

# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class     a__  (    snake_case	):
			"""simple docstring"""







			__lowerCamelCase       				=				42
			__lowerCamelCase       				=				None



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Any		,				SCREAMING_SNAKE_CASE_:							Optional[Any]=0.999		,				SCREAMING_SNAKE_CASE_:							str="cosine"		,				)	->					Optional[int]:




				'''simple docstring'''





				if alpha_transform_type == "cosine":

								def alpha_bar_fn(SCREAMING_SNAKE_CASE_:							Optional[int]					):
												return math.cos((t + 0.008) / 1.008 * math.pi / 2					) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(SCREAMING_SNAKE_CASE_:							List[Any]					):
												return math.exp(t * -12.0					)

				else:
								raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'					)

				A__						=	[]
				for i in range(SCREAMING_SNAKE_CASE_					):
								A__						=	i / num_diffusion_timesteps
								A__						=	(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE_					) / alpha_bar_fn(SCREAMING_SNAKE_CASE_					)		,				SCREAMING_SNAKE_CASE_					)					)
				return torch.tensor(SCREAMING_SNAKE_CASE_		,				dtype=torch.floataa					)





class     a__  (    snake_case							,		snake_case	):
			"""simple docstring"""







			__lowerCamelCase       				=				1


			@register_to_config
			def __init__(			self					,					lowercase = 1000					,					lowercase = 0.0001					,					lowercase = 0.02					,					lowercase = "linear"					,					lowercase = None					,					lowercase = True					,					lowercase = True					,					lowercase = 0					,					lowercase = "epsilon"					,					lowercase = 1.0					,					**lowercase					,					)     ->  Union[str, Any]:
							'''simple docstring'''






							if kwargs.get("set_alpha_to_one"					,					lowercase				) is not None:
											A__						=	(
											    "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
											)
											deprecate("set_alpha_to_one"					,					"1.0.0"					,					lowercase					,					standard_warn=lowercase				)
											A__						=	kwargs["set_alpha_to_one"]
							if trained_betas is not None:
											A__						=	torch.tensor(lowercase					,					dtype=torch.floataa				)
							elif beta_schedule == "linear":
											A__						=	torch.linspace(lowercase					,					lowercase					,					lowercase					,					dtype=torch.floataa				)
							elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											A__						=	(
											    torch.linspace(beta_start**0.5					,					beta_end**0.5					,					lowercase					,					dtype=torch.floataa				) ** 2
											)
							elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											A__						=	betas_for_alpha_bar(lowercase				)
							else:
											raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'				)

							A__						=	1.0 - self.betas
							A__						=	torch.cumprod(self.alphas					,					dim=0				)

							# At every step in inverted ddim, we are looking into the next alphas_cumprod
							# For the final step, there is no next alphas_cumprod, and the index is out of bounds
							# `set_alpha_to_zero` decides whether we set this parameter simply to zero
							# in this case, self.step() just output the predicted noise
							# or whether we use the final alpha of the "non-previous" one.
							A__						=	torch.tensor(0.0				) if set_alpha_to_zero else self.alphas_cumprod[-1]

							# standard deviation of the initial noise distribution
							A__						=	1.0

							# setable values
							A__						=	None
							A__						=	torch.from_numpy(np.arange(0					,					lowercase				).copy().astype(np.intaa				)				)


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase = None				)     ->  torch.FloatTensor:
							'''simple docstring'''






							return sample


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase = None				)     ->  Optional[int]:
							'''simple docstring'''






							if num_inference_steps > self.config.num_train_timesteps:
											raise ValueError(
											    F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
											    F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
											    F' maximal {self.config.num_train_timesteps} timesteps.'				)

							A__						=	num_inference_steps
							A__						=	self.config.num_train_timesteps // self.num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# casting to int to avoid issues when num_inference_step is power of 3
							A__						=	(np.arange(0					,					lowercase				) * step_ratio).round().copy().astype(np.intaa				)
							A__						=	torch.from_numpy(lowercase				).to(lowercase				)
							self.timesteps += self.config.steps_offset


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase					,					lowercase					,					lowercase = 0.0					,					lowercase = False					,					lowercase = None					,					lowercase = True					,					)     ->  Union[DDIMSchedulerOutput, Tuple]:
							'''simple docstring'''






							A__						=	timestep + self.config.num_train_timesteps // self.num_inference_steps

							# 2. compute alphas, betas
							# change original implementation to exactly match noise levels for analogous forward process
							A__						=	self.alphas_cumprod[timestep]
							A__						=	(
							    self.alphas_cumprod[prev_timestep]
							    if prev_timestep < self.config.num_train_timesteps
							    else self.final_alpha_cumprod
							)

							A__						=	1 - alpha_prod_t

							# 3. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							if self.config.prediction_type == "epsilon":
											A__						=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											A__						=	model_output
							elif self.config.prediction_type == "sample":
											A__						=	model_output
											A__						=	(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
							elif self.config.prediction_type == "v_prediction":
											A__						=	(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
											A__						=	(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
							else:
											raise ValueError(
											    F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
											    " `v_prediction`"				)

							# 4. Clip or threshold "predicted x_0"
							if self.config.clip_sample:
											A__						=	pred_original_sample.clamp(
											    -self.config.clip_sample_range					,					self.config.clip_sample_range				)

							# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							A__						=	(1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

							# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							A__						=	alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

							if not return_dict:
											return (prev_sample, pred_original_sample)
							return DDIMSchedulerOutput(prev_sample=lowercase					,					pred_original_sample=lowercase				)


			def __len__(			self				)     ->  Optional[Any]:
							'''simple docstring'''






							return self.config.num_train_timesteps









"""simple docstring"""




import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list






@require_torchaudio
@require_sentencepiece
class        UpperCamelCase			(	unittest.TestCase      ):


 def _lowercase	(     self		:						int							)					->       List[str]:
   _a  :       Any             =	"""laion/clap-htsat-unfused"""
   _a  :       Union[str, Any]             =	tempfile.mkdtemp()


 def _lowercase	(     self		:						List[Any]							,						**UpperCAmelCase__		:						Any							)					->       Dict:
   return RobertaTokenizer.from_pretrained(self.checkpoint							,						**UpperCAmelCase__							)


 def _lowercase	(     self		:						List[Any]							,						**UpperCAmelCase__		:						List[str]							)					->       int:
   return ClapFeatureExtractor.from_pretrained(self.checkpoint							,						**UpperCAmelCase__							)


 def _lowercase	(     self		:						List[Any]							)					->       Tuple:
   shutil.rmtree(self.tmpdirname							)


 def _lowercase	(     self		:						List[str]							)					->       Optional[int]:
   _a  :       List[str]             =	self.get_tokenizer()
   _a  :       Any             =	self.get_feature_extractor()

   _a  :       Optional[Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   processor.save_pretrained(self.tmpdirname							)
   _a  :       List[str]             =	ClapProcessor.from_pretrained(self.tmpdirname							)

   self.assertEqual(processor.tokenizer.get_vocab()							,						tokenizer.get_vocab()							)
   self.assertIsInstance(processor.tokenizer							,						UpperCAmelCase__							)

   self.assertEqual(processor.feature_extractor.to_json_string()							,						feature_extractor.to_json_string()							)
   self.assertIsInstance(processor.feature_extractor							,						UpperCAmelCase__							)


 def _lowercase	(     self		:						Dict							)					->       Optional[int]:
   _a  :       Tuple             =	ClapProcessor(tokenizer=self.get_tokenizer()							,						feature_extractor=self.get_feature_extractor()							)
   processor.save_pretrained(self.tmpdirname							)

   _a  :       Dict             =	self.get_tokenizer(bos_token="""(BOS)"""							,						eos_token="""(EOS)"""							)
   _a  :       Union[str, Any]             =	self.get_feature_extractor(do_normalize=UpperCAmelCase__							,						padding_value=1.0							)

   _a  :       Union[str, Any]             =	ClapProcessor.from_pretrained(
       self.tmpdirname							,						bos_token="""(BOS)"""							,						eos_token="""(EOS)"""							,						do_normalize=UpperCAmelCase__							,						padding_value=1.0							)

   self.assertEqual(processor.tokenizer.get_vocab()							,						tokenizer_add_kwargs.get_vocab()							)
   self.assertIsInstance(processor.tokenizer							,						UpperCAmelCase__							)

   self.assertEqual(processor.feature_extractor.to_json_string()							,						feature_extractor_add_kwargs.to_json_string()							)
   self.assertIsInstance(processor.feature_extractor							,						UpperCAmelCase__							)


 def _lowercase	(     self		:						List[str]							)					->       Optional[Any]:
   _a  :       Optional[int]             =	self.get_feature_extractor()
   _a  :       Tuple             =	self.get_tokenizer()

   _a  :       List[Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   _a  :       Any             =	floats_list((3, 1000)							)

   _a  :       List[Any]             =	feature_extractor(UpperCAmelCase__							,						return_tensors="""np"""							)
   _a  :       List[str]             =	processor(audios=UpperCAmelCase__							,						return_tensors="""np"""							)

   for key in input_feat_extract.keys():
     self.assertAlmostEqual(input_feat_extract[key].sum()							,						input_processor[key].sum()							,						delta=1E-2							)


 def _lowercase	(     self		:						Tuple							)					->       Optional[int]:
   _a  :       List[str]             =	self.get_feature_extractor()
   _a  :       Any             =	self.get_tokenizer()

   _a  :       Any             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   _a  :       Optional[int]             =	"""This is a test string"""

   _a  :       Tuple             =	processor(text=UpperCAmelCase__							)

   _a  :       int             =	tokenizer(UpperCAmelCase__							)

   for key in encoded_tok.keys():
     self.assertListEqual(encoded_tok[key]							,						encoded_processor[key]							)


 def _lowercase	(     self		:						List[Any]							)					->       Any:
   _a  :       str             =	self.get_feature_extractor()
   _a  :       List[str]             =	self.get_tokenizer()

   _a  :       List[Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   _a  :       Any             =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

   _a  :       Dict             =	processor.batch_decode(UpperCAmelCase__							)
   _a  :       Any             =	tokenizer.batch_decode(UpperCAmelCase__							)

   self.assertListEqual(UpperCAmelCase__							,						UpperCAmelCase__							)







 def _lowercase	(     self		:						Dict							)					->       List[str]:
   _a  :       str             =	self.get_feature_extractor()
   _a  :       Optional[Any]             =	self.get_tokenizer()

   _a  :       Union[str, Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   self.assertListEqual(
       processor.model_input_names[2:]							,						feature_extractor.model_input_names							,						msg="""`processor` and `feature_extractor` model input names do not match"""							,						)

