

from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings




@maybe_allow_in_graph
class      UpperCamelCase (     nn.Module   ):

    def __init__(			self :      Optional[int]							,    snake_case__ :      int							,    snake_case__ :      int							,    snake_case__ :      int							,    snake_case__ :      Optional[int]=0.0							,    snake_case__ :      Optional[int] = None							,    snake_case__ :      str = "geglu"							,    snake_case__ :      Optional[int] = None							,    snake_case__ :      bool = False							,    snake_case__ :      bool = False							,    snake_case__ :      bool = False							,    snake_case__ :      bool = False							,    snake_case__ :      bool = True							,    snake_case__ :      str = "layer_norm"							,    snake_case__ :      bool = False							,    ):

     """simple docstring"""






     super().__init__()
     SCREAMING_SNAKE_CASE								=			only_cross_attention

     SCREAMING_SNAKE_CASE								=			(num_embeds_ada_norm is not None) and norm_type == 'ada_norm_zero'
     SCREAMING_SNAKE_CASE								=			(num_embeds_ada_norm is not None) and norm_type == 'ada_norm'

     if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
      raise ValueError(
          F"""`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"""
          F""" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."""    )

     # Define 3 blocks. Each block has its own normalization layer.
     # 1. Self-Attn
     if self.use_ada_layer_norm:
      SCREAMING_SNAKE_CASE								=			AdaLayerNorm(snake_case__							,    snake_case__    )
     elif self.use_ada_layer_norm_zero:
      SCREAMING_SNAKE_CASE								=			AdaLayerNormZero(snake_case__							,    snake_case__    )
     else:
      SCREAMING_SNAKE_CASE								=			nn.LayerNorm(snake_case__							,    elementwise_affine=snake_case__    )
     SCREAMING_SNAKE_CASE								=			Attention(
         query_dim=snake_case__							,    heads=snake_case__							,    dim_head=snake_case__							,    dropout=snake_case__							,    bias=snake_case__							,    cross_attention_dim=cross_attention_dim if only_cross_attention else None							,    upcast_attention=snake_case__							,    )

     # 2. Cross-Attn
     if cross_attention_dim is not None or double_self_attention:
      # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
      # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
      # the second cross attention block.
      SCREAMING_SNAKE_CASE								=			(
          AdaLayerNorm(snake_case__							,    snake_case__    )
          if self.use_ada_layer_norm
          else nn.LayerNorm(snake_case__							,    elementwise_affine=snake_case__    )
      )
      SCREAMING_SNAKE_CASE								=			Attention(
          query_dim=snake_case__							,    cross_attention_dim=cross_attention_dim if not double_self_attention else None							,    heads=snake_case__							,    dim_head=snake_case__							,    dropout=snake_case__							,    bias=snake_case__							,    upcast_attention=snake_case__							,    )  # is self-attn if encoder_hidden_states is none
     else:
      SCREAMING_SNAKE_CASE								=			None
      SCREAMING_SNAKE_CASE								=			None

     # 3. Feed-forward
     SCREAMING_SNAKE_CASE								=			nn.LayerNorm(snake_case__							,    elementwise_affine=snake_case__    )
     SCREAMING_SNAKE_CASE								=			FeedForward(snake_case__							,    dropout=snake_case__							,    activation_fn=snake_case__							,    final_dropout=snake_case__    )

     # let chunk size default to None
     SCREAMING_SNAKE_CASE								=			None
     SCREAMING_SNAKE_CASE								=			0

    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      Optional[int]							,    snake_case__ :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			chunk_size
     SCREAMING_SNAKE_CASE								=			dim




    def UpperCamelCase			(			self :      Tuple							,    snake_case__ :      torch.FloatTensor							,    snake_case__ :      Optional[torch.FloatTensor] = None							,    snake_case__ :      Optional[torch.FloatTensor] = None							,    snake_case__ :      Optional[torch.FloatTensor] = None							,    snake_case__ :      Optional[torch.LongTensor] = None							,    snake_case__ :      Dict[str, Any] = None							,    snake_case__ :      Optional[torch.LongTensor] = None							,    ):

