

"""simple docstring"""

import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
    # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
    # setup instructions, if using on-demand hardware

    # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
    # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
    # Throw an error if user passes both BYO and on-demand cluster args
    # Otherwise, use default values
    _UpperCamelCase		     =    argparse.ArgumentParser()
    parser.add_argument("""--user""", type=str, default="""ubuntu""")
    parser.add_argument("""--host""", type=str, default="""localhost""")
    parser.add_argument("""--key_path""", type=str, default=None)
    parser.add_argument("""--instance""", type=str, default="""V100:1""")
    parser.add_argument("""--provider""", type=str, default="""cheapest""")
    parser.add_argument("""--use_spot""", type=bool, default=False)
    parser.add_argument("""--example""", type=str, default="""pytorch/text-generation/run_generation.py""")
    _UpperCamelCase     ,  _UpperCamelCase		     =    parser.parse_known_args()
    if args.host != "localhost":
        if args.instance != "V100:1" or args.provider != "cheapest":
            raise ValueError("""Cannot specify both BYO and on-demand cluster args""")
        _UpperCamelCase		     =    rh.cluster(
            name="""rh-cluster""", ips=[args.host], ssh_creds={"""ssh_user""": args.user, """ssh_private_key""": args.key_path}
        )
    else:
        _UpperCamelCase		     =    rh.cluster(
            name="""rh-cluster""", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
        )
    _UpperCamelCase		     =    args.example.rsplit("""/""", 1)[0]

    # Set up remote environment
    cluster.install_packages(["""pip:./"""])  # Installs transformers from local source
    # Note transformers is copied into the home directory on the remote machine, so we can install from there
    cluster.run([f"pip install -r transformers/examples/{example_dir}/requirements.txt"])
    cluster.run(["""pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117"""])

    # Run example. You can bypass the CLI wrapper and paste your own code here.
    cluster.run([f"python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"])

    # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
    # from my_script... import train
    # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
    # launch_train_gpu = rh.function(fn=train,
    #                                system=gpu,
    #                                reqs=reqs,
    #                                name='train_bert_glue')
    #
    # We can pass in arguments just like we would to a function:
    # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
    #                  stream_logs=True)









"""simple docstring"""

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

_UpperCamelCase		     =    random.Random()



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__=1.0					,					lowercase__=None					,					lowercase__=None   )			->						Optional[Any]:
 if rng is None:
  lowerCAmelCase__   :				Union[str, Any] 			=				global_rng

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

 return values






class 			__a					(						unittest.TestCase       ):





       """simple docstring"""


       def __init__(   self			,					snake_case			,					snake_case=7			,					snake_case=400			,					snake_case=2_000			,					snake_case=2_048			,					snake_case=128			,					snake_case=1			,					snake_case=512			,					snake_case=30			,					snake_case=44_100			,					):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[int] 			=				parent
        lowerCAmelCase__   :				Optional[int] 			=				batch_size
        lowerCAmelCase__   :				Optional[Any] 			=				min_seq_length
        lowerCAmelCase__   :				Optional[int] 			=				max_seq_length
        lowerCAmelCase__   :				Dict 			=				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        lowerCAmelCase__   :				Dict 			=				spectrogram_length
        lowerCAmelCase__   :				Any 			=				feature_size
        lowerCAmelCase__   :				int 			=				num_audio_channels
        lowerCAmelCase__   :				Optional[int] 			=				hop_length
        lowerCAmelCase__   :				List[str] 			=				chunk_length
        lowerCAmelCase__   :				Optional[Any] 			=				sampling_rate






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        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 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case=False			,					snake_case=False   ):





        """simple docstring"""

        def _flatten(snake_case   ):
         return list(itertools.chain(*snake_case   )   )

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






@require_torch
@require_torchaudio
class 			__a					(						__magic_name__       ,	unittest.TestCase       ):





       """simple docstring"""


       __UpperCamelCase					:      Optional[Any]    =						TvltFeatureExtractor






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[Any] 			=				TvltFeatureExtractionTester(self   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[str] 			=				self.feature_extraction_class(**self.feat_extract_dict   )
        self.assertTrue(hasattr(snake_case			,					"spectrogram_length"   )   )
        self.assertTrue(hasattr(snake_case			,					"feature_size"   )   )
        self.assertTrue(hasattr(snake_case			,					"num_audio_channels"   )   )
        self.assertTrue(hasattr(snake_case			,					"hop_length"   )   )
        self.assertTrue(hasattr(snake_case			,					"chunk_length"   )   )
        self.assertTrue(hasattr(snake_case			,					"sampling_rate"   )   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				int 			=				self.feature_extraction_class(**self.feat_extract_dict   )

        with tempfile.TemporaryDirectory() as tmpdirname:
         lowerCAmelCase__   :				Union[str, Any] 			=				feat_extract_first.save_pretrained(snake_case   )[0]
         check_json_file_has_correct_format(snake_case   )
         lowerCAmelCase__   :				Tuple 			=				self.feature_extraction_class.from_pretrained(snake_case   )

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				self.feature_extraction_class(**self.feat_extract_dict   )

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

        lowerCAmelCase__   :				Optional[Any] 			=				feat_extract_first.to_dict()
        lowerCAmelCase__   :				Dict 			=				feat_extract_second.to_dict()
        lowerCAmelCase__   :				Tuple 			=				dict_first.pop("mel_filters"   )
        lowerCAmelCase__   :				Dict 			=				dict_second.pop("mel_filters"   )
        self.assertTrue(np.allclose(snake_case			,					snake_case   )   )
        self.assertEqual(snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[int] 			=				self.feature_extraction_class(**self.feat_extract_dict   )

        # create three inputs of length 800, 1000, and 1200
        lowerCAmelCase__   :				Union[str, Any] 			=				[floats_list((1, x)   )[0] for x in range(800			,					1_400			,					200   )]
        lowerCAmelCase__   :				Optional[int] 			=				[np.asarray(snake_case   ) for speech_input in speech_inputs]

        # Test not batched input
        lowerCAmelCase__   :				Tuple 			=				feature_extractor(np_speech_inputs[0]			,					return_tensors="np"			,					sampling_rate=44_100   ).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
        lowerCAmelCase__   :				Optional[int] 			=				feature_extractor(snake_case			,					return_tensors="np"			,					sampling_rate=44_100   ).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
        lowerCAmelCase__   :				List[str] 			=				feature_extractor(
            snake_case			,					return_tensors="np"			,					sampling_rate=44_100			,					mask_audio=snake_case   ).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.
        lowerCAmelCase__   :				List[str] 			=				[floats_list((1, x)   )[0] for x in (800, 800, 800)]
        lowerCAmelCase__   :				Tuple 			=				np.asarray(snake_case   )
        lowerCAmelCase__   :				List[Any] 			=				feature_extractor(snake_case			,					return_tensors="np"			,					sampling_rate=44_100   ).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 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				load_dataset("hf-internal-testing/librispeech_asr_dummy"			,					"clean"			,					split="validation"   )
        # automatic decoding with librispeech
        lowerCAmelCase__   :				int 			=				ds.sort("id"   ).select(range(snake_case   )   )[:num_samples]["audio"]

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				self._load_datasamples(1   )
        lowerCAmelCase__   :				Optional[Any] 			=				TvltFeatureExtractor()
        lowerCAmelCase__   :				Dict 			=				feature_extractor(snake_case			,					return_tensors="pt"   ).audio_values

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

        lowerCAmelCase__   :				Dict 			=				torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]]   )
        self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]			,					snake_case			,					atol=1e-4   )   )






