

import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[Any]  ,      lowerCAmelCase:  Optional[Any] )   ->       Any:
    _UpperCAmelCase       :		Optional[Any] 		=    []
    for i in range(encoder_config.num_hidden_layers ):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append(
            (F'encoder.deit.blocks.{i}.norm1.weight', F'encoder.encoder.layer.{i}.layernorm_before.weight') )
        rename_keys.append((F'encoder.deit.blocks.{i}.norm1.bias', F'encoder.encoder.layer.{i}.layernorm_before.bias') )
        rename_keys.append(
            (F'encoder.deit.blocks.{i}.attn.proj.weight', F'encoder.encoder.layer.{i}.attention.output.dense.weight') )
        rename_keys.append(
            (F'encoder.deit.blocks.{i}.attn.proj.bias', F'encoder.encoder.layer.{i}.attention.output.dense.bias') )
        rename_keys.append(
            (F'encoder.deit.blocks.{i}.norm2.weight', F'encoder.encoder.layer.{i}.layernorm_after.weight') )
        rename_keys.append((F'encoder.deit.blocks.{i}.norm2.bias', F'encoder.encoder.layer.{i}.layernorm_after.bias') )
        rename_keys.append(
            (F'encoder.deit.blocks.{i}.mlp.fc1.weight', F'encoder.encoder.layer.{i}.intermediate.dense.weight') )
        rename_keys.append(
            (F'encoder.deit.blocks.{i}.mlp.fc1.bias', F'encoder.encoder.layer.{i}.intermediate.dense.bias') )
        rename_keys.append(
            (F'encoder.deit.blocks.{i}.mlp.fc2.weight', F'encoder.encoder.layer.{i}.output.dense.weight') )
        rename_keys.append((F'encoder.deit.blocks.{i}.mlp.fc2.bias', F'encoder.encoder.layer.{i}.output.dense.bias') )

    # cls token, position embeddings and patch embeddings of encoder
    rename_keys.extend(
        [
            ("encoder.deit.cls_token", "encoder.embeddings.cls_token"),
            ("encoder.deit.pos_embed", "encoder.embeddings.position_embeddings"),
            ("encoder.deit.patch_embed.proj.weight", "encoder.embeddings.patch_embeddings.projection.weight"),
            ("encoder.deit.patch_embed.proj.bias", "encoder.embeddings.patch_embeddings.projection.bias"),
            ("encoder.deit.norm.weight", "encoder.layernorm.weight"),
            ("encoder.deit.norm.bias", "encoder.layernorm.bias"),
        ] )

    return rename_keys

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[Any]  ,      lowerCAmelCase:  Dict )   ->       List[Any]:
    for i in range(encoder_config.num_hidden_layers ):
        # queries, keys and values (only weights, no biases)
        _UpperCAmelCase       :		List[str] 		=    state_dict.pop(F'encoder.deit.blocks.{i}.attn.qkv.weight' )

        _UpperCAmelCase       :		str 		=    in_proj_weight[
            : encoder_config.hidden_size, :
        ]
        _UpperCAmelCase       :		Any 		=    in_proj_weight[
            encoder_config.hidden_size : encoder_config.hidden_size * 2, :
        ]
        _UpperCAmelCase       :		str 		=    in_proj_weight[
            -encoder_config.hidden_size :, :
        ]

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  List[str]  ,      lowerCAmelCase:  Optional[int] )   ->       Any:
    _UpperCAmelCase       :		int 		=    dct.pop(lowerCAmelCase )
    _UpperCAmelCase       :		str 		=    val

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Union[str, Any] )   ->       Union[str, Any]:
    if "handwritten" in checkpoint_url:
        _UpperCAmelCase       :		List[Any] 		=    "https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg"  # industry
        # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
        # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
        # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
        # url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
    elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
        _UpperCAmelCase       :		Optional[int] 		=    "https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"
    _UpperCAmelCase       :		Tuple 		=    Image.open(requests.get(lowerCAmelCase  ,      stream=lowerCAmelCase ).raw ).convert("RGB" )
    return im

@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[str]  ,      lowerCAmelCase:  Union[str, Any] )   ->       Tuple:
    _UpperCAmelCase       :		Dict 		=    ViTConfig(image_size=384  ,      qkv_bias=lowerCAmelCase )
    _UpperCAmelCase       :		str 		=    TrOCRConfig()

    # size of the architecture
    if "base" in checkpoint_url:
        _UpperCAmelCase       :		str 		=    768
    elif "large" in checkpoint_url:
        # use ViT-large encoder
        _UpperCAmelCase       :		Optional[Any] 		=    1024
        _UpperCAmelCase       :		Union[str, Any] 		=    4096
        _UpperCAmelCase       :		Optional[int] 		=    24
        _UpperCAmelCase       :		str 		=    16
        _UpperCAmelCase       :		str 		=    1024
    else:
        raise ValueError("Should either find 'base' or 'large' in checkpoint URL" )

