



'''simple docstring'''





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

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

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


logging.set_verbosity_warning()

lowercase_		       =  2

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

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




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a      =  dict((re.sub(r'''@@$'''  ,	''''''  ,	_A), v) if k.endswith('''@@''') else (re.sub(r'''$'''  ,	'''</w>'''  ,	_A), v) for k, v in d.items())
     _a      =  '''<s> <pad> </s> <unk>'''.split()
     # restore the special tokens
     for k in keep_keys:
          del da[F'''{k}</w>''']
          _a      =  d[k]  # restore
     return da




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     assert os.path.exists(_A)
     os.makedirs(_A  ,	exist_ok=_A)
     print(F'''Writing results to {pytorch_dump_folder_path}''')

     # handle various types of models

     _a      =  basename(_A)
     _a      =  dirname(_A)

     _a      =  fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
     _a      =  cls.hub_models()
     _a      =  {'''bpe''': '''fastbpe''', '''tokenizer''': '''moses'''}
     _a      =  '''.'''
     # 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}''')
     _a      =  hub_utils.from_pretrained(
         _A  ,	_A  ,	_A  ,	archive_map=_A  ,	**_A)

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

     _a      =  args['''source_lang''']
     _a      =  args['''target_lang''']

     _a      =  dirname(_A)
     _a      =  basename(_A)

     # dicts
     _a      =  os.path.join(_A  ,	F'''dict.{src_lang}.txt''')
     _a      =  os.path.join(_A  ,	F'''dict.{tgt_lang}.txt''')

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

     # 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
     _a      =  True
     for k in src_vocab.keys():
          if not k.islower():
               _a      =  False
               break

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

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

     # model config
     _a      =  os.path.join(_A  ,	'''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']}'''

     _a      =  {
         '''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
     _a      =  5
     _a      =  False
     if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
          _a      =  best_score_hparams[model_dir]['''length_penalty''']
     else:
          _a      =  1.0

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

     # tokenizer config
     _a      =  os.path.join(_A  ,	_A)

     _a      =  {
         '''langs''': [src_lang, tgt_lang],
         '''model_max_length''': 1_024,
         '''do_lower_case''': do_lower_case,
     }

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

     # model
     _a      =  chkpt['''models'''][0]
     _a      =  model.state_dict()

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

     # remove unneeded keys
     _a      =  [
         '''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(_A  ,	_A)

     _a      =  FSMTConfig.from_pretrained(_A)
     _a      =  FSMTForConditionalGeneration(_A)

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

     # save
     _a      =  os.path.join(_A  ,	_A)
     print(F'''Generating {pytorch_weights_dump_path}''')
     torch.save(_A  ,	_A)

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


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




'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowercase          =  {
    '''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ResNetConfig''', '''ResNetOnnxConfig''']
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''RESNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''ResNetForImageClassification''',
						    '''ResNetModel''',
						    '''ResNetPreTrainedModel''',
						    '''ResNetBackbone''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFResNetForImageClassification''',
						    '''TFResNetModel''',
						    '''TFResNetPreTrainedModel''',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''FlaxResNetForImageClassification''',
						    '''FlaxResNetModel''',
						    '''FlaxResNetPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_resnet import (
												    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ResNetBackbone,
												    ResNetForImageClassification,
												    ResNetModel,
												    ResNetPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_resnet import (
												    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFResNetForImageClassification,
												    TFResNetModel,
												    TFResNetPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
						import sys

						__lowercase          =  _LazyModule(__name__, globals()['''__file__'''], _import_structure)




