

"""simple docstring"""





# 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  :      str							      =		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  :      Any							      =		{
    # fairseq:
    '''wmt19-ru-en''': {'''length_penalty''': 1.1},
    '''wmt19-en-ru''': {'''length_penalty''': 1.1_5},
    '''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  :      List[Any]							      =		{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
							__UpperCamelCase  :      Dict							      =		'''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  :      List[str]							      =		'''allenai'''

def   __SCREAMING_SNAKE_CASE  (      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}
	lowerCAmelCase__	:						int  				=	dict((re.sub(r'''@@$'''						,     ''''''						,     A_ ), v) if k.endswith('''@@''' ) else (re.sub(r'''$'''						,     '''</w>'''						,     A_ ), v) for k, v in d.items() )
	lowerCAmelCase__	:						List[str]  				=	'''<s> <pad> </s> <unk>'''.split()
	# restore the special tokens
	for k in keep_keys:
		del da[f'{k}</w>']
		lowerCAmelCase__	:						int  				=	d[k]  # restore
	return da

def   __SCREAMING_SNAKE_CASE  (      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

	lowerCAmelCase__	:						int  				=	basename(A_ )
	lowerCAmelCase__	:						Tuple  				=	dirname(A_ )

	lowerCAmelCase__	:						Optional[int]  				=	fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
	lowerCAmelCase__	:						Tuple  				=	cls.hub_models()
	lowerCAmelCase__	:						Any  				=	{'''bpe''': '''fastbpe''', '''tokenizer''': '''moses'''}
	lowerCAmelCase__	:						Optional[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}' )
	lowerCAmelCase__	:						str  				=	hub_utils.from_pretrained(
	    A_						,     A_						,     A_						,     archive_map=A_						,     **A_ )

	lowerCAmelCase__	:						Optional[int]  				=	vars(chkpt['''args''']['''model'''] )

	lowerCAmelCase__	:						Any  				=	args['''source_lang''']
	lowerCAmelCase__	:						int  				=	args['''target_lang''']

	lowerCAmelCase__	:						Optional[Any]  				=	dirname(A_ )
	lowerCAmelCase__	:						Tuple  				=	basename(A_ )

	# dicts
	lowerCAmelCase__	:						Tuple  				=	os.path.join(A_						,     f'dict.{src_lang}.txt' )
	lowerCAmelCase__	:						int  				=	os.path.join(A_						,     f'dict.{tgt_lang}.txt' )

	lowerCAmelCase__	:						str  				=	Dictionary.load(A_ )
	lowerCAmelCase__	:						Any  				=	rewrite_dict_keys(src_dict.indices )
	lowerCAmelCase__	:						Optional[Any]  				=	len(A_ )
	lowerCAmelCase__	:						Tuple  				=	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
	lowerCAmelCase__	:						Union[str, Any]  				=	True
	for k in src_vocab.keys():
		if not k.islower():
			lowerCAmelCase__	:						Union[str, Any]  				=	False
			break

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

	lowerCAmelCase__	:						List[str]  				=	{
	    '''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
	lowerCAmelCase__	:						List[str]  				=	5
	lowerCAmelCase__	:						List[str]  				=	False
	if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
		lowerCAmelCase__	:						Tuple  				=	best_score_hparams[model_dir]['''length_penalty''']
	else:
		lowerCAmelCase__	:						int  				=	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
	lowerCAmelCase__	:						Union[str, Any]  				=	os.path.join(A_						,     A_ )

	lowerCAmelCase__	:						List[Any]  				=	{
	    '''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
	lowerCAmelCase__	:						Optional[int]  				=	chkpt['''models'''][0]
	lowerCAmelCase__	:						List[str]  				=	model.state_dict()

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

	# remove unneeded keys
	lowerCAmelCase__	:						str  				=	[
	    '''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_ )

	lowerCAmelCase__	:						Optional[int]  				=	FSMTConfig.from_pretrained(A_ )
	lowerCAmelCase__	:						Any  				=	FSMTForConditionalGeneration(A_ )

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

	# save
	lowerCAmelCase__	:						str  				=	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  :      Union[str, 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.'''
							)
							__UpperCamelCase  :      List[Any]							      =		parser.parse_args()
							convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)







"""simple docstring"""





import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


__UpperCamelCase  :      List[str]							      =		'''true'''

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=82						,     A_=16 ):
	set_seed(42 )
	lowerCAmelCase__	:						Union[str, Any]  				=	RegressionModel()
	lowerCAmelCase__	:						Optional[int]  				=	deepcopy(A_ )
	lowerCAmelCase__	:						Any  				=	RegressionDataset(length=A_ )
	lowerCAmelCase__	:						List[str]  				=	DataLoader(A_						,     batch_size=A_ )
	model.to(accelerator.device )
	lowerCAmelCase__				,lowerCAmelCase__	:						Dict  				=	accelerator.prepare(A_						,     A_ )
	return model, ddp_model, dataloader