    # the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
    if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
        _UpperCAmelCase       :		Dict 		=    False
        _UpperCAmelCase       :		Union[str, Any] 		=    "relu"
        _UpperCAmelCase       :		str 		=    1024
        _UpperCAmelCase       :		str 		=    True
        _UpperCAmelCase       :		Any 		=    False
        _UpperCAmelCase       :		List[str] 		=    False

    # load HuggingFace model
    _UpperCAmelCase       :		Optional[int] 		=    ViTModel(lowerCAmelCase  ,      add_pooling_layer=lowerCAmelCase )
    _UpperCAmelCase       :		List[Any] 		=    TrOCRForCausalLM(lowerCAmelCase )
    _UpperCAmelCase       :		str 		=    VisionEncoderDecoderModel(encoder=lowerCAmelCase  ,      decoder=lowerCAmelCase )
    model.eval()

    # load state_dict of original model, rename some keys
    _UpperCAmelCase       :		Optional[int] 		=    torch.hub.load_state_dict_from_url(lowerCAmelCase  ,      map_location="cpu"  ,      check_hash=lowerCAmelCase )["model"]

    _UpperCAmelCase       :		List[str] 		=    create_rename_keys(lowerCAmelCase  ,      lowerCAmelCase )
    for src, dest in rename_keys:
        rename_key(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )
    read_in_q_k_v(lowerCAmelCase  ,      lowerCAmelCase )

    # remove parameters we don't need
    del state_dict["encoder.deit.head.weight"]
    del state_dict["encoder.deit.head.bias"]
    del state_dict["decoder.version"]

    # add prefix to decoder keys
    for key, val in state_dict.copy().items():
        _UpperCAmelCase       :		List[Any] 		=    state_dict.pop(lowerCAmelCase )
        if key.startswith("decoder" ) and "output_projection" not in key:
            _UpperCAmelCase       :		int 		=    val
        else:
            _UpperCAmelCase       :		int 		=    val

    # load state dict
    model.load_state_dict(lowerCAmelCase )

    # Check outputs on an image
    _UpperCAmelCase       :		Any 		=    ViTImageProcessor(size=encoder_config.image_size )
    _UpperCAmelCase       :		Union[str, Any] 		=    RobertaTokenizer.from_pretrained("roberta-large" )
    _UpperCAmelCase       :		List[str] 		=    TrOCRProcessor(lowerCAmelCase  ,      lowerCAmelCase )

    _UpperCAmelCase       :		int 		=    processor(images=prepare_img(lowerCAmelCase )  ,      return_tensors="pt" ).pixel_values

    # verify logits
    _UpperCAmelCase       :		Any 		=    torch.tensor([[model.config.decoder.decoder_start_token_id]] )
    _UpperCAmelCase       :		Tuple 		=    model(pixel_values=lowerCAmelCase  ,      decoder_input_ids=lowerCAmelCase )
    _UpperCAmelCase       :		str 		=    outputs.logits

    _UpperCAmelCase       :		str 		=    torch.Size([1, 1, 5_0265] )
    if "trocr-base-handwritten" in checkpoint_url:
        _UpperCAmelCase       :		Optional[int] 		=    torch.tensor(
            [-1.4_502, -4.6_683, -0.5_347, -2.9_291, 9.1_435, -3.0_571, 8.9_764, 1.7_560, 8.7_358, -1.5_311] )
    elif "trocr-large-handwritten" in checkpoint_url:
        _UpperCAmelCase       :		Optional[int] 		=    torch.tensor(
            [-2.6_437, -1.3_129, -2.2_596, -5.3_455, 6.3_539, 1.7_604, 5.4_991, 1.4_702, 5.6_113, 2.0_170] )
    elif "trocr-base-printed" in checkpoint_url:
        _UpperCAmelCase       :		Tuple 		=    torch.tensor(
            [-5.6_816, -5.8_388, 1.1_398, -6.9_034, 6.8_505, -2.4_393, 1.2_284, -1.0_232, -1.9_661, -3.9_210] )
    elif "trocr-large-printed" in checkpoint_url:
        _UpperCAmelCase       :		str 		=    torch.tensor(
            [-6.0_162, -7.0_959, 4.4_155, -5.1_063, 7.0_468, -3.1_631, 2.6_466, -0.3_081, -0.8_106, -1.7_535] )

    if "stage1" not in checkpoint_url:
        assert logits.shape == expected_shape, "Shape of logits not as expected"
        assert torch.allclose(logits[0, 0, :10]  ,      lowerCAmelCase  ,      atol=1E-3 ), "First elements of logits not as expected"

