

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_rag import save_json



def 				snake_case_(_UpperCamelCase					)							->     Union[str, Any]:



  """simple docstring"""






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


__A      		=     logging.getLogger(__name__)



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     List[str]:



  """simple docstring"""






  if metric == "rouge2":
    _snake_case						=							'''{val_avg_rouge2:.4f}-{step_count}'''
  elif metric == "bleu":
    _snake_case						=							'''{val_avg_bleu:.4f}-{step_count}'''
  elif metric == "em":
    _snake_case						=							'''{val_avg_em:.4f}-{step_count}'''
  elif metric == "loss":
    _snake_case						=							'''{val_avg_loss:.4f}-{step_count}'''
  else:
    raise NotImplementedError(
        F"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
        ''' function.'''					)

  _snake_case						=							ModelCheckpoint(
      dirpath=_UpperCamelCase			,	filename=_UpperCamelCase			,	monitor=F"""val_{metric}"""			,	mode='''max'''			,	save_top_k=1			,	every_n_epochs=1			,	)

  return checkpoint_callback



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     Tuple:



  """simple docstring"""






  return EarlyStopping(
      monitor=F"""val_{metric}"""			,	mode='''min''' if '''loss''' in metric else '''max'''			,	patience=_UpperCamelCase			,	verbose=_UpperCamelCase			,	)



class   lowercase_   (  pl.Callback					):



      def 						UpperCamelCase_		(      self				:    Optional[Any]      , A__				:    Optional[Any]      , A__				:    Optional[int]		)   ->					List[str]:
        _snake_case						=							{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 						UpperCamelCase_		(      self				:    Optional[int]      , A__				:    pl.Trainer      , A__				:    pl.LightningModule      , A__				:    str      , A__				:    List[str]=True		)   ->					None:
        logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""		)
        _snake_case						=							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
        _snake_case						=							Path(pl_module.hparams.output_dir		)
        if type_path == "test":
          _snake_case						=							od / '''test_results.txt'''
          _snake_case						=							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.
          _snake_case						=							od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
          _snake_case						=							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
            _snake_case						=							metrics[key]
            if isinstance(A__      , torch.Tensor		):
              _snake_case						=							val.item()
            _snake_case						=							f"""{key}: {val:.6f}\n"""
            writer.write(A__		)

        if not save_generations:
          return

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



      @rank_zero_only
      def 						UpperCamelCase_		(      self				:    Tuple      , A__				:    Any      , A__				:    List[str]		)   ->					Any:
        try:
          _snake_case						=							pl_module.model.model.num_parameters()
        except AttributeError:
          _snake_case						=							pl_module.model.num_parameters()

        _snake_case						=							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 						UpperCamelCase_		(      self				:    List[str]      , A__				:    pl.Trainer      , A__				:    pl.LightningModule		)   ->					Union[str, Any]:
        save_json(pl_module.metrics      , pl_module.metrics_save_path		)
        return self._write_logs(A__      , A__      , '''test'''		)







      @rank_zero_only
      def 						UpperCamelCase_		(      self				:    Union[str, Any]      , A__				:    pl.Trainer      , A__				:    Optional[Any]		)   ->					Union[str, Any]:
        save_json(pl_module.metrics      , pl_module.metrics_save_path		)
        # Uncommenting this will save val generations
        # return self._write_logs(trainer, pl_module, "valid")




import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
 import torch

if is_datasets_available():
 from datasets import load_dataset

__A      		=     random.Random()



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase=1.0			,	_UpperCamelCase=None			,	_UpperCamelCase=None					)							->     Optional[int]:



  """simple docstring"""






  if rng is None:
    _snake_case						=							global_rng

  _snake_case						=							[]
  for batch_idx in range(shape[0]					):
    values.append([]					)
    for _ in range(shape[1]					):
      values[-1].append(rng.random() * scale					)

  return values



class   lowercase_   (  unittest.TestCase					):



      def __init__(      self				:    List[Any]      , A__				:    List[Any]      , A__				:    int=7      , A__				:    Tuple=400      , A__				:    int=2000      , A__				:    Any=2048      , A__				:    List[Any]=128      , A__				:    Optional[int]=1      , A__				:    Optional[Any]=512      , A__				:    Any=30      , A__				:    Any=44100      , )   ->					int:
        _snake_case						=							parent
        _snake_case						=							batch_size
        _snake_case						=							min_seq_length
        _snake_case						=							max_seq_length
        _snake_case						=							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        _snake_case						=							spectrogram_length
        _snake_case						=							feature_size
        _snake_case						=							num_audio_channels
        _snake_case						=							hop_length
        _snake_case						=							chunk_length
        _snake_case						=							sampling_rate



      def 						UpperCamelCase_		(      self				:    str		)   ->					Optional[int]:
        return {
            "spectrogram_length": self.spectrogram_length,
            "feature_size": self.feature_size,
            "num_audio_channels": self.num_audio_channels,
            "hop_length": self.hop_length,
            "chunk_length": self.chunk_length,
            "sampling_rate": self.sampling_rate,
        }







      def 						UpperCamelCase_		(      self				:    Any      , A__				:    Any=False      , A__				:    List[str]=False		)   ->					Tuple:
        def _flatten(A__				:    List[str]		):
          return list(itertools.chain(*A__		)		)

        if equal_length:
          _snake_case						=							[floats_list((self.max_seq_length, self.feature_size)		) for _ in range(self.batch_size		)]
        else:
          # make sure that inputs increase in size
          _snake_case						=							[
              floats_list((x, self.feature_size)		)
              for x in range(self.min_seq_length      , self.max_seq_length      , self.seq_length_diff		)
          ]
        if numpify:
          _snake_case						=							[np.asarray(A__		) for x in speech_inputs]
        return speech_inputs



