

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
a_    :						Any 		=      logging.get_logger(__name__)


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


 SCREAMING_SNAKE_CASE								=			DPTConfig()

 if "large" in checkpoint_url:
  SCREAMING_SNAKE_CASE								=			10_24
  SCREAMING_SNAKE_CASE								=			40_96
  SCREAMING_SNAKE_CASE								=			24
  SCREAMING_SNAKE_CASE								=			16
  SCREAMING_SNAKE_CASE								=			[5, 11, 17, 23]
  SCREAMING_SNAKE_CASE								=			[2_56, 5_12, 10_24, 10_24]
  SCREAMING_SNAKE_CASE								=			(1, 3_84, 3_84)

 if "ade" in checkpoint_url:
  SCREAMING_SNAKE_CASE								=			True

  SCREAMING_SNAKE_CASE								=			1_50
  SCREAMING_SNAKE_CASE								=			'huggingface/label-files'
  SCREAMING_SNAKE_CASE								=			'ade20k-id2label.json'
  SCREAMING_SNAKE_CASE								=			json.load(open(cached_download(hf_hub_url(_UpperCamelCase		,							_UpperCamelCase		,							repo_type='dataset'	)	)		,							'r'	)	)
  SCREAMING_SNAKE_CASE								=			{int(_UpperCamelCase	): v for k, v in idalabel.items()}
  SCREAMING_SNAKE_CASE								=			idalabel
  SCREAMING_SNAKE_CASE								=			{v: k for k, v in idalabel.items()}
  SCREAMING_SNAKE_CASE								=			[1, 1_50, 4_80, 4_80]

 return config, expected_shape


def        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[Any]	)		->							Union[str, Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			['pretrained.model.head.weight', 'pretrained.model.head.bias']
 for k in ignore_keys:
  state_dict.pop(_UpperCamelCase		,							_UpperCamelCase	)


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


 if (
     "pretrained.model" in name
     and "cls_token" not in name
     and "pos_embed" not in name
     and "patch_embed" not in name
 ):
  SCREAMING_SNAKE_CASE								=			name.replace('pretrained.model'		,							'dpt.encoder'	)
 if "pretrained.model" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('pretrained.model'		,							'dpt.embeddings'	)
 if "patch_embed" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('patch_embed'		,							'patch_embeddings'	)
 if "pos_embed" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('pos_embed'		,							'position_embeddings'	)
 if "attn.proj" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('attn.proj'		,							'attention.output.dense'	)
 if "proj" in name and "project" not in name:
  SCREAMING_SNAKE_CASE								=			name.replace('proj'		,							'projection'	)
 if "blocks" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('blocks'		,							'layer'	)
 if "mlp.fc1" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('mlp.fc1'		,							'intermediate.dense'	)
 if "mlp.fc2" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('mlp.fc2'		,							'output.dense'	)
 if "norm1" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('norm1'		,							'layernorm_before'	)
 if "norm2" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('norm2'		,							'layernorm_after'	)
 if "scratch.output_conv" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('scratch.output_conv'		,							'head'	)
 if "scratch" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('scratch'		,							'neck'	)
 if "layer1_rn" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('layer1_rn'		,							'convs.0'	)
 if "layer2_rn" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('layer2_rn'		,							'convs.1'	)
 if "layer3_rn" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('layer3_rn'		,							'convs.2'	)
 if "layer4_rn" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('layer4_rn'		,							'convs.3'	)
 if "refinenet" in name:
  SCREAMING_SNAKE_CASE								=			int(name[len('neck.refinenet'	) : len('neck.refinenet'	) + 1]	)
  # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
  SCREAMING_SNAKE_CASE								=			name.replace(f"""refinenet{layer_idx}"""		,							f"""fusion_stage.layers.{abs(layer_idx-4	)}"""	)
 if "out_conv" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('out_conv'		,							'projection'	)
 if "resConfUnit1" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('resConfUnit1'		,							'residual_layer1'	)
 if "resConfUnit2" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('resConfUnit2'		,							'residual_layer2'	)
 if "conv1" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('conv1'		,							'convolution1'	)
 if "conv2" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('conv2'		,							'convolution2'	)
 # readout blocks
 if "pretrained.act_postprocess1.0.project.0" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('pretrained.act_postprocess1.0.project.0'		,							'neck.reassemble_stage.readout_projects.0.0'	)
 if "pretrained.act_postprocess2.0.project.0" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('pretrained.act_postprocess2.0.project.0'		,							'neck.reassemble_stage.readout_projects.1.0'	)
 if "pretrained.act_postprocess3.0.project.0" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('pretrained.act_postprocess3.0.project.0'		,							'neck.reassemble_stage.readout_projects.2.0'	)
 if "pretrained.act_postprocess4.0.project.0" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('pretrained.act_postprocess4.0.project.0'		,							'neck.reassemble_stage.readout_projects.3.0'	)
 # resize blocks
 if "pretrained.act_postprocess1.3" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('pretrained.act_postprocess1.3'		,							'neck.reassemble_stage.layers.0.projection'	)
 if "pretrained.act_postprocess1.4" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('pretrained.act_postprocess1.4'		,							'neck.reassemble_stage.layers.0.resize'	)
 if "pretrained.act_postprocess2.3" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('pretrained.act_postprocess2.3'		,							'neck.reassemble_stage.layers.1.projection'	)
 if "pretrained.act_postprocess2.4" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('pretrained.act_postprocess2.4'		,							'neck.reassemble_stage.layers.1.resize'	)
 if "pretrained.act_postprocess3.3" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('pretrained.act_postprocess3.3'		,							'neck.reassemble_stage.layers.2.projection'	)
 if "pretrained.act_postprocess4.3" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('pretrained.act_postprocess4.3'		,							'neck.reassemble_stage.layers.3.projection'	)
 if "pretrained.act_postprocess4.4" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('pretrained.act_postprocess4.4'		,							'neck.reassemble_stage.layers.3.resize'	)
 if "pretrained" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('pretrained'		,							'dpt'	)
 if "bn" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('bn'		,							'batch_norm'	)
 if "head" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('head'		,							'head.head'	)
 if "encoder.norm" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('encoder.norm'		,							'layernorm'	)
 if "auxlayer" in name:
  SCREAMING_SNAKE_CASE								=			name.replace('auxlayer'		,							'auxiliary_head.head'	)

