






# 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()

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

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

def _UpperCAmelCase   (	a						:	Tuple  ,					a						:	Optional[Any] ):
							# prep
							assert os.path.exists(a )
							os.makedirs(a  ,					exist_ok=a )
							print(F'''Writing results to {pytorch_dump_folder_path}''' )

							# handle various types of models

							snake_case__	  =       basename(a )
							snake_case__	  =       dirname(a )

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

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

							snake_case__	  =       args["""source_lang"""]
							snake_case__	  =       args["""target_lang"""]

							snake_case__	  =       dirname(a )
							snake_case__	  =       basename(a )

							# dicts
							snake_case__	  =       os.path.join(a  ,					F'''dict.{src_lang}.txt''' )
							snake_case__	  =       os.path.join(a  ,					F'''dict.{tgt_lang}.txt''' )

							snake_case__	  =       Dictionary.load(a )
							snake_case__	  =       rewrite_dict_keys(src_dict.indices )
							snake_case__	  =       len(a )
							snake_case__	  =       os.path.join(a  ,					"""vocab-src.json""" )
							print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records''' )
							with open(a  ,					"""w"""  ,					encoding="""utf-8""" ) as f:
														f.write(json.dumps(a  ,					ensure_ascii=a  ,					indent=a ) )

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

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

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

							# model config
							snake_case__	  =       os.path.join(a  ,					"""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']}'''

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

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

							# tokenizer config
							snake_case__	  =       os.path.join(a  ,					a )

							snake_case__	  =       {
							    """langs""": [src_lang, tgt_lang],
							    """model_max_length""": 1024,
							    """do_lower_case""": do_lower_case,
							}

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

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

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

							# remove unneeded keys
							snake_case__	  =       [
							    """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(a  ,					a )

							snake_case__	  =       FSMTConfig.from_pretrained(a )
							snake_case__	  =       FSMTForConditionalGeneration(a )

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

							# save
							snake_case__	  =       os.path.join(a  ,					a )
							print(F'''Generating {pytorch_weights_dump_path}''' )
							torch.save(a  ,					a )

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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a__       =     {
    """configuration_clip""": [
        """CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """CLIPConfig""",
        """CLIPOnnxConfig""",
        """CLIPTextConfig""",
        """CLIPVisionConfig""",
    ],
    """processing_clip""": ["""CLIPProcessor"""],
    """tokenization_clip""": ["""CLIPTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     ["""CLIPTokenizerFast"""]

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     ["""CLIPFeatureExtractor"""]
	a__       =     ["""CLIPImageProcessor"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """CLIPModel""",
	    """CLIPPreTrainedModel""",
	    """CLIPTextModel""",
	    """CLIPTextModelWithProjection""",
	    """CLIPVisionModel""",
	    """CLIPVisionModelWithProjection""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFCLIPModel""",
	    """TFCLIPPreTrainedModel""",
	    """TFCLIPTextModel""",
	    """TFCLIPVisionModel""",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """FlaxCLIPModel""",
	    """FlaxCLIPPreTrainedModel""",
	    """FlaxCLIPTextModel""",
	    """FlaxCLIPTextPreTrainedModel""",
	    """FlaxCLIPVisionModel""",
	    """FlaxCLIPVisionPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_clip import (
	    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    CLIPConfig,
	    CLIPOnnxConfig,
	    CLIPTextConfig,
	    CLIPVisionConfig,
	)
	from .processing_clip import CLIPProcessor
	from .tokenization_clip import CLIPTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_clip_fast import CLIPTokenizerFast

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_clip import CLIPFeatureExtractor
		from .image_processing_clip import CLIPImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_clip import (
		    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CLIPModel,
		    CLIPPreTrainedModel,
		    CLIPTextModel,
		    CLIPTextModelWithProjection,
		    CLIPVisionModel,
		    CLIPVisionModelWithProjection,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_clip import (
		    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFCLIPModel,
		    TFCLIPPreTrainedModel,
		    TFCLIPTextModel,
		    TFCLIPVisionModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_clip import (
		    FlaxCLIPModel,
		    FlaxCLIPPreTrainedModel,
		    FlaxCLIPTextModel,
		    FlaxCLIPTextPreTrainedModel,
		    FlaxCLIPVisionModel,
		    FlaxCLIPVisionPreTrainedModel,
		)


else:
	import sys

	a__       =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
