



"""simple docstring"""






from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder

@dataclass
class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							42





class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_		,       lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							True



					@register_to_config
					def __init__(	self							:  Union[str, Any],  lowerCamelCase							:  int = 3,  lowerCamelCase							:  int = 3,  lowerCamelCase							:  Tuple[str] = ("DownEncoderBlock2D",),  lowerCamelCase							:  Tuple[str] = ("UpDecoderBlock2D",),  lowerCamelCase							:  Tuple[int] = (64,),  lowerCamelCase							:  int = 1,  lowerCamelCase							:  str = "silu",  lowerCamelCase							:  int = 4,  lowerCamelCase							:  int = 32,  lowerCamelCase							:  int = 32,  lowerCamelCase							:  float = 0.18_215,  )-> Any:
								super().__init__()

								# pass init params to Encoder
								lowerCamelCase__		:		Union[str, Any]		     =Encoder(
								    in_channels=lowerCamelCase,  out_channels=lowerCamelCase,  down_block_types=lowerCamelCase,  block_out_channels=lowerCamelCase,  layers_per_block=lowerCamelCase,  act_fn=lowerCamelCase,  norm_num_groups=lowerCamelCase,  double_z=lowerCamelCase,  )

								# pass init params to Decoder
								lowerCamelCase__		:		int		     =Decoder(
								    in_channels=lowerCamelCase,  out_channels=lowerCamelCase,  up_block_types=lowerCamelCase,  block_out_channels=lowerCamelCase,  layers_per_block=lowerCamelCase,  norm_num_groups=lowerCamelCase,  act_fn=lowerCamelCase,  )

								lowerCamelCase__		:		List[Any]		     =nn.Convad(2 * latent_channels,  2 * latent_channels,  1					)
								lowerCamelCase__		:		Tuple		     =nn.Convad(lowerCamelCase,  lowerCamelCase,  1					)

								lowerCamelCase__		:		str		     =False
								lowerCamelCase__		:		Optional[Any]		     =False

								# only relevant if vae tiling is enabled
								lowerCamelCase__		:		str		     =self.config.sample_size
								lowerCamelCase__		:		Any		     =(
								    self.config.sample_size[0]
								    if isinstance(self.config.sample_size,  (list, tuple)					)
								    else self.config.sample_size
								)
								lowerCamelCase__		:		Optional[int]		     =int(sample_size / (2 ** (len(self.config.block_out_channels					) - 1))					)
								lowerCamelCase__		:		List[Any]		     =0.25



					def 		snake_case	(	self							:  List[Any],  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  Union[str, Any]=False					)-> Optional[Any]:
								if isinstance(lowerCamelCase,  (Encoder, Decoder)					):
											lowerCamelCase__		:		Tuple		     =value



					def 		snake_case	(	self							:  str,  lowerCamelCase							:  bool = True					)-> Tuple:
								lowerCamelCase__		:		str		     =use_tiling



					def 		snake_case	(	self							:  int					)-> Optional[int]:
								self.enable_tiling(lowerCamelCase					)



					def 		snake_case	(	self							:  Optional[Any]					)-> Any:
								lowerCamelCase__		:		str		     =True



					def 		snake_case	(	self							:  Optional[Any]					)-> int:
								lowerCamelCase__		:		int		     =False



					@property
					# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
					def 		snake_case	(	self							:  List[str]					)-> Dict[str, AttentionProcessor]:
								lowerCamelCase__		:		str		     ={}

								def fn_recursive_add_processors(lowerCamelCase							:  str,  lowerCamelCase							:  torch.nn.Module,  lowerCamelCase							:  Dict[str, AttentionProcessor]					):
											if hasattr(lowerCamelCase,  '''set_processor'''					):
														lowerCamelCase__		:		Optional[int]		     =module.processor

											for sub_name, child in module.named_children():
														fn_recursive_add_processors(F'''{name}.{sub_name}''',  lowerCamelCase,  lowerCamelCase					)

											return processors

								for name, module in self.named_children():
											fn_recursive_add_processors(lowerCamelCase,  lowerCamelCase,  lowerCamelCase					)

								return processors



					def 		snake_case	(	self							:  str,  lowerCamelCase							:  Union[AttentionProcessor, Dict[str, AttentionProcessor]]					)-> Any:
								lowerCamelCase__		:		Any		     =len(self.attn_processors.keys()					)

