'''simple docstring'''





# 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_:					Optional[int]  =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_:					List[str]  ={
    # 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_:					Optional[int]  ={}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
    SCREAMING_SNAKE_CASE_:					Any  ='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_:					int  ='allenai'





def      lowerCAmelCase_    ( snake_case_	:			List[str]      )   ->				Dict:


     '''simple docstring'''




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





def      lowerCAmelCase_    ( snake_case_	:			Dict ,   snake_case_	:			Dict      )   ->				Optional[int]:


     '''simple docstring'''




     assert os.path.exists(snake_case_      )
     os.makedirs(snake_case_ ,   exist_ok=snake_case_      )
     print(f"""Writing results to {pytorch_dump_folder_path}"""      )

     # handle various types of models

     UpperCAmelCase_	   =       basename(snake_case_      )
     UpperCAmelCase_	   =       dirname(snake_case_      )

     UpperCAmelCase_	   =       fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
     UpperCAmelCase_	   =       cls.hub_models()
     UpperCAmelCase_	   =       {"bpe": "fastbpe", "tokenizer": "moses"}
     UpperCAmelCase_	   =       "."
     # 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_	   =       hub_utils.from_pretrained(
         snake_case_ ,   snake_case_ ,   snake_case_ ,   archive_map=snake_case_ ,   **snake_case_      )

     UpperCAmelCase_	   =       vars(chkpt["args"]["model"]      )

     UpperCAmelCase_	   =       args["source_lang"]
     UpperCAmelCase_	   =       args["target_lang"]

     UpperCAmelCase_	   =       dirname(snake_case_      )
     UpperCAmelCase_	   =       basename(snake_case_      )

     # dicts
     UpperCAmelCase_	   =       os.path.join(snake_case_ ,   f"""dict.{src_lang}.txt"""      )
     UpperCAmelCase_	   =       os.path.join(snake_case_ ,   f"""dict.{tgt_lang}.txt"""      )

     UpperCAmelCase_	   =       Dictionary.load(snake_case_      )
     UpperCAmelCase_	   =       rewrite_dict_keys(src_dict.indices      )
     UpperCAmelCase_	   =       len(snake_case_      )
     UpperCAmelCase_	   =       os.path.join(snake_case_ ,   "vocab-src.json"      )
     print(f"""Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records"""      )
     with open(snake_case_ ,   "w" ,   encoding="utf-8"      ) as f:
          f.write(json.dumps(snake_case_ ,   ensure_ascii=snake_case_ ,   indent=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
     UpperCAmelCase_	   =       True
     for k in src_vocab.keys():
          if not k.islower():
               UpperCAmelCase_	   =       False
               break

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

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

     # model config
     UpperCAmelCase_	   =       os.path.join(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"]}"""

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

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

     # tokenizer config
     UpperCAmelCase_	   =       os.path.join(snake_case_ ,   snake_case_      )

     UpperCAmelCase_	   =       {
         "langs": [src_lang, tgt_lang],
         "model_max_length": 10_24,
         "do_lower_case": do_lower_case,
     }

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

     # model
     UpperCAmelCase_	   =       chkpt["models"][0]
     UpperCAmelCase_	   =       model.state_dict()

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

     # remove unneeded keys
     UpperCAmelCase_	   =       [
         "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(snake_case_ ,   snake_case_      )

     UpperCAmelCase_	   =       FSMTConfig.from_pretrained(snake_case_      )
     UpperCAmelCase_	   =       FSMTForConditionalGeneration(snake_case_      )

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

     # save
     UpperCAmelCase_	   =       os.path.join(snake_case_ ,   snake_case_      )
     print(f"""Generating {pytorch_weights_dump_path}"""      )
     torch.save(snake_case_ ,   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__":
    SCREAMING_SNAKE_CASE_:					List[str]  =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_:					int  =parser.parse_args()
    convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)





'''simple docstring'''





# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
    import jax
    import jaxlib

SCREAMING_SNAKE_CASE_:					List[str]  =get_logger()

SCREAMING_SNAKE_CASE_:					Optional[dict]  =None




class 	__A				(			TensorFormatter[Mapping, """jax.Array""", Mapping]     ):



    def __init__(self   :   List[Any]       ,      __a   :   Optional[int]=None       ,      __a   :   Any=None       ,      **__a   :   Dict			):
         super().__init__(features=__a			)
         import jax
         from jaxlib.xla_client import Device

