



import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

_lowercase: int      			=		2



class 	_lowercase							:





	"""simple docstring"""




	def __init__(self		,       *,  # begin keyword-only arguments
	    lowerCamelCase_="<s>"		,       lowerCamelCase_="<pad>"		,       lowerCamelCase_="</s>"		,       lowerCamelCase_="<unk>"		,       lowerCamelCase_=None		,       ):
						"""simple docstring"""

						a     ,				a     ,				a     ,				a					     =   bos, unk, pad, eos
						a					     =   []
						a					     =   []
						a					     =   {}
						a					     =   self.add_symbol(lowerCamelCase_		)
						a					     =   self.add_symbol(lowerCamelCase_		)
						a					     =   self.add_symbol(lowerCamelCase_		)
						a					     =   self.add_symbol(lowerCamelCase_		)
						if extra_special_symbols:
											for s in extra_special_symbols:
																self.add_symbol(lowerCamelCase_		)
						a					     =   len(self.symbols		)




	def __eq__(self		,       lowerCamelCase_		):
						"""simple docstring"""

						return self.indices == other.indices




	def __getitem__(self		,       lowerCamelCase_		):
						"""simple docstring"""

						if idx < len(self.symbols		):
											return self.symbols[idx]
						return self.unk_word




	def __len__(self		):
						"""simple docstring"""

						return len(self.symbols		)




	def __contains__(self		,       lowerCamelCase_		):
						"""simple docstring"""

						return sym in self.indices




	@classmethod
	def      UpperCamelCase_       (cls		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   cls()
						d.add_from_file(lowerCamelCase_		)
						return d




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=1		,       lowerCamelCase_=False		):
						"""simple docstring"""

						if word in self.indices and not overwrite:
											a					     =   self.indices[word]
											a					     =   self.count[idx] + n
											return idx
						else:
											a					     =   len(self.symbols		)
											a					     =   idx
											self.symbols.append(lowerCamelCase_		)
											self.count.append(lowerCamelCase_		)
											return idx




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						return 0




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						if isinstance(lowerCamelCase_		,       lowerCamelCase_		):
											try:
																with open(lowerCamelCase_		,       "r"		,       encoding="utf-8"		) as fd:
																					self.add_from_file(lowerCamelCase_		)
											except FileNotFoundError as fnfe:
																raise fnfe
											except UnicodeError:
																raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(lowerCamelCase_		)		)
											return

						a					     =   f.readlines()
						a					     =   self._load_meta(lowerCamelCase_		)

						for line in lines[indices_start_line:]:
											try:
																a     ,				a					     =   line.rstrip().rsplit(" "		,       1		)
																if field == "#fairseq:overwrite":
																					a					     =   True
																					a     ,				a					     =   line.rsplit(" "		,       1		)
																else:
																					a					     =   False
																a					     =   int(lowerCamelCase_		)
																a					     =   line
																if word in self and not overwrite:
																					raise RuntimeError(
																					    "Duplicate word found when loading Dictionary: '{}'. "
																					    "Duplicate words can overwrite earlier ones by adding the "
																					    "#fairseq:overwrite flag at the end of the corresponding row "
																					    "in the dictionary file. If using the Camembert model, please "
																					    "download an updated copy of the model file.".format(lowerCamelCase_		)		)
																self.add_symbol(lowerCamelCase_		,       n=lowerCamelCase_		,       overwrite=lowerCamelCase_		)
											except ValueError:
																raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'"		)






def     a(      A       :    List[str]       )		->       int:
					"""simple docstring"""





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






def     a(      A       :    Union[str, Any] ,     A       :    int       )		->       Optional[Any]:
					"""simple docstring"""





					if not os.path.exists(A       ):
										raise ValueError(f'''path {biogpt_checkpoint_path} does not exist!'''       )
					os.makedirs(A ,     exist_ok=A       )
					print(f'''Writing results to {pytorch_dump_folder_path}'''       )

					# handle various types of models

					a					     =   os.path.join(A ,     "checkpoint.pt"       )
					if not os.path.isfile(A       ):
										raise ValueError(f'''path to the file {checkpoint_file} does not exist!'''       )
					a					     =   torch.load(A ,     map_location="cpu"       )

					a					     =   chkpt["cfg"]["model"]

					# dicts
					a					     =   os.path.join(A ,     "dict.txt"       )
					if not os.path.isfile(A       ):
										raise ValueError(f'''path to the file {dict_file} does not exist!'''       )
					a					     =   Dictionary.load(A       )
					a					     =   rewrite_dict_keys(src_dict.indices       )
					a					     =   len(A       )
					a					     =   os.path.join(A ,     VOCAB_FILES_NAMES["vocab_file"]       )
					print(f'''Generating {src_vocab_file} of {src_vocab_size} records'''       )
					with open(A ,     "w" ,     encoding="utf-8"       ) as f:
										f.write(json.dumps(A ,     ensure_ascii=A ,     indent=A       )       )

