




import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
UpperCamelCase__              =						'stas/tiny-wmt19-en-ru'


class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase   ,     unittest.TestCase  ):
					_lowerCamelCase		  = FSMTTokenizer
					_lowerCamelCase		  = False

					def 	lowercase_							(				self					):







						'''simple docstring'''




						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						lowerCamelCase_							:		Any							   =							[
						    'l',
						    'o',
						    'w',
						    'e',
						    'r',
						    's',
						    't',
						    'i',
						    'd',
						    'n',
						    'w</w>',
						    'r</w>',
						    't</w>',
						    'lo',
						    'low',
						    'er</w>',
						    'low</w>',
						    'lowest</w>',
						    'newer</w>',
						    'wider</w>',
						    '<unk>',
						]
						lowerCamelCase_							:		List[Any]							   =							dict(zip(__lowercase ,       range(len(__lowercase					)					)					)					)
						lowerCamelCase_							:		int							   =							['l o 123', 'lo w 1456', 'e r</w> 1789', '']

						lowerCamelCase_							:		Dict							   =							['en', 'ru']
						lowerCamelCase_							:		Union[str, Any]							   =							{
						    'langs': self.langs,
						    'src_vocab_size': 10,
						    'tgt_vocab_size': 20,
						}

						lowerCamelCase_							:		int							   =							os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES['src_vocab_file']					)
						lowerCamelCase_							:		str							   =							os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES['tgt_vocab_file']					)
						lowerCamelCase_							:		Union[str, Any]							   =							os.path.join(self.tmpdirname ,       'tokenizer_config.json'					)
						lowerCamelCase_							:		Optional[int]							   =							os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES['merges_file']					)
						with open(self.src_vocab_file ,       'w'					) as fp:
							fp.write(json.dumps(__lowercase					)					)
						with open(self.tgt_vocab_file ,       'w'					) as fp:
							fp.write(json.dumps(__lowercase					)					)
						with open(self.merges_file ,       'w'					) as fp:
							fp.write('\n'.join(__lowercase					)					)
						with open(__lowercase ,       'w'					) as fp:
							fp.write(json.dumps(__lowercase					)					)

					@cached_property
					def 	lowercase_							(				self					):







						'''simple docstring'''




						return FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'					)

					@cached_property
					def 	lowercase_							(				self					):







						'''simple docstring'''




						return FSMTTokenizer.from_pretrained('facebook/wmt19-en-ru'					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							   =							FSMTTokenizer.from_pretrained(__lowercase					)
						self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang] ,       ['en', 'ru']					)
						self.assertEqual(tokenizer.src_vocab_size ,       21					)
						self.assertEqual(tokenizer.tgt_vocab_size ,       21					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							   =							FSMTTokenizer(self.langs ,       self.src_vocab_file ,       self.tgt_vocab_file ,       self.merges_file					)

						lowerCamelCase_							:		int							   =							'lower'
						lowerCamelCase_							:		List[Any]							   =							['low', 'er</w>']
						lowerCamelCase_							:		int							   =							tokenizer.tokenize(__lowercase					)
						self.assertListEqual(__lowercase ,       __lowercase					)

						lowerCamelCase_							:		Optional[int]							   =							tokens + ['<unk>']
						lowerCamelCase_							:		str							   =							[14, 15, 20]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase					) ,       __lowercase					)

					@slow
					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							   =							self.tokenizer_ru_en

						lowerCamelCase_							:		Optional[int]							   =							tokenizer.encode('sequence builders' ,       add_special_tokens=__lowercase					)
						lowerCamelCase_							:		int							   =							tokenizer.encode('multi-sequence build' ,       add_special_tokens=__lowercase					)

						lowerCamelCase_							:		Optional[Any]							   =							tokenizer.build_inputs_with_special_tokens(__lowercase					)
						lowerCamelCase_							:		int							   =							tokenizer.build_inputs_with_special_tokens(__lowercase ,       __lowercase					)

						assert encoded_sentence == text + [2]
						assert encoded_pair == text + [2] + text_a + [2]

					@slow
					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Any							   =							self.tokenizer_en_ru
						lowerCamelCase_							:		Optional[Any]							   =							self.tokenizer_ru_en

						lowerCamelCase_							:		str							   =							[
						    [
						        'Here\'s a little song I wrote. Don\'t worry, be happy.',
						        [2_470, 39, 11, 2_349, 7_222, 70, 5_979, 7, 8_450, 1_050, 13_160, 5, 26, 6_445, 7, 2],
						    ],
						    ['This is it. No more. I\'m done!', [132, 21, 37, 7, 1_434, 86, 7, 70, 6_476, 1_305, 427, 2]],
						]

