





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()

UpperCamelCase__     =	2

class     __SCREAMING_SNAKE_CASE   :






							def __init__(  self					,	*,  # begin keyword-only arguments
							    __lowerCAmelCase="<s>"					,	__lowerCAmelCase="<pad>"					,	__lowerCAmelCase="</s>"					,	__lowerCAmelCase="<unk>"					,	__lowerCAmelCase=None					,	):
														UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__			       =     bos, unk, pad, eos
														UpperCamelCase__			       =     []
														UpperCamelCase__			       =     []
														UpperCamelCase__			       =     {}
														UpperCamelCase__			       =     self.add_symbol(a_					)
														UpperCamelCase__			       =     self.add_symbol(a_					)
														UpperCamelCase__			       =     self.add_symbol(a_					)
														UpperCamelCase__			       =     self.add_symbol(a_					)
														if extra_special_symbols:
																					for s in extra_special_symbols:
																												self.add_symbol(a_					)
														UpperCamelCase__			       =     len(self.symbols					)






							def __eq__(  self					,	__lowerCAmelCase					):
														return self.indices == other.indices






							def __getitem__(  self					,	__lowerCAmelCase					):



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






							def __len__(  self					):
														return len(self.symbols					)






							def __contains__(  self					,	__lowerCAmelCase					):
														return sym in self.indices






							@classmethod
							def 			_lowerCamelCase						(  cls					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     cls()
														d.add_from_file(a_					)
														return d






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase=1					,	__lowerCAmelCase=False					):



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






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														return 0







							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):



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

														UpperCamelCase__			       =     f.readlines()
														UpperCamelCase__			       =     self._load_meta(a_					)

														for line in lines[indices_start_line:]:
																					try:
																												UpperCamelCase__       ,       UpperCamelCase__			       =     line.rstrip().rsplit(""" """					,	1					)
																												if field == "#fairseq:overwrite":
																																			UpperCamelCase__			       =     True
																																			UpperCamelCase__       ,       UpperCamelCase__			       =     line.rsplit(""" """					,	1					)
																												else:
																																			UpperCamelCase__			       =     False
																												UpperCamelCase__			       =     int(a_					)
																												UpperCamelCase__			       =     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(a_					)					)
																												self.add_symbol(a_					,	n=a_					,	overwrite=a_					)
																					except ValueError:
																												raise ValueError("""Incorrect dictionary format, expected '<token> <cnt> [flags]'"""					)






def       _UpperCamelCase       (a__				:int    ):






							"""simple docstring"""





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





def       _UpperCamelCase       (a__				:int				,     a__				:str    ):






							"""simple docstring"""





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

							# handle various types of models

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

							UpperCamelCase__			       =     chkpt["""cfg"""]["""model"""]

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

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

							UpperCamelCase__			       =     os.path.join(_UpperCamelCase				,     VOCAB_FILES_NAMES["""merges_file"""]    )
							shutil.copyfile(_UpperCamelCase				,     _UpperCamelCase    )

							# model config
							UpperCamelCase__			       =     os.path.join(_UpperCamelCase				,     """config.json"""    )

							UpperCamelCase__			       =     {
							    """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-12,
							    """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(_UpperCamelCase				,     """w"""				,     encoding="""utf-8"""    ) as f:
														f.write(json.dumps(_UpperCamelCase				,     ensure_ascii=_UpperCamelCase				,     indent=_UpperCamelCase    )    )

							# tokenizer config
							UpperCamelCase__			       =     os.path.join(_UpperCamelCase				,     _UpperCamelCase    )

							UpperCamelCase__			       =     {
							    """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(_UpperCamelCase				,     """w"""				,     encoding="""utf-8"""    ) as f:
														f.write(json.dumps(_UpperCamelCase				,     ensure_ascii=_UpperCamelCase				,     indent=_UpperCamelCase    )    )

							# model
							UpperCamelCase__			       =     chkpt["""model"""]

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

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

							UpperCamelCase__			       =     BioGptConfig.from_pretrained(_UpperCamelCase    )
							UpperCamelCase__			       =     BioGptForCausalLM(_UpperCamelCase    )

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

							# save
							UpperCamelCase__			       =     os.path.join(_UpperCamelCase				,     _UpperCamelCase    )
							print(f"""Generating {pytorch_weights_dump_path}"""    )
							torch.save(_UpperCamelCase				,     _UpperCamelCase    )

							print("""Conversion is done!"""    )


if __name__ == "__main__":
					UpperCamelCase__     =	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."
					)
					UpperCamelCase__     =	parser.parse_args()
					convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)











import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


UpperCamelCase__     =	"sshleifer/mar_enro_6_3_student"

class     __SCREAMING_SNAKE_CASE   (       _a    ):






							def 			_lowerCamelCase						(  self					):
														super().setUp()

