
"""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()

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



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							Dict:







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



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_				)->							Optional[Any]:







       """simple docstring"""
       # prep
       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

       UpperCamelCase					 =		basename(UpperCAmelCase_				)
       UpperCamelCase					 =		dirname(UpperCAmelCase_				)

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

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

       UpperCamelCase					 =		args["source_lang"]
       UpperCamelCase					 =		args["target_lang"]

       UpperCamelCase					 =		dirname(UpperCAmelCase_				)
       UpperCamelCase					 =		basename(UpperCAmelCase_				)

       # dicts
       UpperCamelCase					 =		os.path.join(UpperCAmelCase_		,      F"dict.{src_lang}.txt"				)
       UpperCamelCase					 =		os.path.join(UpperCAmelCase_		,      F"dict.{tgt_lang}.txt"				)

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

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

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

       UpperCamelCase					 =		{
           "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(UpperCAmelCase_		,      "w"		,      encoding="utf-8"				) as f:
              f.write(json.dumps(UpperCAmelCase_		,      ensure_ascii=UpperCAmelCase_		,      indent=UpperCAmelCase_				)				)

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

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

       # remove unneeded keys
       UpperCamelCase					 =		[
           "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_				)

       UpperCamelCase					 =		FSMTConfig.from_pretrained(UpperCAmelCase_				)
       UpperCamelCase					 =		FSMTForConditionalGeneration(UpperCAmelCase_				)

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

       # save
       UpperCamelCase					 =		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__":
      SCREAMING_SNAKE_CASE      =     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."""
      )
      SCREAMING_SNAKE_CASE      =     parser.parse_args()
      convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)







"""simple docstring"""


import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)



class   __a  (      unittest.TestCase	):






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]			)->  int:







              """simple docstring"""
              UpperCamelCase					 =		["a", "b", "c"]

              # Defaults to last layer if both are None
              UpperCamelCase ,       UpperCamelCase					 =		get_aligned_output_features_output_indices(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)
              self.assertEqual(UpperCAmelCase_       ,      ["c"]			)
              self.assertEqual(UpperCAmelCase_       ,      [2]			)

              # Out indices set to match out features
              UpperCamelCase ,       UpperCamelCase					 =		get_aligned_output_features_output_indices(["a", "c"]       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)
              self.assertEqual(UpperCAmelCase_       ,      ["a", "c"]			)
              self.assertEqual(UpperCAmelCase_       ,      [0, 2]			)

              # Out features set to match out indices
              UpperCamelCase ,       UpperCamelCase					 =		get_aligned_output_features_output_indices(UpperCAmelCase_       ,      [0, 2]       ,      UpperCAmelCase_			)
              self.assertEqual(UpperCAmelCase_       ,      ["a", "c"]			)
              self.assertEqual(UpperCAmelCase_       ,      [0, 2]			)

              # Out features selected from negative indices
              UpperCamelCase ,       UpperCamelCase					 =		get_aligned_output_features_output_indices(UpperCAmelCase_       ,      [-3, -1]       ,      UpperCAmelCase_			)
              self.assertEqual(UpperCAmelCase_       ,      ["a", "c"]			)
              self.assertEqual(UpperCAmelCase_       ,      [-3, -1]			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  Union[str, Any]:







              """simple docstring"""
              # Stage names must be set
              with self.assertRaises(UpperCAmelCase_			):
                     verify_out_features_out_indices(["a", "b"]       ,      (0, 1)       ,      UpperCAmelCase_			)

              # Out features must be a list
              with self.assertRaises(UpperCAmelCase_			):
                     verify_out_features_out_indices(("a", "b")       ,      (0, 1)       ,      ["a", "b"]			)

              # Out features must be a subset of stage names
              with self.assertRaises(UpperCAmelCase_			):
                     verify_out_features_out_indices(["a", "b"]       ,      (0, 1)       ,      ["a"]			)

              # Out indices must be a list or tuple
              with self.assertRaises(UpperCAmelCase_			):
                     verify_out_features_out_indices(UpperCAmelCase_       ,      0       ,      ["a", "b"]			)

              # Out indices must be a subset of stage names
              with self.assertRaises(UpperCAmelCase_			):
                     verify_out_features_out_indices(UpperCAmelCase_       ,      (0, 1)       ,      ["a"]			)

              # Out features and out indices must be the same length
              with self.assertRaises(UpperCAmelCase_			):
                     verify_out_features_out_indices(["a", "b"]       ,      (0,)       ,      ["a", "b", "c"]			)

              # Out features should match out indices
              with self.assertRaises(UpperCAmelCase_			):
                     verify_out_features_out_indices(["a", "b"]       ,      (0, 2)       ,      ["a", "b", "c"]			)

              # Out features and out indices should be in order
              with self.assertRaises(UpperCAmelCase_			):
                     verify_out_features_out_indices(["b", "a"]       ,      (0, 1)       ,      ["a", "b"]			)

              # Check passes with valid inputs
              verify_out_features_out_indices(["a", "b", "d"]       ,      (0, 1, -1)       ,      ["a", "b", "c", "d"]			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  List[Any]:







              """simple docstring"""
              UpperCamelCase					 =		BackboneMixin()

              UpperCamelCase					 =		["a", "b", "c"]
              UpperCamelCase					 =		["a", "c"]
              UpperCamelCase					 =		[0, 2]

              # Check that the output features and indices are set correctly
              self.assertEqual(backbone.out_features       ,      ["a", "c"]			)
              self.assertEqual(backbone.out_indices       ,      [0, 2]			)

              # Check out features and indices are updated correctly
              UpperCamelCase					 =		["a", "b"]
              self.assertEqual(backbone.out_features       ,      ["a", "b"]			)
              self.assertEqual(backbone.out_indices       ,      [0, 1]			)

              UpperCamelCase					 =		[-3, -1]
              self.assertEqual(backbone.out_features       ,      ["a", "c"]			)
              self.assertEqual(backbone.out_indices       ,      [-3, -1]			)





