


'''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()

__snake_case				 =	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
__snake_case				 =	{
    # 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
__snake_case				 =	{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
		__snake_case				 =	'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",
]:
		__snake_case				 =	'allenai'


def      a (     __a  )    ->      Tuple:



					'''simple docstring'''






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


def      a (     __a					,   __a  )    ->      Optional[int]:



					'''simple docstring'''






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

					# handle various types of models

					UpperCamelCase__  :Dict				   =					basename(lowercase_  )
					UpperCamelCase__  :int				   =					dirname(lowercase_  )

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

					UpperCamelCase__  :Any				   =					vars(chkpt['''args''']['''model''']  )

					UpperCamelCase__  :Optional[Any]				   =					args["""source_lang"""]
					UpperCamelCase__  :Optional[Any]				   =					args["""target_lang"""]

					UpperCamelCase__  :Dict				   =					dirname(lowercase_  )
					UpperCamelCase__  :Optional[Any]				   =					basename(lowercase_  )

					# dicts
					UpperCamelCase__  :Optional[int]				   =					os.path.join(lowercase_					,   f'''dict.{src_lang}.txt'''  )
					UpperCamelCase__  :int				   =					os.path.join(lowercase_					,   f'''dict.{tgt_lang}.txt'''  )

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

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

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

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

					# model config
					UpperCamelCase__  :Optional[Any]				   =					os.path.join(lowercase_					,   '''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"]}'''

					UpperCamelCase__  :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""": 0.0_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
					UpperCamelCase__  :Tuple				   =					5
					UpperCamelCase__  :List[str]				   =					False
					if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
										UpperCamelCase__  :int				   =					best_score_hparams[model_dir]["""length_penalty"""]
					else:
										UpperCamelCase__  :List[Any]				   =					1.0

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

					# tokenizer config
					UpperCamelCase__  :Dict				   =					os.path.join(lowercase_					,   lowercase_  )

					UpperCamelCase__  :str				   =					{
					    """langs""": [src_lang, tgt_lang],
					    """model_max_length""": 1024,
					    """do_lower_case""": do_lower_case,
					}

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

					# model
					UpperCamelCase__  :int				   =					chkpt["""models"""][0]
					UpperCamelCase__  :Dict				   =					model.state_dict()

					# rename keys to start with 'model.'
					UpperCamelCase__  :Union[str, Any]				   =					OrderedDict(('''model.''' + k, v) for k, v in model_state_dict.items()  )

					# remove unneeded keys
					UpperCamelCase__  :List[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(lowercase_					,   lowercase_  )

					UpperCamelCase__  :str				   =					FSMTConfig.from_pretrained(lowercase_  )
					UpperCamelCase__  :Dict				   =					FSMTForConditionalGeneration(lowercase_  )

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

					# save
					UpperCamelCase__  :int				   =					os.path.join(lowercase_					,   lowercase_  )
					print(f'''Generating {pytorch_weights_dump_path}'''  )
					torch.save(lowercase_					,   lowercase_  )

					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__":
		__snake_case				 =	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.'''
		)
		__snake_case				 =	parser.parse_args()
		convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)


import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A_			:					Tuple    			=     logging.get_logger(__name__)

A_			:					Tuple    			=     {'vocab_file': 'spiece.model'}

A_			:					Optional[Any]    			=     {
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    }
}

A_			:					Tuple    			=     {
    'google/bigbird-roberta-base': 4096,
    'google/bigbird-roberta-large': 4096,
    'google/bigbird-base-trivia-itc': 4096,
}



class        _a		(__magic_name__							):


					'''simple docstring'''






					UpperCAmelCase__:  Optional[int]    				=			VOCAB_FILES_NAMES
					UpperCAmelCase__:  str    				=			PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:  List[str]    				=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:  str    				=			['''input_ids''', '''attention_mask''']
					UpperCAmelCase__:  List[int]    				=			[]






					def __init__(					self       ,			A__       ,			A__="<unk>"       ,			A__="<s>"       ,			A__="</s>"       ,			A__="<pad>"       ,			A__="[SEP]"       ,			A__="[MASK]"       ,			A__="[CLS]"       ,			A__ = None       ,			**A__       ,			):
						A__				:       Tuple	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else bos_token
						A__				:       int	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else eos_token
						A__				:       Tuple	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else unk_token
						A__				:       str	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else pad_token
						A__				:       Dict	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else cls_token
						A__				:       Any	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else sep_token

						# Mask token behave like a normal word, i.e. include the space before it
						A__				:       Optional[Any]	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else mask_token

						A__				:       Optional[Any]	  =						{} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    bos_token=A__       ,			eos_token=A__       ,			unk_token=A__       ,			pad_token=A__       ,			sep_token=A__       ,			mask_token=A__       ,			cls_token=A__       ,			sp_model_kwargs=self.sp_model_kwargs       ,			**A__       ,			)

						A__				:       Any	  =						vocab_file

						A__				:       Optional[int]	  =						spm.SentencePieceProcessor(**self.sp_model_kwargs     )
						self.sp_model.Load(A__     )