    Path(lowerCAmelCase ).mkdir(exist_ok=lowerCAmelCase )
    print(F'Saving model to {pytorch_dump_folder_path}' )
    model.save_pretrained(lowerCAmelCase )
    print(F'Saving processor to {pytorch_dump_folder_path}' )
    processor.save_pretrained(lowerCAmelCase )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_        =      argparse.ArgumentParser()

     parser.add_argument(
         '--checkpoint_url',
         default='https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt',
         type=str,
         help='URL to the original PyTorch checkpoint (.pth file).',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
     )
     SCREAMING_SNAKE_CASE_        =      parser.parse_args()
     convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





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

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

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

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


logging.set_verbosity_warning()

SCREAMING_SNAKE_CASE_        =      2

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

# this remaps the different models to their organization names
SCREAMING_SNAKE_CASE_        =      {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
					SCREAMING_SNAKE_CASE_        =      'facebook'
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
					SCREAMING_SNAKE_CASE_        =      'allenai'

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Dict )   ->       List[Any]:
				# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
				# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
				_UpperCAmelCase       :		Optional[int] 		=    dict((re.sub(R"@@$"  ,      ""  ,      lowerCAmelCase ), v) if k.endswith("@@" ) else (re.sub(R"$"  ,      "</w>"  ,      lowerCAmelCase ), v) for k, v in d.items() )
				_UpperCAmelCase       :		Optional[int] 		=    "<s> <pad> </s> <unk>".split()
				# restore the special tokens
				for k in keep_keys:
								del da[F'{k}</w>']
								_UpperCAmelCase       :		Any 		=    d[k]  # restore
				return da

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Dict  ,      lowerCAmelCase:  Union[str, Any] )   ->       int:
				# prep
				assert os.path.exists(lowerCAmelCase )
				os.makedirs(lowerCAmelCase  ,      exist_ok=lowerCAmelCase )
				print(F'Writing results to {pytorch_dump_folder_path}' )

				# handle various types of models

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

				_UpperCAmelCase       :		Any 		=    fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
				_UpperCAmelCase       :		Optional[int] 		=    cls.hub_models()
				_UpperCAmelCase       :		Dict 		=    {"bpe": "fastbpe", "tokenizer": "moses"}
				_UpperCAmelCase       :		Tuple 		=    "."
				# note: since the model dump is old, fairseq has upgraded its model some
				# time later, and it does a whole lot of rewrites and splits on the saved
				# weights, therefore we can't use torch.load() directly on the model file.
				# see: upgrade_state_dict(state_dict) in fairseq_model.py
				print(F'using checkpoint {checkpoint_file}' )
				_UpperCAmelCase       :		int 		=    hub_utils.from_pretrained(
				    lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase  ,      archive_map=lowerCAmelCase  ,      **lowerCAmelCase )

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

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

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

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

				_UpperCAmelCase       :		Union[str, Any] 		=    Dictionary.load(lowerCAmelCase )
				_UpperCAmelCase       :		Any 		=    rewrite_dict_keys(src_dict.indices )
				_UpperCAmelCase       :		int 		=    len(lowerCAmelCase )
				_UpperCAmelCase       :		List[Any] 		=    os.path.join(lowerCAmelCase  ,      "vocab-src.json" )
				print(F'Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records' )
				with open(lowerCAmelCase  ,      "w"  ,      encoding="utf-8" ) as f:
								f.write(json.dumps(lowerCAmelCase  ,      ensure_ascii=lowerCAmelCase  ,      indent=lowerCAmelCase ) )

				# detect whether this is a do_lower_case situation, which can be derived by checking whether we
				# have at least one uppercase letter in the source vocab
				_UpperCAmelCase       :		Union[str, Any] 		=    True
				for k in src_vocab.keys():
								if not k.islower():
												_UpperCAmelCase       :		Optional[int] 		=    False
												break

				_UpperCAmelCase       :		Dict 		=    Dictionary.load(lowerCAmelCase )
				_UpperCAmelCase       :		List[str] 		=    rewrite_dict_keys(tgt_dict.indices )
				_UpperCAmelCase       :		Union[str, Any] 		=    len(lowerCAmelCase )
				_UpperCAmelCase       :		Dict 		=    os.path.join(lowerCAmelCase  ,      "vocab-tgt.json" )
				print(F'Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records' )
				with open(lowerCAmelCase  ,      "w"  ,      encoding="utf-8" ) as f:
								f.write(json.dumps(lowerCAmelCase  ,      ensure_ascii=lowerCAmelCase  ,      indent=lowerCAmelCase ) )

