






# 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()

lowerCamelCase			:					Union[str, 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
lowerCamelCase			:					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
lowerCamelCase			:					Optional[int]         =	{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
 lowerCamelCase			:					Optional[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",
]:
 lowerCamelCase			:					Optional[Any]         =	'''allenai'''





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





def       __lowerCAmelCase      (						__snake_case					, __snake_case       ):
     # prep
     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

     __lowerCAmelCase								=					basename(__snake_case       )
     __lowerCAmelCase								=					dirname(__snake_case       )

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

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

     __lowerCAmelCase								=					args["source_lang"]
     __lowerCAmelCase								=					args["target_lang"]

     __lowerCAmelCase								=					dirname(__snake_case       )
     __lowerCAmelCase								=					basename(__snake_case       )

     # dicts
     __lowerCAmelCase								=					os.path.join(__snake_case					, F"""dict.{src_lang}.txt"""       )
     __lowerCAmelCase								=					os.path.join(__snake_case					, F"""dict.{tgt_lang}.txt"""       )

     __lowerCAmelCase								=					Dictionary.load(__snake_case       )
     __lowerCAmelCase								=					rewrite_dict_keys(src_dict.indices       )
     __lowerCAmelCase								=					len(__snake_case       )
     __lowerCAmelCase								=					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
     __lowerCAmelCase								=					True
     for k in src_vocab.keys():
          if not k.islower():
               __lowerCAmelCase								=					False
               break

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

     __lowerCAmelCase								=					{
         "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
     __lowerCAmelCase								=					5
     __lowerCAmelCase								=					False
     if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
          __lowerCAmelCase								=					best_score_hparams[model_dir]["length_penalty"]
     else:
          __lowerCAmelCase								=					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
     __lowerCAmelCase								=					os.path.join(__snake_case					, __snake_case       )

     __lowerCAmelCase								=					{
         "langs": [src_lang, tgt_lang],
         "model_max_length": 1024,
         "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
     __lowerCAmelCase								=					chkpt["models"][0]
     __lowerCAmelCase								=					model.state_dict()

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

     # remove unneeded keys
     __lowerCAmelCase								=					[
         "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       )

     __lowerCAmelCase								=					FSMTConfig.from_pretrained(__snake_case       )
     __lowerCAmelCase								=					FSMTForConditionalGeneration(__snake_case       )

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

     # save
     __lowerCAmelCase								=					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__":
 lowerCamelCase			:					Optional[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.'''
 )
 lowerCamelCase			:					int         =	parser.parse_args()
 convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)














import os
import sys
import unittest


lowerCamelCase			:					int         =	os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
lowerCamelCase			:					Any         =	os.path.join(git_repo_path, '''src''', '''diffusers''')



class 							_UpperCamelCase			(unittest.TestCase			):


      def  __UpperCAmelCase		(	self	)->	Optional[int]:
           __lowerCAmelCase								=					find_backend("    if not is_torch_available():"	)
           self.assertEqual(__UpperCamelCase					,						"torch"	)

           # backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
           # self.assertEqual(backend_with_underscore, "tensorflow_text")

           __lowerCAmelCase								=					find_backend("    if not (is_torch_available() and is_transformers_available()):"	)
           self.assertEqual(__UpperCamelCase					,						"torch_and_transformers"	)

           # double_backend_with_underscore = find_backend(
           #    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
           # )
           # self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

           __lowerCAmelCase								=					find_backend(
               "    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"	)
           self.assertEqual(__UpperCamelCase					,						"torch_and_transformers_and_onnx"	)


      def  __UpperCAmelCase		(	self	)->	Dict:
           __lowerCAmelCase								=					read_init()
           # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
           self.assertIn("torch"					,						__UpperCamelCase	)
           self.assertIn("torch_and_transformers"					,						__UpperCamelCase	)
           self.assertIn("flax_and_transformers"					,						__UpperCamelCase	)
           self.assertIn("torch_and_transformers_and_onnx"					,						__UpperCamelCase	)

           # Likewise, we can't assert on the exact content of a key
           self.assertIn("UNet2DModel"					,						objects["torch"]	)
           self.assertIn("FlaxUNet2DConditionModel"					,						objects["flax"]	)
           self.assertIn("StableDiffusionPipeline"					,						objects["torch_and_transformers"]	)
           self.assertIn("FlaxStableDiffusionPipeline"					,						objects["flax_and_transformers"]	)
           self.assertIn("LMSDiscreteScheduler"					,						objects["torch_and_scipy"]	)
           self.assertIn("OnnxStableDiffusionPipeline"					,						objects["torch_and_transformers_and_onnx"]	)


      def  __UpperCAmelCase		(	self	)->	Any:
           __lowerCAmelCase								=					create_dummy_object("CONSTANT"					,						"'torch'"	)
           self.assertEqual(__UpperCamelCase					,						"\nCONSTANT = None\n"	)

           __lowerCAmelCase								=					create_dummy_object("function"					,						"'torch'"	)
           self.assertEqual(
               __UpperCamelCase					,						"\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"	)

           __lowerCAmelCase								=					"\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n"
           __lowerCAmelCase								=					create_dummy_object("FakeClass"					,						"'torch'"	)
           self.assertEqual(__UpperCamelCase					,						__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	Tuple:
           __lowerCAmelCase								=					"# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n"
           __lowerCAmelCase								=					create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}	)
           self.assertEqual(dummy_files["torch"]					,						__UpperCamelCase	)






