# 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()

__A						:							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
__A						:							Optional[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						:							Optional[int]							=  {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
		__A						:							Optional[Any]							=  """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						:							Tuple							=  """allenai"""
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					dict((re.sub(r"""@@$"""					,	""""""					,	SCREAMING_SNAKE_CASE   ), v) if k.endswith("""@@"""   ) else (re.sub(r"""$"""					,	"""</w>"""					,	SCREAMING_SNAKE_CASE   ), v) for k, v in d.items()   )
			SCREAMING_SNAKE_CASE           =					"""<s> <pad> </s> <unk>""".split()
			# restore the special tokens
			for k in keep_keys:
						del da[f"""{k}</w>"""]
						SCREAMING_SNAKE_CASE           =					d[k]  # restore
			return da
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			assert os.path.exists(SCREAMING_SNAKE_CASE   )
			os.makedirs(SCREAMING_SNAKE_CASE					,	exist_ok=SCREAMING_SNAKE_CASE   )
			print(f"""Writing results to {pytorch_dump_folder_path}"""   )

			# handle various types of models

			SCREAMING_SNAKE_CASE           =					basename(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					dirname(SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
			SCREAMING_SNAKE_CASE           =					cls.hub_models()
			SCREAMING_SNAKE_CASE           =					{"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
			SCREAMING_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}"""   )
			SCREAMING_SNAKE_CASE           =					hub_utils.from_pretrained(
			    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	archive_map=SCREAMING_SNAKE_CASE					,	**SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					vars(chkpt["""args"""]["""model"""]   )

			SCREAMING_SNAKE_CASE           =					args["""source_lang"""]
			SCREAMING_SNAKE_CASE           =					args["""target_lang"""]

			SCREAMING_SNAKE_CASE           =					dirname(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					basename(SCREAMING_SNAKE_CASE   )

			# dicts
			SCREAMING_SNAKE_CASE           =					os.path.join(SCREAMING_SNAKE_CASE					,	f"""dict.{src_lang}.txt"""   )
			SCREAMING_SNAKE_CASE           =					os.path.join(SCREAMING_SNAKE_CASE					,	f"""dict.{tgt_lang}.txt"""   )

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

			# 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
			SCREAMING_SNAKE_CASE           =					True
			for k in src_vocab.keys():
						if not k.islower():
									SCREAMING_SNAKE_CASE           =					False
									break

			SCREAMING_SNAKE_CASE           =					Dictionary.load(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					rewrite_dict_keys(tgt_dict.indices   )
			SCREAMING_SNAKE_CASE           =					len(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					os.path.join(SCREAMING_SNAKE_CASE					,	"""vocab-tgt.json"""   )
			print(f"""Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records"""   )
			with open(SCREAMING_SNAKE_CASE					,	"""w"""					,	encoding="""utf-8"""   ) as f:
						f.write(json.dumps(SCREAMING_SNAKE_CASE					,	ensure_ascii=SCREAMING_SNAKE_CASE					,	indent=SCREAMING_SNAKE_CASE   )   )

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

			# model config
			SCREAMING_SNAKE_CASE           =					os.path.join(SCREAMING_SNAKE_CASE					,	"""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"]}"""

			SCREAMING_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""": 0.02,
			    """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
			SCREAMING_SNAKE_CASE           =					5
			SCREAMING_SNAKE_CASE           =					False
			if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
						SCREAMING_SNAKE_CASE           =					best_score_hparams[model_dir]["""length_penalty"""]
			else:
						SCREAMING_SNAKE_CASE           =					1.0

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

			# tokenizer config
			SCREAMING_SNAKE_CASE           =					os.path.join(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					{
			    """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(SCREAMING_SNAKE_CASE					,	"""w"""					,	encoding="""utf-8"""   ) as f:
						f.write(json.dumps(SCREAMING_SNAKE_CASE					,	ensure_ascii=SCREAMING_SNAKE_CASE					,	indent=SCREAMING_SNAKE_CASE   )   )

			# model
			SCREAMING_SNAKE_CASE           =					chkpt["""models"""][0]
			SCREAMING_SNAKE_CASE           =					model.state_dict()

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

			# remove unneeded keys
			SCREAMING_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(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					FSMTConfig.from_pretrained(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					FSMTForConditionalGeneration(SCREAMING_SNAKE_CASE   )

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

			# save
			SCREAMING_SNAKE_CASE           =					os.path.join(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
			print(f"""Generating {pytorch_weights_dump_path}"""   )
			torch.save(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )

			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						:							Dict							=  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						:							Optional[Any]							=  parser.parse_args()
		convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)












from __future__ import annotations




class 			UpperCAmelCase_      :




							'''simple docstring'''



							def __init__(					self       :	Optional[int]       ,     a       :	int = 0			)			->					str:
										SCREAMING_SNAKE_CASE           =					key





							def    _UpperCAmelCase     (					self       :	Tuple       ,     a       :	str       ,     a       :	int			)			->					list[str]:
										assert isinstance(a       ,     a			) and isinstance(a       ,     a			)

										SCREAMING_SNAKE_CASE           =					key or self.__key or 1

										# make sure key is an appropriate size
										key %= 255

										return [chr(ord(a			) ^ key			) for ch in content]





							def    _UpperCAmelCase     (					self       :	Union[str, Any]       ,     a       :	str       ,     a       :	int			)			->					list[str]:
										assert isinstance(a       ,     a			) and isinstance(a       ,     a			)

										SCREAMING_SNAKE_CASE           =					key or self.__key or 1

										# make sure key is an appropriate size
										key %= 255

										return [chr(ord(a			) ^ key			) for ch in content]





							def    _UpperCAmelCase     (					self       :	Union[str, Any]       ,     a       :	str       ,     a       :	int = 0			)			->					str:
										assert isinstance(a       ,     a			) and isinstance(a       ,     a			)

										SCREAMING_SNAKE_CASE           =					key or self.__key or 1

										# make sure key can be any size
										while key > 255:
													key -= 255

										# This will be returned
										SCREAMING_SNAKE_CASE           =					""""""

										for ch in content:
													ans += chr(ord(a			) ^ key			)

										return ans





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	str       ,     a       :	int = 0			)			->					str:
										assert isinstance(a       ,     a			) and isinstance(a       ,     a			)

										SCREAMING_SNAKE_CASE           =					key or self.__key or 1

										# make sure key can be any size
										while key > 255:
													key -= 255

										# This will be returned
										SCREAMING_SNAKE_CASE           =					""""""

										for ch in content:
													ans += chr(ord(a			) ^ key			)

										return ans





							def    _UpperCAmelCase     (					self       :	Optional[int]       ,     a       :	str       ,     a       :	int = 0			)			->					bool:
										assert isinstance(a       ,     a			) and isinstance(a       ,     a			)

										try:
													with open(a			) as fin, open("""encrypt.out"""       ,     """w+"""			) as fout:
																# actual encrypt-process
																for line in fin:
																			fout.write(self.encrypt_string(a       ,     a			)			)

										except OSError:
													return False

										return True







							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	str       ,     a       :	int			)			->					bool:
										assert isinstance(a       ,     a			) and isinstance(a       ,     a			)

										try:
													with open(a			) as fin, open("""decrypt.out"""       ,     """w+"""			) as fout:
																# actual encrypt-process
																for line in fin:
																			fout.write(self.decrypt_string(a       ,     a			)			)

										except OSError:
													return False

										return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")






