
'''simple docstring'''



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: Tuple										=  (DDPMScheduler,)




						def    _lowercase      (   self       ,  **_lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					{
												    """num_train_timesteps""": 1_000,
												    """beta_start""": 0.0001,
												    """beta_end""": 0.02,
												    """beta_schedule""": """linear""",
												    """variance_type""": """fixed_small""",
												    """clip_sample""": True,
												}

												config.update(**_lowercase )
												return config




						def    _lowercase      (   self ):

												"""simple docstring"""



												for timesteps in [1, 5, 100, 1_000]:
																		self.check_over_configs(num_train_timesteps=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]       ,  [0.002, 0.02, 0.2, 2] ):
																		self.check_over_configs(beta_start=_lowercase       ,  beta_end=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



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




						def    _lowercase      (   self ):

												"""simple docstring"""



												for variance in ["fixed_small", "fixed_large", "other"]:
																		self.check_over_configs(variance_type=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



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




						def    _lowercase      (   self ):

												"""simple docstring"""



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




						def    _lowercase      (   self ):

												"""simple docstring"""



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




						def    _lowercase      (   self ):

												"""simple docstring"""



												for t in [0, 500, 999]:
																		self.check_over_forward(time_step=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



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

												assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0979 ) ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1e-5




						def    _lowercase      (   self ):

												"""simple docstring"""



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

												_lowerCAmelCase    		=					len(_lowercase )

												_lowerCAmelCase    		=					self.dummy_model()
												_lowerCAmelCase    		=					self.dummy_sample_deter
												_lowerCAmelCase    		=					torch.manual_seed(0 )

												for t in reversed(range(_lowercase ) ):
																		# 1. predict noise residual
																		_lowerCAmelCase    		=					model(_lowercase       ,  _lowercase )

																		# 2. predict previous mean of sample x_t-1
																		_lowerCAmelCase    		=					scheduler.step(_lowercase       ,  _lowercase       ,  _lowercase       ,  generator=_lowercase ).prev_sample

																		# if t > 0:
																		#     noise = self.dummy_sample_deter
																		#     variance = scheduler.get_variance(t) ** (0.5) * noise
																		#
																		# sample = pred_prev_sample + variance
																		_lowerCAmelCase    		=					pred_prev_sample

												_lowerCAmelCase    		=					torch.sum(torch.abs(_lowercase ) )
												_lowerCAmelCase    		=					torch.mean(torch.abs(_lowercase ) )

												assert abs(result_sum.item() - 258.9606 ) < 1e-2
												assert abs(result_mean.item() - 0.3372 ) < 1e-3




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.scheduler_classes[0]
												_lowerCAmelCase    		=					self.get_scheduler_config(prediction_type="""v_prediction""" )
												_lowerCAmelCase    		=					scheduler_class(**_lowercase )

												_lowerCAmelCase    		=					len(_lowercase )

												_lowerCAmelCase    		=					self.dummy_model()
												_lowerCAmelCase    		=					self.dummy_sample_deter
												_lowerCAmelCase    		=					torch.manual_seed(0 )

												for t in reversed(range(_lowercase ) ):
																		# 1. predict noise residual
																		_lowerCAmelCase    		=					model(_lowercase       ,  _lowercase )

																		# 2. predict previous mean of sample x_t-1
																		_lowerCAmelCase    		=					scheduler.step(_lowercase       ,  _lowercase       ,  _lowercase       ,  generator=_lowercase ).prev_sample

																		# if t > 0:
																		#     noise = self.dummy_sample_deter
																		#     variance = scheduler.get_variance(t) ** (0.5) * noise
																		#
																		# sample = pred_prev_sample + variance
																		_lowerCAmelCase    		=					pred_prev_sample

												_lowerCAmelCase    		=					torch.sum(torch.abs(_lowercase ) )
												_lowerCAmelCase    		=					torch.mean(torch.abs(_lowercase ) )

												assert abs(result_sum.item() - 202.0296 ) < 1e-2
												assert abs(result_mean.item() - 0.2631 ) < 1e-3




						def    _lowercase      (   self ):

												"""simple docstring"""



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

												_lowerCAmelCase    		=					[100, 87, 50, 1, 0]

												scheduler.set_timesteps(timesteps=_lowercase )

												_lowerCAmelCase    		=					scheduler.timesteps

												for i, timestep in enumerate(_lowercase ):
																		if i == len(_lowercase ) - 1:
																								_lowerCAmelCase    		=					-1
																		else:
																								_lowerCAmelCase    		=					timesteps[i + 1]

																		_lowerCAmelCase    		=					scheduler.previous_timestep(_lowercase )
																		_lowerCAmelCase    		=					prev_t.item()

																		self.assertEqual(_lowercase       ,  _lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



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

												_lowerCAmelCase    		=					[100, 87, 50, 51, 0]

												with self.assertRaises(_lowercase       ,  msg="""`custom_timesteps` must be in descending order.""" ):
																		scheduler.set_timesteps(timesteps=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



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

												_lowerCAmelCase    		=					[100, 87, 50, 1, 0]
												_lowerCAmelCase    		=					len(_lowercase )

												with self.assertRaises(_lowercase       ,  msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
																		scheduler.set_timesteps(num_inference_steps=_lowercase       ,  timesteps=_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



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

												_lowerCAmelCase    		=					[scheduler.config.num_train_timesteps]

												with self.assertRaises(
												    _lowercase       ,  msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""       ,  ):
																		scheduler.set_timesteps(timesteps=_lowercase )


'''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
