



import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor


class 	lowerCamelCase__ (		unittest.TestCase   ):


      @property
      def   _lowerCamelCase			( self						:  List[str]			):
             a__:       List[str]											=1
             a__:       Union[str, Any]											=3
             a__:       Dict											=8
             a__:       Optional[Any]											=8

             a__:       Any											=torch.rand((batch_size, num_channels, height, width)			)

             return sample


      def   _lowerCamelCase			( self						:  Union[str, Any]      ,       _a						:  int			):




             if isinstance(image[0]      ,       PIL.Image.Image			):
                    return np.stack([np.array(lowerCAmelCase__			) for i in image]      ,       axis=0			)
             elif isinstance(lowerCAmelCase__      ,       torch.Tensor			):
                    return image.cpu().numpy().transpose(0      ,       2      ,       3      ,       1			)
             return image


      def   _lowerCamelCase			( self						:  Optional[Any]			):
             a__:       int											=VaeImageProcessor(do_resize=lowerCAmelCase__      ,       do_normalize=lowerCAmelCase__			)

             a__:       Optional[int]											=self.dummy_sample
             a__:       str											=self.to_np(lowerCAmelCase__			)

             for output_type in ["pt", "np", "pil"]:
                    a__:       Optional[int]											=image_processor.postprocess(
                        image_processor.preprocess(lowerCAmelCase__			)      ,       output_type=lowerCAmelCase__      ,       )
                    a__:       Dict											=self.to_np(lowerCAmelCase__			)
                    a__:       int											=(input_np * 2_5_5).round() if output_type == "pil" else input_np
                    assert (
                        np.abs(in_np - out_np			).max() < 1e-6
                    ), F"decoded output does not match input for output_type {output_type}"


      def   _lowerCamelCase			( self						:  str			):
             a__:       int											=VaeImageProcessor(do_resize=lowerCAmelCase__      ,       do_normalize=lowerCAmelCase__			)
             a__:       Optional[int]											=self.dummy_sample.cpu().numpy().transpose(0      ,       2      ,       3      ,       1			)

             for output_type in ["pt", "np", "pil"]:
                    a__:       List[Any]											=image_processor.postprocess(image_processor.preprocess(lowerCAmelCase__			)      ,       output_type=lowerCAmelCase__			)

                    a__:       Union[str, Any]											=self.to_np(lowerCAmelCase__			)
                    a__:       Optional[int]											=(input_np * 2_5_5).round() if output_type == "pil" else input_np
                    assert (
                        np.abs(in_np - out_np			).max() < 1e-6
                    ), F"decoded output does not match input for output_type {output_type}"


      def   _lowerCamelCase			( self						:  Union[str, Any]			):
             a__:       List[Any]											=VaeImageProcessor(do_resize=lowerCAmelCase__      ,       do_normalize=lowerCAmelCase__			)

             a__:       Union[str, Any]											=self.dummy_sample.cpu().numpy().transpose(0      ,       2      ,       3      ,       1			)
             a__:       List[Any]											=image_processor.numpy_to_pil(lowerCAmelCase__			)

             for output_type in ["pt", "np", "pil"]:
                    a__:       List[str]											=image_processor.postprocess(image_processor.preprocess(lowerCAmelCase__			)      ,       output_type=lowerCAmelCase__			)
                    for i, o in zip(lowerCAmelCase__      ,       lowerCAmelCase__			):
                           a__:       List[str]											=np.array(lowerCAmelCase__			)
                           a__:       Optional[int]											=self.to_np(lowerCAmelCase__			) if output_type == "pil" else (self.to_np(lowerCAmelCase__			) * 2_5_5).round()
                           assert (
                               np.abs(in_np - out_np			).max() < 1e-6
                           ), F"decoded output does not match input for output_type {output_type}"


      def   _lowerCamelCase			( self						:  Tuple			):
             a__:       Tuple											=VaeImageProcessor(do_resize=lowerCAmelCase__      ,       do_normalize=lowerCAmelCase__			)

             a__:       Optional[int]											=self.dummy_sample
             a__:       List[str]											=input_pt_ad.squeeze(0			)

             a__:       Any											=image_processor.postprocess(
                 image_processor.preprocess(lowerCAmelCase__			)      ,       output_type="np"      ,       )
             a__:       Optional[int]											=image_processor.postprocess(
                 image_processor.preprocess(lowerCAmelCase__			)      ,       output_type="np"      ,       )

             a__:       Optional[Any]											=self.to_np(self.dummy_sample			)
             a__:       Tuple											=input_np_ad.squeeze(0			)