@require_torch
@require_torchaudio
class   lowercase_   (  __lowercase       ,							unittest.TestCase					):
      UpperCamelCase_      :		Optional[int]										=    TvltFeatureExtractor



      def 						UpperCamelCase_		(      self				:    Dict		)   ->					List[str]:
        _snake_case						=							TvltFeatureExtractionTester(self		)



      def 						UpperCamelCase_		(      self				:    int		)   ->					Optional[int]:
        _snake_case						=							self.feature_extraction_class(**self.feat_extract_dict		)
        self.assertTrue(hasattr(A__      , '''spectrogram_length'''		)		)
        self.assertTrue(hasattr(A__      , '''feature_size'''		)		)
        self.assertTrue(hasattr(A__      , '''num_audio_channels'''		)		)
        self.assertTrue(hasattr(A__      , '''hop_length'''		)		)
        self.assertTrue(hasattr(A__      , '''chunk_length'''		)		)
        self.assertTrue(hasattr(A__      , '''sampling_rate'''		)		)



      def 						UpperCamelCase_		(      self				:    Any		)   ->					Union[str, Any]:
        _snake_case						=							self.feature_extraction_class(**self.feat_extract_dict		)

        with tempfile.TemporaryDirectory() as tmpdirname:
          _snake_case						=							feat_extract_first.save_pretrained(A__		)[0]
          check_json_file_has_correct_format(A__		)
          _snake_case						=							self.feature_extraction_class.from_pretrained(A__		)

        _snake_case						=							feat_extract_first.to_dict()
        _snake_case						=							feat_extract_second.to_dict()
        _snake_case						=							dict_first.pop('''mel_filters'''		)
        _snake_case						=							dict_second.pop('''mel_filters'''		)
        self.assertTrue(np.allclose(A__      , A__		)		)
        self.assertEqual(A__      , A__		)



      def 						UpperCamelCase_		(      self				:    int		)   ->					Union[str, Any]:
        _snake_case						=							self.feature_extraction_class(**self.feat_extract_dict		)

        with tempfile.TemporaryDirectory() as tmpdirname:
          _snake_case						=							os.path.join(A__      , '''feat_extract.json'''		)
          feat_extract_first.to_json_file(A__		)
          _snake_case						=							self.feature_extraction_class.from_json_file(A__		)

        _snake_case						=							feat_extract_first.to_dict()
        _snake_case						=							feat_extract_second.to_dict()
        _snake_case						=							dict_first.pop('''mel_filters'''		)
        _snake_case						=							dict_second.pop('''mel_filters'''		)
        self.assertTrue(np.allclose(A__      , A__		)		)
        self.assertEqual(A__      , A__		)



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					Any:
        # Initialize feature_extractor
        _snake_case						=							self.feature_extraction_class(**self.feat_extract_dict		)

        # create three inputs of length 800, 1000, and 1200
        _snake_case						=							[floats_list((1, x)		)[0] for x in range(800      , 1400      , 200		)]
        _snake_case						=							[np.asarray(A__		) for speech_input in speech_inputs]

        # Test not batched input
        _snake_case						=							feature_extractor(np_speech_inputs[0]      , return_tensors='''np'''      , sampling_rate=44100		).audio_values

        self.assertTrue(encoded_audios.ndim == 4		)
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)

        # Test batched
        _snake_case						=							feature_extractor(A__      , return_tensors='''np'''      , sampling_rate=44100		).audio_values

        self.assertTrue(encoded_audios.ndim == 4		)
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)

        # Test audio masking
        _snake_case						=							feature_extractor(
            A__      , return_tensors='''np'''      , sampling_rate=44100      , mask_audio=A__		).audio_values

        self.assertTrue(encoded_audios.ndim == 4		)
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)

        # Test 2-D numpy arrays are batched.
        _snake_case						=							[floats_list((1, x)		)[0] for x in (800, 800, 800)]
        _snake_case						=							np.asarray(A__		)
        _snake_case						=							feature_extractor(A__      , return_tensors='''np'''      , sampling_rate=44100		).audio_values
        self.assertTrue(encoded_audios.ndim == 4		)
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)



      def 						UpperCamelCase_		(      self				:    Optional[Any]      , A__				:    Any		)   ->					Optional[int]:
        _snake_case						=							load_dataset('''hf-internal-testing/librispeech_asr_dummy'''      , '''clean'''      , split='''validation'''		)
        # automatic decoding with librispeech
        _snake_case						=							ds.sort('''id'''		).select(range(A__		)		)[:num_samples]['''audio''']

        return [x["array"] for x in speech_samples]







      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Optional[Any]:
        _snake_case						=							self._load_datasamples(1		)
        _snake_case						=							TvltFeatureExtractor()
        _snake_case						=							feature_extractor(A__      , return_tensors='''pt'''		).audio_values

        self.assertEquals(audio_values.shape      , (1, 1, 192, 128)		)

        _snake_case						=							torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]		)
        self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]      , A__      , atol=1e-4		)		)

