'''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()

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





def lowerCamelCase      (      lowerCAmelCase    :     List[str]		):





	"""simple docstring"""







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





def lowerCamelCase      (      lowerCAmelCase    :     Any		,		lowerCAmelCase    :     int		):





	"""simple docstring"""







	assert os.path.exists(lowerCAmelCase		)
	os.makedirs(lowerCAmelCase		,		exist_ok=lowerCAmelCase		)
	print(f'Writing results to {pytorch_dump_folder_path}'		)

	# handle various types of models

	__magic_name__	:   Any					     =					basename(lowerCAmelCase		)
	__magic_name__	:   Any					     =					dirname(lowerCAmelCase		)

	__magic_name__	:   Union[str, Any]					     =					fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
	__magic_name__	:   Dict					     =					cls.hub_models()
	__magic_name__	:   Dict					     =					{'bpe': 'fastbpe', 'tokenizer': 'moses'}
	__magic_name__	:   str					     =					'.'
	# 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}'		)
	__magic_name__	:   List[Any]					     =					hub_utils.from_pretrained(
	    lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		archive_map=lowerCAmelCase		,		**lowerCAmelCase		)

	__magic_name__	:   Tuple					     =					vars(chkpt['args']['model']		)

	__magic_name__	:   Dict					     =					args['source_lang']
	__magic_name__	:   Optional[int]					     =					args['target_lang']

	__magic_name__	:   Optional[Any]					     =					dirname(lowerCAmelCase		)
	__magic_name__	:   Union[str, Any]					     =					basename(lowerCAmelCase		)

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

	__magic_name__	:   str					     =					Dictionary.load(lowerCAmelCase		)
	__magic_name__	:   str					     =					rewrite_dict_keys(src_dict.indices		)
	__magic_name__	:   int					     =					len(lowerCAmelCase		)
	__magic_name__	:   str					     =					os.path.join(lowerCAmelCase		,		'vocab-src.json'		)
	print(f'Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records'		)
	with open(lowerCAmelCase		,		'w'		,		encoding='utf-8'		) as f:
		f.write(json.dumps(lowerCAmelCase		,		ensure_ascii=lowerCAmelCase		,		indent=lowerCAmelCase		)		)

	# 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
	__magic_name__	:   Optional[Any]					     =					True
	for k in src_vocab.keys():
		if not k.islower():
			__magic_name__	:   Dict					     =					False
			break

	__magic_name__	:   Tuple					     =					Dictionary.load(lowerCAmelCase		)
	__magic_name__	:   List[str]					     =					rewrite_dict_keys(tgt_dict.indices		)
	__magic_name__	:   Any					     =					len(lowerCAmelCase		)
	__magic_name__	:   Tuple					     =					os.path.join(lowerCAmelCase		,		'vocab-tgt.json'		)
	print(f'Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records'		)
	with open(lowerCAmelCase		,		'w'		,		encoding='utf-8'		) as f:
		f.write(json.dumps(lowerCAmelCase		,		ensure_ascii=lowerCAmelCase		,		indent=lowerCAmelCase		)		)

	# merges_file (bpecodes)
	__magic_name__	:   Optional[Any]					     =					os.path.join(lowerCAmelCase		,		VOCAB_FILES_NAMES['merges_file']		)
	for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
		__magic_name__	:   Union[str, Any]					     =					os.path.join(lowerCAmelCase		,		lowerCAmelCase		)
		if os.path.exists(lowerCAmelCase		):
			break
	with open(lowerCAmelCase		,		encoding='utf-8'		) as fin:
		__magic_name__	:   int					     =					fin.read()
	__magic_name__	:   str					     =					re.sub(R' \d+$'		,		''		,		lowerCAmelCase		,		0		,		re.M		)  # remove frequency number
	print(f'Generating {merges_file}'		)
	with open(lowerCAmelCase		,		'w'		,		encoding='utf-8'		) as fout:
		fout.write(lowerCAmelCase		)

	# model config
	__magic_name__	:   List[str]					     =					os.path.join(lowerCAmelCase		,		'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"]}'

	__magic_name__	:   Optional[Any]					     =					{
	    '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
	__magic_name__	:   List[str]					     =					5
	__magic_name__	:   str					     =					False
	if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
		__magic_name__	:   Union[str, Any]					     =					best_score_hparams[model_dir]['length_penalty']
	else:
		__magic_name__	:   Any					     =					1.0

	print(f'Generating {fsmt_model_config_file}'		)
	with open(lowerCAmelCase		,		'w'		,		encoding='utf-8'		) as f:
		f.write(json.dumps(lowerCAmelCase		,		ensure_ascii=lowerCAmelCase		,		indent=lowerCAmelCase		)		)

