



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


_snake_case						=    logging.get_logger(__name__)

_snake_case						=    {
    '''facebook/nllb-moe-54B''': '''https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json''',
}



class     UpperCAmelCase_       (			a_):
							lowerCamelCase__       =						'nllb-moe'
							lowerCamelCase__       =						['past_key_values']
							lowerCamelCase__       =						{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}







							def __init__(  self,     __a=12_8112,     __a=1024,     __a=12,     __a=4096,     __a=16,     __a=12,     __a=4096,     __a=16,     __a=0.05,     __a=0.05,     __a=True,     __a=True,     __a="relu",     __a=1024,     __a=0.1,     __a=0.1,     __a=0.0,     __a=0.02,     __a=2,     __a=True,     __a=False,     __a="float32",     __a=False,     __a=128,     __a=64,     __a=4,     __a=4,     __a=0.001,     __a=0.001,     __a="all",     __a=False,     __a=False,     __a=1.0,     __a=0.2,     __a=1,     __a=0,     __a=2,     __a=False,     **__a,     ):

														'''simple docstring'''

														_lowerCAmelCase	:				str					  =       vocab_size
														_lowerCAmelCase	:				str					  =       max_position_embeddings
														_lowerCAmelCase	:				Dict					  =       d_model
														_lowerCAmelCase	:				List[str]					  =       encoder_ffn_dim
														_lowerCAmelCase	:				Optional[int]					  =       encoder_layers
														_lowerCAmelCase	:				List[Any]					  =       encoder_attention_heads
														_lowerCAmelCase	:				str					  =       decoder_ffn_dim
														_lowerCAmelCase	:				List[Any]					  =       decoder_layers
														_lowerCAmelCase	:				List[Any]					  =       decoder_attention_heads
														_lowerCAmelCase	:				List[Any]					  =       dropout
														_lowerCAmelCase	:				Any					  =       attention_dropout
														_lowerCAmelCase	:				Optional[int]					  =       activation_dropout
														_lowerCAmelCase	:				str					  =       activation_function
														_lowerCAmelCase	:				List[Any]					  =       init_std
														_lowerCAmelCase	:				Tuple					  =       encoder_layerdrop
														_lowerCAmelCase	:				List[Any]					  =       decoder_layerdrop
														_lowerCAmelCase	:				Union[str, Any]					  =       use_cache
														_lowerCAmelCase	:				int					  =       encoder_layers
														_lowerCAmelCase	:				Optional[int]					  =       scale_embedding  # scale factor will be sqrt(d_model) if True
														_lowerCAmelCase	:				Dict					  =       router_z_loss_coef
														_lowerCAmelCase	:				Any					  =       router_aux_loss_coef
														_lowerCAmelCase	:				str					  =       decoder_sparse_step
														_lowerCAmelCase	:				Optional[int]					  =       encoder_sparse_step
														_lowerCAmelCase	:				Optional[int]					  =       num_experts
														_lowerCAmelCase	:				Union[str, Any]					  =       expert_capacity
														_lowerCAmelCase	:				Tuple					  =       router_bias
														if router_dtype not in ["float32", "float16", "bfloat16"]:
																					raise ValueError(f"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}")
														_lowerCAmelCase	:				List[Any]					  =       router_dtype

														_lowerCAmelCase	:				str					  =       router_ignore_padding_tokens
														_lowerCAmelCase	:				int					  =       batch_prioritized_routing
														_lowerCAmelCase	:				Any					  =       second_expert_policy
														_lowerCAmelCase	:				Optional[int]					  =       normalize_router_prob_before_dropping
														_lowerCAmelCase	:				Union[str, Any]					  =       moe_eval_capacity_token_fraction
														_lowerCAmelCase	:				Optional[Any]					  =       moe_token_dropout
														_lowerCAmelCase	:				int					  =       output_router_logits
														super().__init__(
														    pad_token_id=__UpperCamelCase,     bos_token_id=__UpperCamelCase,     eos_token_id=__UpperCamelCase,     is_encoder_decoder=__UpperCamelCase,     decoder_start_token_id=__UpperCamelCase,     **__UpperCamelCase,     )








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 			__UpperCAmelCase		(						self						)->						Optional[int]:
           __lowerCAmelCase             =       "laion/clap-htsat-unfused"
           __lowerCAmelCase             =       tempfile.mkdtemp()


      def 			__UpperCAmelCase		(						self       ,    **__UpperCamelCase						)->						Optional[int]:
           return RobertaTokenizer.from_pretrained(self.checkpoint       ,    **__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self       ,    **__UpperCamelCase						)->						List[Any]:
           return ClapFeatureExtractor.from_pretrained(self.checkpoint       ,    **__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           shutil.rmtree(self.tmpdirname						)


      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.get_tokenizer()
           __lowerCAmelCase             =       self.get_feature_extractor()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

           processor.save_pretrained(self.tmpdirname						)
           __lowerCAmelCase             =       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 			__UpperCAmelCase		(						self						)->						List[str]:
           __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=__UpperCamelCase       ,    padding_value=1.0						)

           __lowerCAmelCase             =       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 			__UpperCAmelCase		(						self						)->						List[str]:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

           __lowerCAmelCase             =       floats_list((3, 1_0_0_0)						)

           __lowerCAmelCase             =       feature_extractor(__UpperCamelCase       ,    return_tensors="np"						)
           __lowerCAmelCase             =       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 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

           __lowerCAmelCase             =       "This is a test string"

           __lowerCAmelCase             =       processor(text=__UpperCamelCase						)

           __lowerCAmelCase             =       tokenizer(__UpperCamelCase						)

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


      def 			__UpperCAmelCase		(						self						)->						int:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

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

           __lowerCAmelCase             =       processor.batch_decode(__UpperCamelCase						)
           __lowerCAmelCase             =       tokenizer.batch_decode(__UpperCamelCase						)

           self.assertListEqual(__UpperCamelCase       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       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"       ,    )