     """simple docstring"""






     if self.use_ada_layer_norm:
      SCREAMING_SNAKE_CASE								=			self.norma(snake_case__							,    snake_case__    )
     elif self.use_ada_layer_norm_zero:
      SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.norma(
          snake_case__							,    snake_case__							,    snake_case__							,    hidden_dtype=hidden_states.dtype    )
     else:
      SCREAMING_SNAKE_CASE								=			self.norma(snake_case__    )

     SCREAMING_SNAKE_CASE								=			cross_attention_kwargs if cross_attention_kwargs is not None else {}

     SCREAMING_SNAKE_CASE								=			self.attna(
         snake_case__							,    encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None							,    attention_mask=snake_case__							,    **snake_case__							,    )
     if self.use_ada_layer_norm_zero:
      SCREAMING_SNAKE_CASE								=			gate_msa.unsqueeze(1    ) * attn_output
     SCREAMING_SNAKE_CASE								=			attn_output + hidden_states

     # 2. Cross-Attention
     if self.attna is not None:
      SCREAMING_SNAKE_CASE								=			(
          self.norma(snake_case__							,    snake_case__    ) if self.use_ada_layer_norm else self.norma(snake_case__    )
      )

      SCREAMING_SNAKE_CASE								=			self.attna(
          snake_case__							,    encoder_hidden_states=snake_case__							,    attention_mask=snake_case__							,    **snake_case__							,    )
      SCREAMING_SNAKE_CASE								=			attn_output + hidden_states

     # 3. Feed-forward
     SCREAMING_SNAKE_CASE								=			self.norma(snake_case__    )

     if self.use_ada_layer_norm_zero:
      SCREAMING_SNAKE_CASE								=			norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

     if self._chunk_size is not None:
      # "feed_forward_chunk_size" can be used to save memory
      if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
       raise ValueError(
           F"""`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."""    )

      SCREAMING_SNAKE_CASE								=			norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
      SCREAMING_SNAKE_CASE								=			torch.cat(
          [self.ff(snake_case__    ) for hid_slice in norm_hidden_states.chunk(snake_case__							,    dim=self._chunk_dim    )]							,    dim=self._chunk_dim							,    )
     else:
      SCREAMING_SNAKE_CASE								=			self.ff(snake_case__    )

     if self.use_ada_layer_norm_zero:
      SCREAMING_SNAKE_CASE								=			gate_mlp.unsqueeze(1    ) * ff_output

     SCREAMING_SNAKE_CASE								=			ff_output + hidden_states

     return hidden_states




class      UpperCamelCase (     nn.Module   ):

    def __init__(			self :      Union[str, Any]							,    snake_case__ :      int							,    snake_case__ :      Optional[int] = None							,    snake_case__ :      int = 4							,    snake_case__ :      float = 0.0							,    snake_case__ :      str = "geglu"							,    snake_case__ :      bool = False							,    ):

     """simple docstring"""






     super().__init__()
     SCREAMING_SNAKE_CASE								=			int(dim * mult    )
     SCREAMING_SNAKE_CASE								=			dim_out if dim_out is not None else dim

     if activation_fn == "gelu":
      SCREAMING_SNAKE_CASE								=			GELU(snake_case__							,    snake_case__    )
     if activation_fn == "gelu-approximate":
      SCREAMING_SNAKE_CASE								=			GELU(snake_case__							,    snake_case__							,    approximate='tanh'    )
     elif activation_fn == "geglu":
      SCREAMING_SNAKE_CASE								=			GEGLU(snake_case__							,    snake_case__    )
     elif activation_fn == "geglu-approximate":
      SCREAMING_SNAKE_CASE								=			ApproximateGELU(snake_case__							,    snake_case__    )

