

import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints

import yaml


SCREAMING_SNAKE_CASE_        =      NewType('DataClass', Any)
SCREAMING_SNAKE_CASE_        =      NewType('DataClassType', Any)

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Union[str, Any] )   ->       int:
				if isinstance(lowerCAmelCase  ,      lowerCAmelCase ):
								return v
				if v.lower() in ("yes", "true", "t", "y", "1"):
								return True
				elif v.lower() in ("no", "false", "f", "n", "0"):
								return False
				else:
								raise ArgumentTypeError(
								    F'Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).' )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list )   ->       Callable[[str], Any]:
				_UpperCAmelCase       :		List[Any] 		=    {str(lowerCAmelCase ): choice for choice in choices}
				return lambda lowerCAmelCase : str_to_choice.get(lowerCAmelCase  ,      lowerCAmelCase )

def 							__SCREAMING_SNAKE_CASE							(				*,
    lowerCAmelCase:  Union[str, List[str]] = None  ,      lowerCAmelCase:  str = None  ,      lowerCAmelCase:  Any = dataclasses.MISSING  ,      lowerCAmelCase:  Callable[[], Any] = dataclasses.MISSING  ,      lowerCAmelCase:  dict = None  ,      **lowerCAmelCase:  Optional[Any]  ,      )   ->       dataclasses.Field:
				if metadata is None:
								# Important, don't use as default param in function signature because dict is mutable and shared across function calls
								_UpperCAmelCase       :		Optional[Any] 		=    {}
				if aliases is not None:
								_UpperCAmelCase       :		Any 		=    aliases
				if help is not None:
								_UpperCAmelCase       :		Union[str, Any] 		=    help

				return dataclasses.field(metadata=lowerCAmelCase  ,      default=lowerCAmelCase  ,      default_factory=lowerCAmelCase  ,      **lowerCAmelCase )





class 				a  (	UpperCAmelCase				):
						_lowercase    						=			42


						def __init__(       self						,					A_						,					**A_	):







										'''simple docstring'''


										if "formatter_class" not in kwargs:
														_UpperCAmelCase       :		Optional[Any] 		=    ArgumentDefaultsHelpFormatter
										super().__init__(**A_	)
										if dataclasses.is_dataclass(A_	):
														_UpperCAmelCase       :		Optional[int] 		=    [dataclass_types]
										_UpperCAmelCase       :		Optional[int] 		=    list(A_	)
										for dtype in self.dataclass_types:
														self._add_dataclass_arguments(A_	)


						@staticmethod
						def        _UpperCAmelCase				(       A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    f'--{field.name}'
										_UpperCAmelCase       :		List[str] 		=    field.metadata.copy()
										# field.metadata is not used at all by Data Classes,
										# it is provided as a third-party extension mechanism.
										if isinstance(field.type						,					A_	):
														raise RuntimeError(
														    "Unresolved type detected, which should have been done with the help of "
														    "`typing.get_type_hints` method by default"	)

										_UpperCAmelCase       :		List[Any] 		=    kwargs.pop("aliases"						,					[]	)
										if isinstance(A_						,					A_	):
														_UpperCAmelCase       :		Dict 		=    [aliases]

										_UpperCAmelCase       :		List[Any] 		=    getattr(field.type						,					"__origin__"						,					field.type	)
										if origin_type is Union or (hasattr(A_						,					"UnionType"	) and isinstance(A_						,					types.UnionType	)):
														if str not in field.type.__args__ and (
														    len(field.type.__args__	) != 2 or type(A_	) not in field.type.__args__
														):
																		raise ValueError(
																		    "Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because"
																		    " the argument parser only supports one type per argument."
																		    f' Problem encountered in field \'{field.name}\'.'	)
														if type(A_	) not in field.type.__args__:
																		# filter `str` in Union
																		_UpperCAmelCase       :		int 		=    field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
																		_UpperCAmelCase       :		int 		=    getattr(field.type						,					"__origin__"						,					field.type	)
														elif bool not in field.type.__args__:
																		# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
																		_UpperCAmelCase       :		Dict 		=    (
																		    field.type.__args__[0] if isinstance(A_						,					field.type.__args__[1]	) else field.type.__args__[1]
																		)
																		_UpperCAmelCase       :		Optional[int] 		=    getattr(field.type						,					"__origin__"						,					field.type	)

