import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


lowercase									=				datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
		import pyspark





@dataclass
class 	__lowercase    (			datasets.BuilderConfig       ):




				'''simple docstring'''


				_A      :		Optional[datasets.Features]   						=			None






def        lowerCamelCase_					(     UpperCamelCase__       :						"pyspark.sql.DataFrame",		UpperCamelCase__       :						List[int],		):







				'''simple docstring'''







				import pyspark

				def generate_fn():
								UpperCamelCase__					=				df.select('''*''',		pyspark.sql.functions.spark_partition_id().alias('''part_id'''			)			)
								for partition_id in partition_order:
												UpperCamelCase__					=				df_with_partition_id.select('''*'''			).where(F"""part_id = {partition_id}"""			).drop('''part_id'''			)
												UpperCamelCase__					=				partition_df.collect()
												UpperCamelCase__					=				0
												for row in rows:
																yield F"""{partition_id}_{row_id}""", row.asDict()
																row_id += 1

				return generate_fn





class 	__lowercase    (			_BaseExamplesIterable       ):




				'''simple docstring'''


				def __init__(		self    :		Optional[Any]      ,							_a    :		Dict      ,							_a    :		Optional[int]=None      ,							):
								UpperCamelCase__					=				df
								UpperCamelCase__					=				partition_order or range(self.df.rdd.getNumPartitions()				)
								UpperCamelCase__					=				_generate_iterable_examples(self.df      ,							self.partition_order				)





				def __iter__(		self    :		int				):
								yield from self.generate_examples_fn()





				def      A_							(		self    :		Optional[int]      ,							_a    :		Dict				):
								UpperCamelCase__					=				list(range(self.df.rdd.getNumPartitions()				)				)
								generator.shuffle(A__				)
								return SparkExamplesIterable(self.df      ,							partition_order=A__				)





				def      A_							(		self    :		int      ,							_a    :		List[Any]      ,							_a    :		int				):
								UpperCamelCase__					=				self.split_shard_indices_by_worker(A__      ,							A__				)
								return SparkExamplesIterable(self.df      ,							partition_order=A__				)





				@property
				def      A_							(		self    :		str				):
								return len(self.partition_order				)






class 	__lowercase    (			datasets.DatasetBuilder       ):




				'''simple docstring'''


				_A      :		Union[str, Any]   						=			SparkConfig





				def __init__(		self    :		List[Any]      ,							_a    :		List[Any]      ,							_a    :		Tuple = None      ,							_a    :		Dict = None      ,							**_a    :		Dict      ,							):
								import pyspark

								UpperCamelCase__					=				pyspark.sql.SparkSession.builder.getOrCreate()
								UpperCamelCase__					=				df
								UpperCamelCase__					=				working_dir

								super().__init__(
								    cache_dir=A__      ,							config_name=str(self.df.semanticHash()				)      ,							**A__      ,							)





				def      A_							(		self    :		Tuple				):
								# Returns the path of the created file.
								def create_cache_and_write_probe(_a    :		Dict				):
												# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
												# already exist.
												os.makedirs(self._cache_dir      ,							exist_ok=A__				)
												UpperCamelCase__					=				os.path.join(self._cache_dir      ,							'''fs_test''' + uuid.uuida().hex				)
												# Opening the file in append mode will create a new file unless it already exists, in which case it will not
												# change the file contents.
												open(A__      ,							'''a'''				)
												return [probe_file]

								if self._spark.conf.get('''spark.master'''      ,							''''''				).startswith('''local'''				):
												return

								# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
								# accessible to the driver.
								# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
								if self._cache_dir:
												UpperCamelCase__					=				(
												    self._spark.sparkContext.parallelize(range(1				)      ,							1				).mapPartitions(A__				).collect()
												)
												if os.path.isfile(probe[0]				):
																return

								raise ValueError(
								    '''When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir'''				)





				def      A_							(		self    :		Tuple				):
								return datasets.DatasetInfo(features=self.config.features				)





				def      A_							(		self    :		str      ,							_a    :		List[Any]				):
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN				)]





				def      A_							(		self    :		str      ,							_a    :		Optional[int]				):
								import pyspark

								def get_arrow_batch_size(_a    :		Union[str, Any]				):
												for batch in it:
																yield pa.RecordBatch.from_pydict({'''batch_bytes''': [batch.nbytes]}				)