     SCREAMING_SNAKE_CASE								=			nn.ModuleList([]    )
     # project in
     self.net.append(snake_case__    )
     # project dropout
     self.net.append(nn.Dropout(snake_case__    )    )
     # project out
     self.net.append(nn.Linear(snake_case__							,    snake_case__    )    )
     # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
     if final_dropout:
      self.net.append(nn.Dropout(snake_case__    )    )




    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      int    ):

     """simple docstring"""






     for module in self.net:
      SCREAMING_SNAKE_CASE								=			module(snake_case__    )
     return hidden_states




class      UpperCamelCase (     nn.Module   ):

    def __init__(			self :      Dict							,    snake_case__ :      int							,    snake_case__ :      int							,    snake_case__ :      str = "none"    ):

     """simple docstring"""






     super().__init__()
     SCREAMING_SNAKE_CASE								=			nn.Linear(snake_case__							,    snake_case__    )
     SCREAMING_SNAKE_CASE								=			approximate

    def UpperCamelCase			(			self :      str							,    snake_case__ :      Any    ):

     """simple docstring"""






     if gate.device.type != "mps":
      return F.gelu(snake_case__							,    approximate=self.approximate    )
     # mps: gelu is not implemented for float16
     return F.gelu(gate.to(dtype=torch.floataa    )							,    approximate=self.approximate    ).to(dtype=gate.dtype    )




    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.proj(snake_case__    )
     SCREAMING_SNAKE_CASE								=			self.gelu(snake_case__    )
     return hidden_states




class      UpperCamelCase (     nn.Module   ):

    def __init__(			self :      Optional[int]							,    snake_case__ :      int							,    snake_case__ :      int    ):

     """simple docstring"""






     super().__init__()
     SCREAMING_SNAKE_CASE								=			nn.Linear(snake_case__							,    dim_out * 2    )

    def UpperCamelCase			(			self :      int							,    snake_case__ :      List[str]    ):

     """simple docstring"""






     if gate.device.type != "mps":
      return F.gelu(snake_case__    )
     # mps: gelu is not implemented for float16
     return F.gelu(gate.to(dtype=torch.floataa    )    ).to(dtype=gate.dtype    )




    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self.proj(snake_case__    ).chunk(2							,    dim=-1    )
     return hidden_states * self.gelu(snake_case__    )




class      UpperCamelCase (     nn.Module   ):

    def __init__(			self :      List[str]							,    snake_case__ :      int							,    snake_case__ :      int    ):

     """simple docstring"""






     super().__init__()
     SCREAMING_SNAKE_CASE								=			nn.Linear(snake_case__							,    snake_case__    )




    def UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.proj(snake_case__    )
     return x * torch.sigmoid(1.702 * x    )




class      UpperCamelCase (     nn.Module   ):

    def __init__(			self :      Any							,    snake_case__ :      Optional[Any]							,    snake_case__ :      Any    ):

     """simple docstring"""






     super().__init__()
     SCREAMING_SNAKE_CASE								=			nn.Embedding(snake_case__							,    snake_case__    )
     SCREAMING_SNAKE_CASE								=			nn.SiLU()
     SCREAMING_SNAKE_CASE								=			nn.Linear(snake_case__							,    embedding_dim * 2    )
     SCREAMING_SNAKE_CASE								=			nn.LayerNorm(snake_case__							,    elementwise_affine=snake_case__    )




    def UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      Dict							,    snake_case__ :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.linear(self.silu(self.emb(snake_case__    )    )    )
     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			torch.chunk(snake_case__							,    2    )
     SCREAMING_SNAKE_CASE								=			self.norm(snake_case__    ) * (1 + scale) + shift
     return x




class      UpperCamelCase (     nn.Module   ):

    def __init__(			self :      Optional[int]							,    snake_case__ :      int							,    snake_case__ :      Optional[int]    ):

     """simple docstring"""






     super().__init__()

     SCREAMING_SNAKE_CASE								=			CombinedTimestepLabelEmbeddings(snake_case__							,    snake_case__    )