         if isinstance(__a       ,      __a			):
              raise ValueError(
                  f"""Expected {device} to be a `str` not {type(__a			)}, as `jaxlib.xla_extension.Device` """
                  "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
                  "the device with `str()` to get its string identifier that will be internally mapped "
                  "to the actual `jaxlib.xla_extension.Device`."			)
         UpperCAmelCase_	   =       device if isinstance(__a       ,      __a			) else str(jax.devices()[0]			)
         # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
         # with `pickle` nor with `dill`, so we need to use a global variable instead
         global DEVICE_MAPPING
         if DEVICE_MAPPING is None:
              UpperCAmelCase_	   =       self._map_devices_to_str()
         if self.device not in list(DEVICE_MAPPING.keys()			):
              logger.warning(
                  f"""Device with string identifier {self.device} not listed among the available """
                  f"""devices: {list(DEVICE_MAPPING.keys()			)}, so falling back to the default """
                  f"""device: {str(jax.devices()[0]			)}."""			)
              UpperCAmelCase_	   =       str(jax.devices()[0]			)
         UpperCAmelCase_	   =       jnp_array_kwargs



    @staticmethod
    def  _lowercase						():
         import jax

         return {str(__a			): device for device in jax.devices()}



    def  _lowercase						(self   :   str       ,      __a   :   Tuple			):
         import jax
         import jax.numpy as jnp

         if isinstance(__a       ,      __a			) and column:
              if all(
                  isinstance(__a       ,      jax.Array			) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column			):
                   return jnp.stack(__a       ,      axis=0			)
         return column



    def  _lowercase						(self   :   Any       ,      __a   :   Optional[int]			):
         import jax
         import jax.numpy as jnp

         if isinstance(__a       ,      (str, bytes, type(__a			))			):
              return value
         elif isinstance(__a       ,      (np.character, np.ndarray)			) and np.issubdtype(value.dtype       ,      np.character			):
              return value.tolist()

         UpperCAmelCase_	   =       {}

         if isinstance(__a       ,      (np.number, np.ndarray)			) and np.issubdtype(value.dtype       ,      np.integer			):
              # the default int precision depends on the jax config
              # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
              if jax.config.jax_enable_xaa:
                   UpperCAmelCase_	   =       {"dtype": jnp.intaa}
              else:
                   UpperCAmelCase_	   =       {"dtype": jnp.intaa}
         elif isinstance(__a       ,      (np.number, np.ndarray)			) and np.issubdtype(value.dtype       ,      np.floating			):
              UpperCAmelCase_	   =       {"dtype": jnp.floataa}
         elif config.PIL_AVAILABLE and "PIL" in sys.modules:
              import PIL.Image

              if isinstance(__a       ,      PIL.Image.Image			):
                   UpperCAmelCase_	   =       np.asarray(__a			)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
         global DEVICE_MAPPING
         if DEVICE_MAPPING is None:
              UpperCAmelCase_	   =       self._map_devices_to_str()

         with jax.default_device(DEVICE_MAPPING[self.device]			):
              # calling jnp.array on a np.ndarray does copy the data
              # see https://github.com/google/jax/issues/4486
              return jnp.array(__a       ,      **{**default_dtype, **self.jnp_array_kwargs}			)



    def  _lowercase						(self   :   int       ,      __a   :   Any			):
         import jax

         # support for torch, tf, jax etc.
         if config.TORCH_AVAILABLE and "torch" in sys.modules:
              import torch

              if isinstance(__a       ,      torch.Tensor			):
                   return self._tensorize(data_struct.detach().cpu().numpy()[()]			)
         if hasattr(__a       ,      "__array__"			) and not isinstance(__a       ,      jax.Array			):
              UpperCAmelCase_	   =       data_struct.__array__()
         # support for nested types like struct of list of struct
         if isinstance(__a       ,      np.ndarray			):
              if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
                   return self._consolidate([self.recursive_tensorize(__a			) for substruct in data_struct]			)
         elif isinstance(__a       ,      (list, tuple)			):
              return self._consolidate([self.recursive_tensorize(__a			) for substruct in data_struct]			)
         return self._tensorize(__a			)



    def  _lowercase						(self   :   Union[str, Any]       ,      __a   :   dict			):
         return map_nested(self._recursive_tensorize       ,      __a       ,      map_list=__a			)



    def  _lowercase						(self   :   str       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_row(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_row(__a			)
         return self.recursive_tensorize(__a			)



    def  _lowercase						(self   :   Tuple       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_column(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_column(__a       ,      pa_table.column_names[0]			)
         UpperCAmelCase_	   =       self.recursive_tensorize(__a			)
         UpperCAmelCase_	   =       self._consolidate(__a			)
         return column






    def  _lowercase						(self   :   str       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_batch(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_batch(__a			)
         UpperCAmelCase_	   =       self.recursive_tensorize(__a			)
         for column_name in batch:
              UpperCAmelCase_	   =       self._consolidate(batch[column_name]			)
         return batch




