


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


lowerCAmelCase       		=    logging.get_logger(__name__)




@add_end_docstrings(_UpperCamelCase )
class 							lowerCamelCase   (  _UpperCamelCase ):


	def __init__(    self				,     **lowercase__):
							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__):
							return super().__call__(lowercase__				,     **lowercase__)


	def  A(    self				,     **lowercase__):
							__UpperCAmelCase			:							Optional[int]	    =	{}
							if "candidate_labels" in kwargs:
													__UpperCAmelCase			:							List[str]	    =	kwargs['''candidate_labels''']
							if "hypothesis_template" in kwargs:
													__UpperCAmelCase			:							Union[str, Any]	    =	kwargs['''hypothesis_template''']

							return preprocess_params, {}, {}


	def  A(    self				,     lowercase__				,     lowercase__=None				,     lowercase__="This is a sound of {}."):
							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
																			__UpperCAmelCase			:							Any	    =	requests.get(lowercase__).content
													else:
																			with open(lowercase__				,     '''rb''') as f:
																									__UpperCAmelCase			:							Union[str, Any]	    =	f.read()

							if isinstance(lowercase__				,     lowercase__):
													__UpperCAmelCase			:							Any	    =	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''')

							__UpperCAmelCase			:							str	    =	self.feature_extractor(
							    [audio]				,     sampling_rate=self.feature_extractor.sampling_rate				,     return_tensors='''pt''')
							__UpperCAmelCase			:							Tuple	    =	candidate_labels
							__UpperCAmelCase			:							str	    =	[hypothesis_template.format(lowercase__) for x in candidate_labels]
							__UpperCAmelCase			:							Optional[int]	    =	self.tokenizer(lowercase__				,     return_tensors=self.framework				,     padding=lowercase__)
							__UpperCAmelCase			:							Optional[Any]	    =	[text_inputs]
							return inputs


	def  A(    self				,     lowercase__):
							__UpperCAmelCase			:							List[str]	    =	model_inputs.pop('''candidate_labels''')
							__UpperCAmelCase			:							str	    =	model_inputs.pop('''text_inputs''')
							if isinstance(text_inputs[0]				,     lowercase__):
													__UpperCAmelCase			:							Dict	    =	text_inputs[0]
							else:
													# Batching case.
													__UpperCAmelCase			:							Any	    =	text_inputs[0][0]

							__UpperCAmelCase			:							Any	    =	self.model(**lowercase__				,     **lowercase__)

							__UpperCAmelCase			:							Dict	    =	{
							    '''candidate_labels''': candidate_labels,
							    '''logits''': outputs.logits_per_audio,
							}
							return model_outputs


	def  A(    self				,     lowercase__):
							__UpperCAmelCase			:							str	    =	model_outputs.pop('''candidate_labels''')
							__UpperCAmelCase			:							Tuple	    =	model_outputs['''logits'''][0]

							if self.framework == "pt":
													__UpperCAmelCase			:							Dict	    =	logits.softmax(dim=0)
													__UpperCAmelCase			:							Union[str, Any]	    =	probs.tolist()
							else:
													raise ValueError('''`tf` framework not supported.''')

							__UpperCAmelCase			:							Dict	    =	[
							    {'''score''': score, '''label''': candidate_label}
							    for score, candidate_label in sorted(zip(lowercase__				,     lowercase__)				,     key=lambda lowercase__: -x[0])
							]
							return result





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin




@dataclass
class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		torch.FloatTensor
	_lowerCAmelCase     :		torch.FloatTensor
	_lowerCAmelCase     :		Optional[torch.FloatTensor]										=						None






class 							lowerCamelCase   (  _UpperCamelCase							,						_UpperCamelCase ):
	_lowerCAmelCase     :		Tuple										=						2


	@register_to_config
	def __init__(    self				,     lowercase__ = 0.0_2				,     lowercase__ = 1_0_0				,     lowercase__ = 1.0_0_7				,     lowercase__ = 8_0				,     lowercase__ = 0.0_5				,     lowercase__ = 5_0				,     ):
							# standard deviation of the initial noise distribution
							__UpperCAmelCase			:							Union[str, Any]	    =	sigma_max

							# setable values
							__UpperCAmelCase			:							int	    =	None
							__UpperCAmelCase			:							np.IntTensor	    =	None
							__UpperCAmelCase			:							torch.FloatTensor	    =	None  # sigma(t_i)


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							return sample


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							__UpperCAmelCase			:							int	    =	num_inference_steps
							__UpperCAmelCase			:							List[str]	    =	np.arange(0				,     self.num_inference_steps)[::-1].copy()
							__UpperCAmelCase			:							Any	    =	torch.from_numpy(lowercase__).to(lowercase__)
							__UpperCAmelCase			:							Union[str, Any]	    =	[
							    (
							        self.config.sigma_max**2
							        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
							    )
							    for i in self.timesteps
							]
							__UpperCAmelCase			:							Tuple	    =	torch.tensor(lowercase__				,     dtype=torch.floataa				,     device=lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__ = None):
							if self.config.s_min <= sigma <= self.config.s_max:
													__UpperCAmelCase			:							int	    =	min(self.config.s_churn / self.num_inference_steps				,     2**0.5 - 1)
							else:
													__UpperCAmelCase			:							int	    =	0

							# sample eps ~ N(0, S_noise^2 * I)
							__UpperCAmelCase			:							List[str]	    =	self.config.s_noise * randn_tensor(sample.shape				,     generator=lowercase__).to(sample.device)
							__UpperCAmelCase			:							Optional[int]	    =	sigma + gamma * sigma
							__UpperCAmelCase			:							List[Any]	    =	sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

							return sample_hat, sigma_hat


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = True				,     ):
							__UpperCAmelCase			:							str	    =	sample_hat + sigma_hat * model_output
							__UpperCAmelCase			:							Tuple	    =	(sample_hat - pred_original_sample) / sigma_hat
							__UpperCAmelCase			:							str	    =	sample_hat + (sigma_prev - sigma_hat) * derivative

							if not return_dict:
													return (sample_prev, derivative)

							return KarrasVeOutput(
							    prev_sample=lowercase__				,     derivative=lowercase__				,     pred_original_sample=lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = True				,     ):
							__UpperCAmelCase			:							Any	    =	sample_prev + sigma_prev * model_output
							__UpperCAmelCase			:							List[str]	    =	(sample_prev - pred_original_sample) / sigma_prev
							__UpperCAmelCase			:							Union[str, Any]	    =	sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

							if not return_dict:
													return (sample_prev, derivative)

							return KarrasVeOutput(
							    prev_sample=lowercase__				,     derivative=lowercase__				,     pred_original_sample=lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__):
							raise NotImplementedError()