     SCREAMING_SNAKE_CASE								=			nn.SiLU()
     SCREAMING_SNAKE_CASE								=			nn.Linear(snake_case__							,    6 * embedding_dim							,    bias=snake_case__    )
     SCREAMING_SNAKE_CASE								=			nn.LayerNorm(snake_case__							,    elementwise_affine=snake_case__							,    eps=1E-6    )




    def UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      Tuple							,    snake_case__ :      int							,    snake_case__ :      Dict							,    snake_case__ :      Any=None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.linear(self.silu(self.emb(snake_case__							,    snake_case__							,    hidden_dtype=snake_case__    )    )    )
     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			emb.chunk(6							,    dim=1    )
     SCREAMING_SNAKE_CASE								=			self.norm(snake_case__    ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
     return x, gate_msa, shift_mlp, scale_mlp, gate_mlp




class      UpperCamelCase (     nn.Module   ):

    def __init__(			self :      Dict							,    snake_case__ :      int							,    snake_case__ :      int							,    snake_case__ :      int							,    snake_case__ :      Optional[str] = None							,    snake_case__ :      float = 1E-5    ):

     """simple docstring"""






     super().__init__()
     SCREAMING_SNAKE_CASE								=			num_groups
     SCREAMING_SNAKE_CASE								=			eps

     if act_fn is None:
      SCREAMING_SNAKE_CASE								=			None
     else:
      SCREAMING_SNAKE_CASE								=			get_activation(snake_case__    )

     SCREAMING_SNAKE_CASE								=			nn.Linear(snake_case__							,    out_dim * 2    )




    def UpperCamelCase			(			self :      Dict							,    snake_case__ :      int							,    snake_case__ :      List[str]    ):

     """simple docstring"""






     if self.act:
      SCREAMING_SNAKE_CASE								=			self.act(snake_case__    )
     SCREAMING_SNAKE_CASE								=			self.linear(snake_case__    )
     SCREAMING_SNAKE_CASE								=			emb[:, :, None, None]
     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			emb.chunk(2							,    dim=1    )

     SCREAMING_SNAKE_CASE								=			F.group_norm(snake_case__							,    self.num_groups							,    eps=self.eps    )
     SCREAMING_SNAKE_CASE								=			x * (1 + scale) + shift
     return x




# 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_    :						str    		=      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_    :						int    		=      {
    # 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_    :						str    		=      {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
      a_    :						List[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_    :						Any    		=      "allenai"


def        __lowerCAmelCase    (       _UpperCamelCase		:  List[Any]	)		->							Any:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			dict((re.sub(R'@@$'		,							''		,							_UpperCamelCase	), v) if k.endswith('@@'	) else (re.sub(R'$'		,							'</w>'		,							_UpperCamelCase	), 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    (       _UpperCamelCase		:  Dict		,							_UpperCamelCase		:  List[str]	)		->							Dict:
 '''simple docstring'''


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

 # handle various types of models

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

 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(
     _UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							archive_map=_UpperCamelCase		,							**_UpperCamelCase	)

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

 SCREAMING_SNAKE_CASE								=			args['source_lang']
 SCREAMING_SNAKE_CASE								=			args['target_lang']

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

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

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

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

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

 # model config
 SCREAMING_SNAKE_CASE								=			os.path.join(_UpperCamelCase		,							'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(_UpperCamelCase		,							'w'		,							encoding='utf-8'	) as f:
  f.write(json.dumps(_UpperCamelCase		,							ensure_ascii=_UpperCamelCase		,							indent=_UpperCamelCase	)	)

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

 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(_UpperCamelCase		,							'w'		,							encoding='utf-8'	) as f:
  f.write(json.dumps(_UpperCamelCase		,							ensure_ascii=_UpperCamelCase		,							indent=_UpperCamelCase	)	)

 # 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(_UpperCamelCase		,							_UpperCamelCase	)

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

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

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

 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_    :						List[str]    		=      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_    :						int    		=      parser.parse_args()
      convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)

