
'''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()

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    :							Optional[Any]      =					{
    # 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    :							Optional[int]      =					{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
					lowercase    :							Tuple      =					"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    :							Any      =					"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	)       ->	Union[str, Any]:
	# 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    :							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."
					)
					lowercase    :							Any      =					parser.parse_args()
					convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)





'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class 			lowerCamelCase_   (	__a      ):






      def __init__(					self				:    List[str]						,						_A				:    NestedDataStructureLike[PathLike]						,						_A				:    Optional[NamedSplit] = None						,						_A				:    Optional[Features] = None						,						_A				:    str = None						,						_A				:    bool = False						,						_A				:    bool = False						,						_A				:    Optional[str] = None						,						_A				:    Optional[int] = None						,						**_A				:    Union[str, Any]						,						):


          '''simple docstring'''


          super().__init__(
              _A						,						split=_A						,						features=_A						,						cache_dir=_A						,						keep_in_memory=_A						,						streaming=_A						,						num_proc=_A						,						**_A						,						)
          UpperCAmelCase__			:		List[str]													=				field
          UpperCAmelCase__			:		Optional[Any]													=				path_or_paths if isinstance(_A						,						_A ) else {self.split: path_or_paths}
          UpperCAmelCase__			:		Any													=				Json(
              cache_dir=_A						,						data_files=_A						,						features=_A						,						field=_A						,						**_A						,						)






      def lowercase_							(					self				:    Dict ):


          '''simple docstring'''


          if self.streaming:
              UpperCAmelCase__			:		int													=				self.builder.as_streaming_dataset(split=self.split )
          # Build regular (map-style) dataset
          else:
              UpperCAmelCase__			:		List[str]													=				None
              UpperCAmelCase__			:		Dict													=				None
              UpperCAmelCase__			:		Optional[int]													=				None
              UpperCAmelCase__			:		List[Any]													=				None

              self.builder.download_and_prepare(
                  download_config=_A						,						download_mode=_A						,						verification_mode=_A						,						base_path=_A						,						num_proc=self.num_proc						,						)
              UpperCAmelCase__			:		str													=				self.builder.as_dataset(
                  split=self.split						,						verification_mode=_A						,						in_memory=self.keep_in_memory )
          return dataset




class 			lowerCamelCase_   :






      def __init__(					self				:    Optional[Any]						,						_A				:    Dataset						,						_A				:    Union[PathLike, BinaryIO]						,						_A				:    Optional[int] = None						,						_A				:    Optional[int] = None						,						**_A				:    List[str]						,						):


          '''simple docstring'''


          if num_proc is not None and num_proc <= 0:
              raise ValueError(f"""num_proc {num_proc} must be an integer > 0.""" )

          UpperCAmelCase__			:		Dict													=				dataset
          UpperCAmelCase__			:		Any													=				path_or_buf
          UpperCAmelCase__			:		List[str]													=				batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
          UpperCAmelCase__			:		Tuple													=				num_proc
          UpperCAmelCase__			:		Any													=				'''utf-8'''
          UpperCAmelCase__			:		Optional[int]													=				to_json_kwargs






      def lowercase_							(					self				:    int ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				self.to_json_kwargs.pop('''path_or_buf'''						,						_A )
          UpperCAmelCase__			:		Optional[int]													=				self.to_json_kwargs.pop('''orient'''						,						'''records''' )
          UpperCAmelCase__			:		Tuple													=				self.to_json_kwargs.pop('''lines'''						,						True if orient == '''records''' else False )
          UpperCAmelCase__			:		str													=				self.to_json_kwargs.pop('''index'''						,						False if orient in ['''split''', '''table'''] else True )
          UpperCAmelCase__			:		Optional[Any]													=				self.to_json_kwargs.pop('''compression'''						,						_A )

          if compression not in [None, "infer", "gzip", "bz2", "xz"]:
              raise NotImplementedError(f"""`datasets` currently does not support {compression} compression""" )

          if isinstance(self.path_or_buf						,						(str, bytes, os.PathLike) ):
              with fsspec.open(self.path_or_buf						,						'''wb'''						,						compression=_A ) as buffer:
                  UpperCAmelCase__			:		Union[str, Any]													=				self._write(file_obj=_A						,						orient=_A						,						lines=_A						,						index=_A						,						**self.to_json_kwargs )
          else:
              if compression:
                  raise NotImplementedError(
                      f"""The compression parameter is not supported when writing to a buffer, but compression={compression}"""
                      ''' was passed. Please provide a local path instead.''' )
              UpperCAmelCase__			:		Union[str, Any]													=				self._write(
                  file_obj=self.path_or_buf						,						orient=_A						,						lines=_A						,						index=_A						,						**self.to_json_kwargs )
          return written






      def lowercase_							(					self				:    Optional[int]						,						_A				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__			:		Dict													=				args

          UpperCAmelCase__			:		Dict													=				query_table(
              table=self.dataset.data						,						key=slice(_A						,						offset + self.batch_size )						,						indices=self.dataset._indices						,						)
          UpperCAmelCase__			:		str													=				batch.to_pandas().to_json(
              path_or_buf=_A						,						orient=_A						,						lines=_A						,						index=_A						,						**_A )
          if not json_str.endswith('''\n''' ):
              json_str += "\n"
          return json_str.encode(self.encoding )






      def lowercase_							(					self				:    Union[str, Any]						,						_A				:    BinaryIO						,						_A				:    Optional[int]						,						_A				:    int						,						_A				:    Any						,						**_A				:    Any						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				0

          if self.num_proc is None or self.num_proc == 1:
              for offset in logging.tqdm(
                  range(0						,						len(self.dataset )						,						self.batch_size )						,						unit='''ba'''						,						disable=not logging.is_progress_bar_enabled()						,						desc='''Creating json from Arrow format'''						,						):
                  UpperCAmelCase__			:		Optional[int]													=				self._batch_json((offset, orient, lines, index, to_json_kwargs) )
                  written += file_obj.write(_A )
          else:
              UpperCAmelCase__						,						UpperCAmelCase__			:		str													=				len(self.dataset ), self.batch_size
              with multiprocessing.Pool(self.num_proc ) as pool:
                  for json_str in logging.tqdm(
                      pool.imap(
                          self._batch_json						,						[(offset, orient, lines, index, to_json_kwargs) for offset in range(0						,						_A						,						_A )]						,						)						,						total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size						,						unit='''ba'''						,						disable=not logging.is_progress_bar_enabled()						,						desc='''Creating json from Arrow format'''						,						):
                      written += file_obj.write(_A )

          return written
