"""simple docstring"""





import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_torch_available():
				from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

_SCREAMING_SNAKE_CASE       :  Tuple        = logging.get_logger(__name__)



def        _lowerCAmelCase      (						UpperCAmelCase     :							bytes      ,							UpperCAmelCase     :							int				):


					'''simple docstring'''






					UpperCamelCase__		:    List[str]         =F'''{sampling_rate}'''
					UpperCamelCase__		:    Optional[int]         ='''1'''
					UpperCamelCase__		:    Optional[Any]         ='''f32le'''
					UpperCamelCase__		:    int         =[
					    '''ffmpeg''',
					    '''-i''',
					    '''pipe:0''',
					    '''-ac''',
					    ac,
					    '''-ar''',
					    ar,
					    '''-f''',
					    format_for_conversion,
					    '''-hide_banner''',
					    '''-loglevel''',
					    '''quiet''',
					    '''pipe:1''',
					]

					try:
										UpperCamelCase__		:    Optional[Any]         =subprocess.Popen(lowercase__      ,							stdin=subprocess.PIPE      ,							stdout=subprocess.PIPE				)
					except FileNotFoundError:
										raise ValueError('''ffmpeg was not found but is required to load audio files from filename'''				)
					UpperCamelCase__		:    Optional[Any]         =ffmpeg_process.communicate(lowercase__				)
					UpperCamelCase__		:    Optional[Any]         =output_stream[0]

					UpperCamelCase__		:    int         =np.frombuffer(lowercase__      ,							np.floataa				)
					if audio.shape[0] == 0:
										raise ValueError('''Malformed soundfile'''				)
					return audio







@add_end_docstrings(snake_case__    )
class     __a   (      snake_case__    ):

			"""simple docstring"""

			def __init__(							self     :					Union[str, Any]						,  *lowercase_     :					int						,  **lowercase_     :					List[str]						):
								# Default, might be overriden by the model.config.
								UpperCamelCase__		:    Optional[Any]         =5
								super().__init__(*A_						,  **A_						)

								if self.framework != "pt":
													raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''						)

								self.check_model_type(A_						)






			def __call__(							self     :					Optional[int]						,  lowercase_     :					Union[np.ndarray, bytes, str]						,  **lowercase_     :					Optional[int]						,  ):
								return super().__call__(A_						,  **A_						)






			def    _lowerCAmelCase			(							self     :					Union[str, Any]						,  lowercase_     :					Optional[Any]=None						,  **lowercase_     :					Union[str, Any]						):
								# No parameters on this pipeline right now
								UpperCamelCase__		:    str         ={}
								if top_k is not None:
													if top_k > self.model.config.num_labels:
																		UpperCamelCase__		:    Tuple         =self.model.config.num_labels
													UpperCamelCase__		:    str         =top_k
								return {}, {}, postprocess_params






			def    _lowerCAmelCase			(							self     :					Optional[Any]						,  lowercase_     :					Optional[int]						):
								if isinstance(A_						,  A_						):
													if inputs.startswith('''http://'''						) or inputs.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
																		UpperCamelCase__		:    int         =requests.get(A_						).content
													else:
																		with open(A_						,  '''rb'''						) as f:
																							UpperCamelCase__		:    List[str]         =f.read()

								if isinstance(A_						,  A_						):
													UpperCamelCase__		:    Optional[int]         =ffmpeg_read(A_						,  self.feature_extractor.sampling_rate						)

								if isinstance(A_						,  A_						):
													# Accepting `"array"` which is the key defined in `datasets` for
													# better integration
													if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
																		raise ValueError(
																		    '''When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a '''
																		    '''\"raw\" key containing the numpy array representing the audio and a \"sampling_rate\" key, '''
																		    '''containing the sampling_rate associated with that array'''						)

													UpperCamelCase__		:    Optional[int]         =inputs.pop('''raw'''						,  A_						)
													if _inputs is None:
																		# Remove path which will not be used from `datasets`.
																		inputs.pop('''path'''						,  A_						)
																		UpperCamelCase__		:    List[str]         =inputs.pop('''array'''						,  A_						)
													UpperCamelCase__		:    Dict         =inputs.pop('''sampling_rate'''						)
													UpperCamelCase__		:    Optional[int]         =_inputs
													if in_sampling_rate != self.feature_extractor.sampling_rate:
																		import torch

																		if is_torchaudio_available():
																							from torchaudio import functional as F
																		else:
																							raise ImportError(
																							    '''torchaudio is required to resample audio samples in AudioClassificationPipeline. '''
																							    '''The torchaudio package can be installed through: `pip install torchaudio`.'''						)

																		UpperCamelCase__		:    Dict         =F.resample(
																		    torch.from_numpy(A_						)						,  A_						,  self.feature_extractor.sampling_rate						).numpy()

								if not isinstance(A_						,  np.ndarray						):
													raise ValueError('''We expect a numpy ndarray as input'''						)
								if len(inputs.shape						) != 1:
													raise ValueError('''We expect a single channel audio input for AudioClassificationPipeline'''						)

								UpperCamelCase__		:    Tuple         =self.feature_extractor(
								    A_						,  sampling_rate=self.feature_extractor.sampling_rate						,  return_tensors='''pt'''						)
								return processed






			def    _lowerCAmelCase			(							self     :					List[str]						,  lowercase_     :					Union[str, Any]						):
								UpperCamelCase__		:    str         =self.model(**A_						)
								return model_outputs






			def    _lowerCAmelCase			(							self     :					str						,  lowercase_     :					Any						,  lowercase_     :					Dict=5						):
								UpperCamelCase__		:    Optional[Any]         =model_outputs.logits[0].softmax(-1						)
								UpperCamelCase__  ,			UpperCamelCase__		:    str         =probs.topk(A_						)

								UpperCamelCase__		:    Union[str, Any]         =scores.tolist()
								UpperCamelCase__		:    Union[str, Any]         =ids.tolist()

								UpperCamelCase__		:    str         =[{'''score''': score, '''label''': self.model.config.idalabel[_id]} for score, _id in zip(A_						,  A_						)]

								return labels





"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


_SCREAMING_SNAKE_CASE       :  Union[str, Any]        = {
    """configuration_xlm_prophetnet""": ["""XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMProphetNetConfig"""],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Any        = ["""XLMProphetNetTokenizer"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Optional[Any]        = [
				    """XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """XLMProphetNetDecoder""",
				    """XLMProphetNetEncoder""",
				    """XLMProphetNetForCausalLM""",
				    """XLMProphetNetForConditionalGeneration""",
				    """XLMProphetNetModel""",
				    """XLMProphetNetPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xlm_prophetnet import (
								    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    XLMProphetNetDecoder,
								    XLMProphetNetEncoder,
								    XLMProphetNetForCausalLM,
								    XLMProphetNetForConditionalGeneration,
								    XLMProphetNetModel,
								    XLMProphetNetPreTrainedModel,
								)

else:
				import sys

				_SCREAMING_SNAKE_CASE       :  Dict        = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


