
"""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()

_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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Optional[int]:







							"""simple docstring"""


							__snake_case     			=       dict((re.sub(r"@@$"							,    ""							,    SCREAMING_SNAKE_CASE ), v) if k.endswith("@@" ) else (re.sub(r"$"							,    "</w>"							,    SCREAMING_SNAKE_CASE ), v) for k, v in d.items() )
							__snake_case     			=       "<s> <pad> </s> <unk>".split()
							# restore the special tokens
							for k in keep_keys:
														del da[F'''{k}</w>''']
														__snake_case     			=       d[k]  # restore
							return da
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Dict:







							"""simple docstring"""


							assert os.path.exists(SCREAMING_SNAKE_CASE )
							os.makedirs(SCREAMING_SNAKE_CASE							,    exist_ok=SCREAMING_SNAKE_CASE )
							print(F'''Writing results to {pytorch_dump_folder_path}''' )

							# handle various types of models

							__snake_case     			=       basename(SCREAMING_SNAKE_CASE )
							__snake_case     			=       dirname(SCREAMING_SNAKE_CASE )

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

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

							__snake_case     			=       args["source_lang"]
							__snake_case     			=       args["target_lang"]

							__snake_case     			=       dirname(SCREAMING_SNAKE_CASE )
							__snake_case     			=       basename(SCREAMING_SNAKE_CASE )

							# dicts
							__snake_case     			=       os.path.join(SCREAMING_SNAKE_CASE							,    F'''dict.{src_lang}.txt''' )
							__snake_case     			=       os.path.join(SCREAMING_SNAKE_CASE							,    F'''dict.{tgt_lang}.txt''' )

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

							# 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     			=       True
							for k in src_vocab.keys():
														if not k.islower():
																					__snake_case     			=       False
																					break

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

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

							# model config
							__snake_case     			=       os.path.join(SCREAMING_SNAKE_CASE							,    "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     			=       {
							    "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
							__snake_case     			=       5
							__snake_case     			=       False
							if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
														__snake_case     			=       best_score_hparams[model_dir]["length_penalty"]
							else:
														__snake_case     			=       1.0

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

							# tokenizer config
							__snake_case     			=       os.path.join(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )

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

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

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

							# remove unneeded keys
							__snake_case     			=       [
							    "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(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )

							__snake_case     			=       FSMTConfig.from_pretrained(SCREAMING_SNAKE_CASE )
							__snake_case     			=       FSMTForConditionalGeneration(SCREAMING_SNAKE_CASE )

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

							# save
							__snake_case     			=       os.path.join(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							print(F'''Generating {pytorch_weights_dump_path}''' )
							torch.save(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )

							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"""







from __future__ import annotations

from typing import Any
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     int:







							"""simple docstring"""


							if not postfix_notation:
														return 0

							__snake_case     			=       {"+", "-", "*", "/"}
							__snake_case     			=       []

							for token in postfix_notation:
														if token in operations:
																					__snake_case			,   __snake_case     			=       stack.pop(), stack.pop()
																					if token == "+":
																												stack.append(a + b )
																					elif token == "-":
																												stack.append(a - b )
																					elif token == "*":
																												stack.append(a * b )
																					else:
																												if a * b < 0 and a % b != 0:
																																			stack.append(a // b + 1 )
																												else:
																																			stack.append(a // b )
														else:
																					stack.append(int(SCREAMING_SNAKE_CASE ) )

							return stack.pop()


if __name__ == "__main__":
		import doctest

		doctest.testmod()
