

# 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()

_UpperCamelCase 			=		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
_UpperCamelCase 			=		{
    # 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
_UpperCamelCase 			=		{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
 _UpperCamelCase 			=		"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",
]:
 _UpperCamelCase 			=		"allenai"







def 		_lowercase (     lowercase__						):
      # (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					:						Union[str, Any]					=					dict((re.sub(r'''@@$'''   ,	''''''   ,	lowercase__						), v) if k.endswith('''@@'''						) else (re.sub(r'''$'''   ,	'''</w>'''   ,	lowercase__						), 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					:						int					=					d[k]  # restore
      return da







def 		_lowercase (     lowercase__   ,	lowercase__						):
      # prep
      assert os.path.exists(lowercase__						)
      os.makedirs(lowercase__   ,	exist_ok=lowercase__						)
      print(f"""Writing results to {pytorch_dump_folder_path}"""						)

      # handle various types of models

      __lowerCAmelCase					:						Optional[Any]					=					basename(lowercase__						)
      __lowerCAmelCase					:						Union[str, Any]					=					dirname(lowercase__						)

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

      __lowerCAmelCase					:						Dict					=					vars(chkpt['''args''']['''model''']						)

      __lowerCAmelCase					:						Union[str, Any]					=					args['''source_lang''']
      __lowerCAmelCase					:						List[Any]					=					args['''target_lang''']

      __lowerCAmelCase					:						int					=					dirname(lowercase__						)
      __lowerCAmelCase					:						int					=					basename(lowercase__						)

      # dicts
      __lowerCAmelCase					:						Optional[Any]					=					os.path.join(lowercase__   ,	f"""dict.{src_lang}.txt"""						)
      __lowerCAmelCase					:						Optional[Any]					=					os.path.join(lowercase__   ,	f"""dict.{tgt_lang}.txt"""						)

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

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

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

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

      # model config
      __lowerCAmelCase					:						str					=					os.path.join(lowercase__   ,	'''config.json'''						)

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

      __lowerCAmelCase					:						Dict					=					{
          '''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''': 0.0_2,
          '''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					:						int					=					5
      __lowerCAmelCase					:						Optional[int]					=					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					:						Optional[Any]					=					1.0

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

      # tokenizer config
      __lowerCAmelCase					:						Tuple					=					os.path.join(lowercase__   ,	lowercase__						)

      __lowerCAmelCase					:						int					=					{
          '''langs''': [src_lang, tgt_lang],
          '''model_max_length''': 1_0_2_4,
          '''do_lower_case''': do_lower_case,
      }

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

      # model
      __lowerCAmelCase					:						List[Any]					=					chkpt['''models'''][0]
      __lowerCAmelCase					:						List[Any]					=					model.state_dict()

      # rename keys to start with 'model.'
      __lowerCAmelCase					:						Any					=					OrderedDict(('''model.''' + k, v) for k, v in model_state_dict.items()						)

      # remove unneeded keys
      __lowerCAmelCase					:						List[str]					=					[
          '''model.model''',
          '''model.encoder.version''',
          '''model.decoder.version''',
          '''model.encoder_embed_tokens.weight''',
          '''model.decoder_embed_tokens.weight''',
          '''model.encoder.embed_positions._float_tensor''',
          '''model.decoder.embed_positions._float_tensor''',
      ]
      for k in ignore_keys:
            model_state_dict.pop(lowercase__   ,	lowercase__						)

      __lowerCAmelCase					:						Optional[Any]					=					FSMTConfig.from_pretrained(lowercase__						)
      __lowerCAmelCase					:						Any					=					FSMTForConditionalGeneration(lowercase__						)

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

      # save
      __lowerCAmelCase					:						Dict					=					os.path.join(lowercase__   ,	lowercase__						)
      print(f"""Generating {pytorch_weights_dump_path}"""						)
      torch.save(lowercase__   ,	lowercase__						)

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


if __name__ == "__main__":
 _UpperCamelCase 			=		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."
 )
 _UpperCamelCase 			=		parser.parse_args()
 convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_UpperCamelCase 			=		logging.get_logger(__name__)

_UpperCamelCase 			=		{
    "microsoft/table-transformer-detection": (
        "https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
    ),
}







