






"""simple docstring"""







import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list


@require_torchaudio
@require_sentencepiece
class        __UpperCAmelCase(  unittest.TestCase				):




						"""simple docstring"""




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												lowercase__							: int=			'''laion/clap-htsat-unfused'''
												lowercase__							: Optional[int]=			tempfile.mkdtemp()




						def 						UpperCAmelCase_			(  self			,    **snake_case__     ):
												'''simple docstring'''
												return RobertaTokenizer.from_pretrained(self.checkpoint			,    **SCREAMING_SNAKE_CASE__     )




						def 						UpperCAmelCase_			(  self			,    **snake_case__     ):
												'''simple docstring'''
												return ClapFeatureExtractor.from_pretrained(self.checkpoint			,    **SCREAMING_SNAKE_CASE__     )




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												shutil.rmtree(self.tmpdirname     )




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												lowercase__							: Union[str, Any]=			self.get_tokenizer()
												lowercase__							: Union[str, Any]=			self.get_feature_extractor()

												lowercase__							: Optional[Any]=			ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE__			,    feature_extractor=SCREAMING_SNAKE_CASE__     )

												processor.save_pretrained(self.tmpdirname     )
												lowercase__							: Any=			ClapProcessor.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     ):
												'''simple docstring'''
												lowercase__							: List[str]=			ClapProcessor(tokenizer=self.get_tokenizer()			,    feature_extractor=self.get_feature_extractor()     )
												processor.save_pretrained(self.tmpdirname     )

												lowercase__							: Optional[int]=			self.get_tokenizer(bos_token="(BOS)"			,    eos_token="(EOS)"     )
												lowercase__							: Dict=			self.get_feature_extractor(do_normalize=SCREAMING_SNAKE_CASE__			,    padding_value=1.0     )

												lowercase__							: Optional[int]=			ClapProcessor.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     ):
												'''simple docstring'''
												lowercase__							: List[Any]=			self.get_feature_extractor()
												lowercase__							: List[str]=			self.get_tokenizer()

												lowercase__							: Union[str, Any]=			ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE__			,    feature_extractor=SCREAMING_SNAKE_CASE__     )

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

												lowercase__							: Optional[int]=			feature_extractor(SCREAMING_SNAKE_CASE__			,    return_tensors="np"     )
												lowercase__							: Optional[int]=			processor(audios=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     ):
												'''simple docstring'''
												lowercase__							: Union[str, Any]=			self.get_feature_extractor()
												lowercase__							: Optional[int]=			self.get_tokenizer()

												lowercase__							: List[str]=			ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE__			,    feature_extractor=SCREAMING_SNAKE_CASE__     )

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

												lowercase__							: str=			processor(text=SCREAMING_SNAKE_CASE__     )

												lowercase__							: Optional[Any]=			tokenizer(SCREAMING_SNAKE_CASE__     )

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




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												lowercase__							: int=			self.get_feature_extractor()
												lowercase__							: Union[str, Any]=			self.get_tokenizer()

												lowercase__							: Tuple=			ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE__			,    feature_extractor=SCREAMING_SNAKE_CASE__     )

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

												lowercase__							: str=			processor.batch_decode(SCREAMING_SNAKE_CASE__     )
												lowercase__							: Dict=			tokenizer.batch_decode(SCREAMING_SNAKE_CASE__     )

												self.assertListEqual(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__     )




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												lowercase__							: Union[str, Any]=			self.get_feature_extractor()
												lowercase__							: Any=			self.get_tokenizer()

												lowercase__							: Optional[int]=			ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE__			,    feature_extractor=SCREAMING_SNAKE_CASE__     )

												self.assertListEqual(
												    processor.model_input_names[2:]			,    feature_extractor.model_input_names			,    msg="`processor` and `feature_extractor` model input names do not match"			,    )










"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase__				:Union[str, Any]			   =	logging.get_logger(__name__)

UpperCamelCase__				:Union[str, Any]			   =	{
    """google/mobilenet_v2_1.4_224""": """https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json""",
    """google/mobilenet_v2_1.0_224""": """https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v2_0.75_160""": """https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json""",
    """google/mobilenet_v2_0.35_96""": """https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json""",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							"mobilenet_v2"
		def __init__( self					,				SCREAMING_SNAKE_CASE__=3					,				SCREAMING_SNAKE_CASE__=2_24					,				SCREAMING_SNAKE_CASE__=1.0					,				SCREAMING_SNAKE_CASE__=8					,				SCREAMING_SNAKE_CASE__=8					,				SCREAMING_SNAKE_CASE__=6					,				SCREAMING_SNAKE_CASE__=32					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__="relu6"					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=0.8					,				SCREAMING_SNAKE_CASE__=0.0_2					,				SCREAMING_SNAKE_CASE__=0.0_0_1					,				SCREAMING_SNAKE_CASE__=2_55					,				**SCREAMING_SNAKE_CASE__					,				)      ->	int:

					"""simple docstring"""



					super().__init__(**SCREAMING_SNAKE_CASE__   )

					if depth_multiplier <= 0:
								raise ValueError('''depth_multiplier must be greater than zero.'''   )

					_UpperCamelCase					:Optional[int]					     =	num_channels
					_UpperCamelCase					:Tuple					     =	image_size
					_UpperCamelCase					:Union[str, Any]					     =	depth_multiplier
					_UpperCamelCase					:Any					     =	depth_divisible_by
					_UpperCamelCase					:Optional[int]					     =	min_depth
					_UpperCamelCase					:Union[str, Any]					     =	expand_ratio
					_UpperCamelCase					:str					     =	output_stride
					_UpperCamelCase					:Tuple					     =	first_layer_is_expansion
					_UpperCamelCase					:Any					     =	finegrained_output
					_UpperCamelCase					:Union[str, Any]					     =	hidden_act
					_UpperCamelCase					:Any					     =	tf_padding
					_UpperCamelCase					:str					     =	classifier_dropout_prob
					_UpperCamelCase					:Optional[Any]					     =	initializer_range
					_UpperCamelCase					:int					     =	layer_norm_eps
					_UpperCamelCase					:Union[str, Any]					     =	semantic_loss_ignore_index






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							version.parse("1.11"		)
		@property
		def   _UpperCamelCase( self   )      ->	Mapping[str, Mapping[int, str]]:

					"""simple docstring"""



					return OrderedDict([('''pixel_values''', {0: '''batch'''})]   )
		@property
		def   _UpperCamelCase( self   )      ->	Mapping[str, Mapping[int, str]]:

					"""simple docstring"""



					if self.task == "image-classification":
								return OrderedDict([('''logits''', {0: '''batch'''})]   )
					else:
								return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]   )





		@property
		def   _UpperCamelCase( self   )      ->	float:

					"""simple docstring"""



					return 1E-4

