





# 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()

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     _UpperCamelCase     (a__   :int       ):






							"""simple docstring"""





							UpperCamelCase__				     =			dict((re.sub(r"""@@$"""   ,			""""""   ,			a__       ), v) if k.endswith("""@@"""       ) else (re.sub(r"""$"""   ,			"""</w>"""   ,			a__       ), 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     _UpperCamelCase     (a__   :List[str]   ,			a__   :List[Any]       ):






							"""simple docstring"""





							assert os.path.exists(a__       )
							os.makedirs(a__   ,			exist_ok=a__       )
							print(f"""Writing results to {pytorch_dump_folder_path}"""       )

							# handle various types of models

							UpperCamelCase__				     =			basename(a__       )
							UpperCamelCase__				     =			dirname(a__       )

							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(
							    a__   ,			a__   ,			a__   ,			archive_map=a__   ,			**a__       )

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

							UpperCamelCase__				     =			args["""source_lang"""]
							UpperCamelCase__				     =			args["""target_lang"""]

							UpperCamelCase__				     =			dirname(a__       )
							UpperCamelCase__				     =			basename(a__       )

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

							UpperCamelCase__				     =			Dictionary.load(a__       )
							UpperCamelCase__				     =			rewrite_dict_keys(src_dict.indices       )
							UpperCamelCase__				     =			len(a__       )
							UpperCamelCase__				     =			os.path.join(a__   ,			"""vocab-src.json"""       )
							print(f"""Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records"""       )
							with open(a__   ,			"""w"""   ,			encoding="""utf-8"""       ) as f:
														f.write(json.dumps(a__   ,			ensure_ascii=a__   ,			indent=a__       )       )

							# 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(a__       )
							UpperCamelCase__				     =			rewrite_dict_keys(tgt_dict.indices       )
							UpperCamelCase__				     =			len(a__       )
							UpperCamelCase__				     =			os.path.join(a__   ,			"""vocab-tgt.json"""       )
							print(f"""Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records"""       )
							with open(a__   ,			"""w"""   ,			encoding="""utf-8"""       ) as f:
														f.write(json.dumps(a__   ,			ensure_ascii=a__   ,			indent=a__       )       )

							# merges_file (bpecodes)
							UpperCamelCase__				     =			os.path.join(a__   ,			VOCAB_FILES_NAMES["""merges_file"""]       )
							for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
														UpperCamelCase__				     =			os.path.join(a__   ,			a__       )
														if os.path.exists(a__       ):
																					break
							with open(a__   ,			encoding="""utf-8"""       ) as fin:
														UpperCamelCase__				     =			fin.read()
							UpperCamelCase__				     =			re.sub(r""" \d+$"""   ,			""""""   ,			a__   ,			0   ,			re.M       )  # remove frequency number
							print(f"""Generating {merges_file}"""       )
							with open(a__   ,			"""w"""   ,			encoding="""utf-8"""       ) as fout:
														fout.write(a__       )

							# model config
							UpperCamelCase__				     =			os.path.join(a__   ,			"""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(a__   ,			"""w"""   ,			encoding="""utf-8"""       ) as f:
														f.write(json.dumps(a__   ,			ensure_ascii=a__   ,			indent=a__       )       )

							# tokenizer config
							UpperCamelCase__				     =			os.path.join(a__   ,			a__       )

							UpperCamelCase__				     =			{
							    """langs""": [src_lang, tgt_lang],
							    """model_max_length""": 1024,
							    """do_lower_case""": do_lower_case,
							}

							print(f"""Generating {fsmt_tokenizer_config_file}"""       )
							with open(a__   ,			"""w"""   ,			encoding="""utf-8"""       ) as f:
														f.write(json.dumps(a__   ,			ensure_ascii=a__   ,			indent=a__       )       )

							# 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(a__   ,			a__       )

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

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

							# save
							UpperCamelCase__				     =			os.path.join(a__   ,			a__       )
							print(f"""Generating {pytorch_weights_dump_path}"""       )
							torch.save(a__   ,			a__       )

							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)











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


UpperCamelCase__											= logging.get_logger(__name__)

UpperCamelCase__											= {
    "studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json",
    "studio-ousia/luke-large": "https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json",
}

class        __SCREAMING_SNAKE_CASE					(     _a       ):
							snake_case     :      Optional[int]						     =       """luke"""






							def __init__(						self  , __lowerCAmelCase=50267  , __lowerCAmelCase=500000  , __lowerCAmelCase=768  , __lowerCAmelCase=256  , __lowerCAmelCase=12  , __lowerCAmelCase=12  , __lowerCAmelCase=3072  , __lowerCAmelCase="gelu"  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.1  , __lowerCAmelCase=512  , __lowerCAmelCase=2  , __lowerCAmelCase=0.02  , __lowerCAmelCase=1E-12  , __lowerCAmelCase=True  , __lowerCAmelCase=None  , __lowerCAmelCase=1  , __lowerCAmelCase=0  , __lowerCAmelCase=2  , **__lowerCAmelCase  , ):
														super().__init__(pad_token_id=__lowerCAmelCase  , bos_token_id=__lowerCAmelCase  , eos_token_id=__lowerCAmelCase  , **__lowerCAmelCase  )

														UpperCamelCase__				     =			vocab_size
														UpperCamelCase__				     =			entity_vocab_size
														UpperCamelCase__				     =			hidden_size
														UpperCamelCase__				     =			entity_emb_size
														UpperCamelCase__				     =			num_hidden_layers
														UpperCamelCase__				     =			num_attention_heads
														UpperCamelCase__				     =			hidden_act
														UpperCamelCase__				     =			intermediate_size
														UpperCamelCase__				     =			hidden_dropout_prob
														UpperCamelCase__				     =			attention_probs_dropout_prob
														UpperCamelCase__				     =			max_position_embeddings
														UpperCamelCase__				     =			type_vocab_size
														UpperCamelCase__				     =			initializer_range
														UpperCamelCase__				     =			layer_norm_eps
														UpperCamelCase__				     =			use_entity_aware_attention
														UpperCamelCase__				     =			classifier_dropout




