import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger


class 					UpperCAmelCase		(_UpperCAmelCase						):



    """simple docstring"""

    _UpperCAmelCase			:str							=				"config.json"






    @register_to_config
    def __init__(     self					,    _UpperCAmelCase=2					,    _UpperCAmelCase=5					,    _UpperCAmelCase=(2, 5)					,    _UpperCAmelCase="for diffusion"					,    _UpperCAmelCase=[1, 3]					,    ):
       pass



class 					UpperCAmelCase		(_UpperCAmelCase						):



    """simple docstring"""

    _UpperCAmelCase			:Union[str, Any]							=				"config.json"






    @register_to_config
    def __init__(     self					,    _UpperCAmelCase=2					,    _UpperCAmelCase=5					,    _UpperCAmelCase=(2, 5)					,    _UpperCAmelCase="for diffusion"					,    _UpperCAmelCase=[1, 3]					,    ):
       pass



class 					UpperCAmelCase		(_UpperCAmelCase						):



    """simple docstring"""

    _UpperCAmelCase			:Union[str, Any]							=				"config.json"






    @register_to_config
    def __init__(     self					,    _UpperCAmelCase=2					,    _UpperCAmelCase=5					,    _UpperCAmelCase=(2, 5)					,    _UpperCAmelCase="for diffusion"					,    _UpperCAmelCase=[1, 3]					,    _UpperCAmelCase=[1, 3]					,    ):
       pass



class 					UpperCAmelCase		(_UpperCAmelCase						):



    """simple docstring"""

    _UpperCAmelCase			:Tuple							=				"config.json"






    @register_to_config
    def __init__(     self					,    _UpperCAmelCase=2					,    _UpperCAmelCase=5					,    _UpperCAmelCase=(2, 5)					,    _UpperCAmelCase="for diffusion"					,    _UpperCAmelCase=[1, 5]					,    _UpperCAmelCase=[5, 4]					,    ):
       pass



class 					UpperCAmelCase		(unittest.TestCase						):



    """simple docstring"""

    def _snake_case    (     self							):
       with self.assertRaises(_UpperCAmelCase							):
          ConfigMixin.load_config('''dummy_path'''							)






    def _snake_case    (     self							):
       lowercase__:     Dict      =   SampleObject()
       lowercase__:     Dict      =   obj.config
       assert config["a"] == 2
       assert config["b"] == 5
       assert config["c"] == (2, 5)
       assert config["d"] == "for diffusion"
       assert config["e"] == [1, 3]

       # init ignore private arguments
       lowercase__:     int      =   SampleObject(_name_or_path='''lalala'''							)
       lowercase__:     int      =   obj.config
       assert config["a"] == 2
       assert config["b"] == 5
       assert config["c"] == (2, 5)
       assert config["d"] == "for diffusion"
       assert config["e"] == [1, 3]

       # can override default
       lowercase__:     Union[str, Any]      =   SampleObject(c=6							)
       lowercase__:     Dict      =   obj.config
       assert config["a"] == 2
       assert config["b"] == 5
       assert config["c"] == 6
       assert config["d"] == "for diffusion"
       assert config["e"] == [1, 3]

       # can use positional arguments.
       lowercase__:     List[str]      =   SampleObject(1					,    c=6							)
       lowercase__:     List[Any]      =   obj.config
       assert config["a"] == 1
       assert config["b"] == 5
       assert config["c"] == 6
       assert config["d"] == "for diffusion"
       assert config["e"] == [1, 3]






    def _snake_case    (     self							):
       lowercase__:     int      =   SampleObject()
       lowercase__:     Any      =   obj.config

       assert config["a"] == 2
       assert config["b"] == 5
       assert config["c"] == (2, 5)
       assert config["d"] == "for diffusion"
       assert config["e"] == [1, 3]

       with tempfile.TemporaryDirectory() as tmpdirname:
          obj.save_config(_UpperCAmelCase							)
          lowercase__:     List[str]      =   SampleObject.from_config(SampleObject.load_config(_UpperCAmelCase							)							)
          lowercase__:     Tuple      =   new_obj.config

