



"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase    :int     =					logging.get_logger(__name__)

_lowerCAmelCase    :List[Any]     =					{
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									='''roformer'''





     def __init__(					self       ,	A=5_0_0_0_0       ,	A=None       ,	A=7_6_8       ,	A=1_2       ,	A=1_2       ,	A=3_0_7_2       ,	A="gelu"       ,	A=0.1       ,	A=0.1       ,	A=1_5_3_6       ,	A=2       ,	A=0.02       ,	A=1E-12       ,	A=0       ,	A=False       ,	A=True       ,	**A       ,	)							->					int:
         super().__init__(pad_token_id=A       ,	**A		)

         _UpperCAmelCase							:  Union[str, Any]											=    vocab_size
         _UpperCAmelCase							:  Optional[int]											=    hidden_size if embedding_size is None else embedding_size
         _UpperCAmelCase							:  List[str]											=    hidden_size
         _UpperCAmelCase							:  Tuple											=    num_hidden_layers
         _UpperCAmelCase							:  Union[str, Any]											=    num_attention_heads
         _UpperCAmelCase							:  Any											=    hidden_act
         _UpperCAmelCase							:  int											=    intermediate_size
         _UpperCAmelCase							:  List[Any]											=    hidden_dropout_prob
         _UpperCAmelCase							:  Dict											=    attention_probs_dropout_prob
         _UpperCAmelCase							:  Dict											=    max_position_embeddings
         _UpperCAmelCase							:  Union[str, Any]											=    type_vocab_size
         _UpperCAmelCase							:  List[Any]											=    initializer_range
         _UpperCAmelCase							:  Optional[Any]											=    layer_norm_eps
         _UpperCAmelCase							:  Optional[Any]											=    rotary_value
         _UpperCAmelCase							:  List[Any]											=    use_cache



class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     @property
     def  __lowerCAmelCase	(					self		)							->					Mapping[str, Mapping[int, str]]:
         if self.task == "multiple-choice":
             _UpperCAmelCase							:  Any											=    {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
             _UpperCAmelCase							:  List[str]											=    {0: '''batch''', 1: '''sequence'''}
         _UpperCAmelCase							:  Optional[int]											=    {0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
                 ('''token_type_ids''', dynamic_axis),
             ]		)







"""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


