# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union

from packaging import version

from ..utils import is_torch_available, logging


if is_torch_available():
		import torch


lowercase									=				logging.get_logger(__name__)





@dataclass
class 	__lowercase    :




				'''simple docstring'''


				def __init__(		self    :		Optional[Any]      ,							_a    :		List[str]=False      ,							_a    :		str=False      ,							_a    :		Optional[int]=6.0      ,							_a    :		int=None      ,							_a    :		Optional[Any]=False      ,							_a    :		Any=False      ,							_a    :		str=None      ,							_a    :		List[str]="fp4"      ,							_a    :		Dict=False      ,							**_a    :		List[Any]      ,							):
								UpperCamelCase__					=				load_in_abit
								UpperCamelCase__					=				load_in_abit
								UpperCamelCase__					=				llm_inta_threshold
								UpperCamelCase__					=				llm_inta_skip_modules
								UpperCamelCase__					=				llm_inta_enable_fpaa_cpu_offload
								UpperCamelCase__					=				llm_inta_has_fpaa_weight
								UpperCamelCase__					=				bnb_abit_quant_type
								UpperCamelCase__					=				bnb_abit_use_double_quant

								if bnb_abit_compute_dtype is None:
												UpperCamelCase__					=				torch.floataa
								elif isinstance(A__      ,							A__				):
												UpperCamelCase__					=				getattr(A__      ,							A__				)
								elif isinstance(A__      ,							torch.dtype				):
												UpperCamelCase__					=				bnb_abit_compute_dtype
								else:
												raise ValueError('''bnb_4bit_compute_dtype must be a string or a torch.dtype'''				)

								self.post_init()





				def      A_							(		self    :		str				):
								if not isinstance(self.llm_inta_threshold      ,							A__				):
												raise ValueError('''llm_int8_threshold must be a float'''				)

								if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules      ,							A__				):
												raise ValueError('''llm_int8_skip_modules must be a list of strings'''				)
								if not isinstance(self.llm_inta_enable_fpaa_cpu_offload      ,							A__				):
												raise ValueError('''llm_int8_enable_fp32_cpu_offload must be a boolean'''				)

								if not isinstance(self.llm_inta_has_fpaa_weight      ,							A__				):
												raise ValueError('''llm_int8_has_fp16_weight must be a boolean'''				)

								if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype      ,							torch.dtype				):
												raise ValueError('''bnb_4bit_compute_dtype must be torch.dtype'''				)

								if not isinstance(self.bnb_abit_quant_type      ,							A__				):
												raise ValueError('''bnb_4bit_quant_type must be a string'''				)

								if not isinstance(self.bnb_abit_use_double_quant      ,							A__				):
												raise ValueError('''bnb_4bit_use_double_quant must be a boolean'''				)

								if self.load_in_abit and not version.parse(importlib.metadata.version('''bitsandbytes'''				)				) >= version.parse(
								    '''0.39.0'''				):
												raise ValueError(
												    '''4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version'''				)





				def      A_							(		self    :		Tuple				):
								return self.load_in_abit or self.load_in_abit





				def      A_							(		self    :		List[Any]				):
								if self.load_in_abit:
												return "llm_int8"
								elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
												return "fp4"
								elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
												return "nf4"
								else:
												return None





				@classmethod
				def      A_							(		cls    :		Optional[Any]      ,							_a    :		Any      ,							_a    :		Tuple      ,							**_a    :		Tuple				):
								UpperCamelCase__					=				cls(**A__				)

								UpperCamelCase__					=				[]
								for key, value in kwargs.items():
												if hasattr(A__      ,							A__				):
																setattr(A__      ,							A__      ,							A__				)
																to_remove.append(A__				)
								for key in to_remove:
												kwargs.pop(A__      ,							A__				)

								if return_unused_kwargs:
												return config, kwargs
								else:
												return config





				def      A_							(		self    :		List[str]      ,							_a    :		Any				):
								with open(A__      ,							'''w'''      ,							encoding='''utf-8'''				) as writer:
												UpperCamelCase__					=				self.to_dict()
												UpperCamelCase__					=				json.dumps(A__      ,							indent=2      ,							sort_keys=A__				) + """\n"""

												writer.write(A__				)





				def      A_							(		self    :		List[str]				):
								UpperCamelCase__					=				copy.deepcopy(self.__dict__				)
								UpperCamelCase__					=				str(output['''bnb_4bit_compute_dtype''']				).split('''.'''				)[1]

								return output





				def __repr__(		self    :		Union[str, Any]				):
								return F"""{self.__class__.__name__} {self.to_json_string()}"""





				def      A_							(		self    :		Optional[int]      ,							_a    :		str = True				):
								if use_diff is True:
												UpperCamelCase__					=				self.to_diff_dict()
								else:
												UpperCamelCase__					=				self.to_dict()
								return json.dumps(A__      ,							indent=2      ,							sort_keys=A__				) + "\n"





				def      A_							(		self    :		List[str]				):
								UpperCamelCase__					=				self.to_dict()

