
'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

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


_lowercase          =						logging.get_logger(__name__)

_lowercase          =						{
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: List[Any]										=  '''data2vec-text'''




						def __init__(   self       ,  _lowercase=30_522       ,  _lowercase=768       ,  _lowercase=12       ,  _lowercase=12       ,  _lowercase=3_072       ,  _lowercase="gelu"       ,  _lowercase=0.1       ,  _lowercase=0.1       ,  _lowercase=512       ,  _lowercase=2       ,  _lowercase=0.02       ,  _lowercase=1e-12       ,  _lowercase=1       ,  _lowercase=0       ,  _lowercase=2       ,  _lowercase="absolute"       ,  _lowercase=True       ,  _lowercase=None       ,  **_lowercase       ,  ):

												"""simple docstring"""



												super().__init__(pad_token_id=_lowercase       ,  bos_token_id=_lowercase       ,  eos_token_id=_lowercase       ,  **_lowercase )

												_lowerCAmelCase    		=					vocab_size
												_lowerCAmelCase    		=					hidden_size
												_lowerCAmelCase    		=					num_hidden_layers
												_lowerCAmelCase    		=					num_attention_heads
												_lowerCAmelCase    		=					hidden_act
												_lowerCAmelCase    		=					intermediate_size
												_lowerCAmelCase    		=					hidden_dropout_prob
												_lowerCAmelCase    		=					attention_probs_dropout_prob
												_lowerCAmelCase    		=					max_position_embeddings
												_lowerCAmelCase    		=					type_vocab_size
												_lowerCAmelCase    		=					initializer_range
												_lowerCAmelCase    		=					layer_norm_eps
												_lowerCAmelCase    		=					position_embedding_type
												_lowerCAmelCase    		=					use_cache
												_lowerCAmelCase    		=					classifier_dropout






class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												if self.task == "multiple-choice":
																		_lowerCAmelCase    		=					{0: """batch""", 1: """choice""", 2: """sequence"""}
												else:
																		_lowerCAmelCase    		=					{0: """batch""", 1: """sequence"""}
												return OrderedDict(
												    [
												        ("""input_ids""", dynamic_axis),
												        ("""attention_mask""", dynamic_axis),
												    ] )


'''simple docstring'''



from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


_lowercase          =						logging.get_logger(__name__)




@add_end_docstrings(_SCREAMING_SNAKE_CASE					)
class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						def __init__(   self       ,  **_lowercase ):

												"""simple docstring"""



												super().__init__(**_lowercase )

												if self.framework != "pt":
																		raise ValueError(F'The {self.__class__} is only available in PyTorch.' )
												# No specific FOR_XXX available yet




						def __call__(   self       ,  _lowercase       ,  **_lowercase ):

												"""simple docstring"""



												return super().__call__(_lowercase       ,  **_lowercase )




						def    _lowercase      (   self       ,  **_lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					{}
												if "candidate_labels" in kwargs:
																		_lowerCAmelCase    		=					kwargs["""candidate_labels"""]
												if "hypothesis_template" in kwargs:
																		_lowerCAmelCase    		=					kwargs["""hypothesis_template"""]

												return preprocess_params, {}, {}




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase=None       ,  _lowercase="This is a sound of {}." ):

												"""simple docstring"""



												if isinstance(_lowercase       ,  _lowercase ):
																		if audio.startswith("""http://""" ) or audio.startswith("""https://""" ):
																								# We need to actually check for a real protocol, otherwise it's impossible to use a local file
																								# like http_huggingface_co.png
																								_lowerCAmelCase    		=					requests.get(_lowercase ).content
																		else:
																								with open(_lowercase       ,  """rb""" ) as f:
																														_lowerCAmelCase    		=					f.read()

												if isinstance(_lowercase       ,  _lowercase ):
																		_lowerCAmelCase    		=					ffmpeg_read(_lowercase       ,  self.feature_extractor.sampling_rate )

												if not isinstance(_lowercase       ,  np.ndarray ):
																		raise ValueError("""We expect a numpy ndarray as input""" )
												if len(audio.shape ) != 1:
																		raise ValueError("""We expect a single channel audio input for ZeroShotAudioClassificationPipeline""" )

												_lowerCAmelCase    		=					self.feature_extractor(
												    [audio]       ,  sampling_rate=self.feature_extractor.sampling_rate       ,  return_tensors="""pt""" )
												_lowerCAmelCase    		=					candidate_labels
												_lowerCAmelCase    		=					[hypothesis_template.format(_lowercase ) for x in candidate_labels]
												_lowerCAmelCase    		=					self.tokenizer(_lowercase       ,  return_tensors=self.framework       ,  padding=_lowercase )
												_lowerCAmelCase    		=					[text_inputs]
												return inputs




						def    _lowercase      (   self       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					model_inputs.pop("""candidate_labels""" )
												_lowerCAmelCase    		=					model_inputs.pop("""text_inputs""" )
												if isinstance(text_inputs[0]       ,  _lowercase ):
																		_lowerCAmelCase    		=					text_inputs[0]
												else:
																		# Batching case.
																		_lowerCAmelCase    		=					text_inputs[0][0]

												_lowerCAmelCase    		=					self.model(**_lowercase       ,  **_lowercase )

												_lowerCAmelCase    		=					{
												    """candidate_labels""": candidate_labels,
												    """logits""": outputs.logits_per_audio,
												}
												return model_outputs




						def    _lowercase      (   self       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					model_outputs.pop("""candidate_labels""" )
												_lowerCAmelCase    		=					model_outputs["""logits"""][0]

												if self.framework == "pt":
																		_lowerCAmelCase    		=					logits.softmax(dim=0 )
																		_lowerCAmelCase    		=					probs.tolist()
												else:
																		raise ValueError("""`tf` framework not supported.""" )

												_lowerCAmelCase    		=					[
												    {"""score""": score, """label""": candidate_label}
												    for score, candidate_label in sorted(zip(_lowercase       ,  _lowercase )       ,  key=lambda _lowercase : -x[0] )
												]
												return result
