


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


lowerCamelCase		=						random.Random()






def 	lowerCamelCase_       (			_a      ,  _a=1.0      ,  _a=None      ,  _a=None			):




		"""simple docstring"""






		if rng is None:
				lowerCAmelCase__  :     int       	=   global_rng

		lowerCAmelCase__  :     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    _a				(							unittest.TestCase):




	def 				UpperCAmelCase__(   self		:						List[Any]						)->			Optional[int]:
			lowerCAmelCase__  :     List[Any]       	=   '''facebook/musicgen-small'''
			lowerCAmelCase__  :     Any       	=   tempfile.mkdtemp()




	def 				UpperCAmelCase__(   self		:						Optional[Any]						,   **_SCREAMING_SNAKE_CASE		:						Union[str, Any]						)->			List[str]:
			return TaTokenizer.from_pretrained(self.checkpoint						,   **_SCREAMING_SNAKE_CASE						)




	def 				UpperCAmelCase__(   self		:						List[str]						,   **_SCREAMING_SNAKE_CASE		:						Optional[Any]						)->			Tuple:
			return EncodecFeatureExtractor.from_pretrained(self.checkpoint						,   **_SCREAMING_SNAKE_CASE						)




	def 				UpperCAmelCase__(   self		:						Dict						)->			Dict:
			shutil.rmtree(self.tmpdirname						)




	def 				UpperCAmelCase__(   self		:						List[Any]						)->			Any:
			lowerCAmelCase__  :     List[str]       	=   self.get_tokenizer()
			lowerCAmelCase__  :     Tuple       	=   self.get_feature_extractor()

			lowerCAmelCase__  :     Optional[int]       	=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE						,   feature_extractor=_SCREAMING_SNAKE_CASE						)

			processor.save_pretrained(self.tmpdirname						)
			lowerCAmelCase__  :     Dict       	=   MusicgenProcessor.from_pretrained(self.tmpdirname						)

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

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




	def 				UpperCAmelCase__(   self		:						Dict						)->			str:
			lowerCAmelCase__  :     Optional[Any]       	=   MusicgenProcessor(tokenizer=self.get_tokenizer()						,   feature_extractor=self.get_feature_extractor()						)
			processor.save_pretrained(self.tmpdirname						)

			lowerCAmelCase__  :     Any       	=   self.get_tokenizer(bos_token='''(BOS)'''						,   eos_token='''(EOS)'''						)
			lowerCAmelCase__  :     List[str]       	=   self.get_feature_extractor(do_normalize=_SCREAMING_SNAKE_CASE						,   padding_value=1.0						)

			lowerCAmelCase__  :     List[str]       	=   MusicgenProcessor.from_pretrained(
			    self.tmpdirname						,   bos_token='''(BOS)'''						,   eos_token='''(EOS)'''						,   do_normalize=_SCREAMING_SNAKE_CASE						,   padding_value=1.0						)

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

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




	def 				UpperCAmelCase__(   self		:						Optional[int]						)->			Tuple:
			lowerCAmelCase__  :     Optional[Any]       	=   self.get_feature_extractor()
			lowerCAmelCase__  :     int       	=   self.get_tokenizer()

			lowerCAmelCase__  :     Tuple       	=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE						,   feature_extractor=_SCREAMING_SNAKE_CASE						)

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

			lowerCAmelCase__  :     List[str]       	=   feature_extractor(_SCREAMING_SNAKE_CASE						,   return_tensors='''np'''						)
			lowerCAmelCase__  :     List[Any]       	=   processor(_SCREAMING_SNAKE_CASE						,   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 				UpperCAmelCase__(   self		:						Dict						)->			List[Any]:
			lowerCAmelCase__  :     Optional[Any]       	=   self.get_feature_extractor()
			lowerCAmelCase__  :     Dict       	=   self.get_tokenizer()

			lowerCAmelCase__  :     Any       	=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE						,   feature_extractor=_SCREAMING_SNAKE_CASE						)

			lowerCAmelCase__  :     str       	=   '''This is a test string'''

			lowerCAmelCase__  :     str       	=   processor(text=_SCREAMING_SNAKE_CASE						)

			lowerCAmelCase__  :     Dict       	=   tokenizer(_SCREAMING_SNAKE_CASE						)

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




	def 				UpperCAmelCase__(   self		:						int						)->			List[str]:
			lowerCAmelCase__  :     Optional[int]       	=   self.get_feature_extractor()
			lowerCAmelCase__  :     Optional[int]       	=   self.get_tokenizer()

			lowerCAmelCase__  :     List[str]       	=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE						,   feature_extractor=_SCREAMING_SNAKE_CASE						)

			lowerCAmelCase__  :     Dict       	=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

			lowerCAmelCase__  :     Any       	=   processor.batch_decode(sequences=_SCREAMING_SNAKE_CASE						)
			lowerCAmelCase__  :     Tuple       	=   tokenizer.batch_decode(_SCREAMING_SNAKE_CASE						)

			self.assertListEqual(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						)




	def 				UpperCAmelCase__(   self		:						Union[str, Any]						)->			List[Any]:
			lowerCAmelCase__  :     List[str]       	=   self.get_feature_extractor()
			lowerCAmelCase__  :     Dict       	=   self.get_tokenizer()

			lowerCAmelCase__  :     List[str]       	=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE						,   feature_extractor=_SCREAMING_SNAKE_CASE						)

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




	def 				UpperCAmelCase__(   self		:						List[str]						)->			Any:
			lowerCAmelCase__  :     int       	=   self.get_feature_extractor(padding_side='''left'''						)
			lowerCAmelCase__  :     Any       	=   self.get_tokenizer()

			lowerCAmelCase__  :     Any       	=   MusicgenProcessor(tokenizer=_SCREAMING_SNAKE_CASE						,   feature_extractor=_SCREAMING_SNAKE_CASE						)

			lowerCAmelCase__  :     Tuple       	=   [floats_list((1, x)						)[0] for x in range(5						,   20						,   5						)]
			lowerCAmelCase__  :     Optional[int]       	=   processor(_SCREAMING_SNAKE_CASE						).padding_mask

			lowerCAmelCase__  :     List[Any]       	=   np.asarray(floats_list((3, 20)						)						)[:, None, :]
			lowerCAmelCase__  :     List[str]       	=   processor.batch_decode(_SCREAMING_SNAKE_CASE						,   padding_mask=_SCREAMING_SNAKE_CASE						)

			self.assertIsInstance(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						)

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

			self.assertTrue(decoded_audios[0].shape == (1, 10)						)
			self.assertTrue(decoded_audios[1].shape == (1, 15)						)
			self.assertTrue(decoded_audios[2].shape == (1, 20)						)








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


lowerCamelCase		=						get_logger(__name__)






def 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






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




