






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()

__snake_case      :      Optional[Any]             =       2


class       __SCREAMING_SNAKE_CASE   :






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






												"""simple docstring"""
												lowerCAmelCase__  ,    lowerCAmelCase__  ,    lowerCAmelCase__  ,    lowerCAmelCase__        =  bos, unk, pad, eos
												lowerCAmelCase__        =  []
												lowerCAmelCase__        =  []
												lowerCAmelCase__        =  {}
												lowerCAmelCase__        =  self.add_symbol(a_				)
												lowerCAmelCase__        =  self.add_symbol(a_				)
												lowerCAmelCase__        =  self.add_symbol(a_				)
												lowerCAmelCase__        =  self.add_symbol(a_				)
												if extra_special_symbols:
																		for s in extra_special_symbols:
																								self.add_symbol(a_				)
												lowerCAmelCase__        =  len(self.symbols				)






						def __eq__(		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												return self.indices == other.indices






						def __getitem__(		self       ,  _UpperCamelCase				):






												"""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       ,  _UpperCamelCase				):






												"""simple docstring"""
												return sym in self.indices






						@classmethod
						def  UpperCamelCase__ (		cls       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  cls()
												d.add_from_file(a_				)
												return d






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase=1       ,  _UpperCamelCase=False				):






												"""simple docstring"""



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






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												return 0







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""



												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

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

												for line in lines[indices_start_line:]:
																		try:
																								lowerCAmelCase__  ,    lowerCAmelCase__        =  line.rstrip().rsplit(' '       ,  1				)
																								if field == "#fairseq:overwrite":
																														lowerCAmelCase__        =  True
																														lowerCAmelCase__  ,    lowerCAmelCase__        =  line.rsplit(' '       ,  1				)
																								else:
																														lowerCAmelCase__        =  False
																								lowerCAmelCase__        =  int(a_				)
																								lowerCAmelCase__        =  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				(      UpperCamelCase_    :   int						)					->				List[Any]:

						"""simple docstring"""



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







def 		_UpperCamelCase				(      UpperCamelCase_    :   int      ,    UpperCamelCase_    :   str						)					->				List[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

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

						lowerCAmelCase__        =  chkpt['cfg']['model']

						# dicts
						lowerCAmelCase__        =  os.path.join(_UpperCamelCase      ,    'dict.txt'						)
						if not os.path.isfile(_UpperCamelCase						):
												raise ValueError(F"path to the file {dict_file} does not exist!"						)
						lowerCAmelCase__        =  Dictionary.load(_UpperCamelCase						)
						lowerCAmelCase__        =  rewrite_dict_keys(src_dict.indices						)
						lowerCAmelCase__        =  len(_UpperCamelCase						)
						lowerCAmelCase__        =  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)
						lowerCAmelCase__        =  os.path.join(_UpperCamelCase      ,    'bpecodes'						)
						if not os.path.isfile(_UpperCamelCase						):
												raise ValueError(F"path to the file {bpecodes_file} does not exist!"						)

						lowerCAmelCase__        =  os.path.join(_UpperCamelCase      ,    VOCAB_FILES_NAMES['merges_file']						)
						shutil.copyfile(_UpperCamelCase      ,    _UpperCamelCase						)

						# model config
						lowerCAmelCase__        =  os.path.join(_UpperCamelCase      ,    'config.json'						)

						lowerCAmelCase__        =  {
						    '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
						lowerCAmelCase__        =  os.path.join(_UpperCamelCase      ,    _UpperCamelCase						)

						lowerCAmelCase__        =  {
						    '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
						lowerCAmelCase__        =  chkpt['model']

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

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

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

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

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

						print('Conversion is done!'						)


if __name__ == "__main__":
	__snake_case      :      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."""
	)
	__snake_case      :      Union[str, Any]             =       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


__snake_case      :      Tuple             =       """sshleifer/mar_enro_6_3_student"""


class       __SCREAMING_SNAKE_CASE   (				__lowercase):






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												super().setUp()

