





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							(			snake_case       )		->						List[Any]:
		__lowercase					=    filter(lambda snake_case       : p.requires_grad	,      model.parameters()       )
		__lowercase					=    sum([np.prod(p.size()       ) for p in model_parameters]       )
		return params


SCREAMING_SNAKE_CASE_      :			Union[str, Any]		  =      logging.getLogger(__name__)

class 		snake_case_      (       pl.Callback	):







	'''simple docstring'''




	def       UpperCAmelCase (	self       :    Optional[Any]				,			__lowerCamelCase       :    Optional[Any]				,			__lowerCamelCase       :    Optional[Any]      )					->   Optional[Any]:

			'''simple docstring'''




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




	@rank_zero_only
	def       UpperCAmelCase (	self       :    Any				,			__lowerCamelCase       :    pl.Trainer				,			__lowerCamelCase       :    pl.LightningModule				,			__lowerCamelCase       :    str				,			__lowerCamelCase       :    Optional[Any]=True      )					->   None:

			'''simple docstring'''




			logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"      )
			__lowercase					=    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					=    Path(pl_module.hparams.output_dir      )
			if type_path == "test":
					__lowercase					=    od / 'test_results.txt'
					__lowercase					=    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					=    od / F"{type_path}_results/{trainer.global_step:05d}.txt"
					__lowercase					=    od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
					results_file.parent.mkdir(exist_ok=__lowerCamelCase      )
					generations_file.parent.mkdir(exist_ok=__lowerCamelCase      )
			with open(__lowerCamelCase				,			'a+'      ) as writer:
					for key in sorted(__lowerCamelCase      ):
							if key in ["log", "progress_bar", "preds"]:
									continue
							__lowercase					=    metrics[key]
							if isinstance(__lowerCamelCase				,			torch.Tensor      ):
									__lowercase					=    val.item()
							__lowercase					=    F"{key}: {val:.6f}\n"
							writer.write(__lowerCamelCase      )

			if not save_generations:
					return

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




	@rank_zero_only
	def       UpperCAmelCase (	self       :    Union[str, Any]				,			__lowerCamelCase       :    Optional[int]				,			__lowerCamelCase       :    Tuple      )					->   str:

			'''simple docstring'''




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

			__lowercase					=    count_trainable_parameters(__lowerCamelCase      )
			# 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       UpperCAmelCase (	self       :    List[str]				,			__lowerCamelCase       :    pl.Trainer				,			__lowerCamelCase       :    pl.LightningModule      )					->   Union[str, Any]:

			'''simple docstring'''




			save_json(pl_module.metrics				,			pl_module.metrics_save_path      )
			return self._write_logs(__lowerCamelCase				,			__lowerCamelCase				,			'test'      )






	@rank_zero_only
	def       UpperCAmelCase (	self       :    Tuple				,			__lowerCamelCase       :    pl.Trainer				,			__lowerCamelCase       :    Union[str, Any]      )					->   List[Any]:

			'''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							(			snake_case	,      snake_case	,      snake_case=1	,      snake_case=False       )		->						Tuple:
		if metric == "rouge2":
				__lowercase					=    '{val_avg_rouge2:.4f}-{step_count}'
		elif metric == "bleu":
				__lowercase					=    '{val_avg_bleu:.4f}-{step_count}'
		elif metric == "loss":
				__lowercase					=    '{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					=    ModelCheckpoint(
		    dirpath=snake_case	,      filename=snake_case	,      monitor=F"val_{metric}"	,      mode='min' if 'loss' in metric else 'max'	,      save_top_k=snake_case	,      )
		return checkpoint_callback



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						int:
		return EarlyStopping(
		    monitor=F"val_{metric}"	,      mode='min' if 'loss' in metric else 'max'	,      patience=snake_case	,      verbose=snake_case	,      )











import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
		from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


SCREAMING_SNAKE_CASE_      :			Union[str, Any]		  =      get_tests_dir('''fixtures/test_sentencepiece.model''')

@require_torch
@require_torchaudio
@require_sentencepiece
class 		snake_case_      (       unittest.TestCase	):







	'''simple docstring'''




	def       UpperCAmelCase (	self       :    List[Any]      )					->   str:

			'''simple docstring'''