														UpperCamelCase__			       =     cached_path(
														    """https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"""					,	extract_compressed_file=__lowerCAmelCase					,	)
														UpperCamelCase__			       =     f"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"""






							@slow
							@require_torch_gpu
							def 			_lowerCamelCase						(  self					):
														MarianMTModel.from_pretrained(__lowerCAmelCase					)







							@slow
							@require_torch_gpu
							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     {
														    """$MAX_LEN""": 64,
														    """$BS""": 64,
														    """$GAS""": 1,
														    """$ENRO_DIR""": self.data_dir,
														    """facebook/mbart-large-cc25""": MARIAN_MODEL,
														    # "val_check_interval=0.25": "val_check_interval=1.0",
														    """--learning_rate=3e-5""": """--learning_rate 3e-4""",
														    """--num_train_epochs 6""": """--num_train_epochs 1""",
														}

														# Clean up bash script
														UpperCamelCase__			       =     (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""					)[1].strip()
														UpperCamelCase__			       =     bash_script.replace("""\\\n"""					,	""""""					).strip().replace("""\"$@\""""					,	""""""					)
														for k, v in env_vars_to_replace.items():
																					UpperCamelCase__			       =     bash_script.replace(__lowerCAmelCase					,	str(__lowerCAmelCase					)					)
														UpperCamelCase__			       =     self.get_auto_remove_tmp_dir()

														# bash_script = bash_script.replace("--fp16 ", "")
														UpperCamelCase__			       =     f"""
            --output_dir {output_dir}
            --tokenizer_name Helsinki-NLP/opus-mt-en-ro
            --sortish_sampler
            --do_predict
            --gpus 1
            --freeze_encoder
            --n_train 40000
            --n_val 500
            --n_test 500
            --fp16_opt_level O1
            --num_sanity_val_steps 0
            --eval_beams 2
        """.split()
														# XXX: args.gpus > 1 : handle multi_gpu in the future

														UpperCamelCase__			       =     ["""finetune.py"""] + bash_script.split() + args
														with patch.object(__lowerCAmelCase					,	"""argv"""					,	__lowerCAmelCase					):
																					UpperCamelCase__			       =     argparse.ArgumentParser()
																					UpperCamelCase__			       =     pl.Trainer.add_argparse_args(__lowerCAmelCase					)
																					UpperCamelCase__			       =     SummarizationModule.add_model_specific_args(__lowerCAmelCase					,	os.getcwd()					)
																					UpperCamelCase__			       =     parser.parse_args()
																					UpperCamelCase__			       =     main(__lowerCAmelCase					)

														# Check metrics
														UpperCamelCase__			       =     load_json(model.metrics_save_path					)
														UpperCamelCase__			       =     metrics["""val"""][0]
														UpperCamelCase__			       =     metrics["""val"""][-1]
														self.assertEqual(len(metrics["""val"""]					)					,	(args.max_epochs / args.val_check_interval)					)
														assert isinstance(last_step_stats[f"""val_avg_{model.val_metric}"""]					,	__lowerCAmelCase					)

														self.assertGreater(last_step_stats["""val_avg_gen_time"""]					,	0.01					)
														# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
														self.assertLessEqual(last_step_stats["""val_avg_gen_time"""]					,	1.0					)

														# test learning requirements:

														# 1. BLEU improves over the course of training by more than 2 pts
														self.assertGreater(last_step_stats["""val_avg_bleu"""] - first_step_stats["""val_avg_bleu"""]					,	2					)

														# 2. BLEU finishes above 17
														self.assertGreater(last_step_stats["""val_avg_bleu"""]					,	17					)

														# 3. test BLEU and val BLEU within ~1.1 pt.
														self.assertLess(abs(metrics["""val"""][-1]["""val_avg_bleu"""] - metrics["""test"""][-1]["""test_avg_bleu"""]					)					,	1.1					)

														# check lightning ckpt can be loaded and has a reasonable statedict
														UpperCamelCase__			       =     os.listdir(__lowerCAmelCase					)
														UpperCamelCase__			       =     [x for x in contents if x.endswith(""".ckpt"""					)][0]
														UpperCamelCase__			       =     os.path.join(args.output_dir					,	__lowerCAmelCase					)
														UpperCamelCase__			       =     torch.load(__lowerCAmelCase					,	map_location="""cpu"""					)
														UpperCamelCase__			       =     """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
														assert expected_key in ckpt["state_dict"]
														assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

