





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 					A__   (       unittest.TestCase			):





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   "laion/clap-htsat-unfused"
       UpperCamelCase	:					Union[str, Any]       	=   tempfile.mkdtemp()





      def        __UpperCamelCase( self       ,							**A_   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							**A_   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       shutil.rmtree(self.tmpdirname   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   self.get_tokenizer()
       UpperCamelCase	:					int       	=   self.get_feature_extractor()

       UpperCamelCase	:					Optional[int]       	=   ClapProcessor(tokenizer=A_       ,							feature_extractor=A_   )

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

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

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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




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

       UpperCamelCase	:					int       	=   self.get_tokenizer(bos_token="(BOS)"       ,							eos_token="(EOS)"   )
       UpperCamelCase	:					Tuple       	=   self.get_feature_extractor(do_normalize=A_       ,							padding_value=1.0   )

       UpperCamelCase	:					Any       	=   ClapProcessor.from_pretrained(
           self.tmpdirname       ,							bos_token="(BOS)"       ,							eos_token="(EOS)"       ,							do_normalize=A_       ,							padding_value=1.0   )

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

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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   self.get_feature_extractor()
       UpperCamelCase	:					List[Any]       	=   self.get_tokenizer()

       UpperCamelCase	:					Tuple       	=   ClapProcessor(tokenizer=A_       ,							feature_extractor=A_   )

       UpperCamelCase	:					Optional[Any]       	=   floats_list((3, 1000)   )

       UpperCamelCase	:					Any       	=   feature_extractor(A_       ,							return_tensors="np"   )
       UpperCamelCase	:					Union[str, Any]       	=   processor(audios=A_       ,							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'''




       UpperCamelCase	:					str       	=   self.get_feature_extractor()
       UpperCamelCase	:					Optional[Any]       	=   self.get_tokenizer()

       UpperCamelCase	:					List[Any]       	=   ClapProcessor(tokenizer=A_       ,							feature_extractor=A_   )

       UpperCamelCase	:					List[Any]       	=   "This is a test string"

       UpperCamelCase	:					List[Any]       	=   processor(text=A_   )

       UpperCamelCase	:					List[str]       	=   tokenizer(A_   )

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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   self.get_feature_extractor()
       UpperCamelCase	:					List[Any]       	=   self.get_tokenizer()

       UpperCamelCase	:					Optional[int]       	=   ClapProcessor(tokenizer=A_       ,							feature_extractor=A_   )

       UpperCamelCase	:					Dict       	=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       UpperCamelCase	:					Optional[Any]       	=   processor.batch_decode(A_   )
       UpperCamelCase	:					List[str]       	=   tokenizer.batch_decode(A_   )

       self.assertListEqual(A_       ,							A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




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

       UpperCamelCase	:					Union[str, Any]       	=   ClapProcessor(tokenizer=A_       ,							feature_extractor=A_   )

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




from dataclasses import asdict, dataclass
from typing import Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__   	=							logging.get_logger(__name__)

# TODO Update this
UpperCAmelCase__   	=							{
    '''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
    # See all ESM models at https://huggingface.co/models?filter=esm
}







class       lowerCamelCase__   (       lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			'''esm'''





     def __init__(self		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=7_6_8		,     UpperCAmelCase=1_2		,     UpperCAmelCase=1_2		,     UpperCAmelCase=3_0_7_2		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=1_0_2_6		,     UpperCAmelCase=0.02		,     UpperCAmelCase=1e-12		,     UpperCAmelCase="absolute"		,     UpperCAmelCase=True		,     UpperCAmelCase=None		,     UpperCAmelCase=False		,     UpperCAmelCase=False		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     **UpperCAmelCase		,     )     ->   Tuple:
      super().__init__(pad_token_id=UpperCAmelCase		,     mask_token_id=UpperCAmelCase		,     **UpperCAmelCase       )

      _lowercase							      =vocab_size
      _lowercase							      =hidden_size
      _lowercase							      =num_hidden_layers
      _lowercase							      =num_attention_heads
      _lowercase							      =intermediate_size
      _lowercase							      =hidden_dropout_prob
      _lowercase							      =attention_probs_dropout_prob
      _lowercase							      =max_position_embeddings
      _lowercase							      =initializer_range
      _lowercase							      =layer_norm_eps
      _lowercase							      =position_embedding_type
      _lowercase							      =use_cache
      _lowercase							      =emb_layer_norm_before
      _lowercase							      =token_dropout
      _lowercase							      =is_folding_model
      if is_folding_model:
       if esmfold_config is None:
        logger.info('''No esmfold_config supplied for folding model, using default values.'''       )
        _lowercase							      =EsmFoldConfig()
       elif isinstance(UpperCAmelCase		,     UpperCAmelCase       ):
        _lowercase							      =EsmFoldConfig(**UpperCAmelCase       )
       _lowercase							      =esmfold_config
       if vocab_list is None:
        logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!'''       )
        _lowercase							      =get_default_vocab_list()
       else:
        _lowercase							      =vocab_list
      else:
       _lowercase							      =None
       _lowercase							      =None
      if self.esmfold_config is not None and getattr(self.esmfold_config		,     '''use_esm_attn_map'''		,     UpperCAmelCase       ):
       raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!'''       )





     def      __A					(self       )     ->   List[str]:
      _lowercase							      =super().to_dict()
      if isinstance(self.esmfold_config		,     UpperCAmelCase       ):
       _lowercase							      =self.esmfold_config.to_dict()
      return output







