'''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()

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'''





import os

import numpy
import onnx





def      lowerCAmelCase_    ( snake_case_	:			Union[str, Any] ,   snake_case_	:			Optional[int]      )   ->				Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       a.name
     UpperCAmelCase_	   =       b.name

     UpperCAmelCase_	   =       ""
     UpperCAmelCase_	   =       ""

     UpperCAmelCase_	   =       a == b

     UpperCAmelCase_	   =       name_a
     UpperCAmelCase_	   =       name_b

     return res





def      lowerCAmelCase_    ( snake_case_	:			Any ,   snake_case_	:			Tuple ,   snake_case_	:			Union[str, Any]      )   ->				Any:


     '''simple docstring'''




     for i, input_name in enumerate(node_proto.input      ):
          if input_name == name:
               node_proto.input.insert(snake_case_ ,   snake_case_      )
               node_proto.input.pop(i + 1      )

     if node_proto.op_type == "If":
          _graph_replace_input_with(node_proto.attribute[0].g ,   snake_case_ ,   snake_case_      )
          _graph_replace_input_with(node_proto.attribute[1].g ,   snake_case_ ,   snake_case_      )
     if node_proto.op_type == "Loop":
          _graph_replace_input_with(node_proto.attribute[0].g ,   snake_case_ ,   snake_case_      )





def      lowerCAmelCase_    ( snake_case_	:			List[str] ,   snake_case_	:			List[str] ,   snake_case_	:			Union[str, Any]      )   ->				Optional[int]:


     '''simple docstring'''




     for n in graph_proto.node:
          _node_replace_input_with(snake_case_ ,   snake_case_ ,   snake_case_      )





def      lowerCAmelCase_    ( snake_case_	:			List[str] ,   snake_case_	:			Any ,   snake_case_	:			Optional[Any]      )   ->				Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_	   =       list(model.graph.initializer      )
     UpperCAmelCase_	   =       list(model_without_ext.graph.initializer      )
     for i, ref_i in ind_to_replace:
          assert inits_with_data[i].name == inits[i].name
          assert inits_with_data[ref_i].name == inits[ref_i].name
          assert i > ref_i

          UpperCAmelCase_	   =       inits[i].name
          UpperCAmelCase_	   =       inits[ref_i].name

          model_without_ext.graph.initializer.remove(inits[i]      )

          # for n in model.graph.node:
          _graph_replace_input_with(model_without_ext.graph ,   snake_case_ ,   snake_case_      )






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


     '''simple docstring'''




     UpperCAmelCase_	   =       os.path.dirname(snake_case_      )
     UpperCAmelCase_	   =       os.path.basename(snake_case_      )

     UpperCAmelCase_	   =       onnx.load(os.path.join(snake_case_ ,   snake_case_      )      )

     UpperCAmelCase_	   =       list(model.graph.initializer      )

     UpperCAmelCase_	   =       set()
     UpperCAmelCase_	   =       {}
     UpperCAmelCase_	   =       []

     UpperCAmelCase_	   =       0

     for i in range(len(snake_case_      )      ):
          if i in dup_set:
               continue

          for j in range(i + 1 ,   len(snake_case_      )      ):
               if j in dup_set:
                    continue
               if _is_equal_tensor_proto(inits[i] ,   inits[j]      ):
                    dup_set.add(snake_case_      )
                    dup_set.add(snake_case_      )

                    UpperCAmelCase_	   =       inits[j].data_type
                    UpperCAmelCase_	   =       numpy.prod(inits[j].dims      )
                    if dtype == 1:
                         mem_size *= 4
                    elif dtype == 6:
                         mem_size *= 4
                    elif dtype == 7 or dtype == 11:
                         mem_size *= 8
                    else:
                         print("unexpected data type: " ,   snake_case_      )
                    total_reduced_size += mem_size

                    UpperCAmelCase_	   =       inits[i].name
                    UpperCAmelCase_	   =       inits[j].name

                    if name_i in dup_map:
                         dup_map[name_i].append(snake_case_      )
                    else:
                         UpperCAmelCase_	   =       [name_j]
                    ind_to_replace.append((j, i)      )

     print("total reduced size: " ,   total_reduced_size / 10_24 / 10_24 / 10_24 ,   "GB"      )

     UpperCAmelCase_	   =       sorted(snake_case_      )
     _remove_dup_initializers_from_model(snake_case_ ,   snake_case_ ,   snake_case_      )

     UpperCAmelCase_	   =       "optimized_" + model_file_name
     UpperCAmelCase_	   =       os.path.join(snake_case_ ,   snake_case_      )
     onnx.save(snake_case_ ,   snake_case_      )

     return new_model




