


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 _lowerCamelCase(						unittest.TestCase   ):
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		'laion/clap-htsat-unfused'
           _lowercase       :							Optional[Any]      		=		tempfile.mkdtemp()
       def 			UpperCamelCase     (					self,      **lowerCamelCase)       ->   List[Any]:




           """simple docstring"""

           return RobertaTokenizer.from_pretrained(self.checkpoint,      **lowerCamelCase)
       def 			UpperCamelCase     (					self,      **lowerCamelCase)       ->   str:




           """simple docstring"""

           return ClapFeatureExtractor.from_pretrained(self.checkpoint,      **lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           shutil.rmtree(self.tmpdirname)
       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							str      		=		self.get_tokenizer()
           _lowercase       :							Optional[int]      		=		self.get_feature_extractor()

           _lowercase       :							int      		=		ClapProcessor(tokenizer=lowerCamelCase,      feature_extractor=lowerCamelCase)

           processor.save_pretrained(self.tmpdirname)
           _lowercase       :							Union[str, Any]      		=		ClapProcessor.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 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		ClapProcessor(tokenizer=self.get_tokenizer(),      feature_extractor=self.get_feature_extractor())
           processor.save_pretrained(self.tmpdirname)

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

           _lowercase       :							Tuple      		=		ClapProcessor.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 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		self.get_feature_extractor()
           _lowercase       :							List[Any]      		=		self.get_tokenizer()

           _lowercase       :							Dict      		=		ClapProcessor(tokenizer=lowerCamelCase,      feature_extractor=lowerCamelCase)

           _lowercase       :							Union[str, Any]      		=		floats_list((3, 10_00))

           _lowercase       :							str      		=		feature_extractor(lowerCamelCase,      return_tensors='np')
           _lowercase       :							Tuple      		=		processor(audios=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 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		self.get_feature_extractor()
           _lowercase       :							Any      		=		self.get_tokenizer()

           _lowercase       :							Dict      		=		ClapProcessor(tokenizer=lowerCamelCase,      feature_extractor=lowerCamelCase)

           _lowercase       :							Any      		=		'This is a test string'

           _lowercase       :							List[Any]      		=		processor(text=lowerCamelCase)

           _lowercase       :							Dict      		=		tokenizer(lowerCamelCase)

           for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key],      encoded_processor[key])
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							Tuple      		=		self.get_feature_extractor()
           _lowercase       :							str      		=		self.get_tokenizer()

           _lowercase       :							List[str]      		=		ClapProcessor(tokenizer=lowerCamelCase,      feature_extractor=lowerCamelCase)

           _lowercase       :							str      		=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           _lowercase       :							str      		=		processor.batch_decode(lowerCamelCase)
           _lowercase       :							Any      		=		tokenizer.batch_decode(lowerCamelCase)

           self.assertListEqual(lowerCamelCase,      lowerCamelCase)




       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           _lowercase       :							Dict      		=		self.get_feature_extractor()
           _lowercase       :							Any      		=		self.get_tokenizer()

           _lowercase       :							Tuple      		=		ClapProcessor(tokenizer=lowerCamelCase,      feature_extractor=lowerCamelCase)

           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 dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer







@dataclass
class A__       (						UpperCAmelCase__      ):
      __UpperCamelCase       :  torch.FloatTensor







class A__       (						UpperCAmelCase__ ,     UpperCAmelCase__      ):






      @register_to_config
      def __init__( self			:Optional[Any]     ,						SCREAMING_SNAKE_CASE			:int = 3     ,						SCREAMING_SNAKE_CASE			:int = 3     ,						SCREAMING_SNAKE_CASE			:Tuple[str] = ("DownEncoderBlock2D",)     ,						SCREAMING_SNAKE_CASE			:Tuple[str] = ("UpDecoderBlock2D",)     ,						SCREAMING_SNAKE_CASE			:Tuple[int] = (6_4,)     ,						SCREAMING_SNAKE_CASE			:int = 1     ,						SCREAMING_SNAKE_CASE			:str = "silu"     ,						SCREAMING_SNAKE_CASE			:int = 3     ,						SCREAMING_SNAKE_CASE			:int = 3_2     ,						SCREAMING_SNAKE_CASE			:int = 2_5_6     ,						SCREAMING_SNAKE_CASE			:int = 3_2     ,						SCREAMING_SNAKE_CASE			:Optional[int] = None     ,						SCREAMING_SNAKE_CASE			:float = 0.18_215     ,						SCREAMING_SNAKE_CASE			:str = "group"     ,						)       ->					Optional[int]:




             '''simple docstring'''
             super().__init__()

             # pass init params to Encoder
             _a    : Union[str, Any]     					=Encoder(
                 in_channels=SCREAMING_SNAKE_CASE     ,						out_channels=SCREAMING_SNAKE_CASE     ,						down_block_types=SCREAMING_SNAKE_CASE     ,						block_out_channels=SCREAMING_SNAKE_CASE     ,						layers_per_block=SCREAMING_SNAKE_CASE     ,						act_fn=SCREAMING_SNAKE_CASE     ,						norm_num_groups=SCREAMING_SNAKE_CASE     ,						double_z=SCREAMING_SNAKE_CASE     ,						)

             _a    : Optional[int]     					=vq_embed_dim if vq_embed_dim is not None else latent_channels

             _a    : Optional[int]     					=nn.Convad(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						1	)
             _a    : str     					=VectorQuantizer(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						beta=0.25     ,						remap=SCREAMING_SNAKE_CASE     ,						sane_index_shape=SCREAMING_SNAKE_CASE	)
             _a    : List[str]     					=nn.Convad(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						1	)

             # pass init params to Decoder
             _a    : List[str]     					=Decoder(
                 in_channels=SCREAMING_SNAKE_CASE     ,						out_channels=SCREAMING_SNAKE_CASE     ,						up_block_types=SCREAMING_SNAKE_CASE     ,						block_out_channels=SCREAMING_SNAKE_CASE     ,						layers_per_block=SCREAMING_SNAKE_CASE     ,						act_fn=SCREAMING_SNAKE_CASE     ,						norm_num_groups=SCREAMING_SNAKE_CASE     ,						norm_type=SCREAMING_SNAKE_CASE     ,						)






      @apply_forward_hook
      def __UpperCAmelCase       ( self			:Optional[Any]     ,						SCREAMING_SNAKE_CASE			:torch.FloatTensor     ,						SCREAMING_SNAKE_CASE			:bool = True	)       ->					VQEncoderOutput:




             '''simple docstring'''
             _a    : Optional[int]     					=self.encoder(SCREAMING_SNAKE_CASE	)
             _a    : int     					=self.quant_conv(SCREAMING_SNAKE_CASE	)

             if not return_dict:
                    return (h,)

             return VQEncoderOutput(latents=SCREAMING_SNAKE_CASE	)






      @apply_forward_hook
      def __UpperCAmelCase       ( self			:List[Any]     ,						SCREAMING_SNAKE_CASE			:torch.FloatTensor     ,						SCREAMING_SNAKE_CASE			:bool = False     ,						SCREAMING_SNAKE_CASE			:bool = True	)       ->					Union[DecoderOutput, torch.FloatTensor]:




             '''simple docstring'''
             # also go through quantization layer
             if not force_not_quantize:
                    _a					,    _a					,    _a    : Tuple     					=self.quantize(SCREAMING_SNAKE_CASE	)
             else:
                    _a    : str     					=h
             _a    : Dict     					=self.post_quant_conv(SCREAMING_SNAKE_CASE	)
             _a    : Union[str, Any]     					=self.decoder(SCREAMING_SNAKE_CASE     ,						quant if self.config.norm_type == """spatial""" else None	)

             if not return_dict:
                    return (dec,)

             return DecoderOutput(sample=SCREAMING_SNAKE_CASE	)






      def __UpperCAmelCase       ( self			:Optional[Any]     ,						SCREAMING_SNAKE_CASE			:torch.FloatTensor     ,						SCREAMING_SNAKE_CASE			:bool = True	)       ->					Union[DecoderOutput, torch.FloatTensor]:




             '''simple docstring'''
             _a    : Tuple     					=sample
             _a    : int     					=self.encode(SCREAMING_SNAKE_CASE	).latents
             _a    : List[Any]     					=self.decode(SCREAMING_SNAKE_CASE	).sample

             if not return_dict:
                    return (dec,)

             return DecoderOutput(sample=SCREAMING_SNAKE_CASE	)