        # A variable to store kwargs for a boolean field, if needed
        # so that we can init a `no_*` complement argument (see below)
										_UpperCAmelCase       :		Optional[int] 		=    {}
										if origin_type is Literal or (isinstance(field.type						,					A_	) and issubclass(field.type						,					A_	)):
														if origin_type is Literal:
																		_UpperCAmelCase       :		Dict 		=    field.type.__args__
														else:
																		_UpperCAmelCase       :		int 		=    [x.value for x in field.type]

														_UpperCAmelCase       :		List[Any] 		=    make_choice_type_function(kwargs["choices"]	)

														if field.default is not dataclasses.MISSING:
																		_UpperCAmelCase       :		int 		=    field.default
														else:
																		_UpperCAmelCase       :		Optional[int] 		=    True
										elif field.type is bool or field.type == Optional[bool]:
														# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
														# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
														_UpperCAmelCase       :		Optional[int] 		=    copy(A_	)

														# Hack because type=bool in argparse does not behave as we want.
														_UpperCAmelCase       :		int 		=    string_to_bool
														if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
																		# Default value is False if we have no default when of type bool.
																		_UpperCAmelCase       :		Optional[int] 		=    False if field.default is dataclasses.MISSING else field.default
																		# This is the value that will get picked if we don't include --field_name in any way
																		_UpperCAmelCase       :		Optional[Any] 		=    default
																		# This tells argparse we accept 0 or 1 value after --field_name
																		_UpperCAmelCase       :		Union[str, Any] 		=    "?"
																		# This is the value that will get picked if we do --field_name (without value)
																		_UpperCAmelCase       :		Dict 		=    True
										elif isclass(A_	) and issubclass(A_						,					A_	):
														_UpperCAmelCase       :		Union[str, Any] 		=    field.type.__args__[0]
														_UpperCAmelCase       :		Union[str, Any] 		=    "+"
														if field.default_factory is not dataclasses.MISSING:
																		_UpperCAmelCase       :		int 		=    field.default_factory()
														elif field.default is dataclasses.MISSING:
																		_UpperCAmelCase       :		Dict 		=    True
										else:
														_UpperCAmelCase       :		Optional[int] 		=    field.type
														if field.default is not dataclasses.MISSING:
																		_UpperCAmelCase       :		Union[str, Any] 		=    field.default
														elif field.default_factory is not dataclasses.MISSING:
																		_UpperCAmelCase       :		List[Any] 		=    field.default_factory()
														else:
																		_UpperCAmelCase       :		Optional[Any] 		=    True
										parser.add_argument(A_						,					*A_						,					**A_	)

										# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
										# Order is important for arguments with the same destination!
										# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
										# here and we do not need those changes/additional keys.
										if field.default is True and (field.type is bool or field.type == Optional[bool]):
														_UpperCAmelCase       :		Tuple 		=    False
														parser.add_argument(f'--no_{field.name}'						,					action="store_false"						,					dest=field.name						,					**A_	)


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										if hasattr(A_						,					"_argument_group_name"	):
														_UpperCAmelCase       :		Optional[Any] 		=    self.add_argument_group(dtype._argument_group_name	)
										else:
														_UpperCAmelCase       :		Optional[Any] 		=    self

										try:
														_UpperCAmelCase       :		Dict[str, type] 		=    get_type_hints(A_	)
										except NameError:
														raise RuntimeError(
														    f'Type resolution failed for {dtype}. Try declaring the class in global scope or '
														    "removing line of `from __future__ import annotations` which opts in Postponed "
														    "Evaluation of Annotations (PEP 563)"	)
										except TypeError as ex:
														# Remove this block when we drop Python 3.9 support
														if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(A_	):
																		_UpperCAmelCase       :		List[str] 		=    ".".join(map(A_						,					sys.version_info[:3]	)	)
																		raise RuntimeError(
																		    f'Type resolution failed for {dtype} on Python {python_version}. Try removing '
																		    "line of `from __future__ import annotations` which opts in union types as "
																		    "`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To "
																		    "support Python versions that lower than 3.10, you need to use "
																		    "`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of "
																		    "`X | None`."	) from ex
														raise

										for field in dataclasses.fields(A_	):
														if not field.init:
																		continue
														_UpperCAmelCase       :		Dict 		=    type_hints[field.name]
														self._parse_dataclass_field(A_						,					A_	)


						def        _UpperCAmelCase				(       self						,					A_=None						,					A_=False						,					A_=True						,					A_=None						,					A_=None						,					):







