
# 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()

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


def     __lowerCAmelCase			(			a__  )       ->  int:
				# (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'''@@$'''						,    ''''''						,    a__  ), v) if k.endswith('''@@'''  ) else (re.sub(R'''$'''						,    '''</w>'''						,    a__  ), 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			(			a__						,    a__  )       ->  Optional[int]:
				# 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

				__a										=    basename(a__  )
				__a										=    dirname(a__  )

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

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

				__a										=    args['''source_lang''']
				__a										=    args['''target_lang''']

				__a										=    dirname(a__  )
				__a										=    basename(a__  )

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

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

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

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

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

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

				# 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(a__						,    a__  )

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

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

				# save
				__a										=    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__":
					A     :	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.'
					)
					A     :	List[str]    = parser.parse_args()
					convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)

import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


UpperCAmelCase	      = logging.getLogger(__name__)







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Optional[int]							 =       """sequence-classification"""





	def __init__(       self		,    snake_case       ):
		if type(snake_case       ) == dict:
			lowercase       =    Namespace(**snake_case       )
		lowercase       =    glue_output_modes[hparams.task]
		lowercase       =    glue_tasks_num_labels[hparams.task]

		super().__init__(snake_case		,    snake_case		,    self.mode       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    **snake_case       ):
		return self.model(**snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case       ):
		lowercase       =    {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

		if self.config.model_type not in ["distilbert", "bart"]:
			lowercase       =    batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

		lowercase       =    self(**snake_case       )
		lowercase       =    outputs[0]

		lowercase       =    self.trainer.lr_schedulers[0]['scheduler']
		lowercase       =    {'loss': loss, 'rate': lr_scheduler.get_last_lr()[-1]}
		return {"loss": loss, "log": tensorboard_logs}





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.hparams
		lowercase       =    processors[args.task]()
		lowercase       =    processor.get_labels()

		for mode in ["train", "dev"]:
			lowercase       =    self._feature_file(snake_case       )
			if os.path.exists(snake_case       ) and not args.overwrite_cache:
				logger.info('Loading features from cached file %s'		,    snake_case       )
			else:
				logger.info('Creating features from dataset file at %s'		,    args.data_dir       )
				lowercase       =    (
				    processor.get_dev_examples(args.data_dir       )
				    if mode == 'dev'
				    else processor.get_train_examples(args.data_dir       )
				)
				lowercase       =    convert_examples_to_features(
				    snake_case		,    self.tokenizer		,    max_length=args.max_seq_length		,    label_list=self.labels		,    output_mode=args.glue_output_mode		,    )
				logger.info('Saving features into cached file %s'		,    snake_case       )
				torch.save(snake_case		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = False       ):
		lowercase       =    'dev' if mode == 'test' else mode

		lowercase       =    self._feature_file(snake_case       )
		logger.info('Loading features from cached file %s'		,    snake_case       )
		lowercase       =    torch.load(snake_case       )
		lowercase       =    torch.tensor([f.input_ids for f in features]		,    dtype=torch.long       )
		lowercase       =    torch.tensor([f.attention_mask for f in features]		,    dtype=torch.long       )
		lowercase       =    torch.tensor([f.token_type_ids for f in features]		,    dtype=torch.long       )
		if self.hparams.glue_output_mode == "classification":
			lowercase       =    torch.tensor([f.label for f in features]		,    dtype=torch.long       )
		elif self.hparams.glue_output_mode == "regression":
			lowercase       =    torch.tensor([f.label for f in features]		,    dtype=torch.float       )

		return DataLoader(
		    TensorDataset(snake_case		,    snake_case		,    snake_case		,    snake_case       )		,    batch_size=snake_case		,    shuffle=snake_case		,    )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case       ):
		lowercase       =    {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

		if self.config.model_type not in ["distilbert", "bart"]:
			lowercase       =    batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

		lowercase       =    self(**snake_case       )
		lowercase  ,					lowercase       =    outputs[:2]
		lowercase       =    logits.detach().cpu().numpy()
		lowercase       =    inputs['labels'].detach().cpu().numpy()

		return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		lowercase       =    torch.stack([x['val_loss'] for x in outputs]       ).mean().detach().cpu().item()
		lowercase       =    np.concatenate([x['pred'] for x in outputs]		,    axis=0       )

		if self.hparams.glue_output_mode == "classification":
			lowercase       =    np.argmax(snake_case		,    axis=1       )
		elif self.hparams.glue_output_mode == "regression":
			lowercase       =    np.squeeze(snake_case       )

		lowercase       =    np.concatenate([x['target'] for x in outputs]		,    axis=0       )
		lowercase       =    [[] for _ in range(out_label_ids.shape[0]       )]
		lowercase       =    [[] for _ in range(out_label_ids.shape[0]       )]

		lowercase       =    {**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task		,    snake_case		,    snake_case       )}

		lowercase       =    dict(results.items()       )
		lowercase       =    results
		return ret, preds_list, out_label_list





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		lowercase  ,					lowercase  ,					lowercase       =    self._eval_end(snake_case       )
		lowercase       =    ret['log']
		return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		lowercase  ,					lowercase  ,					lowercase       =    self._eval_end(snake_case       )
		lowercase       =    ret['log']
		# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
		return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





	@staticmethod
	def 	SCREAMING_SNAKE_CASE__						(       snake_case		,    snake_case       ):
		BaseTransformer.add_model_specific_args(snake_case		,    snake_case       )
		parser.add_argument(
		    '--max_seq_length'		,    default=128		,    type=snake_case		,    help=(
		        'The maximum total input sequence length after tokenization. Sequences longer '
		        'than this will be truncated, sequences shorter will be padded.'
		    )		,    )

		parser.add_argument(
		    '--task'		,    default=''		,    type=snake_case		,    required=snake_case		,    help='The GLUE task to run'		,    )
		parser.add_argument(
		    '--gpus'		,    default=0		,    type=snake_case		,    help='The number of GPUs allocated for this, it is by default 0 meaning none'		,    )

		parser.add_argument(
		    '--overwrite_cache'		,    action='store_true'		,    help='Overwrite the cached training and evaluation sets'       )

		return parser






def 							UpperCAmelCase_		(							):
	lowercase       =    argparse.ArgumentParser()
	add_generic_args(__SCREAMING_SNAKE_CASE ,							os.getcwd()							)
	lowercase       =    GLUETransformer.add_model_specific_args(__SCREAMING_SNAKE_CASE ,							os.getcwd()							)
	lowercase       =    parser.parse_args()

	# If output_dir not provided, a folder will be generated in pwd
	if args.output_dir is None:
		lowercase       =    os.path.join(
		    './results' ,							F'''{args.task}_{time.strftime('%Y%m%d_%H%M%S'							)}''' ,							)
		os.makedirs(args.output_dir							)

	lowercase       =    GLUETransformer(__SCREAMING_SNAKE_CASE							)
	lowercase       =    generic_train(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)

	# Optionally, predict on dev set and write to output_dir
	if args.do_predict:
		lowercase       =    sorted(glob.glob(os.path.join(args.output_dir ,							'checkpoint-epoch=*.ckpt'							) ,							recursive=__SCREAMING_SNAKE_CASE							)							)
		lowercase       =    model.load_from_checkpoint(checkpoints[-1]							)
		return trainer.test(__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
	main()