						# if data needs to be recreated or added, run:
						# import torch
						# model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
						# for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

						for src_text, tgt_input_ids in targets:
							lowerCamelCase_							:		Optional[Any]							   =							tokenizer_enc.encode(__lowercase ,       return_tensors=__lowercase					)
							self.assertListEqual(__lowercase ,       __lowercase					)

							# and decode backward, using the reversed languages model
							lowerCamelCase_							:		Any							   =							tokenizer_dec.decode(__lowercase ,       skip_special_tokens=__lowercase					)
							self.assertEqual(__lowercase ,       __lowercase					)

					@slow
					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		str							   =							FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en' ,       do_lower_case=__lowercase					)
						lowerCamelCase_							:		Optional[Any]							   =							tokenizer.tokenize('USA is United States of America'					)
						lowerCamelCase_							:		Optional[int]							   =							['us', 'a</w>', 'is</w>', 'un', 'i', 'ted</w>', 'st', 'ates</w>', 'of</w>', 'am', 'er', 'ica</w>']
						self.assertListEqual(__lowercase ,       __lowercase					)

					@unittest.skip('FSMTConfig.__init__  requires non-optional args'					)
					def 	lowercase_							(				self					):







						'''simple docstring'''




						pass





					@unittest.skip('FSMTConfig.__init__  requires non-optional args'					)
					def 	lowercase_							(				self					):







						'''simple docstring'''




						pass






import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     int:
     a      =      filter(lambda __UpperCamelCase: p.requires_grad		,					model.parameters())
     a      =      sum([np.prod(p.size()) for p in model_parameters])
     return params


lowercase__      :			Optional[int]           =					logging.getLogger(__name__)




class 			a__       (					pl.Callback      ):







     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    str:



          '''simple docstring'''



          a      =      {F'''lr_group_{i}''': param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
          pl_module.logger.log_metrics(A )







     @rank_zero_only
     def 			lowerCAmelCase_				(	self  ,		A  ,		A  ,		A  ,		A=True )  ->    None:



          '''simple docstring'''



          logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****''' )
          a      =      trainer.callback_metrics
          trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]} )
          # Log results
          a      =      Path(pl_module.hparams.output_dir )
          if type_path == "test":
               a      =      od / "test_results.txt"
               a      =      od / "test_generations.txt"
          else:
               # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
               # If people want this it will be easy enough to add back.
               a      =      od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
               a      =      od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
               results_file.parent.mkdir(exist_ok=A )
               generations_file.parent.mkdir(exist_ok=A )
          with open(A  ,		"a+" ) as writer:
               for key in sorted(A ):
                    if key in ["log", "progress_bar", "preds"]:
                         continue
                    a      =      metrics[key]
                    if isinstance(A  ,		torch.Tensor ):
                         a      =      val.item()
                    a      =      F'''{key}: {val:.6f}\n'''
                    writer.write(A )

          if not save_generations:
               return

          if "preds" in metrics:
               a      =      "\n".join(metrics["preds"] )
               generations_file.open("w+" ).write(A )







     @rank_zero_only
     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    Any:



          '''simple docstring'''



          try:
               a      =      pl_module.model.model.num_parameters()
          except AttributeError:
               a      =      pl_module.model.num_parameters()

          a      =      count_trainable_parameters(A )
          # mp stands for million parameters
          trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6} )







     @rank_zero_only
     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    Optional[int]:



          '''simple docstring'''



          save_json(pl_module.metrics  ,		pl_module.metrics_save_path )
          return self._write_logs(A  ,		A  ,		"test" )







     @rank_zero_only
     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    Dict:



          '''simple docstring'''



          save_json(pl_module.metrics  ,		pl_module.metrics_save_path )
          # Uncommenting this will save val generations
          # return self._write_logs(trainer, pl_module, "valid")







def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase		,					__UpperCamelCase=1		,					__UpperCamelCase=False)			->     Optional[int]:
     if metric == "rouge2":
          a      =      "{val_avg_rouge2:.4f}-{step_count}"
     elif metric == "bleu":
          a      =      "{val_avg_bleu:.4f}-{step_count}"
     elif metric == "loss":
          a      =      "{val_avg_loss:.4f}-{step_count}"
     else:
          raise NotImplementedError(
              f'''seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to'''
              " this function.")

     a      =      ModelCheckpoint(
         dirpath=__UpperCamelCase		,					filename=__UpperCamelCase		,					monitor=f'''val_{metric}'''		,					mode="min" if "loss" in metric else "max"		,					save_top_k=__UpperCamelCase		,					)
     return checkpoint_callback





def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase)			->     Union[str, Any]:
     return EarlyStopping(
         monitor=f'''val_{metric}'''		,					mode="min" if "loss" in metric else "max"		,					patience=__UpperCamelCase		,					verbose=__UpperCamelCase		,					)