										'''simple docstring'''


										if args_file_flag or args_filename or (look_for_args_file and len(sys.argv	)):
														_UpperCAmelCase       :		Any 		=    []

														if args_filename:
																		args_files.append(Path(A_	)	)
														elif look_for_args_file and len(sys.argv	):
																		args_files.append(Path(sys.argv[0]	).with_suffix(".args"	)	)

														# args files specified via command line flag should overwrite default args files so we add them last
														if args_file_flag:
																		# Create special parser just to extract the args_file_flag values
																		_UpperCAmelCase       :		Optional[int] 		=    ArgumentParser()
																		args_file_parser.add_argument(A_						,					type=A_						,					action="append"	)

																		# Use only remaining args for further parsing (remove the args_file_flag)
																		_UpperCAmelCase		,							_UpperCAmelCase       :		int 		=    args_file_parser.parse_known_args(args=A_	)
																		_UpperCAmelCase       :		Optional[int] 		=    vars(A_	).get(args_file_flag.lstrip("-"	)						,					A_	)

																		if cmd_args_file_paths:
																						args_files.extend([Path(A_	) for p in cmd_args_file_paths]	)

														_UpperCAmelCase       :		Tuple 		=    []
														for args_file in args_files:
																		if args_file.exists():
																						file_args += args_file.read_text().split()

            # in case of duplicate arguments the last one has precedence
            # args specified via the command line should overwrite args from files, so we add them last
														_UpperCAmelCase       :		int 		=    file_args + args if args is not None else file_args + sys.argv[1:]
										_UpperCAmelCase		,							_UpperCAmelCase       :		List[str] 		=    self.parse_known_args(args=A_	)
										_UpperCAmelCase       :		List[str] 		=    []
										for dtype in self.dataclass_types:
														_UpperCAmelCase       :		Union[str, Any] 		=    {f.name for f in dataclasses.fields(A_	) if f.init}
														_UpperCAmelCase       :		int 		=    {k: v for k, v in vars(A_	).items() if k in keys}
														for k in keys:
																		delattr(A_						,					A_	)
														_UpperCAmelCase       :		str 		=    dtype(**A_	)
														outputs.append(A_	)
										if len(namespace.__dict__	) > 0:
														# additional namespace.
														outputs.append(A_	)
										if return_remaining_strings:
														return (*outputs, remaining_args)
										else:
														if remaining_args:
																		raise ValueError(f'Some specified arguments are not used by the HfArgumentParser: {remaining_args}'	)

														return (*outputs,)


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = False	):







										'''simple docstring'''


										_UpperCAmelCase       :		str 		=    set(args.keys()	)
										_UpperCAmelCase       :		List[Any] 		=    []
										for dtype in self.dataclass_types:
														_UpperCAmelCase       :		Any 		=    {f.name for f in dataclasses.fields(A_	) if f.init}
														_UpperCAmelCase       :		int 		=    {k: v for k, v in args.items() if k in keys}
														unused_keys.difference_update(inputs.keys()	)
														_UpperCAmelCase       :		List[str] 		=    dtype(**A_	)
														outputs.append(A_	)
										if not allow_extra_keys and unused_keys:
														raise ValueError(f'Some keys are not used by the HfArgumentParser: {sorted(A_	)}'	)
										return tuple(A_	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = False	):







										'''simple docstring'''


										with open(Path(A_	)						,					encoding="utf-8"	) as open_json_file:
														_UpperCAmelCase       :		int 		=    json.loads(open_json_file.read()	)
										_UpperCAmelCase       :		Dict 		=    self.parse_dict(A_						,					allow_extra_keys=A_	)
										return tuple(A_	)





						def        _UpperCAmelCase				(       self						,					A_						,					A_ = False	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    self.parse_dict(yaml.safe_load(Path(A_	).read_text()	)						,					allow_extra_keys=A_	)
										return tuple(A_	)





# 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()

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

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Dict )   ->       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}
				_UpperCAmelCase       :		Optional[int] 		=    dict((re.sub(R"@@$"  ,      ""  ,      lowerCAmelCase ), v) if k.endswith("@@" ) else (re.sub(R"$"  ,      "</w>"  ,      lowerCAmelCase ), v) for k, v in d.items() )
				_UpperCAmelCase       :		Optional[int] 		=    "<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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Dict  ,      lowerCAmelCase:  Union[str, Any] )   ->       int:
				# prep
				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