					# merges_file (bpecodes)
					a					     =   os.path.join(A ,     "bpecodes"       )
					if not os.path.isfile(A       ):
										raise ValueError(f'''path to the file {bpecodes_file} does not exist!'''       )

					a					     =   os.path.join(A ,     VOCAB_FILES_NAMES["merges_file"]       )
					shutil.copyfile(A ,     A       )

					# model config
					a					     =   os.path.join(A ,     "config.json"       )

					a					     =   {
					    "activation_dropout": args["activation_dropout"],
					    "architectures": ["BioGptForCausalLM"],
					    "attention_probs_dropout_prob": args["attention_dropout"],
					    "bos_token_id": 0,
					    "eos_token_id": 2,
					    "hidden_act": args["activation_fn"],
					    "hidden_dropout_prob": args["dropout"],
					    "hidden_size": args["decoder_embed_dim"],
					    "initializer_range": 0.02,
					    "intermediate_size": args["decoder_ffn_embed_dim"],
					    "layer_norm_eps": 1e-1_2,
					    "layerdrop": args["decoder_layerdrop"],
					    "max_position_embeddings": args["max_target_positions"],
					    "model_type": "biogpt",
					    "num_attention_heads": args["decoder_attention_heads"],
					    "num_hidden_layers": args["decoder_layers"],
					    "pad_token_id": 1,
					    "scale_embedding": not args["no_scale_embedding"],
					    "tie_word_embeddings": args["share_decoder_input_output_embed"],
					    "vocab_size": src_vocab_size,
					}

					# good hparam defaults to start with

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

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

					a					     =   {
					    "bos_token": "<s>",
					    "eos_token": "</s>",
					    "model_max_length": 1024,
					    "pad_token": "<pad>",
					    "special_tokens_map_file": None,
					    "tokenizer_class": "BioGptTokenizer",
					    "unk_token": "<unk>",
					}

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

					# model
					a					     =   chkpt["model"]

					# remove unneeded keys
					a					     =   [
					    "decoder.version",
					]
					for k in ignore_keys:
										model_state_dict.pop(A ,     A       )

					a					     =   list(model_state_dict.keys()       )
					for layer_name in layer_names:
										if layer_name.endswith("output_projection.weight"       ):
															a					     =   model_state_dict.pop(A       )
										else:
															a					     =   model_state_dict.pop(A       )

					a					     =   BioGptConfig.from_pretrained(A       )
					a					     =   BioGptForCausalLM(A       )

					# check that it loads ok
					model_new.load_state_dict(A       )

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

					print("Conversion is done!"       )


if __name__ == "__main__":
				_lowercase: Optional[Any]      			=		argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--biogpt_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."
				)
				_lowercase: Optional[int]      			=		parser.parse_args()
				convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)





import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_lowercase: Union[str, Any]      			=		get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
				_lowercase: List[str]      			=		json.load(f)



@require_torch
class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						return FSMTTokenizer.from_pretrained(lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   FSMTForConditionalGeneration.from_pretrained(lowerCamelCase_		).to(lowerCamelCase_		)
						if torch_device == "cuda":
											model.half()
						return model




	@parameterized.expand(
	    [
	        ["en-ru", 26.0],
	        ["ru-en", 22.0],
	        ["en-de", 22.0],
	        ["de-en", 29.0],
	    ]		)
	@slow
	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   F'''facebook/wmt19-{pair}'''
						a					     =   self.get_tokenizer(lowerCamelCase_		)
						a					     =   self.get_model(lowerCamelCase_		)

						a					     =   bleu_data[pair]["src"]
						a					     =   bleu_data[pair]["tgt"]

						a					     =   tokenizer(lowerCamelCase_		,       return_tensors="pt"		,       truncation=lowerCamelCase_		,       padding="longest"		).to(lowerCamelCase_		)
						a					     =   model.generate(
						    input_ids=batch.input_ids		,       num_beams=8		,       )
						a					     =   tokenizer.batch_decode(
						    lowerCamelCase_		,       skip_special_tokens=lowerCamelCase_		,       clean_up_tokenization_spaces=lowerCamelCase_		)
						a					     =   calculate_bleu(lowerCamelCase_		,       lowerCamelCase_		)
						print(lowerCamelCase_		)
						self.assertGreaterEqual(scores["bleu"]		,       lowerCamelCase_		)