								if isinstance(lowerCamelCase,  lowerCamelCase					) and len(lowerCamelCase					) != count:
											raise ValueError(
											    F'''A dict of processors was passed, but the number of processors {len(lowerCamelCase					)} does not match the'''
											    F''' number of attention layers: {count}. Please make sure to pass {count} processor classes.'''					)

								def fn_recursive_attn_processor(lowerCamelCase							:  str,  lowerCamelCase							:  torch.nn.Module,  lowerCamelCase							:  str					):
											if hasattr(lowerCamelCase,  '''set_processor'''					):
														if not isinstance(lowerCamelCase,  lowerCamelCase					):
																	module.set_processor(lowerCamelCase					)
														else:
																	module.set_processor(processor.pop(F'''{name}.processor'''					)					)

											for sub_name, child in module.named_children():
														fn_recursive_attn_processor(F'''{name}.{sub_name}''',  lowerCamelCase,  lowerCamelCase					)

								for name, module in self.named_children():
											fn_recursive_attn_processor(lowerCamelCase,  lowerCamelCase,  lowerCamelCase					)



					def 		snake_case	(	self							:  Tuple					)-> Union[str, Any]:
								self.set_attn_processor(AttnProcessor()					)



					@apply_forward_hook
					def 		snake_case	(	self							:  Any,  lowerCamelCase							:  torch.FloatTensor,  lowerCamelCase							:  bool = True					)-> AutoencoderKLOutput:
								if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
											return self.tiled_encode(lowerCamelCase,  return_dict=lowerCamelCase					)

								if self.use_slicing and x.shape[0] > 1:
											lowerCamelCase__		:		Dict		     =[self.encoder(lowerCamelCase					) for x_slice in x.split(1					)]
											lowerCamelCase__		:		Tuple		     =torch.cat(lowerCamelCase					)
								else:
											lowerCamelCase__		:		List[Any]		     =self.encoder(lowerCamelCase					)

								lowerCamelCase__		:		Tuple		     =self.quant_conv(lowerCamelCase					)
								lowerCamelCase__		:		List[str]		     =DiagonalGaussianDistribution(lowerCamelCase					)

								if not return_dict:
											return (posterior,)

								return AutoencoderKLOutput(latent_dist=lowerCamelCase					)



					def 		snake_case	(	self							:  Union[str, Any],  lowerCamelCase							:  torch.FloatTensor,  lowerCamelCase							:  bool = True					)-> Union[DecoderOutput, torch.FloatTensor]:
								if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
											return self.tiled_decode(lowerCamelCase,  return_dict=lowerCamelCase					)

								lowerCamelCase__		:		List[str]		     =self.post_quant_conv(lowerCamelCase					)
								lowerCamelCase__		:		Tuple		     =self.decoder(lowerCamelCase					)

								if not return_dict:
											return (dec,)

								return DecoderOutput(sample=lowerCamelCase					)



					@apply_forward_hook
					def 		snake_case	(	self							:  str,  lowerCamelCase							:  torch.FloatTensor,  lowerCamelCase							:  bool = True					)-> Union[DecoderOutput, torch.FloatTensor]:
								if self.use_slicing and z.shape[0] > 1:
											lowerCamelCase__		:		Tuple		     =[self._decode(lowerCamelCase					).sample for z_slice in z.split(1					)]
											lowerCamelCase__		:		int		     =torch.cat(lowerCamelCase					)
								else:
											lowerCamelCase__		:		List[Any]		     =self._decode(lowerCamelCase					).sample

								if not return_dict:
											return (decoded,)

								return DecoderOutput(sample=lowerCamelCase					)



					def 		snake_case	(	self							:  Optional[Any],  lowerCamelCase							:  int,  lowerCamelCase							:  Optional[Any],  lowerCamelCase							:  str					)-> Any:
								lowerCamelCase__		:		Optional[Any]		     =min(a.shape[2],  b.shape[2],  lowerCamelCase					)
								for y in range(lowerCamelCase					):
											lowerCamelCase__		:		Optional[int]		     =a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
								return b



					def 		snake_case	(	self							:  List[str],  lowerCamelCase							:  List[Any],  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  Tuple					)-> Tuple:
								lowerCamelCase__		:		Optional[Any]		     =min(a.shape[3],  b.shape[3],  lowerCamelCase					)
								for x in range(lowerCamelCase					):
											lowerCamelCase__		:		str		     =a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
								return b



					def 		snake_case	(	self							:  Union[str, Any],  lowerCamelCase							:  torch.FloatTensor,  lowerCamelCase							:  bool = True					)-> AutoencoderKLOutput:
								lowerCamelCase__		:		Union[str, Any]		     =int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)					)
								lowerCamelCase__		:		List[str]		     =int(self.tile_latent_min_size * self.tile_overlap_factor					)
								lowerCamelCase__		:		str		     =self.tile_latent_min_size - blend_extent

