'''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 							SCREAMING_SNAKE_CASE (					unittest.TestCase			):
  """simple docstring"""







  def 	a (	self :       Optional[Any]       ):


        """simple docstring"""



        _lowerCAmelCase		 = 'laion/clap-htsat-unfused'
        _lowerCAmelCase		 = tempfile.mkdtemp()



  def 	a (	self :       List[Any]       ,       **__lowerCAmelCase :       List[Any]       ):


        """simple docstring"""



        return RobertaTokenizer.from_pretrained(self.checkpoint       ,       **__lowercase       )



  def 	a (	self :       List[str]       ,       **__lowerCAmelCase :       List[str]       ):


        """simple docstring"""



        return ClapFeatureExtractor.from_pretrained(self.checkpoint       ,       **__lowercase       )



  def 	a (	self :       List[Any]       ):


        """simple docstring"""



        shutil.rmtree(self.tmpdirname       )



  def 	a (	self :       List[Any]       ):


        """simple docstring"""



        _lowerCAmelCase		 = self.get_tokenizer()
        _lowerCAmelCase		 = self.get_feature_extractor()

        _lowerCAmelCase		 = ClapProcessor(tokenizer=__lowercase       ,       feature_extractor=__lowercase       )

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

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

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



  def 	a (	self :       Any       ):


        """simple docstring"""



        _lowerCAmelCase		 = ClapProcessor(tokenizer=self.get_tokenizer()       ,       feature_extractor=self.get_feature_extractor()       )
        processor.save_pretrained(self.tmpdirname       )

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

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

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

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



  def 	a (	self :       int       ):


        """simple docstring"""



        _lowerCAmelCase		 = self.get_feature_extractor()
        _lowerCAmelCase		 = self.get_tokenizer()

        _lowerCAmelCase		 = ClapProcessor(tokenizer=__lowercase       ,       feature_extractor=__lowercase       )

        _lowerCAmelCase		 = floats_list((3, 1000)       )

        _lowerCAmelCase		 = feature_extractor(__lowercase       ,       return_tensors='np'       )
        _lowerCAmelCase		 = processor(audios=__lowercase       ,       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 :       Union[str, Any]       ):


        """simple docstring"""



        _lowerCAmelCase		 = self.get_feature_extractor()
        _lowerCAmelCase		 = self.get_tokenizer()

        _lowerCAmelCase		 = ClapProcessor(tokenizer=__lowercase       ,       feature_extractor=__lowercase       )

        _lowerCAmelCase		 = 'This is a test string'

        _lowerCAmelCase		 = processor(text=__lowercase       )

        _lowerCAmelCase		 = tokenizer(__lowercase       )

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



  def 	a (	self :       Union[str, Any]       ):


        """simple docstring"""



        _lowerCAmelCase		 = self.get_feature_extractor()
        _lowerCAmelCase		 = self.get_tokenizer()

        _lowerCAmelCase		 = ClapProcessor(tokenizer=__lowercase       ,       feature_extractor=__lowercase       )

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

        _lowerCAmelCase		 = processor.batch_decode(__lowercase       )
        _lowerCAmelCase		 = tokenizer.batch_decode(__lowercase       )

        self.assertListEqual(__lowercase       ,       __lowercase       )



  def 	a (	self :       Optional[int]       ):


        """simple docstring"""



        _lowerCAmelCase		 = self.get_feature_extractor()
        _lowerCAmelCase		 = self.get_tokenizer()

        _lowerCAmelCase		 = ClapProcessor(tokenizer=__lowercase       ,       feature_extractor=__lowercase       )

        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

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin

@dataclass
class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		torch.FloatTensor


class     _lowerCamelCase       (     snake_case_     ,		snake_case_					):




 '''simple docstring'''


 @register_to_config
 def __init__(   self   ,   __lowercase = 16   ,   __lowercase = 88   ,   __lowercase = None   ,   __lowercase = None   ,   __lowercase = 1   ,   __lowercase = 0.0   ,   __lowercase = 32   ,   __lowercase = None   ,   __lowercase = False   ,   __lowercase = None   ,   __lowercase = "geglu"   ,   __lowercase = True   ,   __lowercase = True   ,   ):

  """simple docstring"""







  super().__init__()
  __A	:		Optional[int]	  =      num_attention_heads
  __A	:		Dict	  =      attention_head_dim
  __A	:		Optional[int]	  =      num_attention_heads * attention_head_dim

  __A	:		Optional[Any]	  =      in_channels

  __A	:		Optional[int]	  =      torch.nn.GroupNorm(num_groups=__lowercase   ,   num_channels=__lowercase   ,   eps=1E-6   ,   affine=__lowercase    )
  __A	:		Optional[Any]	  =      nn.Linear(__lowercase   ,   __lowercase    )

  # 3. Define transformers blocks
  __A	:		List[Any]	  =      nn.ModuleList(
      [
          BasicTransformerBlock(
              __lowercase   ,   __lowercase   ,   __lowercase   ,   dropout=__lowercase   ,   cross_attention_dim=__lowercase   ,   activation_fn=__lowercase   ,   attention_bias=__lowercase   ,   double_self_attention=__lowercase   ,   norm_elementwise_affine=__lowercase   ,   )
          for d in range(__lowercase    )
      ]    )

  __A	:		Union[str, Any]	  =      nn.Linear(__lowercase   ,   __lowercase    )







 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=1   ,   __lowercase=None   ,   __lowercase = True   ,   ):

  """simple docstring"""







  __A  ,__A  ,__A  ,__A	:		int	  =      hidden_states.shape
  __A	:		Union[str, Any]	  =      batch_frames // num_frames

  __A	:		Union[str, Any]	  =      hidden_states

  __A	:		List[str]	  =      hidden_states[None, :].reshape(__lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    )
  __A	:		List[Any]	  =      hidden_states.permute(0   ,   2   ,   1   ,   3   ,   4    )

  __A	:		Dict	  =      self.norm(__lowercase    )
  __A	:		List[Any]	  =      hidden_states.permute(0   ,   3   ,   4   ,   2   ,   1    ).reshape(batch_size * height * width   ,   __lowercase   ,   __lowercase    )

  __A	:		Optional[Any]	  =      self.proj_in(__lowercase    )

  # 2. Blocks
  for block in self.transformer_blocks:
   __A	:		List[Any]	  =      block(
       __lowercase   ,   encoder_hidden_states=__lowercase   ,   timestep=__lowercase   ,   cross_attention_kwargs=__lowercase   ,   class_labels=__lowercase   ,   )

  # 3. Output
  __A	:		Optional[int]	  =      self.proj_out(__lowercase    )
  __A	:		int	  =      (
      hidden_states[None, None, :]
      .reshape(__lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    )
      .permute(0   ,   3   ,   4   ,   1   ,   2    )
      .contiguous()
  )
  __A	:		List[str]	  =      hidden_states.reshape(__lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    )

  __A	:		str	  =      hidden_states + residual

  if not return_dict:
   return (output,)

  return TransformerTemporalModelOutput(sample=__lowercase    )






