'''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()

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







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	Dict		    =  dict((re.sub(r'@@$'				,     ''				,     _a			), v) if k.endswith('@@'			) else (re.sub(r'$'				,     '</w>'				,     _a			), 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	:	int		    =  d[k]  # restore
    return da







def  _UpperCamelCase (     _a	:	str				,     _a	:	Optional[Any]			):




    """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

    __UpperCamelCase	:	List[str]		    =  basename(_a			)
    __UpperCamelCase	:	int		    =  dirname(_a			)

    __UpperCamelCase	:	Optional[int]		    =  fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
    __UpperCamelCase	:	Any		    =  cls.hub_models()
    __UpperCamelCase	:	Any		    =  {'bpe': 'fastbpe', 'tokenizer': 'moses'}
    __UpperCamelCase	:	List[str]		    =  '.'
    # 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	:	Tuple		    =  hub_utils.from_pretrained(
        _a				,     _a				,     _a				,     archive_map=_a				,     **_a			)

    __UpperCamelCase	:	List[str]		    =  vars(chkpt['args']['model']			)

    __UpperCamelCase	:	Optional[Any]		    =  args['source_lang']
    __UpperCamelCase	:	Any		    =  args['target_lang']

    __UpperCamelCase	:	List[Any]		    =  dirname(_a			)
    __UpperCamelCase	:	List[Any]		    =  basename(_a			)

    # dicts
    __UpperCamelCase	:	Any		    =  os.path.join(_a				,     f"""dict.{src_lang}.txt"""			)
    __UpperCamelCase	:	str		    =  os.path.join(_a				,     f"""dict.{tgt_lang}.txt"""			)

    __UpperCamelCase	:	Any		    =  Dictionary.load(_a			)
    __UpperCamelCase	:	Optional[Any]		    =  rewrite_dict_keys(src_dict.indices			)
    __UpperCamelCase	:	Dict		    =  len(_a			)
    __UpperCamelCase	:	Optional[int]		    =  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
    __UpperCamelCase	:	Optional[int]		    =  True
    for k in src_vocab.keys():
        if not k.islower():
            __UpperCamelCase	:	Dict		    =  False
            break

    __UpperCamelCase	:	List[str]		    =  Dictionary.load(_a			)
    __UpperCamelCase	:	List[Any]		    =  rewrite_dict_keys(tgt_dict.indices			)
    __UpperCamelCase	:	Optional[int]		    =  len(_a			)
    __UpperCamelCase	:	List[Any]		    =  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)
    __UpperCamelCase	:	int		    =  os.path.join(_a				,     VOCAB_FILES_NAMES['merges_file']			)
    for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
        __UpperCamelCase	:	Any		    =  os.path.join(_a				,     _a			)
        if os.path.exists(_a			):
            break
    with open(_a				,     encoding='utf-8'			) as fin:
        __UpperCamelCase	:	str		    =  fin.read()
    __UpperCamelCase	:	Optional[Any]		    =  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
    __UpperCamelCase	:	Union[str, Any]		    =  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']}"""

    __UpperCamelCase	:	Optional[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	:	str		    =  5
    __UpperCamelCase	:	Any		    =  False
    if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
        __UpperCamelCase	:	List[str]		    =  best_score_hparams[model_dir]['length_penalty']
    else:
        __UpperCamelCase	:	str		    =  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
    __UpperCamelCase	:	Dict		    =  os.path.join(_a				,     _a			)

    __UpperCamelCase	:	List[Any]		    =  {
        '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(_a				,     'w'				,     encoding='utf-8'			) as f:
        f.write(json.dumps(_a				,     ensure_ascii=_a				,     indent=_a			)			)

    # model
    __UpperCamelCase	:	Union[str, Any]		    =  chkpt['models'][0]
    __UpperCamelCase	:	Optional[int]		    =  model.state_dict()

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

    # remove unneeded keys
    __UpperCamelCase	:	List[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(_a				,     _a			)

    __UpperCamelCase	:	int		    =  FSMTConfig.from_pretrained(_a			)
    __UpperCamelCase	:	Any		    =  FSMTForConditionalGeneration(_a			)

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

    # save
    __UpperCamelCase	:	Tuple		    =  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__":
 a=  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=  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_torch_available, is_vision_available


a=  {
    '''configuration_bridgetower''': [
        '''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BridgeTowerConfig''',
        '''BridgeTowerTextConfig''',
        '''BridgeTowerVisionConfig''',
    ],
    '''processing_bridgetower''': ['''BridgeTowerProcessor'''],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  ['''BridgeTowerImageProcessor''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  [
      '''BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''BridgeTowerForContrastiveLearning''',
      '''BridgeTowerForImageAndTextRetrieval''',
      '''BridgeTowerForMaskedLM''',
      '''BridgeTowerModel''',
      '''BridgeTowerPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_bridgetower import (
      BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      BridgeTowerConfig,
      BridgeTowerTextConfig,
      BridgeTowerVisionConfig,
  )
  from .processing_bridgetower import BridgeTowerProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_bridgetower import BridgeTowerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bridgetower import (
        BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
        BridgeTowerForContrastiveLearning,
        BridgeTowerForImageAndTextRetrieval,
        BridgeTowerForMaskedLM,
        BridgeTowerModel,
        BridgeTowerPreTrainedModel,
    )


else:
  import sys

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