             a__:       Any											=image_processor.postprocess(
                 image_processor.preprocess(lowerCAmelCase__			)      ,       output_type="np"      ,       )
             a__:       Optional[int]											=image_processor.postprocess(
                 image_processor.preprocess(lowerCAmelCase__			)      ,       output_type="np"      ,       )

             assert np.abs(out_pt_ad - out_pt_ad			).max() < 1e-6
             assert np.abs(out_np_ad - out_np_ad			).max() < 1e-6







      def   _lowerCamelCase			( self						:  List[str]			):
             a__:       Dict											=VaeImageProcessor(do_resize=lowerCAmelCase__      ,       do_normalize=lowerCAmelCase__			)

             a__:       Union[str, Any]											=self.dummy_sample
             a__:       Optional[int]											=list(lowerCAmelCase__			)

             a__:       List[Any]											=image_processor.postprocess(
                 image_processor.preprocess(lowerCAmelCase__			)      ,       output_type="np"      ,       )

             a__:       Tuple											=image_processor.postprocess(
                 image_processor.preprocess(lowerCAmelCase__			)      ,       output_type="np"      ,       )

             a__:       Tuple											=self.to_np(self.dummy_sample			)
             list(lowerCAmelCase__			)

             a__:       Optional[Any]											=image_processor.postprocess(
                 image_processor.preprocess(lowerCAmelCase__			)      ,       output_type="np"      ,       )

             a__:       Any											=image_processor.postprocess(
                 image_processor.preprocess(lowerCAmelCase__			)      ,       output_type="np"      ,       )

             assert np.abs(out_pt_ad - out_pt_list			).max() < 1e-6
             assert np.abs(out_np_ad - out_np_list			).max() < 1e-6










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

__UpperCAmelCase						  =     logging.get_logger(__name__)





def 						__lowerCamelCase       (      __magic_name__	:  bytes     , __magic_name__	:  int						):
							a__:       Optional[int]											=F"{sampling_rate}"
							a__:       Union[str, Any]											="1"
							a__:       List[str]											="f32le"
							a__:       Dict											=[
							    "ffmpeg",
							    "-i",
							    "pipe:0",
							    "-ac",
							    ac,
							    "-ar",
							    ar,
							    "-f",
							    format_for_conversion,
							    "-hide_banner",
							    "-loglevel",
							    "quiet",
							    "pipe:1",
							]

							try:
														a__:       List[Any]											=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"						)
							a__:       int											=ffmpeg_process.communicate(__magic_name__						)
							a__:       List[str]											=output_stream[0]

							a__:       str											=np.frombuffer(__magic_name__     , np.floataa						)
							if audio.shape[0] == 0:
														raise ValueError("Malformed soundfile"						)
							return audio







@add_end_docstrings(_a   )
class 	lowerCamelCase__ (		_a   ):


						def __init__( self						:  Union[str, Any]      ,       *_a						:  Dict      ,       **_a						:  Tuple			):
													# Default, might be overriden by the model.config.
													a__:       List[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						:  Tuple      ,       _a						:  Union[np.ndarray, bytes, str]      ,       **_a						:  Union[str, Any]      ,       ):
													return super().__call__(_a      ,       **_a			)


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


						def   _lowerCamelCase			( self						:  Any      ,       _a						:  Union[str, Any]			):
													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
																											a__:       List[str]											=requests.get(_a			).content
																				else:
																											with open(_a      ,       "rb"			) as f:
																																		a__:       Dict											=f.read()

													if isinstance(_a      ,       _a			):
																				a__:       Tuple											=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"			)

																				a__:       Tuple											=inputs.pop("raw"      ,       _a			)
																				if _inputs is None:
																											# Remove path which will not be used from `datasets`.
																											inputs.pop("path"      ,       _a			)
																											a__:       int											=inputs.pop("array"      ,       _a			)
																				a__:       Optional[Any]											=inputs.pop("sampling_rate"			)
																				a__:       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`."			)

																											a__:       Union[str, Any]											=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"			)

													a__:       Union[str, Any]											=self.feature_extractor(
													    _a      ,       sampling_rate=self.feature_extractor.sampling_rate      ,       return_tensors="pt"			)
													return processed


						def   _lowerCamelCase			( self						:  Dict      ,       _a						:  Tuple			):
													a__:       str											=self.model(**_a			)
													return model_outputs







						def   _lowerCamelCase			( self						:  Optional[Any]      ,       _a						:  Tuple      ,       _a						:  Tuple=5			):
													a__:       Tuple											=model_outputs.logits[0].softmax(-1			)
													a__							,			a__:       Any											=probs.topk(_a			)

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

													a__:       Optional[int]											=[{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_a      ,       _a			)]

													return labels