					@property
					def     __A     (					self     ):
						return self.sp_model.get_piece_size()






					def     __A     (					self     ):
						A__				:       List[str]	  =						{self.convert_ids_to_tokens(A__     ): i for i in range(self.vocab_size     )}
						vocab.update(self.added_tokens_encoder     )
						return vocab






					def __getstate__(					self     ):
						A__				:       Optional[Any]	  =						self.__dict__.copy()
						A__				:       Optional[Any]	  =						None
						return state






					def __setstate__(					self       ,			A__     ):
						A__				:       Dict	  =						d

						# for backward compatibility
						if not hasattr(self       ,			"""sp_model_kwargs"""     ):
							A__				:       str	  =						{}

						A__				:       Tuple	  =						spm.SentencePieceProcessor(**self.sp_model_kwargs     )
						self.sp_model.Load(self.vocab_file     )






					def     __A     (					self       ,			A__     ):
						return self.sp_model.encode(A__       ,			out_type=A__     )






					def     __A     (					self       ,			A__     ):
						return self.sp_model.piece_to_id(A__     )






					def     __A     (					self       ,			A__     ):
						A__				:       Dict	  =						self.sp_model.IdToPiece(A__     )
						return token






					def     __A     (					self       ,			A__     ):
						A__				:       str	  =						[]
						A__				:       Optional[int]	  =						""""""
						A__				:       Any	  =						False
						for token in tokens:
							# make sure that special tokens are not decoded using sentencepiece model
							if token in self.all_special_tokens:
								if not prev_is_special:
									out_string += " "
								out_string += self.sp_model.decode(A__     ) + token
								A__				:       Union[str, Any]	  =						True
								A__				:       Union[str, Any]	  =						[]
							else:
								current_sub_tokens.append(A__     )
								A__				:       Any	  =						False
						out_string += self.sp_model.decode(A__     )
						return out_string.strip()






					def     __A     (					self       ,			A__       ,			A__ = False       ,			A__ = None       ,			A__ = True       ,			**A__       ,			):
						A__				:       List[str]	  =						kwargs.pop("""use_source_tokenizer"""       ,			A__     )

						A__				:       Union[str, Any]	  =						self.convert_ids_to_tokens(A__       ,			skip_special_tokens=A__     )

						# To avoid mixing byte-level and unicode for byte-level BPT
						# we need to build string separately for added tokens and byte-level tokens
						# cf. https://github.com/huggingface/transformers/issues/1133
						A__				:       Dict	  =						[]
						A__				:       List[Any]	  =						[]
						for token in filtered_tokens:
							if skip_special_tokens and token in self.all_special_ids:
								continue
							if token in self.added_tokens_encoder:
								if current_sub_text:
									sub_texts.append(self.convert_tokens_to_string(A__     )     )
									A__				:       Optional[Any]	  =						[]
								sub_texts.append(A__     )
							else:
								current_sub_text.append(A__     )
						if current_sub_text:
							sub_texts.append(self.convert_tokens_to_string(A__     )     )

						# Mimic the behavior of the Rust tokenizer:
						# No space before [MASK] and [SEP]
						if spaces_between_special_tokens:
							A__				:       List[Any]	  =						re.sub(r""" (\[(MASK|SEP)\])"""       ,			r"""\1"""       ,			""" """.join(A__     )     )
						else:
							A__				:       Optional[Any]	  =						"""""".join(A__     )

						A__				:       Union[str, Any]	  =						(
						    clean_up_tokenization_spaces
						    if clean_up_tokenization_spaces is not None
						    else self.clean_up_tokenization_spaces
						)
						if clean_up_tokenization_spaces:
							A__				:       Union[str, Any]	  =						self.clean_up_tokenization(A__     )
							return clean_text
						else:
							return text






					def     __A     (					self       ,			A__       ,			A__ = None     ):
						if not os.path.isdir(A__     ):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
							return
						A__				:       str	  =						os.path.join(
						    A__       ,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]     )

						if os.path.abspath(self.vocab_file     ) != os.path.abspath(A__     ) and os.path.isfile(self.vocab_file     ):
							copyfile(self.vocab_file       ,			A__     )
						elif not os.path.isfile(self.vocab_file     ):
							with open(A__       ,			"""wb"""     ) as fi:
								A__				:       Dict	  =						self.sp_model.serialized_model_proto()
								fi.write(A__     )

						return (out_vocab_file,)






					def     __A     (					self       ,			A__       ,			A__ = None     ):
						if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						A__				:       List[Any]	  =						[self.cls_token_id]
						A__				:       List[Any]	  =						[self.sep_token_id]
						return cls + token_ids_a + sep + token_ids_a + sep






					def     __A     (					self       ,			A__       ,			A__ = None       ,			A__ = False     ):
						if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=A__       ,			token_ids_a=A__       ,			already_has_special_tokens=A__     )

						if token_ids_a is None:
							return [1] + ([0] * len(A__     )) + [1]
						return [1] + ([0] * len(A__     )) + [1] + ([0] * len(A__     )) + [1]






					def     __A     (					self       ,			A__       ,			A__ = None     ):
						A__				:       Any	  =						[self.sep_token_id]
						A__				:       List[Any]	  =						[self.cls_token_id]
						if token_ids_a is None:
							return len(cls + token_ids_a + sep     ) * [0]
						return len(cls + token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1]