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

				# model config
				_UpperCAmelCase       :		int 		=    os.path.join(lowerCAmelCase  ,      "config.json" )

				# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
				# may have to modify the tokenizer if a different type is used by a future model
				assert args["bpe"] == "fastbpe", F'need to extend tokenizer to support bpe={args["bpe"]}'
				assert args["tokenizer"] == "moses", F'need to extend tokenizer to support bpe={args["tokenizer"]}'

				_UpperCAmelCase       :		Union[str, Any] 		=    {
				    "architectures": ["FSMTForConditionalGeneration"],
				    "model_type": "fsmt",
				    "activation_dropout": args["activation_dropout"],
				    "activation_function": "relu",
				    "attention_dropout": args["attention_dropout"],
				    "d_model": args["decoder_embed_dim"],
				    "dropout": args["dropout"],
				    "init_std": 0.02,
				    "max_position_embeddings": args["max_source_positions"],
				    "num_hidden_layers": args["encoder_layers"],
				    "src_vocab_size": src_vocab_size,
				    "tgt_vocab_size": tgt_vocab_size,
				    "langs": [src_lang, tgt_lang],
				    "encoder_attention_heads": args["encoder_attention_heads"],
				    "encoder_ffn_dim": args["encoder_ffn_embed_dim"],
				    "encoder_layerdrop": args["encoder_layerdrop"],
				    "encoder_layers": args["encoder_layers"],
				    "decoder_attention_heads": args["decoder_attention_heads"],
				    "decoder_ffn_dim": args["decoder_ffn_embed_dim"],
				    "decoder_layerdrop": args["decoder_layerdrop"],
				    "decoder_layers": args["decoder_layers"],
				    "bos_token_id": 0,
				    "pad_token_id": 1,
				    "eos_token_id": 2,
				    "is_encoder_decoder": True,
				    "scale_embedding": not args["no_scale_embedding"],
				    "tie_word_embeddings": args["share_all_embeddings"],
				}

				# good hparam defaults to start with
				_UpperCAmelCase       :		Union[str, Any] 		=    5
				_UpperCAmelCase       :		str 		=    False
				if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
								_UpperCAmelCase       :		str 		=    best_score_hparams[model_dir]["length_penalty"]
				else:
								_UpperCAmelCase       :		str 		=    1.0

				print(F'Generating {fsmt_model_config_file}' )
				with open(lowerCAmelCase  ,      "w"  ,      encoding="utf-8" ) as f:
								f.write(json.dumps(lowerCAmelCase  ,      ensure_ascii=lowerCAmelCase  ,      indent=lowerCAmelCase ) )

				# tokenizer config
				_UpperCAmelCase       :		int 		=    os.path.join(lowerCAmelCase  ,      lowerCAmelCase )

				_UpperCAmelCase       :		str 		=    {
				    "langs": [src_lang, tgt_lang],
				    "model_max_length": 1024,
				    "do_lower_case": do_lower_case,
				}

				print(F'Generating {fsmt_tokenizer_config_file}' )
				with open(lowerCAmelCase  ,      "w"  ,      encoding="utf-8" ) as f:
								f.write(json.dumps(lowerCAmelCase  ,      ensure_ascii=lowerCAmelCase  ,      indent=lowerCAmelCase ) )

				# model
				_UpperCAmelCase       :		Optional[int] 		=    chkpt["models"][0]
				_UpperCAmelCase       :		int 		=    model.state_dict()

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

				# remove unneeded keys
				_UpperCAmelCase       :		Any 		=    [
				    "model.model",
				    "model.encoder.version",
				    "model.decoder.version",
				    "model.encoder_embed_tokens.weight",
				    "model.decoder_embed_tokens.weight",
				    "model.encoder.embed_positions._float_tensor",
				    "model.decoder.embed_positions._float_tensor",
				]
				for k in ignore_keys:
								model_state_dict.pop(lowerCAmelCase  ,      lowerCAmelCase )

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

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

				# save
				_UpperCAmelCase       :		List[str] 		=    os.path.join(lowerCAmelCase  ,      lowerCAmelCase )
				print(F'Generating {pytorch_weights_dump_path}' )
				torch.save(lowerCAmelCase  ,      lowerCAmelCase )

				print("Conversion is done!" )
				print("\nLast step is to upload the files to s3" )
				print(F'cd {data_root}' )
				print(F'transformers-cli upload {model_dir}' )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_        =      argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--fsmt_checkpoint_path',
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        'Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'
					        ' bpecodes, etc.'
					    ),
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					SCREAMING_SNAKE_CASE_        =      parser.parse_args()
					convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)


