






"""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()

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






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






def 			__UpperCamelCase	(		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

	A_		:						Optional[int]	    =   basename(snake_case__					)
	A_		:						Union[str, Any]	    =   dirname(snake_case__					)

	A_		:						Tuple	    =   fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
	A_		:						List[Any]	    =   cls.hub_models()
	A_		:						Dict	    =   {"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
	A_		:						Any	    =   """."""
	# 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}"""					)
	A_		:						Union[str, Any]	    =   hub_utils.from_pretrained(
	    snake_case__   ,   snake_case__   ,   snake_case__   ,   archive_map=snake_case__   ,   **snake_case__					)

	A_		:						Union[str, Any]	    =   vars(chkpt["""args"""]["""model"""]					)

	A_		:						Tuple	    =   args["""source_lang"""]
	A_		:						Any	    =   args["""target_lang"""]

	A_		:						Tuple	    =   dirname(snake_case__					)
	A_		:						Any	    =   basename(snake_case__					)

	# dicts
	A_		:						Optional[Any]	    =   os.path.join(snake_case__   ,   F"""dict.{src_lang}.txt"""					)
	A_		:						Any	    =   os.path.join(snake_case__   ,   F"""dict.{tgt_lang}.txt"""					)

	A_		:						Any	    =   Dictionary.load(snake_case__					)
	A_		:						str	    =   rewrite_dict_keys(src_dict.indices					)
	A_		:						List[str]	    =   len(snake_case__					)
	A_		:						List[Any]	    =   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
	A_		:						List[str]	    =   True
	for k in src_vocab.keys():
		if not k.islower():
			A_		:						List[Any]	    =   False
			break

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

	A_		:						Optional[int]	    =   {
	    """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
	A_		:						Union[str, Any]	    =   5
	A_		:						Dict	    =   False
	if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
		A_		:						Dict	    =   best_score_hparams[model_dir]["""length_penalty"""]
	else:
		A_		:						str	    =   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
	A_		:						Optional[int]	    =   os.path.join(snake_case__   ,   snake_case__					)

	A_		:						Tuple	    =   {
	    """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
	A_		:						Union[str, Any]	    =   chkpt["""models"""][0]
	A_		:						List[str]	    =   model.state_dict()

	# rename keys to start with 'model.'
	A_		:						Optional[int]	    =   OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items()					)

	# remove unneeded keys
	A_		:						Tuple	    =   [
	    """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__					)

	A_		:						List[str]	    =   FSMTConfig.from_pretrained(snake_case__					)
	A_		:						Optional[Any]	    =   FSMTForConditionalGeneration(snake_case__					)

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

	# save
	A_		:						Tuple	    =   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         =							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         =							parser.parse_args()
							convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)









"""simple docstring"""
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_lowerCAmelCase         =							logging.get_logger(__name__)

_lowerCAmelCase         =							{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				List[str]    =				"""deta"""
	_A       :				Tuple    =				{
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	}





	def __init__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=900     ,     lowerCAmelCase_=2048     ,     lowerCAmelCase_=6     ,     lowerCAmelCase_=2048     ,     lowerCAmelCase_=8     ,     lowerCAmelCase_=6     ,     lowerCAmelCase_=1024     ,     lowerCAmelCase_=8     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_="relu"     ,     lowerCAmelCase_=256     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=0.0     ,     lowerCAmelCase_=0.02     ,     lowerCAmelCase_=1.0     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=False     ,     lowerCAmelCase_="sine"     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=300     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=5     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.25     ,     **lowerCAmelCase_     ,     ):
		if backbone_config is None:
			logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""							)
			A_		:						Any	    =   CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]							)
		else:
			if isinstance(lowerCAmelCase_     ,     lowerCAmelCase_							):
				A_		:						List[str]	    =   backbone_config.pop("""model_type"""							)
				A_		:						List[str]	    =   CONFIG_MAPPING[backbone_model_type]
				A_		:						Tuple	    =   config_class.from_dict(lowerCAmelCase_							)

		A_		:						List[Any]	    =   backbone_config
		A_		:						List[str]	    =   num_queries
		A_		:						List[Any]	    =   max_position_embeddings
		A_		:						int	    =   d_model
		A_		:						Optional[Any]	    =   encoder_ffn_dim
		A_		:						Optional[Any]	    =   encoder_layers
		A_		:						List[Any]	    =   encoder_attention_heads
		A_		:						int	    =   decoder_ffn_dim
		A_		:						Dict	    =   decoder_layers
		A_		:						Dict	    =   decoder_attention_heads
		A_		:						Optional[Any]	    =   dropout
		A_		:						str	    =   attention_dropout
		A_		:						Any	    =   activation_dropout
		A_		:						Union[str, Any]	    =   activation_function
		A_		:						int	    =   init_std
		A_		:						List[Any]	    =   init_xavier_std
		A_		:						List[Any]	    =   encoder_layerdrop
		A_		:						Any	    =   auxiliary_loss
		A_		:						Tuple	    =   position_embedding_type
		# deformable attributes
		A_		:						List[str]	    =   num_feature_levels
		A_		:						Tuple	    =   encoder_n_points
		A_		:						str	    =   decoder_n_points
		A_		:						List[Any]	    =   two_stage
		A_		:						Optional[Any]	    =   two_stage_num_proposals
		A_		:						Union[str, Any]	    =   with_box_refine
		A_		:						Optional[int]	    =   assign_first_stage
		if two_stage is True and with_box_refine is False:
			raise ValueError("""If two_stage is True, with_box_refine must be True."""							)
		# Hungarian matcher
		A_		:						List[Any]	    =   class_cost
		A_		:						str	    =   bbox_cost
		A_		:						Tuple	    =   giou_cost
		# Loss coefficients
		A_		:						int	    =   mask_loss_coefficient
		A_		:						Union[str, Any]	    =   dice_loss_coefficient
		A_		:						Union[str, Any]	    =   bbox_loss_coefficient
		A_		:						Optional[Any]	    =   giou_loss_coefficient
		A_		:						List[str]	    =   eos_coefficient
		A_		:						Optional[Any]	    =   focal_alpha
		super().__init__(is_encoder_decoder=lowerCAmelCase_     ,     **lowerCAmelCase_							)





	@property
	def    lowerCamelCase(self							):
		return self.encoder_attention_heads





	@property
	def    lowerCamelCase(self							):
		return self.d_model





	def    lowerCamelCase(self							):
		A_		:						str	    =   copy.deepcopy(self.__dict__							)
		A_		:						int	    =   self.backbone_config.to_dict()
		A_		:						Dict	    =   self.__class__.model_type
		return output

