



"""simple docstring"""
import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     @require_torch
     def  __lowerCAmelCase	(					self		)							->					Any:
         _UpperCAmelCase							:  str											=    pipeline(
             task='''zero-shot-audio-classification'''       ,	model='''hf-internal-testing/tiny-clap-htsat-unfused'''		)
         _UpperCAmelCase							:  List[Any]											=    load_dataset('''ashraq/esc50'''		)
         _UpperCAmelCase							:  Optional[int]											=    dataset['''train''']['''audio'''][-1]['''array''']
         _UpperCAmelCase							:  str											=    audio_classifier(A       ,	candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']		)
         self.assertEqual(
             nested_simplify(A		)       ,	[{'''score''': 0.501, '''label''': '''Sound of a dog'''}, {'''score''': 0.499, '''label''': '''Sound of vaccum cleaner'''}]       ,	)





     @unittest.skip('''No models are available in TF'''		)
     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         pass





     @slow
     @require_torch
     def  __lowerCAmelCase	(					self		)							->					str:
         _UpperCAmelCase							:  Union[str, Any]											=    pipeline(
             task='''zero-shot-audio-classification'''       ,	model='''laion/clap-htsat-unfused'''       ,	)
         # This is an audio of a dog
         _UpperCAmelCase							:  List[Any]											=    load_dataset('''ashraq/esc50'''		)
         _UpperCAmelCase							:  Optional[int]											=    dataset['''train''']['''audio'''][-1]['''array''']
         _UpperCAmelCase							:  Any											=    audio_classifier(A       ,	candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']		)

         self.assertEqual(
             nested_simplify(A		)       ,	[
                 {'''score''': 0.999, '''label''': '''Sound of a dog'''},
                 {'''score''': 0.001, '''label''': '''Sound of vaccum cleaner'''},
             ]       ,	)

         _UpperCAmelCase							:  List[Any]											=    audio_classifier([audio] * 5       ,	candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']		)
         self.assertEqual(
             nested_simplify(A		)       ,	[
                 [
                     {'''score''': 0.999, '''label''': '''Sound of a dog'''},
                     {'''score''': 0.001, '''label''': '''Sound of vaccum cleaner'''},
                 ],
             ]
             * 5       ,	)
         _UpperCAmelCase							:  Tuple											=    audio_classifier(
             [audio] * 5       ,	candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']       ,	batch_size=5		)
         self.assertEqual(
             nested_simplify(A		)       ,	[
                 [
                     {'''score''': 0.999, '''label''': '''Sound of a dog'''},
                     {'''score''': 0.001, '''label''': '''Sound of vaccum cleaner'''},
                 ],
             ]
             * 5       ,	)





     @unittest.skip('''No models are available in TF'''		)
     def  __lowerCAmelCase	(					self		)							->					int:
         pass







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


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

_lowerCAmelCase    :Any     =					{
    'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
    'tiiuae/falcon-7b': 'https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json',
}
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									='''falcon'''
     a__									=['''past_key_values''']





     def __init__(					self       ,	A=6_5_0_2_4       ,	A=4_5_4_4       ,	A=3_2       ,	A=7_1       ,	A=1E-5       ,	A=0.02       ,	A=True       ,	A=0.0       ,	A=0.0       ,	A=None       ,	A=False       ,	A=False       ,	A=True       ,	A=True       ,	A=False       ,	A=1_1       ,	A=1_1       ,	**A       ,	)							->					Any:
         _UpperCAmelCase							:  int											=    vocab_size
         # Backward compatibility with n_embed kwarg
         _UpperCAmelCase							:  Optional[Any]											=    kwargs.pop('''n_embed'''       ,	A		)
         _UpperCAmelCase							:  int											=    hidden_size if n_embed is None else n_embed
         _UpperCAmelCase							:  List[str]											=    num_hidden_layers
         _UpperCAmelCase							:  Tuple											=    num_attention_heads
         _UpperCAmelCase							:  Optional[int]											=    layer_norm_epsilon
         _UpperCAmelCase							:  Tuple											=    initializer_range
         _UpperCAmelCase							:  Optional[int]											=    use_cache
         _UpperCAmelCase							:  Any											=    hidden_dropout
         _UpperCAmelCase							:  Dict											=    attention_dropout

         _UpperCAmelCase							:  Any											=    bos_token_id
         _UpperCAmelCase							:  List[Any]											=    eos_token_id
         _UpperCAmelCase							:  Tuple											=    num_attention_heads if num_kv_heads is None else num_kv_heads
         _UpperCAmelCase							:  Dict											=    alibi
         _UpperCAmelCase							:  Optional[int]											=    new_decoder_architecture
         _UpperCAmelCase							:  str											=    multi_query  # Ignored when new_decoder_architecture is True
         _UpperCAmelCase							:  Optional[int]											=    parallel_attn
         _UpperCAmelCase							:  Optional[int]											=    bias

         super().__init__(bos_token_id=A       ,	eos_token_id=A       ,	**A		)





     @property
     def  __lowerCAmelCase	(					self		)							->					List[str]:
         return self.hidden_size // self.num_attention_heads





     @property
     def  __lowerCAmelCase	(					self		)							->					List[Any]:
         return not self.alibi


