"""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     :     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
_a     :     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
_a     :     List[Any]= {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
	_a     :     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",
]:
	_a     :     List[str]= '''allenai'''
def   __UpperCAmelCase				(    UpperCAmelCase_    :				List[str]  )							->			Tuple:

	'''simple docstring'''





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

	'''simple docstring'''





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

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

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

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

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

	# dicts
	__snake_case			:							Tuple      		= os.path.join(A_			,							F"dict.{src_lang}.txt"  )
	__snake_case			:							int      		= os.path.join(A_			,							F"dict.{tgt_lang}.txt"  )

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

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

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

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

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

	# remove unneeded keys
	__snake_case			:							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_  )

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

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

	# save
	__snake_case			:							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__":
	_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[Any]= parser.parse_args()
	convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)








"""simple docstring"""





import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__UpperCamelCase  :      Any							      =		logging.getLogger(__name__)

if __name__ == "__main__":
							__UpperCamelCase  :      Union[str, Any]							      =		argparse.ArgumentParser(
							    description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
							)
							parser.add_argument(
							    '''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
							)
							parser.add_argument(
							    '''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
							)
							parser.add_argument('''--vocab_size''', default=3_0_5_2_2, type=int)
							__UpperCamelCase  :      Any							      =		parser.parse_args()

							logger.info(F'''Loading data from {args.data_file}''')
							with open(args.data_file, '''rb''') as fp:
														__UpperCamelCase  :      List[Any]							      =		pickle.load(fp)

							logger.info('''Counting occurrences for MLM.''')
							__UpperCamelCase  :      Dict							      =		Counter()
							for tk_ids in data:
														counter.update(tk_ids)
							__UpperCamelCase  :      Optional[Any]							      =		[0] * args.vocab_size
							for k, v in counter.items():
														__UpperCamelCase  :      Dict							      =		v

							logger.info(F'''Dump to {args.token_counts_dump}''')
							with open(args.token_counts_dump, '''wb''') as handle:
														pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)




