





from collections import OrderedDict
from typing import Mapping

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


__lowercase       :str       =  logging.get_logger(__name__)

__lowercase       :Optional[int]       =  {
    "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
    "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
    "kssteven/ibert-roberta-large-mnli": (
        "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
    ),
}



class    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						"ibert"

							def __init__(      self				:     Optional[Any]							,	a				:     Optional[int]=3_05_22							,	a				:     Tuple=7_68							,	a				:     Tuple=12							,	a				:     List[str]=12							,	a				:     str=30_72							,	a				:     str="gelu"							,	a				:     Union[str, Any]=0.1							,	a				:     Any=0.1							,	a				:     Tuple=5_12							,	a				:     Dict=2							,	a				:     str=0.02							,	a				:     Union[str, Any]=1E-12							,	a				:     Tuple=1							,	a				:     List[Any]=0							,	a				:     Optional[Any]=2							,	a				:     List[Any]="absolute"							,	a				:     str=False							,	a				:     Any="none"							,	**a				:     Any							,	)	->List[Any]:
													super().__init__(pad_token_id=a							,	bos_token_id=a							,	eos_token_id=a							,	**a    )

													SCREAMING_SNAKE_CASE__     :				int   =					vocab_size
													SCREAMING_SNAKE_CASE__     :				int   =					hidden_size
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					num_hidden_layers
													SCREAMING_SNAKE_CASE__     :				Any   =					num_attention_heads
													SCREAMING_SNAKE_CASE__     :				int   =					hidden_act
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					intermediate_size
													SCREAMING_SNAKE_CASE__     :				str   =					hidden_dropout_prob
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE__     :				int   =					max_position_embeddings
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					type_vocab_size
													SCREAMING_SNAKE_CASE__     :				List[str]   =					initializer_range
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					layer_norm_eps
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					position_embedding_type
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					quant_mode
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					force_dequant







class    _a     (				lowercase__							):






							"""simple docstring"""

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






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    _a     (				unittest.TestCase							):






							"""simple docstring"""

							@require_torch
							def      A_  (      self				:     Dict    )	->str:
													SCREAMING_SNAKE_CASE__     :				Any   =					pipeline(
													    task="zero-shot-audio-classification"							,	model="hf-internal-testing/tiny-clap-htsat-unfused"    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					load_dataset("ashraq/esc50"    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					dataset["train"]["audio"][-1]["array"]
													SCREAMING_SNAKE_CASE__     :				int   =					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      A_  (      self				:     int    )	->Union[str, Any]:
													pass

							@slow
							@require_torch
							def      A_  (      self				:     int    )	->str:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					pipeline(
													    task="zero-shot-audio-classification"							,	model="laion/clap-htsat-unfused"							,	)
													# This is an audio of a dog
													SCREAMING_SNAKE_CASE__     :				int   =					load_dataset("ashraq/esc50"    )
													SCREAMING_SNAKE_CASE__     :				str   =					dataset["train"]["audio"][-1]["array"]
													SCREAMING_SNAKE_CASE__     :				List[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"},
													    ]							,	)

													SCREAMING_SNAKE_CASE__     :				Optional[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							,	)
													SCREAMING_SNAKE_CASE__     :				int   =					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      A_  (      self				:     Optional[int]    )	->Union[str, Any]:
													pass