




"""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()

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

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

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							Optional[int]:
							# 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

							A         =					basename(lowerCamelCase__			)
							A         =					dirname(lowerCamelCase__			)

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

							A         =					vars(chkpt['args']['model']			)

							A         =					args['source_lang']
							A         =					args['target_lang']

							A         =					dirname(lowerCamelCase__			)
							A         =					basename(lowerCamelCase__			)

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

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

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

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

							A         =					{
							    'langs': [src_lang, tgt_lang],
							    'model_max_length': 1024,
							    '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
							A         =					chkpt['models'][0]
							A         =					model.state_dict()

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

							# remove unneeded keys
							A         =					[
							    '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__			)

							A         =					FSMTConfig.from_pretrained(lowerCamelCase__			)
							A         =					FSMTForConditionalGeneration(lowerCamelCase__			)

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

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








"""simple docstring"""







# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A    =			{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'MraForMaskedLM',
							    'MraForMultipleChoice',
							    'MraForQuestionAnswering',
							    'MraForSequenceClassification',
							    'MraForTokenClassification',
							    'MraLayer',
							    'MraModel',
							    'MraPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mra import (
														    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MraForMaskedLM,
														    MraForMultipleChoice,
														    MraForQuestionAnswering,
														    MraForSequenceClassification,
														    MraForTokenClassification,
														    MraLayer,
														    MraModel,
														    MraPreTrainedModel,
														)
else:
							import sys

							A    =			_LazyModule(__name__, globals()['__file__'], _import_structure)



