






'''simple docstring'''

import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     int   		=			(KDPMaDiscreteScheduler,)
       snake_case__    :     Dict   		=			1_0




       def 					a_					(					self      : str	,    **A__      : str					):


            """simple docstring"""







            __lowerCamelCase						:       int				   =				{
                """num_train_timesteps""": 1100,
                """beta_start""": 0.0001,
                """beta_end""": 0.02,
                """beta_schedule""": """linear""",
            }

            config.update(**A__					)
            return config




       def 					a_					(					self      : Any					):


            """simple docstring"""







            for timesteps in [10, 50, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=A__					)




       def 					a_					(					self      : Tuple					):


            """simple docstring"""







            for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,    [0.0002, 0.002, 0.02]					):
                 self.check_over_configs(beta_start=A__	,    beta_end=A__					)




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







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




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







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




       def 					a_					(					self      : Dict					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				self.scheduler_classes[0]
            __lowerCamelCase						:       Union[str, Any]				   =				self.get_scheduler_config(prediction_type="""v_prediction"""					)
            __lowerCamelCase						:       List[str]				   =				scheduler_class(**A__					)

            scheduler.set_timesteps(self.num_inference_steps					)

            __lowerCamelCase						:       Union[str, Any]				   =				self.dummy_model()
            __lowerCamelCase						:       Any				   =				self.dummy_sample_deter * scheduler.init_noise_sigma
            __lowerCamelCase						:       Optional[int]				   =				sample.to(A__					)

            for i, t in enumerate(scheduler.timesteps					):
                 __lowerCamelCase						:       List[Any]				   =				scheduler.scale_model_input(A__	,    A__					)

                 __lowerCamelCase						:       str				   =				model(A__	,    A__					)

                 __lowerCamelCase						:       Optional[Any]				   =				scheduler.step(A__	,    A__	,    A__					)
                 __lowerCamelCase						:       List[Any]				   =				output.prev_sample

            __lowerCamelCase						:       List[str]				   =				torch.sum(torch.abs(A__					)					)
            __lowerCamelCase						:       Optional[int]				   =				torch.mean(torch.abs(A__					)					)

            if torch_device in ["cpu", "mps"]:
                 assert abs(result_sum.item() - 4.6_9_3_4e-0_7					) < 1e-2
                 assert abs(result_mean.item() - 6.1_1_1_2e-1_0					) < 1e-3
            else:
                 # CUDA
                 assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2e-0_7					) < 1e-2
                 assert abs(result_mean.item() - 0.0002					) < 1e-3




       def 					a_					(					self      : Optional[Any]					):


            """simple docstring"""







            if torch_device == "mps":
                 return
            __lowerCamelCase						:       Tuple				   =				self.scheduler_classes[0]
            __lowerCamelCase						:       Union[str, Any]				   =				self.get_scheduler_config()
            __lowerCamelCase						:       Any				   =				scheduler_class(**A__					)

            scheduler.set_timesteps(self.num_inference_steps					)

            __lowerCamelCase						:       List[str]				   =				self.dummy_model()
            __lowerCamelCase						:       List[Any]				   =				self.dummy_sample_deter * scheduler.init_noise_sigma
            __lowerCamelCase						:       List[str]				   =				sample.to(A__					)

            for i, t in enumerate(scheduler.timesteps					):
                 __lowerCamelCase						:       Union[str, Any]				   =				scheduler.scale_model_input(A__	,    A__					)

                 __lowerCamelCase						:       str				   =				model(A__	,    A__					)

                 __lowerCamelCase						:       Optional[int]				   =				scheduler.step(A__	,    A__	,    A__					)
                 __lowerCamelCase						:       str				   =				output.prev_sample

            __lowerCamelCase						:       int				   =				torch.sum(torch.abs(A__					)					)
            __lowerCamelCase						:       Any				   =				torch.mean(torch.abs(A__					)					)

            if torch_device in ["cpu", "mps"]:
                 assert abs(result_sum.item() - 20.4125					) < 1e-2
                 assert abs(result_mean.item() - 0.0266					) < 1e-3
            else:
                 # CUDA
                 assert abs(result_sum.item() - 20.4125					) < 1e-2
                 assert abs(result_mean.item() - 0.0266					) < 1e-3




