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


lowerCamelCase_						: List[str]         =  logging.get_logger(__name__)

lowerCamelCase_						: Optional[int]         =  {
    "s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			__UpperCamelCase      :						Optional[Any]					      = """open-llama"""

			def __init__(       self :       List[Any]						,	snake_case_ :       Any=10_0000						,	snake_case_ :       Union[str, Any]=4096						,	snake_case_ :       int=1_1008						,	snake_case_ :       Any=32						,	snake_case_ :       int=32						,	snake_case_ :       Union[str, Any]="silu"						,	snake_case_ :       List[str]=2048						,	snake_case_ :       List[Any]=0.02						,	snake_case_ :       Dict=1e-6						,	snake_case_ :       int=True						,	snake_case_ :       Tuple=0						,	snake_case_ :       Tuple=1						,	snake_case_ :       Tuple=2						,	snake_case_ :       List[str]=False						,	snake_case_ :       Optional[Any]=True						,	snake_case_ :       int=0.1						,	snake_case_ :       str=0.1						,	snake_case_ :       Optional[int]=True						,	snake_case_ :       Optional[int]=True						,	snake_case_ :       int=None						,	**snake_case_ :       str						,	):
							UpperCamelCase_:							int	=						vocab_size
							UpperCamelCase_:							int	=						max_position_embeddings
							UpperCamelCase_:							Optional[Any]	=						hidden_size
							UpperCamelCase_:							List[str]	=						intermediate_size
							UpperCamelCase_:							Any	=						num_hidden_layers
							UpperCamelCase_:							Optional[int]	=						num_attention_heads
							UpperCamelCase_:							Any	=						hidden_act
							UpperCamelCase_:							Tuple	=						initializer_range
							UpperCamelCase_:							List[str]	=						rms_norm_eps
							UpperCamelCase_:							List[Any]	=						use_cache
							UpperCamelCase_:							List[str]	=						kwargs.pop(
							    """use_memorry_efficient_attention"""						,	UpperCAmelCase__      )
							UpperCamelCase_:							Optional[int]	=						hidden_dropout_prob
							UpperCamelCase_:							Union[str, Any]	=						attention_dropout_prob
							UpperCamelCase_:							Union[str, Any]	=						use_stable_embedding
							UpperCamelCase_:							List[str]	=						shared_input_output_embedding
							UpperCamelCase_:							Any	=						rope_scaling
							self._rope_scaling_validation()

							super().__init__(
							    pad_token_id=UpperCAmelCase__						,	bos_token_id=UpperCAmelCase__						,	eos_token_id=UpperCAmelCase__						,	tie_word_embeddings=UpperCAmelCase__						,	**UpperCAmelCase__						,	)




			def       lowerCAmelCase__	(       self :       str      ):
							if self.rope_scaling is None:
											return

							if not isinstance(self.rope_scaling						,	UpperCAmelCase__      ) or len(self.rope_scaling      ) != 2:
											raise ValueError(
											    """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
											    f'''got {self.rope_scaling}'''      )
							UpperCamelCase_:							Tuple	=						self.rope_scaling.get("""type"""						,	UpperCAmelCase__      )
							UpperCamelCase_:							Dict	=						self.rope_scaling.get("""factor"""						,	UpperCAmelCase__      )
							if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
											raise ValueError(
											    f'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''      )
							if rope_scaling_factor is None or not isinstance(UpperCAmelCase__						,	UpperCAmelCase__      ) or rope_scaling_factor <= 1.0:
											raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''      )








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  ):






			'''simple docstring'''

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

			def       lowerCAmelCase__	(       self :       Tuple						,	**snake_case_ :       Optional[Any]      ):
							return RobertaTokenizer.from_pretrained(self.checkpoint						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       str						,	**snake_case_ :       Any      ):
							return ClapFeatureExtractor.from_pretrained(self.checkpoint						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       Tuple      ):
							shutil.rmtree(self.tmpdirname      )

			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							Union[str, Any]	=						self.get_tokenizer()
							UpperCamelCase_:							int	=						self.get_feature_extractor()

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

							processor.save_pretrained(self.tmpdirname      )
							UpperCamelCase_:							Any	=						ClapProcessor.from_pretrained(self.tmpdirname      )

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

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

			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							Any	=						ClapProcessor(tokenizer=self.get_tokenizer()						,	feature_extractor=self.get_feature_extractor()      )
							processor.save_pretrained(self.tmpdirname      )

							UpperCamelCase_:							Optional[int]	=						self.get_tokenizer(bos_token="""(BOS)"""						,	eos_token="""(EOS)"""      )
							UpperCamelCase_:							Dict	=						self.get_feature_extractor(do_normalize=snake_case_						,	padding_value=1.0      )

							UpperCamelCase_:							List[str]	=						ClapProcessor.from_pretrained(
							    self.tmpdirname						,	bos_token="""(BOS)"""						,	eos_token="""(EOS)"""						,	do_normalize=snake_case_						,	padding_value=1.0      )

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

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

			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							int	=						self.get_feature_extractor()
							UpperCamelCase_:							Optional[Any]	=						self.get_tokenizer()

							UpperCamelCase_:							Dict	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

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

							UpperCamelCase_:							List[str]	=						feature_extractor(snake_case_						,	return_tensors="""np"""      )
							UpperCamelCase_:							int	=						processor(audios=snake_case_						,	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       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							List[Any]	=						self.get_feature_extractor()
							UpperCamelCase_:							List[str]	=						self.get_tokenizer()

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

							UpperCamelCase_:							Dict	=						"""This is a test string"""

							UpperCamelCase_:							Tuple	=						processor(text=snake_case_      )

							UpperCamelCase_:							Optional[int]	=						tokenizer(snake_case_      )

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

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							List[str]	=						self.get_feature_extractor()
							UpperCamelCase_:							Any	=						self.get_tokenizer()

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

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

							UpperCamelCase_:							Tuple	=						processor.batch_decode(snake_case_      )
							UpperCamelCase_:							str	=						tokenizer.batch_decode(snake_case_      )

							self.assertListEqual(snake_case_						,	snake_case_      )




			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							Any	=						self.get_feature_extractor()
							UpperCamelCase_:							str	=						self.get_tokenizer()

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

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






