import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
 pass

if is_speech_available():
 from transformers import EncodecFeatureExtractor, MusicgenProcessor


UpperCAmelCase							= random.Random()


def SCREAMING_SNAKE_CASE (   lowercase_:		List[str]						,  lowercase_:		Optional[Any]=1.0						,  lowercase_:		str=None						,  lowercase_:		Any=None ):
       if rng is None:
              _UpperCamelCase   :				Any       =     global_rng

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

       return values





@require_torch
@require_sentencepiece
class       __lowerCAmelCase     (				unittest.TestCase  ):



 '''simple docstring'''

 def     __snake_case    (	self				:						Dict     )       ->							Optional[Any]:
        _UpperCamelCase   :				str       =     'facebook/musicgen-small'
        _UpperCamelCase   :				Union[str, Any]       =     tempfile.mkdtemp()

 def     __snake_case    (	self				:						Tuple				,	**lowerCamelCase				:						str     )       ->							List[str]:
        return TaTokenizer.from_pretrained(self.checkpoint				,	**lowerCamelCase     )

 def     __snake_case    (	self				:						List[Any]				,	**lowerCamelCase				:						List[Any]     )       ->							int:
        return EncodecFeatureExtractor.from_pretrained(self.checkpoint				,	**lowerCamelCase     )

 def     __snake_case    (	self				:						Optional[int]     )       ->							Optional[Any]:
        shutil.rmtree(self.tmpdirname     )

 def     __snake_case    (	self				:						Union[str, Any]     )       ->							int:
        _UpperCamelCase   :				Dict       =     self.get_tokenizer()
        _UpperCamelCase   :				List[str]       =     self.get_feature_extractor()

        _UpperCamelCase   :				int       =     MusicgenProcessor(tokenizer=lowerCamelCase				,	feature_extractor=lowerCamelCase     )

        processor.save_pretrained(self.tmpdirname     )
        _UpperCamelCase   :				List[Any]       =     MusicgenProcessor.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     __snake_case    (	self				:						Union[str, Any]     )       ->							Any:
        _UpperCamelCase   :				Any       =     MusicgenProcessor(tokenizer=self.get_tokenizer()				,	feature_extractor=self.get_feature_extractor()     )
        processor.save_pretrained(self.tmpdirname     )

        _UpperCamelCase   :				Dict       =     self.get_tokenizer(bos_token='(BOS)'				,	eos_token='(EOS)'     )
        _UpperCamelCase   :				List[str]       =     self.get_feature_extractor(do_normalize=lowerCamelCase				,	padding_value=1.0     )

        _UpperCamelCase   :				List[str]       =     MusicgenProcessor.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     __snake_case    (	self				:						Optional[int]     )       ->							Optional[Any]:
        _UpperCamelCase   :				Optional[Any]       =     self.get_feature_extractor()
        _UpperCamelCase   :				Tuple       =     self.get_tokenizer()

        _UpperCamelCase   :				Dict       =     MusicgenProcessor(tokenizer=lowerCamelCase				,	feature_extractor=lowerCamelCase     )

        _UpperCamelCase   :				Tuple       =     floats_list((3, 1_0_0_0)     )

        _UpperCamelCase   :				Tuple       =     feature_extractor(lowerCamelCase				,	return_tensors='np'     )
        _UpperCamelCase   :				int       =     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     __snake_case    (	self				:						Any     )       ->							Optional[Any]:
        _UpperCamelCase   :				Optional[Any]       =     self.get_feature_extractor()
        _UpperCamelCase   :				List[Any]       =     self.get_tokenizer()

        _UpperCamelCase   :				List[str]       =     MusicgenProcessor(tokenizer=lowerCamelCase				,	feature_extractor=lowerCamelCase     )

        _UpperCamelCase   :				List[Any]       =     'This is a test string'

        _UpperCamelCase   :				Union[str, Any]       =     processor(text=lowerCamelCase     )

        _UpperCamelCase   :				int       =     tokenizer(lowerCamelCase     )

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

 def     __snake_case    (	self				:						Dict     )       ->							List[Any]:
        _UpperCamelCase   :				Optional[int]       =     self.get_feature_extractor()
        _UpperCamelCase   :				List[str]       =     self.get_tokenizer()

        _UpperCamelCase   :				Any       =     MusicgenProcessor(tokenizer=lowerCamelCase				,	feature_extractor=lowerCamelCase     )

        _UpperCamelCase   :				Optional[int]       =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _UpperCamelCase   :				Any       =     processor.batch_decode(sequences=lowerCamelCase     )
        _UpperCamelCase   :				str       =     tokenizer.batch_decode(lowerCamelCase     )

        self.assertListEqual(lowerCamelCase				,	lowerCamelCase     )

 def     __snake_case    (	self				:						Optional[Any]     )       ->							Any:
        _UpperCamelCase   :				Tuple       =     self.get_feature_extractor()
        _UpperCamelCase   :				List[str]       =     self.get_tokenizer()

        _UpperCamelCase   :				Optional[Any]       =     MusicgenProcessor(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'				,	)

 def     __snake_case    (	self				:						Tuple     )       ->							Dict:
        _UpperCamelCase   :				Optional[int]       =     self.get_feature_extractor(padding_side='left'     )
        _UpperCamelCase   :				Union[str, Any]       =     self.get_tokenizer()

        _UpperCamelCase   :				Tuple       =     MusicgenProcessor(tokenizer=lowerCamelCase				,	feature_extractor=lowerCamelCase     )

        _UpperCamelCase   :				Union[str, Any]       =     [floats_list((1, x)     )[0] for x in range(5				,	2_0				,	5     )]
        _UpperCamelCase   :				Dict       =     processor(lowerCamelCase     ).padding_mask

        _UpperCamelCase   :				List[str]       =     np.asarray(floats_list((3, 2_0)     )     )[:, None, :]
        _UpperCamelCase   :				Optional[Any]       =     processor.batch_decode(lowerCamelCase				,	padding_mask=lowerCamelCase     )

        self.assertIsInstance(lowerCamelCase				,	lowerCamelCase     )

        for audio in decoded_audios:
               self.assertIsInstance(lowerCamelCase				,	np.ndarray     )

        self.assertTrue(decoded_audios[0].shape == (1, 1_0)     )
        self.assertTrue(decoded_audios[1].shape == (1, 1_5)     )
        self.assertTrue(decoded_audios[2].shape == (1, 2_0)     )




import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
 from .testing_utils import (
     floats_tensor,
     load_hf_numpy,
     load_image,
     load_numpy,
     load_pt,
     nightly,
     parse_flag_from_env,
     print_tensor_test,
     require_torch_a,
     require_torch_gpu,
     skip_mps,
     slow,
     torch_all_close,
     torch_device,
 )
 from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


UpperCAmelCase							= get_logger(__name__)


def SCREAMING_SNAKE_CASE (   lowercase_:		Any ):
       if version.parse(lowercase_ ) < version.parse(lowercase_ ):
              if "dev" in min_version:
                     _UpperCamelCase   :				Tuple       =     (
                         'This example requires a source install from HuggingFace diffusers (see '
                         '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
                     )
              else:
                     _UpperCamelCase   :				str       =     F"This example requires a minimum version of {min_version},"
              error_message += F" but the version found is {__version__}.\n"
              raise ImportError(lowercase_ )