														# TODO: turn on args.do_predict when PL bug fixed.
														if args.do_predict:
																					UpperCamelCase__			       =     {os.path.basename(__lowerCAmelCase					) for p in contents}
																					assert "test_generations.txt" in contents
																					assert "test_results.txt" in contents
																					# assert len(metrics["val"]) ==  desired_n_evals
																					assert len(metrics["""test"""]					) == 1






class     __SCREAMING_SNAKE_CASE   (       _a    ):






							@timeout_decorator.timeout(600					)
							@slow
							@require_torch_gpu
							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     f"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
														UpperCamelCase__			       =     {
														    """--fp16_opt_level=O1""": """""",
														    """$MAX_LEN""": 128,
														    """$BS""": 16,
														    """$GAS""": 1,
														    """$ENRO_DIR""": data_dir,
														    """$m""": """sshleifer/student_marian_en_ro_6_1""",
														    """val_check_interval=0.25""": """val_check_interval=1.0""",
														}

														# Clean up bash script
														UpperCamelCase__			       =     (
														    (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""					)[1].strip()
														)
														UpperCamelCase__			       =     bash_script.replace("""\\\n"""					,	""""""					).strip().replace("""\"$@\""""					,	""""""					)
														UpperCamelCase__			       =     bash_script.replace("""--fp16 """					,	""" """					)

														for k, v in env_vars_to_replace.items():
																					UpperCamelCase__			       =     bash_script.replace(__lowerCAmelCase					,	str(__lowerCAmelCase					)					)
														UpperCamelCase__			       =     self.get_auto_remove_tmp_dir()
														UpperCamelCase__			       =     bash_script.replace("""--fp16"""					,	""""""					)
														UpperCamelCase__			       =     6
														UpperCamelCase__			       =     (
														    ["""distillation.py"""]
														    + bash_script.split()
														    + [
														        f"""--output_dir={output_dir}""",
														        """--gpus=1""",
														        """--learning_rate=1e-3""",
														        f"""--num_train_epochs={epochs}""",
														        """--warmup_steps=10""",
														        """--val_check_interval=1.0""",
														        """--do_predict""",
														    ]
														)
														with patch.object(__lowerCAmelCase					,	"""argv"""					,	__lowerCAmelCase					):
																					UpperCamelCase__			       =     argparse.ArgumentParser()
																					UpperCamelCase__			       =     pl.Trainer.add_argparse_args(__lowerCAmelCase					)
																					UpperCamelCase__			       =     SummarizationDistiller.add_model_specific_args(__lowerCAmelCase					,	os.getcwd()					)
																					UpperCamelCase__			       =     parser.parse_args()
																					# assert args.gpus == gpus THIS BREAKS for multi_gpu

																					UpperCamelCase__			       =     distill_main(__lowerCAmelCase					)

														# Check metrics
														UpperCamelCase__			       =     load_json(model.metrics_save_path					)
														UpperCamelCase__			       =     metrics["""val"""][0]
														UpperCamelCase__			       =     metrics["""val"""][-1]
														assert len(metrics["""val"""]					) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

														assert last_step_stats["val_avg_gen_time"] >= 0.01

														assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
														assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
														assert isinstance(last_step_stats[f"""val_avg_{model.val_metric}"""]					,	__lowerCAmelCase					)

														# check lightning ckpt can be loaded and has a reasonable statedict
														UpperCamelCase__			       =     os.listdir(__lowerCAmelCase					)
														UpperCamelCase__			       =     [x for x in contents if x.endswith(""".ckpt"""					)][0]
														UpperCamelCase__			       =     os.path.join(args.output_dir					,	__lowerCAmelCase					)
														UpperCamelCase__			       =     torch.load(__lowerCAmelCase					,	map_location="""cpu"""					)
														UpperCamelCase__			       =     """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
														assert expected_key in ckpt["state_dict"]
														assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

														# TODO: turn on args.do_predict when PL bug fixed.
														if args.do_predict:
																					UpperCamelCase__			       =     {os.path.basename(__lowerCAmelCase					) for p in contents}
																					assert "test_generations.txt" in contents
																					assert "test_results.txt" in contents
																					# assert len(metrics["val"]) ==  desired_n_evals
																					assert len(metrics["""test"""]					) == 1