								# Split the image into 512x512 tiles and encode them separately.
								lowerCamelCase__		:		List[Any]		     =[]
								for i in range(0,  x.shape[2],  lowerCamelCase					):
											lowerCamelCase__		:		Optional[int]		     =[]
											for j in range(0,  x.shape[3],  lowerCamelCase					):
														lowerCamelCase__		:		List[Any]		     =x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
														lowerCamelCase__		:		Optional[Any]		     =self.encoder(lowerCamelCase					)
														lowerCamelCase__		:		Optional[int]		     =self.quant_conv(lowerCamelCase					)
														row.append(lowerCamelCase					)
											rows.append(lowerCamelCase					)
								lowerCamelCase__		:		List[Any]		     =[]
								for i, row in enumerate(lowerCamelCase					):
											lowerCamelCase__		:		Tuple		     =[]
											for j, tile in enumerate(lowerCamelCase					):
														# blend the above tile and the left tile
														# to the current tile and add the current tile to the result row
														if i > 0:
																	lowerCamelCase__		:		Optional[Any]		     =self.blend_v(rows[i - 1][j],  lowerCamelCase,  lowerCamelCase					)
														if j > 0:
																	lowerCamelCase__		:		Dict		     =self.blend_h(row[j - 1],  lowerCamelCase,  lowerCamelCase					)
														result_row.append(tile[:, :, :row_limit, :row_limit]					)
											result_rows.append(torch.cat(lowerCamelCase,  dim=3					)					)

								lowerCamelCase__		:		str		     =torch.cat(lowerCamelCase,  dim=2					)
								lowerCamelCase__		:		Optional[Any]		     =DiagonalGaussianDistribution(lowerCamelCase					)

								if not return_dict:
											return (posterior,)

								return AutoencoderKLOutput(latent_dist=lowerCamelCase					)



					def 		snake_case	(	self							:  Dict,  lowerCamelCase							:  torch.FloatTensor,  lowerCamelCase							:  bool = True					)-> Union[DecoderOutput, torch.FloatTensor]:
								lowerCamelCase__		:		Any		     =int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)					)
								lowerCamelCase__		:		List[str]		     =int(self.tile_sample_min_size * self.tile_overlap_factor					)
								lowerCamelCase__		:		Dict		     =self.tile_sample_min_size - blend_extent

								# Split z into overlapping 64x64 tiles and decode them separately.
								# The tiles have an overlap to avoid seams between tiles.
								lowerCamelCase__		:		int		     =[]
								for i in range(0,  z.shape[2],  lowerCamelCase					):
											lowerCamelCase__		:		List[str]		     =[]
											for j in range(0,  z.shape[3],  lowerCamelCase					):
														lowerCamelCase__		:		Any		     =z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
														lowerCamelCase__		:		Tuple		     =self.post_quant_conv(lowerCamelCase					)
														lowerCamelCase__		:		Optional[int]		     =self.decoder(lowerCamelCase					)
														row.append(lowerCamelCase					)
											rows.append(lowerCamelCase					)
								lowerCamelCase__		:		Tuple		     =[]
								for i, row in enumerate(lowerCamelCase					):
											lowerCamelCase__		:		Any		     =[]
											for j, tile in enumerate(lowerCamelCase					):
														# blend the above tile and the left tile
														# to the current tile and add the current tile to the result row
														if i > 0:
																	lowerCamelCase__		:		Tuple		     =self.blend_v(rows[i - 1][j],  lowerCamelCase,  lowerCamelCase					)
														if j > 0:
																	lowerCamelCase__		:		Union[str, Any]		     =self.blend_h(row[j - 1],  lowerCamelCase,  lowerCamelCase					)
														result_row.append(tile[:, :, :row_limit, :row_limit]					)
											result_rows.append(torch.cat(lowerCamelCase,  dim=3					)					)

