






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__snake_case      :      int             =       """▁"""

__snake_case      :      Optional[Any]             =       {"""vocab_file""": """spiece.model"""}

__snake_case      :      List[str]             =       {
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""}
}

__snake_case      :      List[str]             =       {
    """google/pegasus-xsum""": 5_12,
}


__snake_case      :      Union[str, Any]             =       logging.get_logger(__name__)


class       __SCREAMING_SNAKE_CASE   (				__A):
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   VOCAB_FILES_NAMES

						_SCREAMING_SNAKE_CASE		:    List[str]						=   VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE		:    Tuple						=   PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE		:    Dict						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_SCREAMING_SNAKE_CASE		:    Tuple						=   ['input_ids', 'attention_mask']






						def __init__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase="<pad>"       ,  _UpperCamelCase="</s>"       ,  _UpperCamelCase="<unk>"       ,  _UpperCamelCase="<mask_2>"       ,  _UpperCamelCase="<mask_1>"       ,  _UpperCamelCase=None       ,  _UpperCamelCase=1_03       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  offset
												if additional_special_tokens is not None:
																		if not isinstance(__lowercase       ,  __lowercase				):
																								raise TypeError(
																								    F"additional_special_tokens should be of type {type(__lowercase				)}, but is"
																								    F" {type(__lowercase				)}"				)

																		lowerCAmelCase__        =  (
																		    ([mask_token_sent] + additional_special_tokens)
																		    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
																		    else additional_special_tokens
																		)
																		# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
																		additional_special_tokens_extended += [
																		    F"<unk_{i}>" for i in range(len(__lowercase				)       ,  self.offset - 1				)
																		]

																		if len(set(__lowercase				)				) != len(__lowercase				):
																								raise ValueError(
																								    'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
																								    F" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."				)
																		lowerCAmelCase__        =  additional_special_tokens_extended
												else:
																		lowerCAmelCase__        =  [mask_token_sent] if mask_token_sent is not None else []
																		additional_special_tokens += [F"<unk_{i}>" for i in range(2       ,  self.offset				)]

												lowerCAmelCase__        =  {} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    eos_token=__lowercase       ,  unk_token=__lowercase       ,  mask_token=__lowercase       ,  pad_token=__lowercase       ,  mask_token_sent=__lowercase       ,  offset=__lowercase       ,  additional_special_tokens=__lowercase       ,  sp_model_kwargs=self.sp_model_kwargs       ,  **__lowercase       ,  )
												lowerCAmelCase__        =  mask_token_sent
												lowerCAmelCase__        =  vocab_file
												lowerCAmelCase__        =  spm.SentencePieceProcessor(**self.sp_model_kwargs				)
												self.sp_model.Load(__lowercase				)

												# add special tokens to encoder dict
												lowerCAmelCase__        =  {
												    0: self.pad_token,
												    1: self.eos_token,
												}

												if self.mask_token_sent is not None:
																		self.encoder.update(
																		    {
																		        2: self.mask_token_sent,
																		        3: self.mask_token,
																		    }				)

												if self.offset > 0:
																		# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
																		# mask_token_sent is already added to list -> so start at 1
																		self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1       ,  self.offset - 1				)}				)

												lowerCAmelCase__        =  {v: k for k, v in self.encoder.items()}






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return len(self.sp_model				) + self.offset






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  {self.convert_ids_to_tokens(__lowercase				): i for i in range(self.vocab_size				)}
												vocab.update(self.added_tokens_encoder				)
												return vocab






						def __getstate__(		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.__dict__.copy()
												lowerCAmelCase__        =  None
												return state






						def __setstate__(		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  d

												# for backward compatibility
												if not hasattr(self       ,  'sp_model_kwargs'				):
																		lowerCAmelCase__        =  {}

												lowerCAmelCase__        =  spm.SentencePieceProcessor(**self.sp_model_kwargs				)
												self.sp_model.Load(self.vocab_file				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												return self.sp_model.encode(__lowercase       ,  out_type=__lowercase				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												if token in self.decoder:
																		return self.decoder[token]
												elif token in self.added_tokens_decoder:
																		return self.added_tokens_decoder[token]
												lowerCAmelCase__        =  self.sp_model.piece_to_id(__lowercase				)
												return sp_id + self.offset






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												if index in self.encoder:
																		return self.encoder[index]
												elif index in self.added_tokens_encoder:
																		return self.added_tokens_encoder[index]
												else:
																		lowerCAmelCase__        =  self.sp_model.IdToPiece(index - self.offset				)
												return token






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  []
												lowerCAmelCase__        =  ''
												for token in tokens:
																		# make sure that special tokens are not decoded using sentencepiece model
																		if token in self.all_special_tokens:
																								out_string += self.sp_model.decode(__lowercase				) + token
																								lowerCAmelCase__        =  []
																		else:
																								current_sub_tokens.append(__lowercase				)
												out_string += self.sp_model.decode(__lowercase				)
												return out_string.strip()






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase=False				):






												"""simple docstring"""
												return 1






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  set(self.all_special_ids				)  # call it once instead of inside list comp
												all_special_ids.remove(self.unk_token_id				)  # <unk> is only sometimes special

												return [1 if x in all_special_ids else 0 for x in seq]






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  _UpperCamelCase = False				):






												"""simple docstring"""
												if already_has_special_tokens:
																		return self._special_token_mask(__lowercase				)
												elif token_ids_a is None:
																		return self._special_token_mask(__lowercase				) + [1]
												else:
																		return self._special_token_mask(token_ids_a + token_ids_a				) + [1]






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase=None				):






												"""simple docstring"""
												if token_ids_a is None:
																		return token_ids_a + [self.eos_token_id]
												# We don't expect to process pairs, but leave the pair logic for API consistency
												return token_ids_a + token_ids_a + [self.eos_token_id]







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None				):






												"""simple docstring"""
												if not os.path.isdir(__lowercase				):
																		logger.error(F"Vocabulary path ({save_directory}) should be a directory"				)
																		return
												lowerCAmelCase__        =  os.path.join(
												    __lowercase       ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)

												if os.path.abspath(self.vocab_file				) != os.path.abspath(__lowercase				) and os.path.isfile(self.vocab_file				):
																		copyfile(self.vocab_file       ,  __lowercase				)
												elif not os.path.isfile(self.vocab_file				):
																		with open(__lowercase       ,  'wb'				) as fi:
																								lowerCAmelCase__        =  self.sp_model.serialized_model_proto()
																								fi.write(__lowercase				)

												return (out_vocab_file,)













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





