
'''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()

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







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			List[Any]:
	# (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        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			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

	_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': 0.0_2,
	    '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': 1_024,
	    '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					:							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."
							)
							lowercase					:							List[Any]   =      parser.parse_args()
							convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)



'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase					:							int   =      {
    "configuration_mask2former": [
        "MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Mask2FormerConfig",
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase					:							Optional[int]   =      ["Mask2FormerImageProcessor"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase					:							Optional[Any]   =      [
					    "MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Mask2FormerForUniversalSegmentation",
					    "Mask2FormerModel",
					    "Mask2FormerPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

					lowercase					:							int   =      _LazyModule(__name__, globals()["__file__"], _import_structure)