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  """table-transformer"""
 _UpperCamelCase			  =  ["""past_key_values"""]
 _UpperCamelCase			  =  {
     """hidden_size""": """d_model""",
     """num_attention_heads""": """encoder_attention_heads""",
 }
 def __init__(							self      ,							A_=True      ,							A_=None      ,							A_=3      ,							A_=100      ,							A_=6      ,							A_=2048      ,							A_=8      ,							A_=6      ,							A_=2048      ,							A_=8      ,							A_=0.0      ,							A_=0.0      ,							A_=True      ,							A_="relu"      ,							A_=256      ,							A_=0.1      ,							A_=0.0      ,							A_=0.0      ,							A_=0.02      ,							A_=1.0      ,							A_=False      ,							A_="sine"      ,							A_="resnet50"      ,							A_=True      ,							A_=False      ,							A_=1      ,							A_=5      ,							A_=2      ,							A_=1      ,							A_=1      ,							A_=5      ,							A_=2      ,							A_=0.1      ,							**A_      ,							)					->Any:



       '''simple docstring'''





       if backbone_config is not None and use_timm_backbone:
             raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''					)

       if not use_timm_backbone:
             if backbone_config is None:
                   logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''					)
                   __lowerCAmelCase					:						Optional[Any]					=					CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']					)
             elif isinstance(A_      ,							A_					):
                   __lowerCAmelCase					:						int					=					backbone_config.get('''model_type'''					)
                   __lowerCAmelCase					:						List[str]					=					CONFIG_MAPPING[backbone_model_type]
                   __lowerCAmelCase					:						Any					=					config_class.from_dict(A_					)
             # set timm attributes to None
             __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase					:						List[str]					=					None, None, None

       __lowerCAmelCase					:						Tuple					=					use_timm_backbone
       __lowerCAmelCase					:						Optional[Any]					=					backbone_config
       __lowerCAmelCase					:						List[str]					=					num_channels
       __lowerCAmelCase					:						Tuple					=					num_queries
       __lowerCAmelCase					:						int					=					d_model
       __lowerCAmelCase					:						List[Any]					=					encoder_ffn_dim
       __lowerCAmelCase					:						Optional[int]					=					encoder_layers
       __lowerCAmelCase					:						List[str]					=					encoder_attention_heads
       __lowerCAmelCase					:						str					=					decoder_ffn_dim
       __lowerCAmelCase					:						Union[str, Any]					=					decoder_layers
       __lowerCAmelCase					:						Any					=					decoder_attention_heads
       __lowerCAmelCase					:						Optional[int]					=					dropout
       __lowerCAmelCase					:						Any					=					attention_dropout
       __lowerCAmelCase					:						Tuple					=					activation_dropout
       __lowerCAmelCase					:						Optional[Any]					=					activation_function
       __lowerCAmelCase					:						List[str]					=					init_std
       __lowerCAmelCase					:						Tuple					=					init_xavier_std
       __lowerCAmelCase					:						Any					=					encoder_layerdrop
       __lowerCAmelCase					:						List[Any]					=					decoder_layerdrop
       __lowerCAmelCase					:						Optional[Any]					=					encoder_layers
       __lowerCAmelCase					:						Optional[Any]					=					auxiliary_loss
       __lowerCAmelCase					:						Optional[Any]					=					position_embedding_type
       __lowerCAmelCase					:						Tuple					=					backbone
       __lowerCAmelCase					:						Any					=					use_pretrained_backbone
       __lowerCAmelCase					:						int					=					dilation
       # Hungarian matcher
       __lowerCAmelCase					:						Dict					=					class_cost
       __lowerCAmelCase					:						List[str]					=					bbox_cost
       __lowerCAmelCase					:						int					=					giou_cost
       # Loss coefficients
       __lowerCAmelCase					:						Optional[Any]					=					mask_loss_coefficient
       __lowerCAmelCase					:						Tuple					=					dice_loss_coefficient
       __lowerCAmelCase					:						int					=					bbox_loss_coefficient
       __lowerCAmelCase					:						List[Any]					=					giou_loss_coefficient
       __lowerCAmelCase					:						int					=					eos_coefficient
       super().__init__(is_encoder_decoder=A_      ,							**A_					)
 @property
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       return self.encoder_attention_heads





 @property
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       return self.d_model







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  version.parse("""1.11""" )
 @property
 def 	UpperCamelCase__  (							self					)					->Mapping[str, Mapping[int, str]]:



       '''simple docstring'''





       return OrderedDict(
           [
               ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
               ('''pixel_mask''', {0: '''batch'''}),
           ]					)
 @property
 def 	UpperCamelCase__  (							self					)					->float:



       '''simple docstring'''





       return 1e-5





 @property
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       return 12


