'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
 from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

 from .test_feature_extraction_speechta import floats_list


_A										= get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')


@require_torch
class    UpperCAmelCase__	(							unittest.TestCase      ):






   """simple docstring"""
   def 		_lowerCamelCase			(self					)     ->  Any:
        lowercase_       :							Any  					=       tempfile.mkdtemp()

        lowercase_       :							Union[str, Any]  					=       SpeechTaTokenizer(_a					)
        tokenizer.save_pretrained(self.tmpdirname					)

        lowercase_       :							str  					=       {
            'feature_size': 1,
            'padding_value': 0.0,
            'sampling_rate': 16_000,
            'do_normalize': False,
            'num_mel_bins': 80,
            'hop_length': 16,
            'win_length': 64,
            'win_function': 'hann_window',
            'fmin': 80,
            'fmax': 7_600,
            'mel_floor': 1e-10,
            'reduction_factor': 2,
            'return_attention_mask': True,
        }

        lowercase_       :							Optional[int]  					=       os.path.join(self.tmpdirname							,					_a					)
        with open(self.feature_extraction_file							,					'w'							,					encoding='utf-8'					) as fp:
             fp.write(json.dumps(_a					) + '\n'					)
   def 		_lowerCamelCase			(self							,					**_a					)     ->  Dict:
        return SpeechTaTokenizer.from_pretrained(self.tmpdirname							,					**_a					)
   def 		_lowerCamelCase			(self							,					**_a					)     ->  Optional[Any]:
        return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname							,					**_a					)
   def 		_lowerCamelCase			(self					)     ->  Optional[Any]:
        shutil.rmtree(self.tmpdirname					)
   def 		_lowerCamelCase			(self					)     ->  str:
        lowercase_       :							Optional[Any]  					=       self.get_tokenizer()
        lowercase_       :							Optional[Any]  					=       self.get_feature_extractor()

        lowercase_       :							str  					=       SpeechTaProcessor(tokenizer=_a							,					feature_extractor=_a					)

        processor.save_pretrained(self.tmpdirname					)
        lowercase_       :							Optional[Any]  					=       SpeechTaProcessor.from_pretrained(self.tmpdirname					)

        self.assertEqual(processor.tokenizer.get_vocab()							,					tokenizer.get_vocab()					)
        self.assertIsInstance(processor.tokenizer							,					_a					)

        self.assertEqual(processor.feature_extractor.to_json_string()							,					feature_extractor.to_json_string()					)
        self.assertIsInstance(processor.feature_extractor							,					_a					)
   def 		_lowerCamelCase			(self					)     ->  int:
        lowercase_       :							List[str]  					=       SpeechTaProcessor(tokenizer=self.get_tokenizer()							,					feature_extractor=self.get_feature_extractor()					)
        processor.save_pretrained(self.tmpdirname					)

        lowercase_       :							Tuple  					=       self.get_tokenizer(bos_token='(BOS)'							,					eos_token='(EOS)'					)
        lowercase_       :							List[Any]  					=       self.get_feature_extractor(do_normalize=_a							,					padding_value=1.0					)

        lowercase_       :							List[Any]  					=       SpeechTaProcessor.from_pretrained(
            self.tmpdirname							,					bos_token='(BOS)'							,					eos_token='(EOS)'							,					do_normalize=_a							,					padding_value=1.0					)

        self.assertEqual(processor.tokenizer.get_vocab()							,					tokenizer_add_kwargs.get_vocab()					)
        self.assertIsInstance(processor.tokenizer							,					_a					)

        self.assertEqual(processor.feature_extractor.to_json_string()							,					feature_extractor_add_kwargs.to_json_string()					)
        self.assertIsInstance(processor.feature_extractor							,					_a					)
   def 		_lowerCamelCase			(self					)     ->  Tuple:
        lowercase_       :							Optional[Any]  					=       self.get_feature_extractor()
        lowercase_       :							Any  					=       self.get_tokenizer()

        lowercase_       :							Any  					=       SpeechTaProcessor(tokenizer=_a							,					feature_extractor=_a					)

        lowercase_       :							int  					=       floats_list((3, 1_000)					)

        lowercase_       :							Any  					=       feature_extractor(audio=_a							,					return_tensors='np'					)
        lowercase_       :							Union[str, Any]  					=       processor(audio=_a							,					return_tensors='np'					)

        for key in input_feat_extract.keys():
             self.assertAlmostEqual(input_feat_extract[key].sum()							,					input_processor[key].sum()							,					delta=1e-2					)
   def 		_lowerCamelCase			(self					)     ->  Optional[Any]:
        lowercase_       :							List[str]  					=       self.get_feature_extractor()
        lowercase_       :							str  					=       self.get_tokenizer()

        lowercase_       :							Union[str, Any]  					=       SpeechTaProcessor(tokenizer=_a							,					feature_extractor=_a					)

        lowercase_       :							List[str]  					=       floats_list((3, 1_000)					)

        lowercase_       :							Optional[int]  					=       feature_extractor(audio_target=_a							,					return_tensors='np'					)
        lowercase_       :							List[Any]  					=       processor(audio_target=_a							,					return_tensors='np'					)