	# tokenizer config
	__magic_name__	:   str					     =					os.path.join(lowerCAmelCase		,		lowerCAmelCase		)

	__magic_name__	:   Any					     =					{
	    'langs': [src_lang, tgt_lang],
	    'model_max_length': 1024,
	    'do_lower_case': do_lower_case,
	}

	print(f'Generating {fsmt_tokenizer_config_file}'		)
	with open(lowerCAmelCase		,		'w'		,		encoding='utf-8'		) as f:
		f.write(json.dumps(lowerCAmelCase		,		ensure_ascii=lowerCAmelCase		,		indent=lowerCAmelCase		)		)

	# model
	__magic_name__	:   str					     =					chkpt['models'][0]
	__magic_name__	:   Tuple					     =					model.state_dict()

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

	# remove unneeded keys
	__magic_name__	:   int					     =					[
	    '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(lowerCAmelCase		,		lowerCAmelCase		)

	__magic_name__	:   Optional[int]					     =					FSMTConfig.from_pretrained(lowerCAmelCase		)
	__magic_name__	:   List[Any]					     =					FSMTForConditionalGeneration(lowerCAmelCase		)

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

	# save
	__magic_name__	:   Optional[int]					     =					os.path.join(lowerCAmelCase		,		lowerCAmelCase		)
	print(f'Generating {pytorch_weights_dump_path}'		)
	torch.save(lowerCAmelCase		,		lowerCAmelCase		)

	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__":
							lowerCAmelCase				:Optional[int]        =      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.'''
							)
							lowerCAmelCase				:Optional[int]        =      parser.parse_args()
							convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)



'''simple docstring'''

import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _lowerCamelCase	(						lowercase__		,					unittest.TestCase							):



		'''simple docstring'''







		A_     :      Optional[Any]				   =    LxmertTokenizer
		A_     :      List[Any]				   =    LxmertTokenizerFast
		A_     :      int				   =    True
		A_     :      Any				   =    True







		def        __lowerCAmelCase		( self		:						List[str]      )  ->			Tuple:
			super().setUp()

			__magic_name__	:   str					     =					[
			    '[UNK]',
			    '[CLS]',
			    '[SEP]',
			    'want',
			    '##want',
			    '##ed',
			    'wa',
			    'un',
			    'runn',
			    '##ing',
			    ',',
			    'low',
			    'lowest',
			]
			__magic_name__	:   List[str]					     =					os.path.join(self.tmpdirname  ,					VOCAB_FILES_NAMES['vocab_file']      )
			with open(self.vocab_file  ,					'w'  ,					encoding='utf-8'      ) as vocab_writer:
				vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )







		def        __lowerCAmelCase		( self		:						Any  ,					_A		:						str      )  ->			List[Any]:
			__magic_name__	:   Dict					     =					'UNwant\u00E9d,running'
			__magic_name__	:   Dict					     =					'unwanted, running'
			return input_text, output_text







		def        __lowerCAmelCase		( self		:						Tuple      )  ->			Optional[int]:
			__magic_name__	:   Optional[Any]					     =					self.tokenizer_class(self.vocab_file      )

			__magic_name__	:   List[str]					     =					tokenizer.tokenize('UNwant\u00E9d,running'      )
			self.assertListEqual(_A  ,					['un', '##want', '##ed', ',', 'runn', '##ing']      )
			self.assertListEqual(tokenizer.convert_tokens_to_ids(_A      )  ,					[7, 4, 5, 10, 8, 9]      )







		def        __lowerCAmelCase		( self		:						int      )  ->			List[Any]:
			if not self.test_rust_tokenizer:
				return

			__magic_name__	:   Any					     =					self.get_tokenizer()
			__magic_name__	:   Optional[Any]					     =					self.get_rust_tokenizer()

			__magic_name__	:   Union[str, Any]					     =					'I was born in 92000, and this is falsé.'

			__magic_name__	:   List[Any]					     =					tokenizer.tokenize(_A      )
			__magic_name__	:   Dict					     =					rust_tokenizer.tokenize(_A      )
			self.assertListEqual(_A  ,					_A      )

			__magic_name__	:   int					     =					tokenizer.encode(_A  ,					add_special_tokens=_A      )
			__magic_name__	:   Union[str, Any]					     =					rust_tokenizer.encode(_A  ,					add_special_tokens=_A      )
			self.assertListEqual(_A  ,					_A      )

			__magic_name__	:   List[Any]					     =					self.get_rust_tokenizer()
			__magic_name__	:   str					     =					tokenizer.encode(_A      )
			__magic_name__	:   Optional[int]					     =					rust_tokenizer.encode(_A      )
			self.assertListEqual(_A  ,					_A      )