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






						@slow
						@require_torch_gpu
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												MarianMTModel.from_pretrained(_UpperCamelCase				)







						@slow
						@require_torch_gpu
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  {
												    '$MAX_LEN': 64,
												    '$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
												lowerCAmelCase__        =  (self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py'				)[1].strip()
												lowerCAmelCase__        =  bash_script.replace('\\\n'       ,  ''				).strip().replace('"$@"'       ,  ''				)
												for k, v in env_vars_to_replace.items():
																		lowerCAmelCase__        =  bash_script.replace(_UpperCamelCase       ,  str(_UpperCamelCase				)				)
												lowerCAmelCase__        =  self.get_auto_remove_tmp_dir()

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

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

												# Check metrics
												lowerCAmelCase__        =  load_json(model.metrics_save_path				)
												lowerCAmelCase__        =  metrics['val'][0]
												lowerCAmelCase__        =  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}"]       ,  _UpperCamelCase				)

												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
												lowerCAmelCase__        =  os.listdir(_UpperCamelCase				)
												lowerCAmelCase__        =  [x for x in contents if x.endswith('.ckpt'				)][0]
												lowerCAmelCase__        =  os.path.join(args.output_dir       ,  _UpperCamelCase				)
												lowerCAmelCase__        =  torch.load(_UpperCamelCase       ,  map_location='cpu'				)
												lowerCAmelCase__        =  '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:
																		lowerCAmelCase__        =  {os.path.basename(_UpperCamelCase				) 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   (				__lowercase):






						@timeout_decorator.timeout(6_00				)
						@slow
						@require_torch_gpu
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  F"{self.test_file_dir_str}/test_data/wmt_en_ro"
												lowerCAmelCase__        =  {
												    '--fp16_opt_level=O1': '',
												    '$MAX_LEN': 1_28,
												    '$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
												lowerCAmelCase__        =  (
												    (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py'				)[1].strip()
												)
												lowerCAmelCase__        =  bash_script.replace('\\\n'       ,  ''				).strip().replace('"$@"'       ,  ''				)
												lowerCAmelCase__        =  bash_script.replace('--fp16 '       ,  ' '				)

												for k, v in env_vars_to_replace.items():
																		lowerCAmelCase__        =  bash_script.replace(_UpperCamelCase       ,  str(_UpperCamelCase				)				)
												lowerCAmelCase__        =  self.get_auto_remove_tmp_dir()
												lowerCAmelCase__        =  bash_script.replace('--fp16'       ,  ''				)
												lowerCAmelCase__        =  6
												lowerCAmelCase__        =  (
												    ['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(_UpperCamelCase       ,  'argv'       ,  _UpperCamelCase				):
																		lowerCAmelCase__        =  argparse.ArgumentParser()
																		lowerCAmelCase__        =  pl.Trainer.add_argparse_args(_UpperCamelCase				)
																		lowerCAmelCase__        =  SummarizationDistiller.add_model_specific_args(_UpperCamelCase       ,  os.getcwd()				)
																		lowerCAmelCase__        =  parser.parse_args()
																		# assert args.gpus == gpus THIS BREAKS for multi_gpu

																		lowerCAmelCase__        =  distill_main(_UpperCamelCase				)

												# Check metrics
												lowerCAmelCase__        =  load_json(model.metrics_save_path				)
												lowerCAmelCase__        =  metrics['val'][0]
												lowerCAmelCase__        =  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}"]       ,  _UpperCamelCase				)

												# check lightning ckpt can be loaded and has a reasonable statedict
												lowerCAmelCase__        =  os.listdir(_UpperCamelCase				)
												lowerCAmelCase__        =  [x for x in contents if x.endswith('.ckpt'				)][0]
												lowerCAmelCase__        =  os.path.join(args.output_dir       ,  _UpperCamelCase				)
												lowerCAmelCase__        =  torch.load(_UpperCamelCase       ,  map_location='cpu'				)
												lowerCAmelCase__        =  '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:
																		lowerCAmelCase__        =  {os.path.basename(_UpperCamelCase				) 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