								UpperCamelCase__					=				self.df.count()
								UpperCamelCase__					=				df_num_rows if df_num_rows <= 100 else 100
								# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
								UpperCamelCase__					=				(
								    self.df.limit(A__				)
								    .repartition(1				)
								    .mapInArrow(A__      ,							'''batch_bytes: long'''				)
								    .agg(pyspark.sql.functions.sum('''batch_bytes'''				).alias('''sample_bytes'''				)				)
								    .collect()[0]
								    .sample_bytes
								    / sample_num_rows
								)
								UpperCamelCase__					=				approx_bytes_per_row * df_num_rows
								if approx_total_size > max_shard_size:
												# Make sure there is at least one row per partition.
												UpperCamelCase__					=				min(A__      ,							int(approx_total_size / max_shard_size				)				)
												UpperCamelCase__					=				self.df.repartition(A__				)





				def      A_							(		self    :		int      ,							_a    :		List[Any]      ,							_a    :		Tuple      ,							_a    :		Optional[int]      ,							):
								import pyspark

								UpperCamelCase__					=				ParquetWriter if file_format == """parquet""" else ArrowWriter
								UpperCamelCase__					=				os.path.join(self._working_dir      ,							os.path.basename(A__				)				) if self._working_dir else fpath
								UpperCamelCase__					=				file_format == """parquet"""

								# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
								# pickling the SparkContext.
								UpperCamelCase__					=				self.config.features
								UpperCamelCase__					=				self._writer_batch_size
								UpperCamelCase__					=				self._fs.storage_options

								def write_arrow(_a    :		List[str]				):
												# Within the same SparkContext, no two task attempts will share the same attempt ID.
												UpperCamelCase__					=				pyspark.TaskContext().taskAttemptId()
												UpperCamelCase__					=				next(A__      ,							A__				)
												if first_batch is None:
																# Some partitions might not receive any data.
																return pa.RecordBatch.from_arrays(
																    [[task_id], [0], [0]]      ,							names=['''task_id''', '''num_examples''', '''num_bytes''']      ,							)
												UpperCamelCase__					=				0
												UpperCamelCase__					=				writer_class(
												    features=A__      ,							path=working_fpath.replace('''SSSSS'''      ,							F"""{shard_id:05d}"""				).replace('''TTTTT'''      ,							F"""{task_id:05d}"""				)      ,							writer_batch_size=A__      ,							storage_options=A__      ,							embed_local_files=A__      ,							)
												UpperCamelCase__					=				pa.Table.from_batches([first_batch]				)
												writer.write_table(A__				)
												for batch in it:
																if max_shard_size is not None and writer._num_bytes >= max_shard_size:
																				UpperCamelCase__					=				writer.finalize()
																				writer.close()
																				yield pa.RecordBatch.from_arrays(
																				    [[task_id], [num_examples], [num_bytes]]      ,							names=['''task_id''', '''num_examples''', '''num_bytes''']      ,							)
																				shard_id += 1
																				UpperCamelCase__					=				writer_class(
																				    features=writer._features      ,							path=working_fpath.replace('''SSSSS'''      ,							F"""{shard_id:05d}"""				).replace('''TTTTT'''      ,							F"""{task_id:05d}"""				)      ,							writer_batch_size=A__      ,							storage_options=A__      ,							embed_local_files=A__      ,							)
																UpperCamelCase__					=				pa.Table.from_batches([batch]				)
																writer.write_table(A__				)

												if writer._num_bytes > 0:
																UpperCamelCase__					=				writer.finalize()
																writer.close()
																yield pa.RecordBatch.from_arrays(
																    [[task_id], [num_examples], [num_bytes]]      ,							names=['''task_id''', '''num_examples''', '''num_bytes''']      ,							)

												if working_fpath != fpath:
																for file in os.listdir(os.path.dirname(A__				)				):
																				UpperCamelCase__					=				os.path.join(os.path.dirname(A__				)      ,							os.path.basename(A__				)				)
																				shutil.move(A__      ,							A__				)

								UpperCamelCase__					=				(
								    self.df.mapInArrow(A__      ,							'''task_id: long, num_examples: long, num_bytes: long'''				)
								    .groupBy('''task_id'''				)
								    .agg(
								        pyspark.sql.functions.sum('''num_examples'''				).alias('''total_num_examples'''				)      ,							pyspark.sql.functions.sum('''num_bytes'''				).alias('''total_num_bytes'''				)      ,							pyspark.sql.functions.count('''num_bytes'''				).alias('''num_shards'''				)      ,							pyspark.sql.functions.collect_list('''num_examples'''				).alias('''shard_lengths'''				)      ,							)
								    .collect()
								)
								for row in stats:
												yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)





				def      A_							(		self    :		Dict      ,							_a    :		Optional[Any]      ,							_a    :		List[str] = "arrow"      ,							_a    :		Dict = None      ,							_a    :		Union[str, Any] = None      ,							**_a    :		Union[str, Any]      ,							):
								self._validate_cache_dir()