								# get the default config dict
								UpperCamelCase__					=				BitsAndBytesConfig().to_dict()

								UpperCamelCase__					=				{}

								# only serialize values that differ from the default config
								for key, value in config_dict.items():
												if value != default_config_dict[key]:
																UpperCamelCase__					=				value

								return serializable_config_dict






# 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_			:					List[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
A_			:					Tuple    			=     {
    # 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_			:					Optional[int]    			=     {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
	A_			:					Optional[int]    			=     '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_			:					Optional[int]    			=     'allenai'

def 		UpperCamelCase   (lowercase_:					int   )	-> Tuple:
	# (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__				:       int	  =						dict((re.sub(r"""@@$"""       , """"""       , lowercase_   ), v) if k.endswith("""@@"""   ) else (re.sub(r"""$"""       , """</w>"""       , lowercase_   ), v) for k, v in d.items()   )
	A__				:       str	  =						"""<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   (lowercase_:					Tuple       , lowercase_:					Tuple   )	-> Optional[int]:
	# prep
	assert os.path.exists(lowercase_   )
	os.makedirs(lowercase_       , exist_ok=lowercase_   )
	print(f"""Writing results to {pytorch_dump_folder_path}"""   )

	# handle various types of models

	A__				:       Dict	  =						basename(lowercase_   )
	A__				:       int	  =						dirname(lowercase_   )

	A__				:       List[str]	  =						fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
	A__				:       Union[str, Any]	  =						cls.hub_models()
	A__				:       Optional[Any]	  =						{"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
	A__				:       Union[str, 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__				:       Tuple	  =						hub_utils.from_pretrained(
	    lowercase_       , lowercase_       , lowercase_       , archive_map=lowercase_       , **lowercase_   )

	A__				:       Any	  =						vars(chkpt["""args"""]["""model"""]   )

	A__				:       Optional[Any]	  =						args["""source_lang"""]
	A__				:       Optional[Any]	  =						args["""target_lang"""]

	A__				:       Dict	  =						dirname(lowercase_   )
	A__				:       Optional[Any]	  =						basename(lowercase_   )

	# dicts
	A__				:       Optional[int]	  =						os.path.join(lowercase_       , f"""dict.{src_lang}.txt"""   )
	A__				:       int	  =						os.path.join(lowercase_       , f"""dict.{tgt_lang}.txt"""   )

	A__				:       Dict	  =						Dictionary.load(lowercase_   )
	A__				:       List[str]	  =						rewrite_dict_keys(src_dict.indices   )
	A__				:       Any	  =						len(lowercase_   )
	A__				:       str	  =						os.path.join(lowercase_       , """vocab-src.json"""   )
	print(f"""Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records"""   )
	with open(lowercase_       , """w"""       , encoding="""utf-8"""   ) as f:
		f.write(json.dumps(lowercase_       , ensure_ascii=lowercase_       , indent=lowercase_   )   )

	# 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__				:       Optional[Any]	  =						True
	for k in src_vocab.keys():
		if not k.islower():
			A__				:       Tuple	  =						False
			break

	A__				:       List[str]	  =						Dictionary.load(lowercase_   )
	A__				:       Union[str, Any]	  =						rewrite_dict_keys(tgt_dict.indices   )
	A__				:       str	  =						len(lowercase_   )
	A__				:       int	  =						os.path.join(lowercase_       , """vocab-tgt.json"""   )
	print(f"""Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records"""   )
	with open(lowercase_       , """w"""       , encoding="""utf-8"""   ) as f:
		f.write(json.dumps(lowercase_       , ensure_ascii=lowercase_       , indent=lowercase_   )   )

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

	# model config
	A__				:       Optional[Any]	  =						os.path.join(lowercase_       , """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__				:       List[str]	  =						{
	    """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__				:       Tuple	  =						5
	A__				:       List[str]	  =						False
	if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
		A__				:       int	  =						best_score_hparams[model_dir]["""length_penalty"""]
	else:
		A__				:       List[Any]	  =						1.0

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

	# tokenizer config
	A__				:       Dict	  =						os.path.join(lowercase_       , lowercase_   )

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

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

	# model
	A__				:       int	  =						chkpt["""models"""][0]
	A__				:       Dict	  =						model.state_dict()

	# rename keys to start with 'model.'
	A__				:       Union[str, Any]	  =						OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items()   )

	# remove unneeded keys
	A__				:       List[str]	  =						[
	    """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(lowercase_       , lowercase_   )

	A__				:       str	  =						FSMTConfig.from_pretrained(lowercase_   )
	A__				:       Dict	  =						FSMTForConditionalGeneration(lowercase_   )

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

	# save
	A__				:       int	  =						os.path.join(lowercase_       , lowercase_   )
	print(f"""Generating {pytorch_weights_dump_path}"""   )
	torch.save(lowercase_       , lowercase_   )

	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_			:					Union[str, 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.'
	)
	A_			:					Tuple    			=     parser.parse_args()
	convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)