 return name


def        __lowerCAmelCase    (       _UpperCamelCase		:  int		,							_UpperCamelCase		:  Optional[Any]	)		->							Any:
 '''simple docstring'''


 for i in range(config.num_hidden_layers	):
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  SCREAMING_SNAKE_CASE								=			state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight"""	)
  SCREAMING_SNAKE_CASE								=			state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.bias"""	)
  # next, add query, keys and values (in that order) to the state dict
  SCREAMING_SNAKE_CASE								=			in_proj_weight[: config.hidden_size, :]
  SCREAMING_SNAKE_CASE								=			in_proj_bias[: config.hidden_size]
  SCREAMING_SNAKE_CASE								=			in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  SCREAMING_SNAKE_CASE								=			in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  SCREAMING_SNAKE_CASE								=			in_proj_weight[
      -config.hidden_size :, :
  ]
  SCREAMING_SNAKE_CASE								=			in_proj_bias[-config.hidden_size :]


def        __lowerCAmelCase    (       )		->							Tuple:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			'http://images.cocodataset.org/val2017/000000039769.jpg'
 SCREAMING_SNAKE_CASE								=			Image.open(requests.get(_UpperCamelCase		,							stream=_UpperCamelCase	).raw	)
 return im


@torch.no_grad()
def        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[Any]		,							_UpperCamelCase		:  Dict		,							_UpperCamelCase		:  Dict		,							_UpperCamelCase		:  int	)		->							Any:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			get_dpt_config(_UpperCamelCase	)
 # load original state_dict from URL
 SCREAMING_SNAKE_CASE								=			torch.hub.load_state_dict_from_url(_UpperCamelCase		,							map_location='cpu'	)
 # remove certain keys
 remove_ignore_keys_(_UpperCamelCase	)
 # rename keys
 for key in state_dict.copy().keys():
  SCREAMING_SNAKE_CASE								=			state_dict.pop(_UpperCamelCase	)
  SCREAMING_SNAKE_CASE								=			val
 # read in qkv matrices
 read_in_q_k_v(_UpperCamelCase		,							_UpperCamelCase	)

 # load HuggingFace model
 SCREAMING_SNAKE_CASE								=			DPTForSemanticSegmentation(_UpperCamelCase	) if 'ade' in checkpoint_url else DPTForDepthEstimation(_UpperCamelCase	)
 model.load_state_dict(_UpperCamelCase	)
 model.eval()

 # Check outputs on an image
 SCREAMING_SNAKE_CASE								=			4_80 if 'ade' in checkpoint_url else 3_84
 SCREAMING_SNAKE_CASE								=			DPTImageProcessor(size=_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			prepare_img()
 SCREAMING_SNAKE_CASE								=			image_processor(_UpperCamelCase		,							return_tensors='pt'	)

 # forward pass
 SCREAMING_SNAKE_CASE								=			model(**_UpperCamelCase	).logits if 'ade' in checkpoint_url else model(**_UpperCamelCase	).predicted_depth

 # Assert logits
 SCREAMING_SNAKE_CASE								=			torch.tensor([[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]]	)
 if "ade" in checkpoint_url:
  SCREAMING_SNAKE_CASE								=			torch.tensor([[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]]	)
 assert outputs.shape == torch.Size(_UpperCamelCase	)
 assert (
     torch.allclose(outputs[0, 0, :3, :3]		,							_UpperCamelCase		,							atol=1e-4	)
     if "ade" in checkpoint_url
     else torch.allclose(outputs[0, :3, :3]		,							_UpperCamelCase	)
 )

 Path(_UpperCamelCase	).mkdir(exist_ok=_UpperCamelCase	)
 print(f"""Saving model to {pytorch_dump_folder_path}"""	)
 model.save_pretrained(_UpperCamelCase	)
 print(f"""Saving image processor to {pytorch_dump_folder_path}"""	)
 image_processor.save_pretrained(_UpperCamelCase	)

 if push_to_hub:
  print('Pushing model to hub...'	)
  model.push_to_hub(
      repo_path_or_name=Path(_UpperCamelCase		,							_UpperCamelCase	)		,							organization='nielsr'		,							commit_message='Add model'		,							use_temp_dir=_UpperCamelCase		,							)
  image_processor.push_to_hub(
      repo_path_or_name=Path(_UpperCamelCase		,							_UpperCamelCase	)		,							organization='nielsr'		,							commit_message='Add image processor'		,							use_temp_dir=_UpperCamelCase		,							)


if __name__ == "__main__":
      a_    :						Dict 		=      argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--checkpoint_url",
          default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
          type=str,
          help="URL of the original DPT checkpoint you'd like to convert.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path",
          default=None,
          type=str,
          required=True,
          help="Path to the output PyTorch model directory.",
      )
      parser.add_argument(
          "--push_to_hub",
          action="store_true",
      )
      parser.add_argument(
          "--model_name",
          default="dpt-large",
          type=str,
          help="Name of the model, in case you're pushing to the hub.",
      )

      a_    :						Any 		=      parser.parse_args()
      convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)




# 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)