@dataclass
class       lowerCamelCase__   :
     SCREAMING_SNAKE_CASE__      			=			None
     SCREAMING_SNAKE_CASE__      			=			True
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			0

     SCREAMING_SNAKE_CASE__      			=			True
     SCREAMING_SNAKE_CASE__      			=			False

     SCREAMING_SNAKE_CASE__      			=			128
     SCREAMING_SNAKE_CASE__      			=			None





     def      __A					(self       )     ->   Union[str, Any]:
      if self.trunk is None:
       _lowercase							      =TrunkConfig()
      elif isinstance(self.trunk		,     UpperCAmelCase       ):
       _lowercase							      =TrunkConfig(**self.trunk       )





     def      __A					(self       )     ->   Tuple:
      _lowercase							      =asdict(self       )
      _lowercase							      =self.trunk.to_dict()
      return output







@dataclass
class       lowerCamelCase__   :
     SCREAMING_SNAKE_CASE__      			=			48
     SCREAMING_SNAKE_CASE__      			=			1024
     SCREAMING_SNAKE_CASE__      			=			128
     SCREAMING_SNAKE_CASE__      			=			32
     SCREAMING_SNAKE_CASE__      			=			32
     SCREAMING_SNAKE_CASE__      			=			32
     SCREAMING_SNAKE_CASE__      			=			0
     SCREAMING_SNAKE_CASE__      			=			0
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			4
     SCREAMING_SNAKE_CASE__      			=			128
     SCREAMING_SNAKE_CASE__      			=			None





     def      __A					(self       )     ->   List[str]:
      if self.structure_module is None:
       _lowercase							      =StructureModuleConfig()
      elif isinstance(self.structure_module		,     UpperCAmelCase       ):
       _lowercase							      =StructureModuleConfig(**self.structure_module       )

      if self.max_recycles <= 0:
       raise ValueError(f"`max_recycles` should be positive, got {self.max_recycles}."       )
      if self.sequence_state_dim % self.sequence_state_dim != 0:
       raise ValueError(
           '''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
           f" {self.sequence_state_dim} and {self.sequence_state_dim}."       )
      if self.pairwise_state_dim % self.pairwise_state_dim != 0:
       raise ValueError(
           '''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
           f" {self.pairwise_state_dim} and {self.pairwise_state_dim}."       )

      _lowercase							      =self.sequence_state_dim // self.sequence_head_width
      _lowercase							      =self.pairwise_state_dim // self.pairwise_head_width

      if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
       raise ValueError(
           '''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
           f" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}."       )
      if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
       raise ValueError(
           '''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
           f" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}."       )
      if self.pairwise_state_dim % 2 != 0:
       raise ValueError(f"`pairwise_state_dim` should be even, got {self.pairwise_state_dim}."       )

      if self.dropout >= 0.4:
       raise ValueError(f"`dropout` should not be greater than 0.4, got {self.dropout}."       )





     def      __A					(self       )     ->   Dict:
      _lowercase							      =asdict(self       )
      _lowercase							      =self.structure_module.to_dict()
      return output







@dataclass
class       lowerCamelCase__   :
     SCREAMING_SNAKE_CASE__      			=			384
     SCREAMING_SNAKE_CASE__      			=			128
     SCREAMING_SNAKE_CASE__      			=			16
     SCREAMING_SNAKE_CASE__      			=			128
     SCREAMING_SNAKE_CASE__      			=			12
     SCREAMING_SNAKE_CASE__      			=			4
     SCREAMING_SNAKE_CASE__      			=			8
     SCREAMING_SNAKE_CASE__      			=			0.1
     SCREAMING_SNAKE_CASE__      			=			8
     SCREAMING_SNAKE_CASE__      			=			1
     SCREAMING_SNAKE_CASE__      			=			2
     SCREAMING_SNAKE_CASE__      			=			7
     SCREAMING_SNAKE_CASE__      			=			10
     SCREAMING_SNAKE_CASE__      			=			1E-8
     SCREAMING_SNAKE_CASE__      			=			1E5





     def      __A					(self       )     ->   List[Any]:
      return asdict(self       )



def 				UpperCAmelCase_					(							)     ->			Tuple:







 """simple docstring"""


 return (
     "<cls>",
     "<pad>",
     "<eos>",
     "<unk>",
     "L",
     "A",
     "G",
     "V",
     "S",
     "E",
     "R",
     "T",
     "I",
     "D",
     "P",
     "K",
     "Q",
     "N",
     "F",
     "Y",
     "M",
     "H",
     "W",
     "C",
     "X",
     "B",
     "U",
     "Z",
     "O",
     ".",
     "-",
     "<null_1>",
     "<mask>",
 )

