

'''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

UpperCAmelCase							=					logging.get_logger(__name__)

def 			__UpperCamelCase							(		lowercase__  :					bytes,   lowercase__  :					int	):






     '''simple docstring'''





     __lowercase     						=F'''{sampling_rate}'''
     __lowercase     						='1'
     __lowercase     						='f32le'
     __lowercase     						=[
         'ffmpeg',
         '-i',
         'pipe:0',
         '-ac',
         ac,
         '-ar',
         ar,
         '-f',
         format_for_conversion,
         '-hide_banner',
         '-loglevel',
         'quiet',
         'pipe:1',
     ]

     try:
          __lowercase     						=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'	)
     __lowercase     						=ffmpeg_process.communicate(lowercase__	)
     __lowercase     						=output_stream[0]

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







@add_end_docstrings(A				)
class 					lowerCAmelCase       ( A				):







       def __init__(							self     :	List[Any]  , *__lowercase     :	int  , **__lowercase     :	int ):







            """simple docstring"""







            __lowercase     						=5
            super().__init__(*__lowercase  , **__lowercase )

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

            self.check_model_type(__lowercase )







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







            """simple docstring"""







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







       def      snake_case				(							self     :	List[str]  , __lowercase     :	str=None  , **__lowercase     :	Tuple ):







            """simple docstring"""







            __lowercase     						={}
            if top_k is not None:
                 if top_k > self.model.config.num_labels:
                      __lowercase     						=self.model.config.num_labels
                 __lowercase     						=top_k
            return {}, {}, postprocess_params







       def      snake_case				(							self     :	Optional[int]  , __lowercase     :	int ):







            """simple docstring"""







            if isinstance(__lowercase  , __lowercase ):
                 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
                      __lowercase     						=requests.get(__lowercase ).content
                 else:
                      with open(__lowercase  , 'rb' ) as f:
                           __lowercase     						=f.read()

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

            if isinstance(__lowercase  , __lowercase ):
                 # 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' )

                 __lowercase     						=inputs.pop('raw'  , __lowercase )
                 if _inputs is None:
                      # Remove path which will not be used from `datasets`.
                      inputs.pop('path'  , __lowercase )
                      __lowercase     						=inputs.pop('array'  , __lowercase )
                 __lowercase     						=inputs.pop('sampling_rate' )
                 __lowercase     						=_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`.' )

                      __lowercase     						=F.resample(
                          torch.from_numpy(__lowercase )  , __lowercase  , self.feature_extractor.sampling_rate ).numpy()

            if not isinstance(__lowercase  , 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' )

            __lowercase     						=self.feature_extractor(
                __lowercase  , sampling_rate=self.feature_extractor.sampling_rate  , return_tensors='pt' )
            return processed







       def      snake_case				(							self     :	List[Any]  , __lowercase     :	Tuple ):







            """simple docstring"""







            __lowercase     						=self.model(**__lowercase )
            return model_outputs







       def      snake_case				(							self     :	int  , __lowercase     :	Any  , __lowercase     :	Union[str, Any]=5 ):







            """simple docstring"""







            __lowercase     						=model_outputs.logits[0].softmax(-1 )
            __lowercase						,       __lowercase     						=probs.topk(__lowercase )

            __lowercase     						=scores.tolist()
            __lowercase     						=ids.tolist()

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

            return labels





'''simple docstring'''

import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	(DDIMScheduler,)
       lowerCAmelCase_				=	(("eta", 0.0), ("num_inference_steps", 5_0))







       def      snake_case				(							self     :	List[Any]  , **__lowercase     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						={
                'num_train_timesteps': 1000,
                'beta_start': 0.0_0_0_1,
                'beta_end': 0.0_2,
                'beta_schedule': 'linear',
                'clip_sample': True,
            }

            config.update(**__lowercase )
            return config







       def      snake_case				(							self     :	Optional[Any]  , **__lowercase     :	str ):







            """simple docstring"""







            __lowercase     						=self.scheduler_classes[0]
            __lowercase     						=self.get_scheduler_config(**__lowercase )
            __lowercase     						=scheduler_class(**__lowercase )

