


'''simple docstring'''



from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 							__UpperCamelCase    (		lowerCAmelCase_ ):




	def __init__(       self						,    __a						,    __a						,    __a						,    __a = None						,    ):

						'''simple docstring'''





						super().__init__()
						self.register_modules(transformer=__a						,    vae=__a						,    scheduler=__a							)

						# create a imagenet -> id dictionary for easier use
						__a      :	List[Any]    =  {}
						if idalabel is not None:
											for key, value in idalabel.items():
																for label in value.split(','							):
																					__a      :	str    =  int(__a							)
											__a      :	Union[str, Any]    =  dict(sorted(self.labels.items()							)							)




	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						if not isinstance(__a						,    __a							):
											__a      :	Optional[Any]    =  list(__a							)

						for l in label:
											if l not in self.labels:
																raise ValueError(
																    f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""							)

						return [self.labels[l] for l in label]





	@torch.no_grad()
	def __call__(       self						,    __a						,    __a = 4.0						,    __a = None						,    __a = 50						,    __a = "pil"						,    __a = True						,    ):

						'''simple docstring'''





						__a      :	Optional[int]    =  len(__a							)
						__a      :	str    =  self.transformer.config.sample_size
						__a      :	Optional[int]    =  self.transformer.config.in_channels

						__a      :	Dict    =  randn_tensor(
						    shape=(batch_size, latent_channels, latent_size, latent_size)						,    generator=__a						,    device=self.device						,    dtype=self.transformer.dtype						,    )
						__a      :	Optional[int]    =  torch.cat([latents] * 2							) if guidance_scale > 1 else latents

						__a      :	Optional[Any]    =  torch.tensor(__a						,    device=self.device							).reshape(-1							)
						__a      :	Dict    =  torch.tensor([1000] * batch_size						,    device=self.device							)
						__a      :	Optional[Any]    =  torch.cat([class_labels, class_null]						,    0							) if guidance_scale > 1 else class_labels

						# set step values
						self.scheduler.set_timesteps(__a							)

						for t in self.progress_bar(self.scheduler.timesteps							):
											if guidance_scale > 1:
																__a      :	Tuple    =  latent_model_input[: len(__a							) // 2]
																__a      :	int    =  torch.cat([half, half]						,    dim=0							)
											__a      :	Any    =  self.scheduler.scale_model_input(__a						,    __a							)

											__a      :	Union[str, Any]    =  t
											if not torch.is_tensor(__a							):
																# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																# This would be a good case for the `match` statement (Python 3.10+)
																__a      :	List[str]    =  latent_model_input.device.type == 'mps'
																if isinstance(__a						,    __a							):
																					__a      :	Tuple    =  torch.floataa if is_mps else torch.floataa
																else:
																					__a      :	Optional[int]    =  torch.intaa if is_mps else torch.intaa
																__a      :	List[str]    =  torch.tensor([timesteps]						,    dtype=__a						,    device=latent_model_input.device							)
											elif len(timesteps.shape							) == 0:
																__a      :	Union[str, Any]    =  timesteps[None].to(latent_model_input.device							)
											# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
											__a      :	List[Any]    =  timesteps.expand(latent_model_input.shape[0]							)
											# predict noise model_output
											__a      :	List[str]    =  self.transformer(
											    __a						,    timestep=__a						,    class_labels=__a							).sample

											# perform guidance
											if guidance_scale > 1:
																__a   ,    __a      :	int    =  noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																__a   ,    __a      :	Union[str, Any]    =  torch.split(__a						,    len(__a							) // 2						,    dim=0							)

																__a      :	str    =  uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																__a      :	Tuple    =  torch.cat([half_eps, half_eps]						,    dim=0							)

																__a      :	List[str]    =  torch.cat([eps, rest]						,    dim=1							)

											# learned sigma
											if self.transformer.config.out_channels // 2 == latent_channels:
																__a   ,    __a      :	Optional[Any]    =  torch.split(__a						,    __a						,    dim=1							)
											else:
																__a      :	Optional[int]    =  noise_pred

											# compute previous image: x_t -> x_t-1
											__a      :	List[Any]    =  self.scheduler.step(__a						,    __a						,    __a							).prev_sample

						if guidance_scale > 1:
											__a   ,    __a      :	List[Any]    =  latent_model_input.chunk(2						,    dim=0							)
						else:
											__a      :	Union[str, Any]    =  latent_model_input

						__a      :	int    =  1 / self.vae.config.scaling_factor * latents
						__a      :	str    =  self.vae.decode(__a							).sample

						__a      :	Union[str, Any]    =  (samples / 2 + 0.5).clamp(0						,    1							)

						# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
						__a      :	List[str]    =  samples.cpu().permute(0						,    2						,    3						,    1							).float().numpy()

						if output_type == "pil":
											__a      :	List[str]    =  self.numpy_to_pil(__a							)

						if not return_dict:
											return (samples,)

						return ImagePipelineOutput(images=__a							)







'''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 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  'laion/clap-htsat-unfused'
						__a      :	Optional[Any]    =  tempfile.mkdtemp()




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





						return RobertaTokenizer.from_pretrained(self.checkpoint						,    **__a							)




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





						return ClapFeatureExtractor.from_pretrained(self.checkpoint						,    **__a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						shutil.rmtree(self.tmpdirname							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  self.get_tokenizer()
						__a      :	List[str]    =  self.get_feature_extractor()

						__a      :	Any    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						processor.save_pretrained(self.tmpdirname							)
						__a      :	Tuple    =  ClapProcessor.from_pretrained(self.tmpdirname							)

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

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  ClapProcessor(tokenizer=self.get_tokenizer()						,    feature_extractor=self.get_feature_extractor()							)
						processor.save_pretrained(self.tmpdirname							)

						__a      :	int    =  self.get_tokenizer(bos_token='(BOS)'						,    eos_token='(EOS)'							)
						__a      :	List[str]    =  self.get_feature_extractor(do_normalize=__a						,    padding_value=1.0							)

						__a      :	Tuple    =  ClapProcessor.from_pretrained(
						    self.tmpdirname						,    bos_token='(BOS)'						,    eos_token='(EOS)'						,    do_normalize=__a						,    padding_value=1.0							)

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

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  self.get_feature_extractor()
						__a      :	int    =  self.get_tokenizer()

						__a      :	str    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						__a      :	int    =  floats_list((3, 1000)							)

						__a      :	str    =  feature_extractor(__a						,    return_tensors='np'							)
						__a      :	int    =  processor(audios=__a						,    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 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  self.get_feature_extractor()
						__a      :	Any    =  self.get_tokenizer()

						__a      :	Any    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						__a      :	Union[str, Any]    =  'This is a test string'

						__a      :	Union[str, Any]    =  processor(text=__a							)

						__a      :	Tuple    =  tokenizer(__a							)

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  self.get_feature_extractor()
						__a      :	str    =  self.get_tokenizer()

						__a      :	List[str]    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						__a      :	Dict    =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						__a      :	Optional[int]    =  processor.batch_decode(__a							)
						__a      :	Optional[Any]    =  tokenizer.batch_decode(__a							)

						self.assertListEqual(__a						,    __a							)





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  self.get_feature_extractor()
						__a      :	Optional[int]    =  self.get_tokenizer()

						__a      :	int    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

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