				_UpperCAmelCase       :		Optional[Any] 		=    basename(lowerCAmelCase )
				_UpperCAmelCase       :		Optional[Any] 		=    dirname(lowerCAmelCase )

				_UpperCAmelCase       :		Any 		=    fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
				_UpperCAmelCase       :		Optional[int] 		=    cls.hub_models()
				_UpperCAmelCase       :		Dict 		=    {"bpe": "fastbpe", "tokenizer": "moses"}
				_UpperCAmelCase       :		Tuple 		=    "."
				# 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       :		int 		=    hub_utils.from_pretrained(
				    lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      archive_map=lowerCAmelCase  ,      **lowerCAmelCase )

				_UpperCAmelCase       :		Any 		=    vars(chkpt["args"]["model"] )

				_UpperCAmelCase       :		Optional[int] 		=    args["source_lang"]
				_UpperCAmelCase       :		Dict 		=    args["target_lang"]

				_UpperCAmelCase       :		int 		=    dirname(lowerCAmelCase )
				_UpperCAmelCase       :		str 		=    basename(lowerCAmelCase )

				# dicts
				_UpperCAmelCase       :		List[str] 		=    os.path.join(lowerCAmelCase  ,      F'dict.{src_lang}.txt' )
				_UpperCAmelCase       :		Dict 		=    os.path.join(lowerCAmelCase  ,      F'dict.{tgt_lang}.txt' )

				_UpperCAmelCase       :		Union[str, Any] 		=    Dictionary.load(lowerCAmelCase )
				_UpperCAmelCase       :		Any 		=    rewrite_dict_keys(src_dict.indices )
				_UpperCAmelCase       :		int 		=    len(lowerCAmelCase )
				_UpperCAmelCase       :		List[Any] 		=    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
				_UpperCAmelCase       :		Union[str, Any] 		=    True
				for k in src_vocab.keys():
								if not k.islower():
												_UpperCAmelCase       :		Optional[int] 		=    False
												break

				_UpperCAmelCase       :		Dict 		=    Dictionary.load(lowerCAmelCase )
				_UpperCAmelCase       :		List[str] 		=    rewrite_dict_keys(tgt_dict.indices )
				_UpperCAmelCase       :		Union[str, Any] 		=    len(lowerCAmelCase )
				_UpperCAmelCase       :		Dict 		=    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)
				_UpperCAmelCase       :		int 		=    os.path.join(lowerCAmelCase  ,      VOCAB_FILES_NAMES["merges_file"] )
				for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
								_UpperCAmelCase       :		Tuple 		=    os.path.join(lowerCAmelCase  ,      lowerCAmelCase )
								if os.path.exists(lowerCAmelCase ):
												break
				with open(lowerCAmelCase  ,      encoding="utf-8" ) as fin:
								_UpperCAmelCase       :		List[str] 		=    fin.read()
				_UpperCAmelCase       :		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
				_UpperCAmelCase       :		int 		=    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"]}'

				_UpperCAmelCase       :		Union[str, 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
				_UpperCAmelCase       :		Union[str, Any] 		=    5
				_UpperCAmelCase       :		str 		=    False
				if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
								_UpperCAmelCase       :		str 		=    best_score_hparams[model_dir]["length_penalty"]
				else:
								_UpperCAmelCase       :		str 		=    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
				_UpperCAmelCase       :		int 		=    os.path.join(lowerCAmelCase  ,      lowerCAmelCase )

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

				# model
				_UpperCAmelCase       :		Optional[int] 		=    chkpt["models"][0]
				_UpperCAmelCase       :		int 		=    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       :		Any 		=    [
				    "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 )

				_UpperCAmelCase       :		List[Any] 		=    FSMTConfig.from_pretrained(lowerCAmelCase )
				_UpperCAmelCase       :		List[Any] 		=    FSMTForConditionalGeneration(lowerCAmelCase )

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

				# save
				_UpperCAmelCase       :		List[str] 		=    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__":
					SCREAMING_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.'
					)
					SCREAMING_SNAKE_CASE_        =      parser.parse_args()
					convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)