       # unfreeze configs
       lowercase__:     List[Any]      =   dict(_UpperCAmelCase							)
       lowercase__:     Tuple      =   dict(_UpperCAmelCase							)

       assert config.pop('''c'''							) == (2, 5)  # instantiated as tuple
       assert new_config.pop('''c'''							) == [2, 5]  # saved & loaded as list because of json
       config.pop('''_use_default_values'''							)
       assert config == new_config






    def _snake_case    (     self							):
       lowercase__:     Optional[int]      =   logging.get_logger('''diffusers.configuration_utils'''							)
       # 30 for warning
       logger.setLevel(30							)

       with CaptureLogger(_UpperCAmelCase							) as cap_logger:
          lowercase__:     Dict      =   DDIMScheduler.from_pretrained(
              '''hf-internal-testing/tiny-stable-diffusion-torch'''					,    subfolder='''scheduler'''							)

       assert ddim.__class__ == DDIMScheduler
       # no warning should be thrown
       assert cap_logger.out == ""






    def _snake_case    (     self							):
       lowercase__:     str      =   logging.get_logger('''diffusers.configuration_utils'''							)
       # 30 for warning
       logger.setLevel(30							)

       with CaptureLogger(_UpperCAmelCase							) as cap_logger:
          lowercase__:     List[Any]      =   EulerDiscreteScheduler.from_pretrained(
              '''hf-internal-testing/tiny-stable-diffusion-torch'''					,    subfolder='''scheduler'''							)

       assert euler.__class__ == EulerDiscreteScheduler
       # no warning should be thrown
       assert cap_logger.out == ""






    def _snake_case    (     self							):
       lowercase__:     Union[str, Any]      =   logging.get_logger('''diffusers.configuration_utils'''							)
       # 30 for warning
       logger.setLevel(30							)

       with CaptureLogger(_UpperCAmelCase							) as cap_logger:
          lowercase__:     Optional[Any]      =   EulerAncestralDiscreteScheduler.from_pretrained(
              '''hf-internal-testing/tiny-stable-diffusion-torch'''					,    subfolder='''scheduler'''							)

       assert euler.__class__ == EulerAncestralDiscreteScheduler
       # no warning should be thrown
       assert cap_logger.out == ""






    def _snake_case    (     self							):
       lowercase__:     str      =   logging.get_logger('''diffusers.configuration_utils'''							)
       # 30 for warning
       logger.setLevel(30							)

       with CaptureLogger(_UpperCAmelCase							) as cap_logger:
          lowercase__:     Any      =   PNDMScheduler.from_pretrained(
              '''hf-internal-testing/tiny-stable-diffusion-torch'''					,    subfolder='''scheduler'''							)

       assert pndm.__class__ == PNDMScheduler
       # no warning should be thrown
       assert cap_logger.out == ""






    def _snake_case    (     self							):
       lowercase__:     Union[str, Any]      =   logging.get_logger('''diffusers.configuration_utils'''							)
       # 30 for warning
       logger.setLevel(30							)

       with CaptureLogger(_UpperCAmelCase							) as cap_logger:
          lowercase__:     Optional[Any]      =   DDPMScheduler.from_pretrained(
              '''hf-internal-testing/tiny-stable-diffusion-torch'''					,    subfolder='''scheduler'''					,    prediction_type='''sample'''					,    beta_end=8					,    )

       with CaptureLogger(_UpperCAmelCase							) as cap_logger_a:
          lowercase__:     List[str]      =   DDPMScheduler.from_pretrained('''google/ddpm-celebahq-256'''					,    beta_start=88							)

       assert ddpm.__class__ == DDPMScheduler
       assert ddpm.config.prediction_type == "sample"
       assert ddpm.config.beta_end == 8
       assert ddpm_a.config.beta_start == 88

       # no warning should be thrown
       assert cap_logger.out == ""
       assert cap_logger_a.out == ""






    def _snake_case    (     self							):
       lowercase__:     List[Any]      =   logging.get_logger('''diffusers.configuration_utils'''							)
       # 30 for warning
       logger.setLevel(30							)

       with CaptureLogger(_UpperCAmelCase							) as cap_logger:
          lowercase__:     List[Any]      =   DPMSolverMultistepScheduler.from_pretrained(
              '''hf-internal-testing/tiny-stable-diffusion-torch'''					,    subfolder='''scheduler'''							)

       assert dpm.__class__ == DPMSolverMultistepScheduler
       # no warning should be thrown
       assert cap_logger.out == ""






    def _snake_case    (     self							):
       # let's first save a config that should be in the form
       #    a=2,
       #    b=5,
       #    c=(2, 5),
       #    d="for diffusion",
       #    e=[1, 3],

       lowercase__:     Optional[Any]      =   SampleObject()

       lowercase__:     Dict      =   {k: v for k, v in config.config.items() if not k.startswith('''_'''							)}

       # make sure that default config has all keys in `_use_default_values`
       assert set(config_dict.keys()							) == set(config.config._use_default_values							)

       with tempfile.TemporaryDirectory() as tmpdirname:
          config.save_config(_UpperCAmelCase							)

          # now loading it with SampleObject2 should put f into `_use_default_values`
          lowercase__:     Tuple      =   SampleObjecta.from_config(_UpperCAmelCase							)

          assert "f" in config._use_default_values
          assert config.f == [1, 3]

