



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




def        lowerCamelCase  (				_snake_case  :						List[str]       ):





				'''simple docstring'''


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




def        lowerCamelCase  (				_snake_case  :						Tuple     ,_snake_case  :						Optional[Any]       ):





				'''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

				lowercase__		       =						basename(_snake_case       )
				lowercase__		       =						dirname(_snake_case       )

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

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

				lowercase__		       =						args["source_lang"]
				lowercase__		       =						args["target_lang"]

				lowercase__		       =						dirname(_snake_case       )
				lowercase__		       =						basename(_snake_case       )

				# dicts
				lowercase__		       =						os.path.join(_snake_case     ,f'''dict.{src_lang}.txt'''       )
				lowercase__		       =						os.path.join(_snake_case     ,f'''dict.{tgt_lang}.txt'''       )

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

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

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

				lowercase__		       =						{
				    "langs": [src_lang, tgt_lang],
				    "model_max_length": 1_024,
				    "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
				lowercase__		       =						chkpt["models"][0]
				lowercase__		       =						model.state_dict()

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

				# remove unneeded keys
				lowercase__		       =						[
				    "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       )

				lowercase__		       =						FSMTConfig.from_pretrained(_snake_case       )
				lowercase__		       =						FSMTForConditionalGeneration(_snake_case       )

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

				# save
				lowercase__		       =						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__      					=     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__      					=     parser.parse_args()
	convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)









'''simple docstring'''





import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
 pass

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      					=     {
    "kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}


class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Optional[int]						   =					"align_text_model"



       def __init__(				self     ,UpperCAmelCase_=30_522     ,UpperCAmelCase_=768     ,UpperCAmelCase_=12     ,UpperCAmelCase_=12     ,UpperCAmelCase_=3_072     ,UpperCAmelCase_="gelu"     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=512     ,UpperCAmelCase_=2     ,UpperCAmelCase_=0.02     ,UpperCAmelCase_=1E-1_2     ,UpperCAmelCase_=0     ,UpperCAmelCase_="absolute"     ,UpperCAmelCase_=True     ,**UpperCAmelCase_     ,)  ->							List[str]:
           super().__init__(**UpperCAmelCase_      )

           lowercase__		       =						vocab_size
           lowercase__		       =						hidden_size
           lowercase__		       =						num_hidden_layers
           lowercase__		       =						num_attention_heads
           lowercase__		       =						hidden_act
           lowercase__		       =						intermediate_size
           lowercase__		       =						hidden_dropout_prob
           lowercase__		       =						attention_probs_dropout_prob
           lowercase__		       =						max_position_embeddings
           lowercase__		       =						type_vocab_size
           lowercase__		       =						initializer_range
           lowercase__		       =						layer_norm_eps
           lowercase__		       =						position_embedding_type
           lowercase__		       =						use_cache
           lowercase__		       =						pad_token_id







       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							"PretrainedConfig":
           cls._set_token_in_kwargs(UpperCAmelCase_      )

           lowercase__       ,       lowercase__		       =						cls.get_config_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )

           # get the text config dict if we are loading from AlignConfig
           if config_dict.get("model_type"      ) == "align":
               lowercase__		       =						config_dict["text_config"]

           if "model_type" in config_dict and hasattr(cls     ,"model_type"      ) and config_dict["model_type"] != cls.model_type:
               logger.warning(
                   F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
                   F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''      )

           return cls.from_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )





class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Tuple						   =					"align_vision_model"



       def __init__(				self     ,UpperCAmelCase_ = 3     ,UpperCAmelCase_ = 600     ,UpperCAmelCase_ = 2.0     ,UpperCAmelCase_ = 3.1     ,UpperCAmelCase_ = 8     ,UpperCAmelCase_ = [3, 3, 5, 3, 5, 5, 3]     ,UpperCAmelCase_ = [32, 16, 24, 40, 80, 112, 192]     ,UpperCAmelCase_ = [16, 24, 40, 80, 112, 192, 320]     ,UpperCAmelCase_ = []     ,UpperCAmelCase_ = [1, 2, 2, 2, 1, 2, 1]     ,UpperCAmelCase_ = [1, 2, 2, 3, 3, 4, 1]     ,UpperCAmelCase_ = [1, 6, 6, 6, 6, 6, 6]     ,UpperCAmelCase_ = 0.25     ,UpperCAmelCase_ = "swish"     ,UpperCAmelCase_ = 2_560     ,UpperCAmelCase_ = "mean"     ,UpperCAmelCase_ = 0.02     ,UpperCAmelCase_ = 0.0_01     ,UpperCAmelCase_ = 0.99     ,UpperCAmelCase_ = 0.2     ,**UpperCAmelCase_     ,)  ->							Union[str, Any]:
           super().__init__(**UpperCAmelCase_      )

           lowercase__		       =						num_channels
           lowercase__		       =						image_size
           lowercase__		       =						width_coefficient
           lowercase__		       =						depth_coefficient
           lowercase__		       =						depth_divisor
           lowercase__		       =						kernel_sizes
           lowercase__		       =						in_channels
           lowercase__		       =						out_channels
           lowercase__		       =						depthwise_padding
           lowercase__		       =						strides
           lowercase__		       =						num_block_repeats
           lowercase__		       =						expand_ratios
           lowercase__		       =						squeeze_expansion_ratio
           lowercase__		       =						hidden_act
           lowercase__		       =						hidden_dim
           lowercase__		       =						pooling_type
           lowercase__		       =						initializer_range
           lowercase__		       =						batch_norm_eps
           lowercase__		       =						batch_norm_momentum
           lowercase__		       =						drop_connect_rate
           lowercase__		       =						sum(UpperCAmelCase_      ) * 4







       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							"PretrainedConfig":
           cls._set_token_in_kwargs(UpperCAmelCase_      )

           lowercase__       ,       lowercase__		       =						cls.get_config_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )

           # get the vision config dict if we are loading from AlignConfig
           if config_dict.get("model_type"      ) == "align":
               lowercase__		       =						config_dict["vision_config"]

           if "model_type" in config_dict and hasattr(cls     ,"model_type"      ) and config_dict["model_type"] != cls.model_type:
               logger.warning(
                   F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
                   F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''      )

           return cls.from_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )





class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Union[str, Any]						   =					"align"
       lowerCAmelCase__  :Tuple						   =					True



       def __init__(				self     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,UpperCAmelCase_=640     ,UpperCAmelCase_=1.0     ,UpperCAmelCase_=0.02     ,**UpperCAmelCase_     ,)  ->							Union[str, Any]:
           super().__init__(**UpperCAmelCase_      )

           if text_config is None:
               lowercase__		       =						{}
               logger.info("text_config is None. Initializing the AlignTextConfig with default values."      )

           if vision_config is None:
               lowercase__		       =						{}
               logger.info("vision_config is None. Initializing the AlignVisionConfig with default values."      )

           lowercase__		       =						AlignTextConfig(**UpperCAmelCase_      )
           lowercase__		       =						AlignVisionConfig(**UpperCAmelCase_      )

           lowercase__		       =						projection_dim
           lowercase__		       =						temperature_init_value
           lowercase__		       =						initializer_range



       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							List[str]:
           return cls(text_config=text_config.to_dict()     ,vision_config=vision_config.to_dict()     ,**UpperCAmelCase_      )







       def 							_a      (				self      )  ->							List[Any]:
           lowercase__		       =						copy.deepcopy(self.__dict__      )
           lowercase__		       =						self.text_config.to_dict()
           lowercase__		       =						self.vision_config.to_dict()
           lowercase__		       =						self.__class__.model_type
           return output




