






# 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()

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





def 		__UpperCAmelCase     (	a_):
							# (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_           =				dict((re.sub(R'@@$'				,	''				,	a_), v) if k.endswith('@@') else (re.sub(R'$'				,	'</w>'				,	a_), 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     (	a_				,	a_):
							# prep
							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

							snake_case_           =				basename(a_)
							snake_case_           =				dirname(a_)

							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(
							    a_				,	a_				,	a_				,	archive_map=a_				,	**a_)

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

							snake_case_           =				args['source_lang']
							snake_case_           =				args['target_lang']

							snake_case_           =				dirname(a_)
							snake_case_           =				basename(a_)

							# dicts
							snake_case_           =				os.path.join(a_				,	f'''dict.{src_lang}.txt''')
							snake_case_           =				os.path.join(a_				,	f'''dict.{tgt_lang}.txt''')

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

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

							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(a_				,	'w'				,	encoding='utf-8') as f:
														f.write(json.dumps(a_				,	ensure_ascii=a_				,	indent=a_))

							# tokenizer config
							snake_case_           =				os.path.join(a_				,	a_)

							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(a_				,	'w'				,	encoding='utf-8') as f:
														f.write(json.dumps(a_				,	ensure_ascii=a_				,	indent=a_))

							# 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(a_				,	a_)

							snake_case_           =				FSMTConfig.from_pretrained(a_)
							snake_case_           =				FSMTForConditionalGeneration(a_)

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

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













import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler




@dataclass
class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					0
	lowerCAmelCase						=					False
	lowerCAmelCase						=					3.0






class 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								self.assertDictEqual(MockClass().to_kwargs()			,     {}   )
								self.assertDictEqual(MockClass(a=2   ).to_kwargs()			,     {'a': 2}   )
								self.assertDictEqual(MockClass(a=2			,     b=a   ).to_kwargs()			,     {'a': 2, 'b': True}   )
								self.assertDictEqual(MockClass(a=2			,     c=2.25   ).to_kwargs()			,     {'a': 2, 'c': 2.25}   )




	@require_cuda
	def 				_UpperCamelCase     (			self   )			->       str:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								snake_case_           =				GradScalerKwargs(init_scale=10_24			,     growth_factor=2   )
								AcceleratorState._reset_state()
								snake_case_           =				Accelerator(mixed_precision='fp16'			,     kwargs_handlers=[scaler_handler]   )
								print(accelerator.use_fpaa   )
								snake_case_           =				accelerator.scaler

								# Check the kwargs have been applied
								self.assertEqual(scaler._init_scale			,     1_024.0   )
								self.assertEqual(scaler._growth_factor			,     2.0   )

								# Check the other values are at the default
								self.assertEqual(scaler._backoff_factor			,     0.5   )
								self.assertEqual(scaler._growth_interval			,     20_00   )
								self.assertEqual(scaler._enabled			,     a   )






	@require_multi_gpu
	def 				_UpperCamelCase     (			self   )			->       Optional[Any]:
								snake_case_           =				['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__   )]
								execute_subprocess_async(a			,     env=os.environ.copy()   )


if __name__ == "__main__":
	lowercase    				=   DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
	lowercase    				=   Accelerator(kwargs_handlers=[ddp_scaler])
	lowercase    				=   torch.nn.Linear(100, 200)
	lowercase    				=   accelerator.prepare(model)

	# Check the values changed in kwargs
	lowercase    				=   ""
	lowercase    				=   model.bucket_bytes_cap // (1024 * 1024)
	if observed_bucket_cap_map != 15:
		error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
	if model.find_unused_parameters is not True:
		error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

	# Check the values of the defaults
	if model.dim != 0:
		error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
	if model.broadcast_buffers is not True:
		error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
	if model.gradient_as_bucket_view is not False:
		error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

	# Raise error at the end to make sure we don't stop at the first failure.
	if len(error_msg) > 0:
		raise ValueError(error_msg)