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            if torch_device == "mps":
                 return
            __lowerCamelCase						:       Tuple				   =				self.scheduler_classes[0]
            __lowerCamelCase						:       Union[str, Any]				   =				self.get_scheduler_config()
            __lowerCamelCase						:       Any				   =				scheduler_class(**A__					)

            scheduler.set_timesteps(self.num_inference_steps	,    device=A__					)

            __lowerCamelCase						:       Union[str, Any]				   =				self.dummy_model()
            __lowerCamelCase						:       int				   =				self.dummy_sample_deter.to(A__					) * scheduler.init_noise_sigma

            for t in scheduler.timesteps:
                 __lowerCamelCase						:       Union[str, Any]				   =				scheduler.scale_model_input(A__	,    A__					)

                 __lowerCamelCase						:       Optional[Any]				   =				model(A__	,    A__					)

                 __lowerCamelCase						:       List[Any]				   =				scheduler.step(A__	,    A__	,    A__					)
                 __lowerCamelCase						:       Union[str, Any]				   =				output.prev_sample

            __lowerCamelCase						:       Optional[Any]				   =				torch.sum(torch.abs(A__					)					)
            __lowerCamelCase						:       Any				   =				torch.mean(torch.abs(A__					)					)

            if str(A__					).startswith("""cpu"""					):
                 # The following sum varies between 148 and 156 on mps. Why?
                 assert abs(result_sum.item() - 20.4125					) < 1e-2
                 assert abs(result_mean.item() - 0.0266					) < 1e-3
            else:
                 # CUDA
                 assert abs(result_sum.item() - 20.4125					) < 1e-2
                 assert abs(result_mean.item() - 0.0266					) < 1e-3











'''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 	SCREAMING_SNAKE_CASE	(      unittest.TestCase  ):




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				"""laion/clap-htsat-unfused"""
            __lowerCamelCase						:       List[Any]				   =				tempfile.mkdtemp()




       def 					a_					(					self      : Optional[int]	,    **A__      : Union[str, Any]					):


            """simple docstring"""







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




       def 					a_					(					self      : str	,    **A__      : Any					):


            """simple docstring"""







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




       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            shutil.rmtree(self.tmpdirname					)




       def 					a_					(					self      : str					):


            """simple docstring"""







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

            __lowerCamelCase						:       Dict				   =				ClapProcessor(tokenizer=A__	,    feature_extractor=A__					)

            processor.save_pretrained(self.tmpdirname					)
            __lowerCamelCase						:       Optional[Any]				   =				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 					a_					(					self      : str					):


            """simple docstring"""







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

            __lowerCamelCase						:       Tuple				   =				self.get_tokenizer(bos_token="""(BOS)"""	,    eos_token="""(EOS)"""					)
            __lowerCamelCase						:       Any				   =				self.get_feature_extractor(do_normalize=A__	,    padding_value=1.0					)

            __lowerCamelCase						:       List[Any]				   =				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 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				self.get_feature_extractor()
            __lowerCamelCase						:       Tuple				   =				self.get_tokenizer()

            __lowerCamelCase						:       Optional[int]				   =				ClapProcessor(tokenizer=A__	,    feature_extractor=A__					)

            __lowerCamelCase						:       Dict				   =				floats_list((3, 1000)					)

            __lowerCamelCase						:       str				   =				feature_extractor(A__	,    return_tensors="""np"""					)
            __lowerCamelCase						:       Any				   =				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 					a_					(					self      : Any					):


            """simple docstring"""







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

            __lowerCamelCase						:       str				   =				ClapProcessor(tokenizer=A__	,    feature_extractor=A__					)

            __lowerCamelCase						:       int				   =				"""This is a test string"""

            __lowerCamelCase						:       Any				   =				processor(text=A__					)

            __lowerCamelCase						:       Any				   =				tokenizer(A__					)

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




       def 					a_					(					self      : List[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				self.get_feature_extractor()
            __lowerCamelCase						:       List[str]				   =				self.get_tokenizer()

            __lowerCamelCase						:       int				   =				ClapProcessor(tokenizer=A__	,    feature_extractor=A__					)

            __lowerCamelCase						:       Union[str, Any]				   =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            __lowerCamelCase						:       int				   =				processor.batch_decode(A__					)
            __lowerCamelCase						:       List[Any]				   =				tokenizer.batch_decode(A__					)

            self.assertListEqual(A__	,    A__					)




       def 					a_					(					self      : str					):


            """simple docstring"""







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

            __lowerCamelCase						:       Any				   =				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"""	,    )