       # now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
       # **BECAUSE** it is part of `config._use_default_values`
       lowercase__:     Union[str, Any]      =   SampleObjecta.from_config(config.config							)
       assert new_config.f == [5, 4]

       config.config._use_default_values.pop()
       lowercase__:     Tuple      =   SampleObjecta.from_config(config.config							)
       assert new_config_a.f == [1, 3]

       # Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
       assert new_config_a.e == [1, 3]

"""simple docstring"""






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


__A         =				get_tests_dir("fixtures/test_sentencepiece.model")


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



    """simple docstring"""

    def _snake_case    (     self							):
       lowercase__:     Dict      =   tempfile.mkdtemp()

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

       lowercase__:     Tuple      =   SpeechaTextTokenizer.from_pretrained(self.tmpdirname							)
       tokenizer.save_pretrained(self.tmpdirname							)

       lowercase__:     Optional[int]      =   {
           '''feature_size''': 24,
           '''num_mel_bins''': 24,
           '''padding_value''': 0.0,
           '''sampling_rate''': 16000,
           '''return_attention_mask''': False,
           '''do_normalize''': True,
       }
       save_json(_UpperCAmelCase					,    save_dir / FEATURE_EXTRACTOR_NAME							)






    def _snake_case    (     self					,    **_UpperCAmelCase							):
       return SpeechaTextTokenizer.from_pretrained(self.tmpdirname					,    **_UpperCAmelCase							)






    def _snake_case    (     self					,    **_UpperCAmelCase							):
       return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname					,    **_UpperCAmelCase							)






    def _snake_case    (     self							):
       shutil.rmtree(self.tmpdirname							)






    def _snake_case    (     self							):
       lowercase__:     Tuple      =   self.get_tokenizer()
       lowercase__:     Optional[int]      =   self.get_feature_extractor()

       lowercase__:     int      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

       processor.save_pretrained(self.tmpdirname							)
       lowercase__:     Union[str, Any]      =   SpeechaTextProcessor.from_pretrained(self.tmpdirname							)

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

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






    def _snake_case    (     self							):
       lowercase__:     Tuple      =   SpeechaTextProcessor(
           tokenizer=self.get_tokenizer()					,    feature_extractor=self.get_feature_extractor()							)
       processor.save_pretrained(self.tmpdirname							)

       lowercase__:     Any      =   self.get_tokenizer(bos_token='''(BOS)'''					,    eos_token='''(EOS)'''							)
       lowercase__:     Tuple      =   self.get_feature_extractor(do_normalize=_UpperCAmelCase					,    padding_value=1.0							)

       lowercase__:     List[str]      =   SpeechaTextProcessor.from_pretrained(
           self.tmpdirname					,    bos_token='''(BOS)'''					,    eos_token='''(EOS)'''					,    do_normalize=_UpperCAmelCase					,    padding_value=1.0							)

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

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






    def _snake_case    (     self							):
       lowercase__:     Tuple      =   self.get_feature_extractor()
       lowercase__:     List[Any]      =   self.get_tokenizer()

       lowercase__:     Any      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

       lowercase__:     int      =   floats_list((3, 1000)							)

       lowercase__:     Optional[Any]      =   feature_extractor(_UpperCAmelCase					,    return_tensors='''np'''							)
       lowercase__:     Dict      =   processor(_UpperCAmelCase					,    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 _snake_case    (     self							):
       lowercase__:     Tuple      =   self.get_feature_extractor()
       lowercase__:     Dict      =   self.get_tokenizer()

       lowercase__:     Union[str, Any]      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

       lowercase__:     Dict      =   '''This is a test string'''

       lowercase__:     int      =   processor(text=_UpperCAmelCase							)

       lowercase__:     Union[str, Any]      =   tokenizer(_UpperCAmelCase							)

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






    def _snake_case    (     self							):
       lowercase__:     List[Any]      =   self.get_feature_extractor()
       lowercase__:     Union[str, Any]      =   self.get_tokenizer()

       lowercase__:     Tuple      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

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

       lowercase__:     List[Any]      =   processor.batch_decode(_UpperCAmelCase							)
       lowercase__:     Dict      =   tokenizer.batch_decode(_UpperCAmelCase							)

       self.assertListEqual(_UpperCAmelCase					,    _UpperCAmelCase							)






    def _snake_case    (     self							):
       lowercase__:     Optional[Any]      =   self.get_feature_extractor()
       lowercase__:     List[str]      =   self.get_tokenizer()

       lowercase__:     Union[str, Any]      =   SpeechaTextProcessor(tokenizer=_UpperCAmelCase					,    feature_extractor=_UpperCAmelCase							)

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