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=False ):
	lowerCAmelCase__	:						List[str]  				=	AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased''' )
	lowerCAmelCase__	:						List[str]  				=	load_dataset('''glue'''						,     '''mrpc'''						,     split='''validation''' )

	def tokenize_function(A_ ):
		lowerCAmelCase__	:						Optional[Any]  				=	tokenizer(examples['''sentence1''']						,     examples['''sentence2''']						,     truncation=A_						,     max_length=A_ )
		return outputs

	with accelerator.main_process_first():
		lowerCAmelCase__	:						Dict  				=	dataset.map(
		    A_						,     batched=A_						,     remove_columns=['''idx''', '''sentence1''', '''sentence2''']						,     )

	lowerCAmelCase__	:						int  				=	tokenized_datasets.rename_column('''label'''						,     '''labels''' )

	def collate_fn(A_ ):
		if use_longest:
			return tokenizer.pad(A_						,     padding='''longest'''						,     return_tensors='''pt''' )
		return tokenizer.pad(A_						,     padding='''max_length'''						,     max_length=1_28						,     return_tensors='''pt''' )

	return DataLoader(A_						,     shuffle=A_						,     collate_fn=A_						,     batch_size=16 )

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	lowerCAmelCase__	:						Union[str, Any]  				=	Accelerator(dispatch_batches=A_						,     split_batches=A_ )
	lowerCAmelCase__	:						str  				=	get_dataloader(A_						,     not dispatch_batches )
	lowerCAmelCase__	:						Optional[int]  				=	AutoModelForSequenceClassification.from_pretrained(
	    '''hf-internal-testing/mrpc-bert-base-cased'''						,     return_dict=A_ )
	lowerCAmelCase__				,lowerCAmelCase__	:						Union[str, Any]  				=	accelerator.prepare(A_						,     A_ )
	return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Union[str, Any]  				=	[]
	for batch in dataloader:
		lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	batch.values()
		with torch.no_grad():
			lowerCAmelCase__	:						List[str]  				=	model(A_ )
			lowerCAmelCase__				,lowerCAmelCase__	:						str  				=	accelerator.gather_for_metrics((logit, target) )
			logits_and_targets.append((logit, target) )
	lowerCAmelCase__				,lowerCAmelCase__	:						int  				=	[], []
	for logit, targ in logits_and_targets:
		logits.append(A_ )
		targs.append(A_ )
	lowerCAmelCase__				,lowerCAmelCase__	:						List[str]  				=	torch.cat(A_ ), torch.cat(A_ )
	return logits, targs

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=82						,     A_=False						,     A_=False						,     A_=16 ):
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Union[str, Any]  				=	get_basic_setup(A_						,     A_						,     A_ )
	lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	generate_predictions(A_						,     A_						,     A_ )
	assert (
	    len(A_ ) == num_samples
	), f'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(A_ )}'

def   __SCREAMING_SNAKE_CASE  (      A_ = False						,     A_ = False ):
	lowerCAmelCase__	:						Optional[Any]  				=	evaluate.load('''glue'''						,     '''mrpc''' )
	lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	get_mrpc_setup(A_						,     A_ )
	# First do baseline
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	setup['''no''']
	model.to(A_ )
	model.eval()
	for batch in dataloader:
		batch.to(A_ )
		with torch.inference_mode():
			lowerCAmelCase__	:						Optional[int]  				=	model(**A_ )
		lowerCAmelCase__	:						Dict  				=	outputs.logits.argmax(dim=-1 )
		metric.add_batch(predictions=A_						,     references=batch['''labels'''] )
	lowerCAmelCase__	:						Dict  				=	metric.compute()

	# Then do distributed
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	setup['''ddp''']
	model.eval()
	for batch in dataloader:
		with torch.inference_mode():
			lowerCAmelCase__	:						Union[str, Any]  				=	model(**A_ )
		lowerCAmelCase__	:						int  				=	outputs.logits.argmax(dim=-1 )
		lowerCAmelCase__	:						int  				=	batch['''labels''']
		lowerCAmelCase__				,lowerCAmelCase__	:						int  				=	accelerator.gather_for_metrics((preds, references) )
		metric.add_batch(predictions=A_						,     references=A_ )
	lowerCAmelCase__	:						List[Any]  				=	metric.compute()

	for key in "accuracy f1".split():
		assert math.isclose(
		    baseline[key]						,     distributed[key] ), f'Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						List[str]  				=	Accelerator(split_batches=A_						,     dispatch_batches=A_ )
	if accelerator.is_local_main_process:
		datasets.utils.logging.set_verbosity_warning()
		transformers.utils.logging.set_verbosity_warning()
	else:
		datasets.utils.logging.set_verbosity_error()
		transformers.utils.logging.set_verbosity_error()
	# These are a bit slower so they should only be ran on the GPU or TPU
	if torch.cuda.is_available() or is_tpu_available():
		if accelerator.is_local_main_process:
			print('''**Testing gather_for_metrics**''' )
		for split_batches in [True, False]:
			for dispatch_batches in [True, False]:
				if accelerator.is_local_main_process:
					print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`' )
				test_mrpc(A_						,     A_ )
				accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print('''**Test torch metrics**''' )
	for split_batches in [True, False]:
		for dispatch_batches in [True, False]:
			lowerCAmelCase__	:						Optional[Any]  				=	Accelerator(split_batches=A_						,     dispatch_batches=A_ )
			if accelerator.is_local_main_process:
				print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99' )
			test_torch_metrics(A_						,     99 )
			accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print('''**Test last batch is not dropped when perfectly divisible**''' )
	lowerCAmelCase__	:						List[str]  				=	Accelerator()
	test_torch_metrics(A_						,     5_12 )
	accelerator.state._reset_state()

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
							main()




