



"""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 			snake_case_						(						unittest.TestCase				):






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     "laion/clap-htsat-unfused"
									a_     :      Optional[int] 						=     tempfile.mkdtemp()






							def snake_case_  (   self			,     **a_      ):
									return RobertaTokenizer.from_pretrained(self.checkpoint			,     **a_      )






							def snake_case_  (   self			,     **a_      ):
									return ClapFeatureExtractor.from_pretrained(self.checkpoint			,     **a_      )






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






							def snake_case_  (   self      ):
									a_     :      Dict 						=     self.get_tokenizer()
									a_     :      Optional[Any] 						=     self.get_feature_extractor()

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

									processor.save_pretrained(self.tmpdirname      )
									a_     :      Optional[int] 						=     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 snake_case_  (   self      ):
									a_     :      List[Any] 						=     ClapProcessor(tokenizer=self.get_tokenizer()			,     feature_extractor=self.get_feature_extractor()      )
									processor.save_pretrained(self.tmpdirname      )

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

									a_     :      Optional[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 snake_case_  (   self      ):
									a_     :      Any 						=     self.get_feature_extractor()
									a_     :      Tuple 						=     self.get_tokenizer()

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

									a_     :      str 						=     floats_list((3, 1_0_0_0)      )

									a_     :      Any 						=     feature_extractor(a_			,     return_tensors="np"      )
									a_     :      Optional[int] 						=     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 snake_case_  (   self      ):
									a_     :      List[Any] 						=     self.get_feature_extractor()
									a_     :      Optional[Any] 						=     self.get_tokenizer()

									a_     :      Any 						=     ClapProcessor(tokenizer=a_			,     feature_extractor=a_      )

									a_     :      List[str] 						=     "This is a test string"

									a_     :      Union[str, Any] 						=     processor(text=a_      )

									a_     :      Dict 						=     tokenizer(a_      )

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






							def snake_case_  (   self      ):
									a_     :      Optional[int] 						=     self.get_feature_extractor()
									a_     :      Dict 						=     self.get_tokenizer()

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

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

									a_     :      Optional[int] 						=     processor.batch_decode(a_      )
									a_     :      str 						=     tokenizer.batch_decode(a_      )

									self.assertListEqual(a_			,     a_      )






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     self.get_feature_extractor()
									a_     :      Optional[int] 						=     self.get_tokenizer()

									a_     :      int 						=     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"			,     )




"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				= {
    """google/realm-cc-news-pretrained-embedder""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-encoder""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-scorer""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-openqa""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"""
    ),
    """google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json""",
    """google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json""",
    """google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json""",
    """google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json""",
    # See all REALM models at https://huggingface.co/models?filter=realm
}




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					"realm"






							def __init__(   self			,     a_=3_0_5_2_2			,     a_=7_6_8			,     a_=1_2_8			,     a_=1_2			,     a_=1_2			,     a_=8			,     a_=3_0_7_2			,     a_="gelu_new"			,     a_=0.1			,     a_=0.1			,     a_=5_1_2			,     a_=2			,     a_=0.02			,     a_=1e-12			,     a_=2_5_6			,     a_=1_0			,     a_=1e-3			,     a_=5			,     a_=3_2_0			,     a_=1_3_3_5_3_7_1_8			,     a_=5_0_0_0			,     a_=1			,     a_=0			,     a_=2			,     **a_			,     ):
									super().__init__(pad_token_id=a_			,     bos_token_id=a_			,     eos_token_id=a_			,     **a_      )

									# Common config
									a_     :      Optional[int] 						=     vocab_size
									a_     :      List[Any] 						=     max_position_embeddings
									a_     :      Optional[Any] 						=     hidden_size
									a_     :      Optional[Any] 						=     retriever_proj_size
									a_     :      List[str] 						=     num_hidden_layers
									a_     :      List[Any] 						=     num_attention_heads
									a_     :      Tuple 						=     num_candidates
									a_     :      str 						=     intermediate_size
									a_     :      Optional[int] 						=     hidden_act
									a_     :      List[str] 						=     hidden_dropout_prob
									a_     :      List[str] 						=     attention_probs_dropout_prob
									a_     :      Tuple 						=     initializer_range
									a_     :      Tuple 						=     type_vocab_size
									a_     :      str 						=     layer_norm_eps

									# Reader config
									a_     :      str 						=     span_hidden_size
									a_     :      Union[str, Any] 						=     max_span_width
									a_     :      Tuple 						=     reader_layer_norm_eps
									a_     :      List[Any] 						=     reader_beam_size
									a_     :      str 						=     reader_seq_len

									# Retrieval config
									a_     :      str 						=     num_block_records
									a_     :      int 						=     searcher_beam_size