        for key in input_feat_extract.keys():
             self.assertAlmostEqual(input_feat_extract[key].sum()							,					input_processor[key].sum()							,					delta=1e-2					)
   def 		_lowerCamelCase			(self					)     ->  Dict:
        lowercase_       :							List[Any]  					=       self.get_feature_extractor()
        lowercase_       :							str  					=       self.get_tokenizer()

        lowercase_       :							Any  					=       SpeechTaProcessor(tokenizer=_a							,					feature_extractor=_a					)

        lowercase_       :							Optional[int]  					=       'This is a test string'

        lowercase_       :							Dict  					=       processor(text=_a					)
        lowercase_       :							List[str]  					=       tokenizer(_a					)

        for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]							,					encoded_processor[key]					)
   def 		_lowerCamelCase			(self					)     ->  str:
        lowercase_       :							int  					=       self.get_feature_extractor()
        lowercase_       :							Tuple  					=       self.get_tokenizer()

        lowercase_       :							Any  					=       SpeechTaProcessor(tokenizer=_a							,					feature_extractor=_a					)

        lowercase_       :							Optional[int]  					=       'This is a test string'

        lowercase_       :							List[str]  					=       processor(text_target=_a					)
        lowercase_       :							Union[str, Any]  					=       tokenizer(_a					)

        for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]							,					encoded_processor[key]					)
   def 		_lowerCamelCase			(self					)     ->  List[Any]:
        lowercase_       :							int  					=       self.get_feature_extractor()
        lowercase_       :							Any  					=       self.get_tokenizer()

        lowercase_       :							Tuple  					=       SpeechTaProcessor(tokenizer=_a							,					feature_extractor=_a					)

        lowercase_       :							str  					=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        lowercase_       :							List[Any]  					=       processor.batch_decode(_a					)
        lowercase_       :							Tuple  					=       tokenizer.batch_decode(_a					)

        self.assertListEqual(_a							,					_a					)





   def 		_lowerCamelCase			(self					)     ->  List[Any]:
        lowercase_       :							Dict  					=       self.get_feature_extractor()
        lowercase_       :							Optional[int]  					=       self.get_tokenizer()

        lowercase_       :							Tuple  					=       SpeechTaProcessor(tokenizer=_a							,					feature_extractor=_a					)

        self.assertListEqual(
            processor.model_input_names							,					feature_extractor.model_input_names							,					msg='`processor` and `feature_extractor` model input names do not match'							,					)





'''simple docstring'''




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 _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							Optional[Any]  					=       filter(lambda SCREAMING_SNAKE_CASE_				: p.requires_grad	,							model.parameters()				)
     lowercase_       :							Tuple  					=       sum([np.prod(p.size()				) for p in model_parameters]				)
     return params


_A										= logging.getLogger(__name__)


class    UpperCAmelCase__	(							pl.Callback      ):






   """simple docstring"""
   def 		_lowerCamelCase			(self							,					_a							,					_a					)     ->  str:
        lowercase_       :							List[str]  					=       {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:
        logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''					)
        lowercase_       :							Union[str, Any]  					=       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
        lowercase_       :							Optional[int]  					=       Path(pl_module.hparams.output_dir					)
        if type_path == "test":
             lowercase_       :							Union[str, Any]  					=       od / 'test_results.txt'
             lowercase_       :							Tuple  					=       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.
             lowercase_       :							List[str]  					=       od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
             lowercase_       :							int  					=       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
                  lowercase_       :							Any  					=       metrics[key]
                  if isinstance(_a							,					torch.Tensor					):
                       lowercase_       :							Any  					=       val.item()
                  lowercase_       :							List[str]  					=       f'''{key}: {val:.6f}\n'''
                  writer.write(_a					)

        if not save_generations:
             return

        if "preds" in metrics:
             lowercase_       :							Optional[Any]  					=       '\n'.join(metrics['preds']					)
             generations_file.open('w+'					).write(_a					)
   @rank_zero_only
   def 		_lowerCamelCase			(self							,					_a							,					_a					)     ->  Tuple:
        try:
             lowercase_       :							List[Any]  					=       pl_module.model.model.num_parameters()
        except AttributeError:
             lowercase_       :							Dict  					=       pl_module.model.num_parameters()

        lowercase_       :							Optional[Any]  					=       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					)     ->  int:
        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					)     ->  List[str]:
        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 _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_=1	,							SCREAMING_SNAKE_CASE_=False				):
     if metric == "rouge2":
          lowercase_       :							Optional[int]  					=       '{val_avg_rouge2:.4f}-{step_count}'
     elif metric == "bleu":
          lowercase_       :							int  					=       '{val_avg_bleu:.4f}-{step_count}'
     elif metric == "loss":
          lowercase_       :							List[Any]  					=       '{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.'				)

     lowercase_       :							Tuple  					=       ModelCheckpoint(
         dirpath=SCREAMING_SNAKE_CASE_	,							filename=SCREAMING_SNAKE_CASE_	,							monitor=f'''val_{metric}'''	,							mode='min' if 'loss' in metric else 'max'	,							save_top_k=SCREAMING_SNAKE_CASE_	,							)
     return checkpoint_callback






def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     return EarlyStopping(
         monitor=f'''val_{metric}'''	,							mode='min' if 'loss' in metric else 'max'	,							patience=SCREAMING_SNAKE_CASE_	,							verbose=SCREAMING_SNAKE_CASE_	,							)




