



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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin




@flax.struct.dataclass
class 					snake_case__						:






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	None
     _SCREAMING_SNAKE_CASE						=	None
     _SCREAMING_SNAKE_CASE						=	None  # sigma(t_i)






     @classmethod
     def    lowercase_   (       cls  :  List[str]					)   ->Optional[Any]:
            return cls()




@dataclass
class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	42
     _SCREAMING_SNAKE_CASE						=	42
     _SCREAMING_SNAKE_CASE						=	42




class 					snake_case__						(       lowerCAmelCase_				,     lowerCAmelCase_     ):






     """simple docstring"""


     @property
     def    lowercase_   (       self  :  List[Any]					)   ->List[str]:
            return True






     @register_to_config
     def __init__(       self  :  Tuple, _snake_case  :  float = 0.0_2, _snake_case  :  float = 1_0_0, _snake_case  :  float = 1.0_0_7, _snake_case  :  float = 8_0, _snake_case  :  float = 0.0_5, _snake_case  :  float = 5_0, )   ->List[Any]:
            pass






     def    lowercase_   (       self  :  Union[str, Any]					)   ->str:
            return KarrasVeSchedulerState.create()






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  KarrasVeSchedulerState, _snake_case  :  int, _snake_case  :  Tuple = ()					)   ->KarrasVeSchedulerState:
            snake_case__							:	Tuple   	=	jnp.arange(0, _snake_case					)[::-1].copy()
            snake_case__							:	List[str]   	=	[
                (
                    self.config.sigma_max**2
                    * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
                )
                for i in timesteps
            ]

            return state.replace(
                num_inference_steps=_snake_case, schedule=jnp.array(_snake_case, dtype=jnp.floataa					), timesteps=_snake_case, )






     def    lowercase_   (       self  :  Dict, _snake_case  :  KarrasVeSchedulerState, _snake_case  :  jnp.ndarray, _snake_case  :  float, _snake_case  :  random.KeyArray, )   ->Tuple[jnp.ndarray, float]:
            if self.config.s_min <= sigma <= self.config.s_max:
                   snake_case__							:	Optional[int]   	=	min(self.config.s_churn / state.num_inference_steps, 2**0.5 - 1					)
            else:
                   snake_case__							:	Any   	=	0

            # sample eps ~ N(0, S_noise^2 * I)
            snake_case__							:	List[Any]   	=	random.split(_snake_case, num=1					)
            snake_case__							:	List[str]   	=	self.config.s_noise * random.normal(key=_snake_case, shape=sample.shape					)
            snake_case__							:	Optional[Any]   	=	sigma + gamma * sigma
            snake_case__							:	Optional[Any]   	=	sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

            return sample_hat, sigma_hat






     def    lowercase_   (       self  :  Dict, _snake_case  :  KarrasVeSchedulerState, _snake_case  :  jnp.ndarray, _snake_case  :  float, _snake_case  :  float, _snake_case  :  jnp.ndarray, _snake_case  :  bool = True, )   ->Union[FlaxKarrasVeOutput, Tuple]:
            snake_case__							:	int   	=	sample_hat + sigma_hat * model_output
            snake_case__							:	List[str]   	=	(sample_hat - pred_original_sample) / sigma_hat
            snake_case__							:	int   	=	sample_hat + (sigma_prev - sigma_hat) * derivative

            if not return_dict:
                   return (sample_prev, derivative, state)

            return FlaxKarrasVeOutput(prev_sample=_snake_case, derivative=_snake_case, state=_snake_case					)






     def    lowercase_   (       self  :  List[Any], _snake_case  :  KarrasVeSchedulerState, _snake_case  :  jnp.ndarray, _snake_case  :  float, _snake_case  :  float, _snake_case  :  jnp.ndarray, _snake_case  :  jnp.ndarray, _snake_case  :  jnp.ndarray, _snake_case  :  bool = True, )   ->Union[FlaxKarrasVeOutput, Tuple]:
            snake_case__							:	Optional[Any]   	=	sample_prev + sigma_prev * model_output
            snake_case__							:	str   	=	(sample_prev - pred_original_sample) / sigma_prev
            snake_case__							:	int   	=	sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

            if not return_dict:
                   return (sample_prev, derivative, state)

            return FlaxKarrasVeOutput(prev_sample=_snake_case, derivative=_snake_case, state=_snake_case					)






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  KarrasVeSchedulerState, _snake_case  :  Union[str, Any], _snake_case  :  Union[str, Any], _snake_case  :  Any					)   ->Union[str, Any]:
            raise NotImplementedError()











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


a_   :List[Any]        =      logging.get_logger(__name__)




@add_end_docstrings(lowerCAmelCase_     )
class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     def __init__(       self  :  Optional[Any], **_snake_case  :  str					)   ->Dict:
            super().__init__(**_snake_case					)

            if self.framework != "pt":
                   raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''					)
            # No specific FOR_XXX available yet






     def __call__(       self  :  Union[str, Any], _snake_case  :  Union[np.ndarray, bytes, str], **_snake_case  :  Tuple					)   ->Dict:
            return super().__call__(_snake_case, **_snake_case					)






     def    lowercase_   (       self  :  Tuple, **_snake_case  :  Any					)   ->Union[str, Any]:
            snake_case__							:	str   	=	{}
            if "candidate_labels" in kwargs:
                   snake_case__							:	str   	=	kwargs['candidate_labels']
            if "hypothesis_template" in kwargs:
                   snake_case__							:	str   	=	kwargs['hypothesis_template']

            return preprocess_params, {}, {}






     def    lowercase_   (       self  :  Dict, _snake_case  :  str, _snake_case  :  Optional[int]=None, _snake_case  :  List[str]="This is a sound of {}."					)   ->int:
            if isinstance(_snake_case, _snake_case					):
                   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
                          snake_case__							:	List[Any]   	=	requests.get(_snake_case					).content
                   else:
                          with open(_snake_case, 'rb'					) as f:
                                 snake_case__							:	Union[str, Any]   	=	f.read()

            if isinstance(_snake_case, _snake_case					):
                   snake_case__							:	List[Any]   	=	ffmpeg_read(_snake_case, self.feature_extractor.sampling_rate					)

            if not isinstance(_snake_case, 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'					)

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






     def    lowercase_   (       self  :  Optional[int], _snake_case  :  Optional[Any]					)   ->int:
            snake_case__							:	Optional[int]   	=	model_inputs.pop('candidate_labels'					)
            snake_case__							:	str   	=	model_inputs.pop('text_inputs'					)
            if isinstance(text_inputs[0], _snake_case					):
                   snake_case__							:	Optional[Any]   	=	text_inputs[0]
            else:
                   # Batching case.
                   snake_case__							:	int   	=	text_inputs[0][0]

            snake_case__							:	Any   	=	self.model(**_snake_case, **_snake_case					)

            snake_case__							:	List[Any]   	=	{
                'candidate_labels': candidate_labels,
                'logits': outputs.logits_per_audio,
            }
            return model_outputs






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  str					)   ->List[str]:
            snake_case__							:	int   	=	model_outputs.pop('candidate_labels'					)
            snake_case__							:	List[Any]   	=	model_outputs['logits'][0]

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

            snake_case__							:	Union[str, Any]   	=	[
                {'score': score, 'label': candidate_label}
                for score, candidate_label in sorted(zip(_snake_case, _snake_case					), key=lambda _snake_case					: -x[0]					)
            ]
            return result






