
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			:   List[Any]         =     logging.get_logger(__name__)
def     UpperCAmelCase__							(       __magic_name__						:     bytes							,  __magic_name__						:     int  ):


					'''simple docstring'''

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

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

					lowerCAmelCase		:					Optional[Any]  	=     np.frombuffer(__magic_name__							,  np.floataa  )
					if audio.shape[0] == 0:
										raise ValueError('''Malformed soundfile'''  )
					return audio


@add_end_docstrings(snake_case    )
class 		__magic_name__   (						snake_case    ):





		def __init__(							self    :	Dict						,  *lowerCamelCase__    :	Union[str, Any]						,  **lowerCamelCase__    :	str				):
							# Default, might be overriden by the model.config.
							lowerCAmelCase		:					List[str]  	=     5
							super().__init__(*lowerCamelCase__						,  **lowerCamelCase__				)

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

							self.check_model_type(lowerCamelCase__				)





		def __call__(							self    :	List[Any]						,  lowerCamelCase__    :	Union[np.ndarray, bytes, str]						,  **lowerCamelCase__    :	str						,  ):
							return super().__call__(lowerCamelCase__						,  **lowerCamelCase__				)





		def       _A							(							self    :	Optional[Any]						,  lowerCamelCase__    :	Optional[Any]=None						,  **lowerCamelCase__    :	List[str]				):
							# No parameters on this pipeline right now
							lowerCAmelCase		:					Optional[int]  	=     {}
							if top_k is not None:
												if top_k > self.model.config.num_labels:
																	lowerCAmelCase		:					Optional[int]  	=     self.model.config.num_labels
												lowerCAmelCase		:					str  	=     top_k
							return {}, {}, postprocess_params





		def       _A							(							self    :	Dict						,  lowerCamelCase__    :	Any				):
							if isinstance(lowerCamelCase__						,  lowerCamelCase__				):
												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
																	lowerCAmelCase		:					str  	=     requests.get(lowerCamelCase__				).content
												else:
																	with open(lowerCamelCase__						,  '''rb'''				) as f:
																						lowerCAmelCase		:					Tuple  	=     f.read()

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

							if isinstance(lowerCamelCase__						,  lowerCamelCase__				):
												# 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'''				)

												lowerCAmelCase		:					List[str]  	=     inputs.pop('''raw'''						,  lowerCamelCase__				)
												if _inputs is None:
																	# Remove path which will not be used from `datasets`.
																	inputs.pop('''path'''						,  lowerCamelCase__				)
																	lowerCAmelCase		:					str  	=     inputs.pop('''array'''						,  lowerCamelCase__				)
												lowerCAmelCase		:					Optional[Any]  	=     inputs.pop('''sampling_rate'''				)
												lowerCAmelCase		:					List[str]  	=     _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`.'''				)

																	lowerCAmelCase		:					int  	=     F.resample(
																	    torch.from_numpy(lowerCamelCase__				)						,  lowerCamelCase__						,  self.feature_extractor.sampling_rate				).numpy()

							if not isinstance(lowerCamelCase__						,  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'''				)

							lowerCAmelCase		:					str  	=     self.feature_extractor(
							    lowerCamelCase__						,  sampling_rate=self.feature_extractor.sampling_rate						,  return_tensors='''pt'''				)
							return processed





		def       _A							(							self    :	int						,  lowerCamelCase__    :	int				):
							lowerCAmelCase		:					Any  	=     self.model(**lowerCamelCase__				)
							return model_outputs





		def       _A							(							self    :	Any						,  lowerCamelCase__    :	str						,  lowerCamelCase__    :	List[str]=5				):
							lowerCAmelCase		:					Tuple  	=     model_outputs.logits[0].softmax(-1				)
							lowerCAmelCase	,							lowerCAmelCase		:					str  	=     probs.topk(lowerCamelCase__				)

							lowerCAmelCase		:					Any  	=     scores.tolist()
							lowerCAmelCase		:					Any  	=     ids.tolist()

							lowerCAmelCase		:					int  	=     [{'''score''': score, '''label''': self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase__						,  lowerCamelCase__				)]

							return labels


from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE			:   str         =     {
    'configuration_deta': ['DETA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetaConfig'],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE			:   Optional[int]         =     ['DetaImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE			:   str         =     [
		    'DETA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'DetaForObjectDetection',
		    'DetaModel',
		    'DetaPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_deta import DetaImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deta import (
				    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DetaForObjectDetection,
				    DetaModel,
				    DetaPreTrainedModel,
				)

else:
		import sys

		__SCREAMING_SNAKE_CASE			:   Dict         =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
