




from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def        lowerCAmelCase_			(  snake_case_  ):
      return DownloadCommand(args.model,args.cache_dir,args.force,args.trust_remote_code  )







class 		lowercase							(     UpperCamelCase__	):

     @staticmethod
     def       a__			(						_a       )		->							List[str]:
           _A							:  List[Any]    =		parser.add_parser("""download"""       )
           download_parser.add_argument(
               """--cache-dir"""	,  type=_a	,  default=_a	,  help="""Path to location to store the models"""       )
           download_parser.add_argument(
               """--force"""	,  action="""store_true"""	,  help="""Force the model to be download even if already in cache-dir"""       )
           download_parser.add_argument(
               """--trust-remote-code"""	,  action="""store_true"""	,  help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"""	,  )
           download_parser.add_argument("""model"""	,  type=_a	,  help="""Name of the model to download"""       )
           download_parser.set_defaults(func=_a       )

     def __init__(						self	,  _a	,  _a	,  _a	,  _a       )		->							Tuple:
           _A							:  Dict    =		model
           _A							:  str    =		cache
           _A							:  List[Any]    =		force
           _A							:  Optional[Any]    =		trust_remote_code



     def       a__			(						self       )		->							Any:
           from ..models.auto import AutoModel, AutoTokenizer

           AutoModel.from_pretrained(
               self._model	,  cache_dir=self._cache	,  force_download=self._force	,  trust_remote_code=self._trust_remote_code       )
           AutoTokenizer.from_pretrained(
               self._model	,  cache_dir=self._cache	,  force_download=self._force	,  trust_remote_code=self._trust_remote_code       )







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

_snake_case            = random.Random()







def        lowerCAmelCase_			(  snake_case_,snake_case_=1.0,snake_case_=None,snake_case_=None  ):
      if rng is None:
            _A							:  str    =		global_rng

      _A							:  List[Any]    =		[]
      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	,  _a	,  _a=7	,  _a=400	,  _a=2000	,  _a=2048	,  _a=128	,  _a=1	,  _a=512	,  _a=30	,  _a=4_4100	,  )		->							Tuple:
           _A							:  Any    =		parent
           _A							:  str    =		batch_size
           _A							:  Union[str, Any]    =		min_seq_length
           _A							:  int    =		max_seq_length
           _A							:  List[str]    =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           _A							:  Tuple    =		spectrogram_length
           _A							:  int    =		feature_size
           _A							:  str    =		num_audio_channels
           _A							:  Tuple    =		hop_length
           _A							:  List[str]    =		chunk_length
           _A							:  Union[str, Any]    =		sampling_rate

     def       a__			(						self       )		->							Tuple:
           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       a__			(						self	,  _a=False	,  _a=False       )		->							Optional[int]:
           def _flatten(_a       ):
                 return list(itertools.chain(*_a       )       )

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







@require_torch
@require_torchaudio
class 		lowercase							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		TvltFeatureExtractor

     def       a__			(						self       )		->							Any:
           _A							:  int    =		TvltFeatureExtractionTester(self       )

     def       a__			(						self       )		->							List[Any]:
           _A							:  Tuple    =		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       a__			(						self       )		->							Optional[int]:
           _A							:  str    =		self.feature_extraction_class(**self.feat_extract_dict       )

           with tempfile.TemporaryDirectory() as tmpdirname:
                 _A							:  Tuple    =		feat_extract_first.save_pretrained(_a       )[0]
                 check_json_file_has_correct_format(_a       )
                 _A							:  Optional[int]    =		self.feature_extraction_class.from_pretrained(_a       )

           _A							:  Optional[Any]    =		feat_extract_first.to_dict()
           _A							:  int    =		feat_extract_second.to_dict()
           _A							:  int    =		dict_first.pop("""mel_filters"""       )
           _A							:  Optional[int]    =		dict_second.pop("""mel_filters"""       )
           self.assertTrue(np.allclose(_a	,  _a       )       )
           self.assertEqual(_a	,  _a       )

     def       a__			(						self       )		->							int:
           _A							:  str    =		self.feature_extraction_class(**self.feat_extract_dict       )

           with tempfile.TemporaryDirectory() as tmpdirname:
                 _A							:  List[str]    =		os.path.join(_a	,  """feat_extract.json"""       )
                 feat_extract_first.to_json_file(_a       )
                 _A							:  Union[str, Any]    =		self.feature_extraction_class.from_json_file(_a       )

           _A							:  Optional[Any]    =		feat_extract_first.to_dict()
           _A							:  Union[str, Any]    =		feat_extract_second.to_dict()
           _A							:  List[Any]    =		dict_first.pop("""mel_filters"""       )
           _A							:  Optional[Any]    =		dict_second.pop("""mel_filters"""       )
           self.assertTrue(np.allclose(_a	,  _a       )       )
           self.assertEqual(_a	,  _a       )

     def       a__			(						self       )		->							Optional[Any]:
           # Initialize feature_extractor
           _A							:  Tuple    =		self.feature_extraction_class(**self.feat_extract_dict       )

           # create three inputs of length 800, 1000, and 1200
           _A							:  Optional[int]    =		[floats_list((1, x)       )[0] for x in range(800	,  1400	,  200       )]
           _A							:  List[str]    =		[np.asarray(_a       ) for speech_input in speech_inputs]

           # Test not batched input
           _A							:  Optional[int]    =		feature_extractor(np_speech_inputs[0]	,  return_tensors="""np"""	,  sampling_rate=4_4100       ).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
           _A							:  str    =		feature_extractor(_a	,  return_tensors="""np"""	,  sampling_rate=4_4100       ).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
           _A							:  Optional[Any]    =		feature_extractor(
               _a	,  return_tensors="""np"""	,  sampling_rate=4_4100	,  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.
           _A							:  Dict    =		[floats_list((1, x)       )[0] for x in (800, 800, 800)]
           _A							:  Optional[int]    =		np.asarray(_a       )
           _A							:  Optional[Any]    =		feature_extractor(_a	,  return_tensors="""np"""	,  sampling_rate=4_4100       ).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       a__			(						self	,  _a       )		->							str:
           _A							:  Optional[Any]    =		load_dataset("""hf-internal-testing/librispeech_asr_dummy"""	,  """clean"""	,  split="""validation"""       )
           # automatic decoding with librispeech
           _A							:  Dict    =		ds.sort("""id"""       ).select(range(_a       )       )[:num_samples]["""audio"""]

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



     def       a__			(						self       )		->							Optional[Any]:
           _A							:  List[str]    =		self._load_datasamples(1       )
           _A							:  List[str]    =		TvltFeatureExtractor()
           _A							:  List[Any]    =		feature_extractor(_a	,  return_tensors="""pt"""       ).audio_values

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

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

