


# 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()

_snake_case	:						Any         =							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
_snake_case	:						List[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
_snake_case	:						int         =							{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
			_snake_case	:						Optional[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",
]:
			_snake_case	:						Optional[Any]         =							"allenai"







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







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase      ):
							# 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

							__snake_case						:     Any									=  basename(__lowerCamelCase      )
							__snake_case						:     Union[str, Any]									=  dirname(__lowerCamelCase      )

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

							__snake_case						:     List[Any]									=  vars(chkpt["args"]["model"]      )

							__snake_case						:     int									=  args["source_lang"]
							__snake_case						:     str									=  args["target_lang"]

							__snake_case						:     List[str]									=  dirname(__lowerCamelCase      )
							__snake_case						:     str									=  basename(__lowerCamelCase      )

							# dicts
							__snake_case						:     str									=  os.path.join(__lowerCamelCase				,      F'dict.{src_lang}.txt'      )
							__snake_case						:     List[str]									=  os.path.join(__lowerCamelCase				,      F'dict.{tgt_lang}.txt'      )

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

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

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

							__snake_case						:     Tuple									=  {
							    "langs": [src_lang, tgt_lang],
							    "model_max_length": 1_0_2_4,
							    "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
							__snake_case						:     List[Any]									=  chkpt["models"][0]
							__snake_case						:     Tuple									=  model.state_dict()

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

							# remove unneeded keys
							__snake_case						:     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(__lowerCamelCase				,      __lowerCamelCase      )

							__snake_case						:     Union[str, Any]									=  FSMTConfig.from_pretrained(__lowerCamelCase      )
							__snake_case						:     Union[str, Any]									=  FSMTForConditionalGeneration(__lowerCamelCase      )

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

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



"""simple docstring"""

import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_            =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            =    {
    '''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				List[str]				   =						"sew-d"




      def __init__(				self ,							lowercase_=3_2 ,							lowercase_=7_6_8 ,							lowercase_=1_2 ,							lowercase_=1_2 ,							lowercase_=3_0_7_2 ,							lowercase_=2 ,							lowercase_=5_1_2 ,							lowercase_=2_5_6 ,							lowercase_=True ,							lowercase_=True ,							lowercase_=("p2c", "c2p") ,							lowercase_="layer_norm" ,							lowercase_="gelu_python" ,							lowercase_=0.1 ,							lowercase_=0.1 ,							lowercase_=0.1 ,							lowercase_=0.0 ,							lowercase_=0.1 ,							lowercase_=0.0_2 ,							lowercase_=1E-7 ,							lowercase_=1E-5 ,							lowercase_="group" ,							lowercase_="gelu" ,							lowercase_=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2) ,							lowercase_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) ,							lowercase_=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) ,							lowercase_=False ,							lowercase_=1_2_8 ,							lowercase_=1_6 ,							lowercase_=True ,							lowercase_=0.0_5 ,							lowercase_=1_0 ,							lowercase_=2 ,							lowercase_=0.0 ,							lowercase_=1_0 ,							lowercase_=0 ,							lowercase_="mean" ,							lowercase_=False ,							lowercase_=False ,							lowercase_=2_5_6 ,							lowercase_=0 ,							lowercase_=1 ,							lowercase_=2 ,							**lowercase_ ,							)    ->       Dict:
          super().__init__(**lowercase_ ,							pad_token_id=lowercase_ ,							bos_token_id=lowercase_ ,							eos_token_id=lowercase_				)
          UpperCAmelCase          =       hidden_size
          UpperCAmelCase          =       feat_extract_norm
          UpperCAmelCase          =       feat_extract_activation
          UpperCAmelCase          =       list(lowercase_				)
          UpperCAmelCase          =       list(lowercase_				)
          UpperCAmelCase          =       list(lowercase_				)
          UpperCAmelCase          =       conv_bias
          UpperCAmelCase          =       num_conv_pos_embeddings
          UpperCAmelCase          =       num_conv_pos_embedding_groups
          UpperCAmelCase          =       len(self.conv_dim				)
          UpperCAmelCase          =       num_hidden_layers
          UpperCAmelCase          =       intermediate_size
          UpperCAmelCase          =       squeeze_factor
          UpperCAmelCase          =       max_position_embeddings
          UpperCAmelCase          =       position_buckets
          UpperCAmelCase          =       share_att_key
          UpperCAmelCase          =       relative_attention
          UpperCAmelCase          =       norm_rel_ebd
          UpperCAmelCase          =       list(lowercase_				)
          UpperCAmelCase          =       hidden_act
          UpperCAmelCase          =       num_attention_heads
          UpperCAmelCase          =       hidden_dropout
          UpperCAmelCase          =       attention_dropout
          UpperCAmelCase          =       activation_dropout
          UpperCAmelCase          =       feat_proj_dropout
          UpperCAmelCase          =       final_dropout
          UpperCAmelCase          =       layer_norm_eps
          UpperCAmelCase          =       feature_layer_norm_eps
          UpperCAmelCase          =       initializer_range
          UpperCAmelCase          =       vocab_size

          if (
              (len(self.conv_stride				) != self.num_feat_extract_layers)
              or (len(self.conv_kernel				) != self.num_feat_extract_layers)
              or (len(self.conv_dim				) != self.num_feat_extract_layers)
          ):
              raise ValueError(
                  'Configuration for convolutional layers is incorrect.'
                  'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
                  F"but is `len(config.conv_dim) = {len(self.conv_dim				)}`, `len(config.conv_stride)"
                  F"= {len(self.conv_stride				)}`, `len(config.conv_kernel) = {len(self.conv_kernel				)}`."				)

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          UpperCAmelCase          =       apply_spec_augment
          UpperCAmelCase          =       mask_time_prob
          UpperCAmelCase          =       mask_time_length
          UpperCAmelCase          =       mask_time_min_masks
          UpperCAmelCase          =       mask_feature_prob
          UpperCAmelCase          =       mask_feature_length
          UpperCAmelCase          =       mask_feature_min_masks

          # ctc loss
          UpperCAmelCase          =       ctc_loss_reduction
          UpperCAmelCase          =       ctc_zero_infinity

          # sequence classification
          UpperCAmelCase          =       use_weighted_layer_sum
          UpperCAmelCase          =       classifier_proj_size




      @property
      def      a_   (				self				)    ->       Tuple:
          return functools.reduce(operator.mul ,							self.conv_stride ,							1				)



