

"""simple docstring"""




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


__A     :    Optional[int]    =		random.Random()
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__=1.0			,							lowercase__=None			,							lowercase__=None      ):
					"""simple docstring"""
					if rng is None:
										A			=					global_rng

					A			=					[]
					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 			__UpperCamelCase (   unittest.TestCase						):


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[Any]):
							A			=					"facebook/musicgen-small"
							A			=					tempfile.mkdtemp()


		def 					SCREAMING_SNAKE_CASE__   (self :		List[Any]     ,     **__SCREAMING_SNAKE_CASE :		str):
							return TaTokenizer.from_pretrained(self.checkpoint     ,     **__SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[int]     ,     **__SCREAMING_SNAKE_CASE :		List[str]):
							return EncodecFeatureExtractor.from_pretrained(self.checkpoint     ,     **__SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		Dict):
							shutil.rmtree(self.tmpdirname)


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[int]):
							A			=					self.get_tokenizer()
							A			=					self.get_feature_extractor()

							A			=					MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,     feature_extractor=__SCREAMING_SNAKE_CASE)

							processor.save_pretrained(self.tmpdirname)
							A			=					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 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]):
							A			=					MusicgenProcessor(tokenizer=self.get_tokenizer()     ,     feature_extractor=self.get_feature_extractor())
							processor.save_pretrained(self.tmpdirname)

							A			=					self.get_tokenizer(bos_token="(BOS)"     ,     eos_token="(EOS)")
							A			=					self.get_feature_extractor(do_normalize=__SCREAMING_SNAKE_CASE     ,     padding_value=1.0)

							A			=					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 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]):
							A			=					self.get_feature_extractor()
							A			=					self.get_tokenizer()

							A			=					MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,     feature_extractor=__SCREAMING_SNAKE_CASE)

							A			=					floats_list((3, 1_0_0_0))

							A			=					feature_extractor(__SCREAMING_SNAKE_CASE     ,     return_tensors="np")
							A			=					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 					SCREAMING_SNAKE_CASE__   (self :		int):
							A			=					self.get_feature_extractor()
							A			=					self.get_tokenizer()

							A			=					MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,     feature_extractor=__SCREAMING_SNAKE_CASE)

							A			=					"This is a test string"

							A			=					processor(text=__SCREAMING_SNAKE_CASE)

							A			=					tokenizer(__SCREAMING_SNAKE_CASE)

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


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[Any]):
							A			=					self.get_feature_extractor()
							A			=					self.get_tokenizer()

							A			=					MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,     feature_extractor=__SCREAMING_SNAKE_CASE)

							A			=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							A			=					processor.batch_decode(sequences=__SCREAMING_SNAKE_CASE)
							A			=					tokenizer.batch_decode(__SCREAMING_SNAKE_CASE)

							self.assertListEqual(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		List[str]):
							A			=					self.get_feature_extractor()
							A			=					self.get_tokenizer()

							A			=					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 					SCREAMING_SNAKE_CASE__   (self :		Optional[Any]):
							A			=					self.get_feature_extractor(padding_side="left")
							A			=					self.get_tokenizer()

							A			=					MusicgenProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,     feature_extractor=__SCREAMING_SNAKE_CASE)

							A			=					[floats_list((1, x))[0] for x in range(5     ,     2_0     ,     5)]
							A			=					processor(__SCREAMING_SNAKE_CASE).padding_mask

							A			=					np.asarray(floats_list((3, 2_0)))[:, None, :]
							A			=					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, 1_0))
							self.assertTrue(decoded_audios[1].shape == (1, 1_5))
							self.assertTrue(decoded_audios[2].shape == (1, 2_0))








"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__A     :    int    =		{'configuration_bit': ['BIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BitConfig', 'BitOnnxConfig']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A     :    Optional[Any]    =		[
	    'BIT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'BitForImageClassification',
	    'BitModel',
	    'BitPreTrainedModel',
	    'BitBackbone',
	]


try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A     :    List[Any]    =		['BitImageProcessor']


if TYPE_CHECKING:
	from .configuration_bit import BIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BitConfig, BitOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_bit import (
		    BIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BitBackbone,
		    BitForImageClassification,
		    BitModel,
		    BitPreTrainedModel,
		)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_bit import BitImageProcessor

else:
	import sys

	__A     :    Union[str, Any]    =		_LazyModule(__name__, globals()['__file__'], _import_structure)