								lowerCamelCase__		:		Optional[Any]		     =torch.cat(lowerCamelCase,  dim=2					)
								if not return_dict:
											return (dec,)

								return DecoderOutput(sample=lowerCamelCase					)




					def 		snake_case	(	self							:  Any,  lowerCamelCase							:  torch.FloatTensor,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Optional[torch.Generator] = None,  )-> Union[DecoderOutput, torch.FloatTensor]:
								lowerCamelCase__		:		Union[str, Any]		     =sample
								lowerCamelCase__		:		Union[str, Any]		     =self.encode(lowerCamelCase					).latent_dist
								if sample_posterior:
											lowerCamelCase__		:		str		     =posterior.sample(generator=lowerCamelCase					)
								else:
											lowerCamelCase__		:		List[Any]		     =posterior.mode()
								lowerCamelCase__		:		Optional[int]		     =self.decode(lowerCamelCase					).sample

								if not return_dict:
											return (dec,)

								return DecoderOutput(sample=lowerCamelCase					)









"""simple docstring"""






# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

_lowercase			:			Dict										=						2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
_lowercase			:			str										=						{
    # fairseq:
    "wmt19-ru-en": {"length_penalty": 1.1},
    "wmt19-en-ru": {"length_penalty": 1.15},
    "wmt19-en-de": {"length_penalty": 1.0},
    "wmt19-de-en": {"length_penalty": 1.1},
    # allenai:
    "wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
    "wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
    "wmt16-en-de-12-1": {"length_penalty": 0.8},
    "wmt19-de-en-6-6-base": {"length_penalty": 0.6},
    "wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}

# this remaps the different models to their organization names
_lowercase			:			str										=						{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
		_lowercase			:			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",
]:
		_lowercase			:			List[Any]										=						"allenai"






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






def 					snake_case__       (		__lowerCamelCase    :   Union[str, Any]							,		__lowerCamelCase    :   Dict     ):
			"""simple docstring"""
			# 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

			lowerCamelCase__		:		Union[str, Any]		     =basename(__lowerCamelCase     )
			lowerCamelCase__		:		str		     =dirname(__lowerCamelCase     )

			lowerCamelCase__		:		Dict		     =fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
			lowerCamelCase__		:		Union[str, Any]		     =cls.hub_models()
			lowerCamelCase__		:		Optional[Any]		     ={'''bpe''': '''fastbpe''', '''tokenizer''': '''moses'''}
			lowerCamelCase__		:		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}'''     )
			lowerCamelCase__		:		Optional[int]		     =hub_utils.from_pretrained(
			    __lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		archive_map=__lowerCamelCase							,		**__lowerCamelCase     )

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

			lowerCamelCase__		:		int		     =args['''source_lang''']
			lowerCamelCase__		:		Optional[Any]		     =args['''target_lang''']

			lowerCamelCase__		:		Dict		     =dirname(__lowerCamelCase     )
			lowerCamelCase__		:		str		     =basename(__lowerCamelCase     )

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

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

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

			lowerCamelCase__		:		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
			lowerCamelCase__		:		Optional[int]		     =5
			lowerCamelCase__		:		List[str]		     =False
			if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
						lowerCamelCase__		:		Optional[int]		     =best_score_hparams[model_dir]['''length_penalty''']
			else:
						lowerCamelCase__		:		Union[str, Any]		     =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
			lowerCamelCase__		:		Any		     =os.path.join(__lowerCamelCase							,		__lowerCamelCase     )

			lowerCamelCase__		:		int		     ={
			    '''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
			lowerCamelCase__		:		int		     =chkpt['''models'''][0]
			lowerCamelCase__		:		Union[str, Any]		     =model.state_dict()

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

			# remove unneeded keys
			lowerCamelCase__		:		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(__lowerCamelCase							,		__lowerCamelCase     )

			lowerCamelCase__		:		Dict		     =FSMTConfig.from_pretrained(__lowerCamelCase     )
			lowerCamelCase__		:		int		     =FSMTForConditionalGeneration(__lowerCamelCase     )

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

			# save
			lowerCamelCase__		:		Optional[int]		     =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__":
		_lowercase			:			List[Any]										=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--fsmt_checkpoint_path",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
		        " bpecodes, etc."
		    ),
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		_lowercase			:			str										=						parser.parse_args()
		convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)