								UpperCamelCase__					=				convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE				)
								self._repartition_df_if_needed(A__				)
								UpperCamelCase__					=				not is_remote_filesystem(self._fs				)
								UpperCamelCase__					=				os.path.join if is_local else posixpath.join

								UpperCamelCase__					=				"""-TTTTT-SSSSS-of-NNNNN"""
								UpperCamelCase__					=				F"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
								UpperCamelCase__					=				path_join(self._output_dir      ,							A__				)

								UpperCamelCase__					=				0
								UpperCamelCase__					=				0
								UpperCamelCase__					=				0
								UpperCamelCase__					=				[]
								UpperCamelCase__					=				[]

								for task_id, content in self._prepare_split_single(A__      ,							A__      ,							A__				):
												(
												    UpperCamelCase__  
												)					=				content
												if num_bytes > 0:
																total_num_examples += num_examples
																total_num_bytes += num_bytes
																total_shards += num_shards
																task_id_and_num_shards.append((task_id, num_shards)				)
																all_shard_lengths.extend(A__				)

								UpperCamelCase__					=				total_num_examples
								UpperCamelCase__					=				total_num_bytes

								# should rename everything at the end
								logger.debug(F"""Renaming {total_shards} shards."""				)
								if total_shards > 1:
												UpperCamelCase__					=				all_shard_lengths

												# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
												# pickling error due to pickling the SparkContext.
												UpperCamelCase__					=				self._fs

												# use the -SSSSS-of-NNNNN pattern
												def _rename_shard(
												    _a    :		Optional[Any]      ,							_a    :		int      ,							_a    :		Any      ,							):
																rename(
																    A__      ,							fpath.replace('''SSSSS'''      ,							F"""{shard_id:05d}"""				).replace('''TTTTT'''      ,							F"""{task_id:05d}"""				)      ,							fpath.replace('''TTTTT-SSSSS'''      ,							F"""{global_shard_id:05d}"""				).replace('''NNNNN'''      ,							F"""{total_shards:05d}"""				)      ,							)

												UpperCamelCase__					=				[]
												UpperCamelCase__					=				0
												for i in range(len(A__				)				):
																UpperCamelCase__					=				task_id_and_num_shards[i]
																for shard_id in range(A__				):
																				args.append([task_id, shard_id, global_shard_id]				)
																				global_shard_id += 1
												self._spark.sparkContext.parallelize(A__      ,							len(A__				)				).map(lambda _a				: _rename_shard(*A__				)				).collect()
								else:
												# don't use any pattern
												UpperCamelCase__					=				0
												UpperCamelCase__					=				task_id_and_num_shards[0][0]
												self._rename(
												    fpath.replace('''SSSSS'''      ,							F"""{shard_id:05d}"""				).replace('''TTTTT'''      ,							F"""{task_id:05d}"""				)      ,							fpath.replace(A__      ,							''''''				)      ,							)





				def      A_							(		self    :		Union[str, Any]      ,							_a    :		List[Any]      ,							):
								return SparkExamplesIterable(self.df				)






# 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_			:					List[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
A_			:					Tuple    			=     {
    # 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
A_			:					Optional[int]    			=     {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
	A_			:					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",
]:
	A_			:					Optional[int]    			=     'allenai'

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

def 		UpperCamelCase   (lowercase_:					Tuple       , lowercase_:					Tuple   )	-> Optional[int]:
	# prep
	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

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

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

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

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

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

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

	A__				:       Dict	  =						Dictionary.load(lowercase_   )
	A__				:       List[str]	  =						rewrite_dict_keys(src_dict.indices   )
	A__				:       Any	  =						len(lowercase_   )
	A__				:       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
	A__				:       Optional[Any]	  =						True
	for k in src_vocab.keys():
		if not k.islower():
			A__				:       Tuple	  =						False
			break

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

	A__				:       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""": 0.02,
	    """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
	A__				:       Tuple	  =						5
	A__				:       List[str]	  =						False
	if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
		A__				:       int	  =						best_score_hparams[model_dir]["""length_penalty"""]
	else:
		A__				:       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
	A__				:       Dict	  =						os.path.join(lowercase_       , lowercase_   )

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

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

	# remove unneeded keys
	A__				:       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_   )

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

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

	# save
	A__				:       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__":
	A_			:					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.'
	)
	A_			:					Tuple    			=     parser.parse_args()
	convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)



