

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 				lowercase       ( unittest.TestCase    ):




				def 					a							(      self ):
											snake_case_           =					'laion/clap-htsat-unfused'
											snake_case_           =					tempfile.mkdtemp()




				def 					a							(      self   ,     **snake_case ):
											return RobertaTokenizer.from_pretrained(self.checkpoint   ,     **snake_case )




				def 					a							(      self   ,     **snake_case ):
											return ClapFeatureExtractor.from_pretrained(self.checkpoint   ,     **snake_case )




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




				def 					a							(      self ):
											snake_case_           =					self.get_tokenizer()
											snake_case_           =					self.get_feature_extractor()

											snake_case_           =					ClapProcessor(tokenizer=snake_case   ,     feature_extractor=snake_case )

											processor.save_pretrained(self.tmpdirname )
											snake_case_           =					ClapProcessor.from_pretrained(self.tmpdirname )

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

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




				def 					a							(      self ):
											snake_case_           =					ClapProcessor(tokenizer=self.get_tokenizer()   ,     feature_extractor=self.get_feature_extractor() )
											processor.save_pretrained(self.tmpdirname )

											snake_case_           =					self.get_tokenizer(bos_token='(BOS)'   ,     eos_token='(EOS)' )
											snake_case_           =					self.get_feature_extractor(do_normalize=snake_case   ,     padding_value=1.0 )

											snake_case_           =					ClapProcessor.from_pretrained(
											    self.tmpdirname   ,     bos_token='(BOS)'   ,     eos_token='(EOS)'   ,     do_normalize=snake_case   ,     padding_value=1.0 )

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

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




				def 					a							(      self ):
											snake_case_           =					self.get_feature_extractor()
											snake_case_           =					self.get_tokenizer()

											snake_case_           =					ClapProcessor(tokenizer=snake_case   ,     feature_extractor=snake_case )

											snake_case_           =					floats_list((3, 1000) )

											snake_case_           =					feature_extractor(snake_case   ,     return_tensors='np' )
											snake_case_           =					processor(audios=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 					a							(      self ):
											snake_case_           =					self.get_feature_extractor()
											snake_case_           =					self.get_tokenizer()

											snake_case_           =					ClapProcessor(tokenizer=snake_case   ,     feature_extractor=snake_case )

											snake_case_           =					'This is a test string'

											snake_case_           =					processor(text=snake_case )

											snake_case_           =					tokenizer(snake_case )

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




				def 					a							(      self ):
											snake_case_           =					self.get_feature_extractor()
											snake_case_           =					self.get_tokenizer()

											snake_case_           =					ClapProcessor(tokenizer=snake_case   ,     feature_extractor=snake_case )

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

											snake_case_           =					processor.batch_decode(snake_case )
											snake_case_           =					tokenizer.batch_decode(snake_case )

											self.assertListEqual(snake_case   ,     snake_case )




				def 					a							(      self ):
											snake_case_           =					self.get_feature_extractor()
											snake_case_           =					self.get_tokenizer()

											snake_case_           =					ClapProcessor(tokenizer=snake_case   ,     feature_extractor=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'   ,     )







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	:     int		       = logging.get_logger(__name__)

_UpperCAmelCase	:     Any		       = {
    """facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				int     =    '''levit'''




				def __init__(      self   ,     snake_case=224   ,     snake_case=3   ,     snake_case=3   ,     snake_case=2   ,     snake_case=1   ,     snake_case=16   ,     snake_case=[128, 256, 384]   ,     snake_case=[4, 8, 12]   ,     snake_case=[4, 4, 4]   ,     snake_case=[16, 16, 16]   ,     snake_case=0   ,     snake_case=[2, 2, 2]   ,     snake_case=[2, 2, 2]   ,     snake_case=0.02   ,     **snake_case   ,     ):
											super().__init__(**snake_case )
											snake_case_           =					image_size
											snake_case_           =					num_channels
											snake_case_           =					kernel_size
											snake_case_           =					stride
											snake_case_           =					padding
											snake_case_           =					hidden_sizes
											snake_case_           =					num_attention_heads
											snake_case_           =					depths
											snake_case_           =					key_dim
											snake_case_           =					drop_path_rate
											snake_case_           =					patch_size
											snake_case_           =					attention_ratio
											snake_case_           =					mlp_ratio
											snake_case_           =					initializer_range
											snake_case_           =					[
											    ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
											    ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
											]







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Dict     =    version.parse('''1.11'''    )




				@property
				def 					a							(      self ):
											return OrderedDict(
											    [
											        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
											    ] )




				@property
				def 					a							(      self ):
											return 1e-4




