
"""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			:       Optional[Any]   		=       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			:       Any   		=       {
    # 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			:       Optional[Any]   		=       {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
   _A			:       Dict   		=       """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   __magic_name__	(      __snake_case				:       Dict  ) ->				str:
       # (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}
       lowercase    :				Union[str, Any]       		=							dict((re.sub(r"@@$"     , ""     , _SCREAMING_SNAKE_CASE  ), v) if k.endswith("@@"  ) else (re.sub(r"$"     , "</w>"     , _SCREAMING_SNAKE_CASE  ), v) for k, v in d.items()  )
       lowercase    :				List[str]       		=							'<s> <pad> </s> <unk>'.split()
       # restore the special tokens
       for k in keep_keys:
              del da[f"""{k}</w>"""]
              lowercase    :				Any       		=							d[k]  # restore
       return da


def   __magic_name__	(      __snake_case				:       Optional[int]     , __snake_case				:       Dict  ) ->				Optional[int]:
       # prep
       assert os.path.exists(_SCREAMING_SNAKE_CASE  )
       os.makedirs(_SCREAMING_SNAKE_CASE     , exist_ok=_SCREAMING_SNAKE_CASE  )
       print(f"""Writing results to {pytorch_dump_folder_path}"""  )

       # handle various types of models

       lowercase    :				Optional[int]       		=							basename(_SCREAMING_SNAKE_CASE  )
       lowercase    :				str       		=							dirname(_SCREAMING_SNAKE_CASE  )

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

       lowercase    :				int       		=							vars(chkpt["args"]["model"]  )

       lowercase    :				int       		=							args['source_lang']
       lowercase    :				Optional[Any]       		=							args['target_lang']

       lowercase    :				Optional[int]       		=							dirname(_SCREAMING_SNAKE_CASE  )
       lowercase    :				Optional[int]       		=							basename(_SCREAMING_SNAKE_CASE  )

       # dicts
       lowercase    :				Union[str, Any]       		=							os.path.join(_SCREAMING_SNAKE_CASE     , f"""dict.{src_lang}.txt"""  )
       lowercase    :				Optional[int]       		=							os.path.join(_SCREAMING_SNAKE_CASE     , f"""dict.{tgt_lang}.txt"""  )

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

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

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

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

       # model config
       lowercase    :				List[Any]       		=							os.path.join(_SCREAMING_SNAKE_CASE     , "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"]}"""

       lowercase    :				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
       lowercase    :				Optional[Any]       		=							5
       lowercase    :				Union[str, Any]       		=							False
       if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
              lowercase    :				str       		=							best_score_hparams[model_dir]['length_penalty']
       else:
              lowercase    :				Tuple       		=							1.0

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

       # tokenizer config
       lowercase    :				List[Any]       		=							os.path.join(_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE  )

       lowercase    :				List[Any]       		=							{
           'langs': [src_lang, tgt_lang],
           'model_max_length': 1024,
           'do_lower_case': do_lower_case,
       }

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

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

       # rename keys to start with 'model.'
       lowercase    :				Optional[int]       		=							OrderedDict(("model." + k, v) for k, v in model_state_dict.items()  )

       # remove unneeded keys
       lowercase    :				Union[str, 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(_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE  )

       lowercase    :				Dict       		=							FSMTConfig.from_pretrained(_SCREAMING_SNAKE_CASE  )
       lowercase    :				int       		=							FSMTForConditionalGeneration(_SCREAMING_SNAKE_CASE  )

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

       # save
       lowercase    :				Union[str, Any]       		=							os.path.join(_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE  )
       print(f"""Generating {pytorch_weights_dump_path}"""  )
       torch.save(_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE  )

       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			:       Any   		=       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			:       Any   		=       parser.parse_args()
   convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__					       =	logging.get_logger(__name__)

lowercase__					       =	{
    'edbeeching/decision-transformer-gym-hopper-medium': (
        'https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}




class    __snake_case       (     __lowerCAmelCase				):
      a__        =		"""decision_transformer"""
      a__        =		["""past_key_values"""]
      a__        =		{
          """max_position_embeddings""": """n_positions""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }



      def __init__(						self    ,			lowercase=17    ,			lowercase=4    ,			lowercase=1_28    ,			lowercase=40_96    ,			lowercase=True    ,			lowercase=1    ,			lowercase=10_24    ,			lowercase=3    ,			lowercase=1    ,			lowercase=None    ,			lowercase="relu"    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=1e-5    ,			lowercase=0.02    ,			lowercase=True    ,			lowercase=True    ,			lowercase=5_02_56    ,			lowercase=5_02_56    ,			lowercase=False    ,			lowercase=False    ,			**lowercase    ,			)	->			Tuple:





        '''simple docstring'''





        a__:     List[str]										=	state_dim
        a__:     int										=	act_dim
        a__:     List[Any]										=	hidden_size
        a__:     List[str]										=	max_ep_len
        a__:     List[Any]										=	action_tanh
        a__:     Optional[Any]										=	vocab_size
        a__:     Tuple										=	n_positions
        a__:     Dict										=	n_layer
        a__:     Optional[int]										=	n_head
        a__:     Optional[int]										=	n_inner
        a__:     Any										=	activation_function
        a__:     Union[str, Any]										=	resid_pdrop
        a__:     Any										=	embd_pdrop
        a__:     Any										=	attn_pdrop
        a__:     List[Any]										=	layer_norm_epsilon
        a__:     Optional[Any]										=	initializer_range
        a__:     Any										=	scale_attn_weights
        a__:     Dict										=	use_cache
        a__:     Optional[int]										=	scale_attn_by_inverse_layer_idx
        a__:     List[str]										=	reorder_and_upcast_attn

        a__:     Any										=	bos_token_id
        a__:     int										=	eos_token_id

        super().__init__(bos_token_id=lowercase    ,			eos_token_id=lowercase    ,			**lowercase)