			__lowercase					=    tempfile.mkdtemp()

			__lowercase					=    ['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
			__lowercase					=    dict(zip(__lowerCamelCase				,			range(len(__lowerCamelCase      )      )      )      )
			__lowercase					=    Path(self.tmpdirname      )
			save_json(__lowerCamelCase				,			save_dir / VOCAB_FILES_NAMES['vocab_file']      )
			if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
					copyfile(__lowerCamelCase				,			save_dir / VOCAB_FILES_NAMES['spm_file']      )

			__lowercase					=    SpeechaTextTokenizer.from_pretrained(self.tmpdirname      )
			tokenizer.save_pretrained(self.tmpdirname      )

			__lowercase					=    {
			    'feature_size': 24,
			    'num_mel_bins': 24,
			    'padding_value': 0.0,
			    'sampling_rate': 16_000,
			    'return_attention_mask': False,
			    'do_normalize': True,
			}
			save_json(__lowerCamelCase				,			save_dir / FEATURE_EXTRACTOR_NAME      )




	def       UpperCAmelCase (	self       :    Dict				,			**__lowerCamelCase       :    Dict      )					->   Union[str, Any]:

			'''simple docstring'''




			return SpeechaTextTokenizer.from_pretrained(self.tmpdirname				,			**__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    str				,			**__lowerCamelCase       :    List[str]      )					->   Tuple:

			'''simple docstring'''




			return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname				,			**__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   str:

			'''simple docstring'''




			shutil.rmtree(self.tmpdirname      )




	def       UpperCAmelCase (	self       :    str      )					->   Any:

			'''simple docstring'''




			__lowercase					=    self.get_tokenizer()
			__lowercase					=    self.get_feature_extractor()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			processor.save_pretrained(self.tmpdirname      )
			__lowercase					=    SpeechaTextProcessor.from_pretrained(self.tmpdirname      )

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

			self.assertEqual(processor.feature_extractor.to_json_string()				,			feature_extractor.to_json_string()      )
			self.assertIsInstance(processor.feature_extractor				,			__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    List[str]      )					->   Optional[Any]:

			'''simple docstring'''




			__lowercase					=    SpeechaTextProcessor(
			    tokenizer=self.get_tokenizer()				,			feature_extractor=self.get_feature_extractor()      )
			processor.save_pretrained(self.tmpdirname      )

			__lowercase					=    self.get_tokenizer(bos_token='(BOS)'				,			eos_token='(EOS)'      )
			__lowercase					=    self.get_feature_extractor(do_normalize=__lowerCamelCase				,			padding_value=1.0      )

			__lowercase					=    SpeechaTextProcessor.from_pretrained(
			    self.tmpdirname				,			bos_token='(BOS)'				,			eos_token='(EOS)'				,			do_normalize=__lowerCamelCase				,			padding_value=1.0      )

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

			self.assertEqual(processor.feature_extractor.to_json_string()				,			feature_extractor_add_kwargs.to_json_string()      )
			self.assertIsInstance(processor.feature_extractor				,			__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    Dict      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			__lowercase					=    floats_list((3, 1_000)      )

			__lowercase					=    feature_extractor(__lowerCamelCase				,			return_tensors='np'      )
			__lowercase					=    processor(__lowerCamelCase				,			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       UpperCAmelCase (	self       :    Union[str, Any]      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			__lowercase					=    'This is a test string'

			__lowercase					=    processor(text=__lowerCamelCase      )

			__lowercase					=    tokenizer(__lowerCamelCase      )

			for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key]				,			encoded_processor[key]      )




	def       UpperCAmelCase (	self       :    str      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

			__lowercase					=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

			__lowercase					=    processor.batch_decode(__lowerCamelCase      )
			__lowercase					=    tokenizer.batch_decode(__lowerCamelCase      )

			self.assertListEqual(__lowerCamelCase				,			__lowerCamelCase      )






	def       UpperCAmelCase (	self       :    Tuple      )					->   str:

			'''simple docstring'''




			__lowercase					=    self.get_feature_extractor()
			__lowercase					=    self.get_tokenizer()

			__lowercase					=    SpeechaTextProcessor(tokenizer=__lowerCamelCase				,			feature_extractor=__lowerCamelCase      )

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