            __lowercase						,       __lowercase     						=10, 0.0

            __lowercase     						=self.dummy_model()
            __lowercase     						=self.dummy_sample_deter

            scheduler.set_timesteps(__lowercase )

            for t in scheduler.timesteps:
                 __lowercase     						=model(__lowercase  , __lowercase )
                 __lowercase     						=scheduler.step(__lowercase  , __lowercase  , __lowercase  , __lowercase ).prev_sample

            return sample







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            for timesteps in [100, 500, 1000]:
                 self.check_over_configs(num_train_timesteps=__lowercase )







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=__lowercase )

            __lowercase     						=self.scheduler_classes[0]
            __lowercase     						=self.get_scheduler_config(steps_offset=1 )
            __lowercase     						=scheduler_class(**__lowercase )
            scheduler.set_timesteps(5 )
            assert torch.equal(scheduler.timesteps  , torch.LongTensor([801, 601, 401, 201, 1] ) )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]  , [0.0_0_2, 0.0_2, 0.2, 2] ):
                 self.check_over_configs(beta_start=__lowercase  , beta_end=__lowercase )







       def      snake_case				(							self     :	str ):







            """simple docstring"""







            for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=__lowercase )







       def      snake_case				(							self     :	str ):







            """simple docstring"""







            for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=__lowercase )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=__lowercase )







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            for timestep_spacing in ["trailing", "leading"]:
                 self.check_over_configs(timestep_spacing=__lowercase )







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            for rescale_betas_zero_snr in [True, False]:
                 self.check_over_configs(rescale_betas_zero_snr=__lowercase )







       def      snake_case				(							self     :	int ):







            """simple docstring"""







            self.check_over_configs(thresholding=__lowercase )
            for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "v_prediction"]:
                      self.check_over_configs(
                          thresholding=__lowercase  , prediction_type=__lowercase  , sample_max_value=__lowercase  , )







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            for t in [1, 10, 49]:
                 self.check_over_forward(time_step=__lowercase )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            for t, num_inference_steps in zip([1, 10, 50]  , [10, 50, 500] ):
                 self.check_over_forward(time_step=__lowercase  , num_inference_steps=__lowercase )







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            for t, eta in zip([1, 10, 49]  , [0.0, 0.5, 1.0] ):
                 self.check_over_forward(time_step=__lowercase  , eta=__lowercase )







       def      snake_case				(							self     :	Optional[int] ):







            """simple docstring"""







            __lowercase     						=self.scheduler_classes[0]
            __lowercase     						=self.get_scheduler_config()
            __lowercase     						=scheduler_class(**__lowercase )

            assert torch.sum(torch.abs(scheduler._get_variance(0  , 0 ) - 0.0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(420  , 400 ) - 0.1_4_7_7_1 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(980  , 960 ) - 0.3_2_4_6_0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(0  , 0 ) - 0.0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(487  , 486 ) - 0.0_0_9_7_9 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(999  , 998 ) - 0.0_2 ) ) < 1E-5







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            __lowercase     						=self.full_loop()

            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_7_2.0_0_6_7 ) < 1E-2
            assert abs(result_mean.item() - 0.2_2_3_9_6_7 ) < 1E-3







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            __lowercase     						=self.full_loop(prediction_type='v_prediction' )

            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 5_2.5_3_0_2 ) < 1E-2
            assert abs(result_mean.item() - 0.0_6_8_4 ) < 1E-3







       def      snake_case				(							self     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						=self.full_loop(set_alpha_to_one=__lowercase  , beta_start=0.0_1 )
            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_4_9.8_2_9_5 ) < 1E-2
            assert abs(result_mean.item() - 0.1_9_5_1 ) < 1E-3







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            __lowercase     						=self.full_loop(set_alpha_to_one=__lowercase  , beta_start=0.0_1 )
            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_4_9.0_7_8_4 ) < 1E-2
            assert abs(result_mean.item() - 0.1_9_4_1 ) < 1E-3


