





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     __snake_case       (							snake_case_			):





       '''simple docstring'''







       lowerCAmelCase__      		=     42







class     __snake_case       (							snake_case_   ,			snake_case_			):





       '''simple docstring'''







       @register_to_config
       def __init__(  self :    Dict							,				A :    int = 3							,				A :    int = 3							,				A :    Tuple[str] = ("DownEncoderBlock2D",)							,				A :    Tuple[str] = ("UpDecoderBlock2D",)							,				A :    Tuple[int] = (64,)							,				A :    int = 1							,				A :    str = "silu"							,				A :    int = 3							,				A :    int = 32							,				A :    int = 256							,				A :    int = 32							,				A :    Optional[int] = None							,				A :    float = 0.1_8215							,				A :    str = "group"							,				):
         super().__init__()

         # pass init params to Encoder
         __snake_case:						Dict   =						Encoder(
             in_channels=UpperCAmelCase__							,				out_channels=UpperCAmelCase__							,				down_block_types=UpperCAmelCase__							,				block_out_channels=UpperCAmelCase__							,				layers_per_block=UpperCAmelCase__							,				act_fn=UpperCAmelCase__							,				norm_num_groups=UpperCAmelCase__							,				double_z=UpperCAmelCase__							,				)

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

         __snake_case:						int   =						nn.Convad(UpperCAmelCase__							,				UpperCAmelCase__							,				1		)
         __snake_case:						Optional[int]   =						VectorQuantizer(UpperCAmelCase__							,				UpperCAmelCase__							,				beta=0.25							,				remap=UpperCAmelCase__							,				sane_index_shape=UpperCAmelCase__		)
         __snake_case:						Union[str, Any]   =						nn.Convad(UpperCAmelCase__							,				UpperCAmelCase__							,				1		)

         # pass init params to Decoder
         __snake_case:						List[Any]   =						Decoder(
             in_channels=UpperCAmelCase__							,				out_channels=UpperCAmelCase__							,				up_block_types=UpperCAmelCase__							,				block_out_channels=UpperCAmelCase__							,				layers_per_block=UpperCAmelCase__							,				act_fn=UpperCAmelCase__							,				norm_num_groups=UpperCAmelCase__							,				norm_type=UpperCAmelCase__							,				)




       @apply_forward_hook
       def 							UpperCAmelCase__ (  self :    Any							,				A :    torch.FloatTensor							,				A :    bool = True		):
         __snake_case:						str   =						self.encoder(UpperCAmelCase__		)
         __snake_case:						Union[str, Any]   =						self.quant_conv(UpperCAmelCase__		)

         if not return_dict:
           return (h,)

         return VQEncoderOutput(latents=UpperCAmelCase__		)




       @apply_forward_hook
       def 							UpperCAmelCase__ (  self :    int							,				A :    torch.FloatTensor							,				A :    bool = False							,				A :    bool = True		):
         # also go through quantization layer
         if not force_not_quantize:
           __snake_case:						Any   =						self.quantize(UpperCAmelCase__		)
         else:
           __snake_case:						int   =						h
         __snake_case:						Any   =						self.post_quant_conv(UpperCAmelCase__		)
         __snake_case:						List[Any]   =						self.decoder(UpperCAmelCase__							,				quant if self.config.norm_type == """spatial""" else None		)

         if not return_dict:
           return (dec,)

         return DecoderOutput(sample=UpperCAmelCase__		)






       def 							UpperCAmelCase__ (  self :    str							,				A :    torch.FloatTensor							,				A :    bool = True		):
         __snake_case:						Tuple   =						sample
         __snake_case:						Union[str, Any]   =						self.encode(UpperCAmelCase__		).latents
         __snake_case:						Any   =						self.decode(UpperCAmelCase__		).sample

         if not return_dict:
           return (dec,)

         return DecoderOutput(sample=UpperCAmelCase__		)














"""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      ):


 def _lowercase	(     self		:						int							)					->       List[str]:
   _a  :       Any             =	"""laion/clap-htsat-unfused"""
   _a  :       Union[str, Any]             =	tempfile.mkdtemp()


 def _lowercase	(     self		:						List[Any]							,						**UpperCAmelCase__		:						Any							)					->       Dict:
   return RobertaTokenizer.from_pretrained(self.checkpoint							,						**UpperCAmelCase__							)


 def _lowercase	(     self		:						List[Any]							,						**UpperCAmelCase__		:						List[str]							)					->       int:
   return ClapFeatureExtractor.from_pretrained(self.checkpoint							,						**UpperCAmelCase__							)


 def _lowercase	(     self		:						List[Any]							)					->       Tuple:
   shutil.rmtree(self.tmpdirname							)


 def _lowercase	(     self		:						List[str]							)					->       Optional[int]:
   _a  :       List[str]             =	self.get_tokenizer()
   _a  :       Any             =	self.get_feature_extractor()

   _a  :       Optional[Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   processor.save_pretrained(self.tmpdirname							)
   _a  :       List[str]             =	ClapProcessor.from_pretrained(self.tmpdirname							)

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

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


 def _lowercase	(     self		:						Dict							)					->       Optional[int]:
   _a  :       Tuple             =	ClapProcessor(tokenizer=self.get_tokenizer()							,						feature_extractor=self.get_feature_extractor()							)
   processor.save_pretrained(self.tmpdirname							)

   _a  :       Dict             =	self.get_tokenizer(bos_token="""(BOS)"""							,						eos_token="""(EOS)"""							)
   _a  :       Union[str, Any]             =	self.get_feature_extractor(do_normalize=UpperCAmelCase__							,						padding_value=1.0							)

   _a  :       Union[str, Any]             =	ClapProcessor.from_pretrained(
       self.tmpdirname							,						bos_token="""(BOS)"""							,						eos_token="""(EOS)"""							,						do_normalize=UpperCAmelCase__							,						padding_value=1.0							)

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

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


 def _lowercase	(     self		:						List[str]							)					->       Optional[Any]:
   _a  :       Optional[int]             =	self.get_feature_extractor()
   _a  :       Tuple             =	self.get_tokenizer()

   _a  :       List[Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   _a  :       Any             =	floats_list((3, 1000)							)

   _a  :       List[Any]             =	feature_extractor(UpperCAmelCase__							,						return_tensors="""np"""							)
   _a  :       List[str]             =	processor(audios=UpperCAmelCase__							,						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 _lowercase	(     self		:						Tuple							)					->       Optional[int]:
   _a  :       List[str]             =	self.get_feature_extractor()
   _a  :       Any             =	self.get_tokenizer()

   _a  :       Any             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   _a  :       Optional[int]             =	"""This is a test string"""

   _a  :       Tuple             =	processor(text=UpperCAmelCase__							)

   _a  :       int             =	tokenizer(UpperCAmelCase__							)

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


 def _lowercase	(     self		:						List[Any]							)					->       Any:
   _a  :       str             =	self.get_feature_extractor()
   _a  :       List[str]             =	self.get_tokenizer()

   _a  :       List[Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

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

   _a  :       Dict             =	processor.batch_decode(UpperCAmelCase__							)
   _a  :       Any             =	tokenizer.batch_decode(UpperCAmelCase__							)

   self.assertListEqual(UpperCAmelCase__							,						UpperCAmelCase__							)







 def _lowercase	(     self		:						Dict							)					->       List[str]:
   _a  :       str             =	self.get_feature_extractor()
   _a  :       Optional[Any]             =	self.get_tokenizer()

   _a  :       Union[str, Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

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

