # check lightning ckpt can be loaded and has a reasonable statedict
           a_     :							Dict     						=  os.listdir(lowercase__       )
           a_     :							Union[str, Any]     						=  [x for x in contents if x.endswith(""".ckpt"""       )][0]
           a_     :							Optional[Any]     						=  os.path.join(args.output_dir						,						lowercase__       )
           a_     :							Dict     						=  torch.load(lowercase__						,						map_location="""cpu"""       )
           a_     :							Tuple     						=  """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
           assert expected_key in ckpt["state_dict"]
           assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

           # TODO: turn on args.do_predict when PL bug fixed.
           if args.do_predict:
                a_     :							int     						=  {os.path.basename(lowercase__       ) for p in contents}
                assert "test_generations.txt" in contents
                assert "test_results.txt" in contents
                # assert len(metrics["val"]) ==  desired_n_evals
                assert len(metrics["""test"""]       ) == 1







class      SCREAMING_SNAKE_CASE    (						snake_case_				):






      @timeout_decorator.timeout(600       )
      @slow
      @require_torch_gpu
      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							int     						=  F"{self.test_file_dir_str}/test_data/wmt_en_ro"
           a_     :							Dict     						=  {
               """--fp16_opt_level=O1""": """""",
               """$MAX_LEN""": 128,
               """$BS""": 16,
               """$GAS""": 1,
               """$ENRO_DIR""": data_dir,
               """$m""": """sshleifer/student_marian_en_ro_6_1""",
               """val_check_interval=0.25""": """val_check_interval=1.0""",
           }

           # Clean up bash script
           a_     :							List[str]     						=  (
               (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""       )[1].strip()
           )
           a_     :							List[str]     						=  bash_script.replace("""\\\n"""						,						""""""       ).strip().replace("""\"$@\""""						,						""""""       )
           a_     :							Tuple     						=  bash_script.replace("""--fp16 """						,						""" """       )

           for k, v in env_vars_to_replace.items():
                a_     :							Union[str, Any]     						=  bash_script.replace(lowercase__						,						str(lowercase__       )       )
           a_     :							List[str]     						=  self.get_auto_remove_tmp_dir()
           a_     :							List[str]     						=  bash_script.replace("""--fp16"""						,						""""""       )
           a_     :							str     						=  6
           a_     :							Union[str, Any]     						=  (
               ["""distillation.py"""]
               + bash_script.split()
               + [
                   F"--output_dir={output_dir}",
                   """--gpus=1""",
                   """--learning_rate=1e-3""",
                   F"--num_train_epochs={epochs}",
                   """--warmup_steps=10""",
                   """--val_check_interval=1.0""",
                   """--do_predict""",
               ]
           )
           with patch.object(lowercase__						,						"""argv"""						,						lowercase__       ):
                a_     :							Dict     						=  argparse.ArgumentParser()
                a_     :							Tuple     						=  pl.Trainer.add_argparse_args(lowercase__       )
                a_     :							int     						=  SummarizationDistiller.add_model_specific_args(lowercase__						,						os.getcwd()       )
                a_     :							List[Any]     						=  parser.parse_args()
                # assert args.gpus == gpus THIS BREAKS for multi_gpu

                a_     :							Union[str, Any]     						=  distill_main(lowercase__       )

           # Check metrics
           a_     :							Union[str, Any]     						=  load_json(model.metrics_save_path       )
           a_     :							List[str]     						=  metrics["""val"""][0]
           a_     :							Optional[Any]     						=  metrics["""val"""][-1]
           assert len(metrics["""val"""]       ) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

           assert last_step_stats["val_avg_gen_time"] >= 0.01

           assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
           assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
           assert isinstance(last_step_stats[F"val_avg_{model.val_metric}"]						,						lowercase__       )

           # check lightning ckpt can be loaded and has a reasonable statedict
           a_     :							Optional[int]     						=  os.listdir(lowercase__       )
           a_     :							Dict     						=  [x for x in contents if x.endswith(""".ckpt"""       )][0]
           a_     :							Optional[Any]     						=  os.path.join(args.output_dir						,						lowercase__       )
           a_     :							Optional[Any]     						=  torch.load(lowercase__						,						map_location="""cpu"""       )
           a_     :							List[str]     						=  """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
           assert expected_key in ckpt["state_dict"]
           assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

           # TODO: turn on args.do_predict when PL bug fixed.
           if args.do_predict:
                a_     :							int     						=  {os.path.basename(lowercase__       ) for p in contents}
                assert "test_generations.txt" in contents
                assert "test_results.txt" in contents
                # assert len(metrics["val"]) ==  desired_n_evals
                assert len(metrics["""test"""]       ) == 1
