






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class 				_UpperCamelCase      (a_      ):
      snake_case_	=				(DDPMScheduler,)


      def 			__UpperCAmelCase		(						self       ,    **__UpperCamelCase						)->						List[str]:
           __lowerCAmelCase             =       {
               "num_train_timesteps": 1_0_0_0,
               "beta_start": 0.0_0_0_1,
               "beta_end": 0.0_2,
               "beta_schedule": "linear",
               "variance_type": "fixed_small",
               "clip_sample": True,
           }

           config.update(**__UpperCamelCase						)
           return config


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
                self.check_over_configs(num_train_timesteps=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]       ,    [0.0_0_2, 0.0_2, 0.2, 2]						):
                self.check_over_configs(beta_start=__UpperCamelCase       ,    beta_end=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Tuple:
           for schedule in ["linear", "squaredcos_cap_v2"]:
                self.check_over_configs(beta_schedule=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Optional[Any]:
           for variance in ["fixed_small", "fixed_large", "other"]:
                self.check_over_configs(variance_type=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						str:
           for clip_sample in [True, False]:
                self.check_over_configs(clip_sample=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           self.check_over_configs(thresholding=__UpperCamelCase						)
           for threshold in [0.5, 1.0, 2.0]:
                for prediction_type in ["epsilon", "sample", "v_prediction"]:
                     self.check_over_configs(
                         thresholding=__UpperCamelCase       ,    prediction_type=__UpperCamelCase       ,    sample_max_value=__UpperCamelCase       ,    )


      def 			__UpperCAmelCase		(						self						)->						str:
           for prediction_type in ["epsilon", "sample", "v_prediction"]:
                self.check_over_configs(prediction_type=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           for t in [0, 5_0_0, 9_9_9]:
                self.check_over_forward(time_step=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Tuple:
           __lowerCAmelCase             =       self.scheduler_classes[0]
           __lowerCAmelCase             =       self.get_scheduler_config()
           __lowerCAmelCase             =       scheduler_class(**__UpperCamelCase						)

           assert torch.sum(torch.abs(scheduler._get_variance(0						) - 0.0						)						) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(4_8_7						) - 0.0_0_9_7_9						)						) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(9_9_9						) - 0.0_2						)						) < 1e-5


      def 			__UpperCAmelCase		(						self						)->						int:
           __lowerCAmelCase             =       self.scheduler_classes[0]
           __lowerCAmelCase             =       self.get_scheduler_config()
           __lowerCAmelCase             =       scheduler_class(**__UpperCamelCase						)

           __lowerCAmelCase             =       len(__UpperCamelCase						)

           __lowerCAmelCase             =       self.dummy_model()
           __lowerCAmelCase             =       self.dummy_sample_deter
           __lowerCAmelCase             =       torch.manual_seed(0						)

           for t in reversed(range(__UpperCamelCase						)						):
                # 1. predict noise residual
                __lowerCAmelCase             =       model(__UpperCamelCase       ,    __UpperCamelCase						)

                # 2. predict previous mean of sample x_t-1
                __lowerCAmelCase             =       scheduler.step(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    generator=__UpperCamelCase						).prev_sample

                # if t > 0:
                #     noise = self.dummy_sample_deter
                #     variance = scheduler.get_variance(t) ** (0.5) * noise
                #
                # sample = pred_prev_sample + variance
                __lowerCAmelCase             =       pred_prev_sample

           __lowerCAmelCase             =       torch.sum(torch.abs(__UpperCamelCase						)						)
           __lowerCAmelCase             =       torch.mean(torch.abs(__UpperCamelCase						)						)

           assert abs(result_sum.item() - 2_5_8.9_6_0_6						) < 1e-2
           assert abs(result_mean.item() - 0.3_3_7_2						) < 1e-3


      def 			__UpperCAmelCase		(						self						)->						List[str]:
           __lowerCAmelCase             =       self.scheduler_classes[0]
           __lowerCAmelCase             =       self.get_scheduler_config(prediction_type="v_prediction"						)
           __lowerCAmelCase             =       scheduler_class(**__UpperCamelCase						)

           __lowerCAmelCase             =       len(__UpperCamelCase						)

           __lowerCAmelCase             =       self.dummy_model()
           __lowerCAmelCase             =       self.dummy_sample_deter
           __lowerCAmelCase             =       torch.manual_seed(0						)

           for t in reversed(range(__UpperCamelCase						)						):
                # 1. predict noise residual
                __lowerCAmelCase             =       model(__UpperCamelCase       ,    __UpperCamelCase						)

                # 2. predict previous mean of sample x_t-1
                __lowerCAmelCase             =       scheduler.step(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    generator=__UpperCamelCase						).prev_sample

                # if t > 0:
                #     noise = self.dummy_sample_deter
                #     variance = scheduler.get_variance(t) ** (0.5) * noise
                #
                # sample = pred_prev_sample + variance
                __lowerCAmelCase             =       pred_prev_sample

           __lowerCAmelCase             =       torch.sum(torch.abs(__UpperCamelCase						)						)
           __lowerCAmelCase             =       torch.mean(torch.abs(__UpperCamelCase						)						)

           assert abs(result_sum.item() - 2_0_2.0_2_9_6						) < 1e-2
           assert abs(result_mean.item() - 0.2_6_3_1						) < 1e-3


      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           __lowerCAmelCase             =       self.scheduler_classes[0]
           __lowerCAmelCase             =       self.get_scheduler_config()
           __lowerCAmelCase             =       scheduler_class(**__UpperCamelCase						)

           __lowerCAmelCase             =       [1_0_0, 8_7, 5_0, 1, 0]

           scheduler.set_timesteps(timesteps=__UpperCamelCase						)

           __lowerCAmelCase             =       scheduler.timesteps

           for i, timestep in enumerate(__UpperCamelCase						):
                if i == len(__UpperCamelCase						) - 1:
                     __lowerCAmelCase             =       -1
                else:
                     __lowerCAmelCase             =       timesteps[i + 1]

                __lowerCAmelCase             =       scheduler.previous_timestep(__UpperCamelCase						)
                __lowerCAmelCase             =       prev_t.item()

                self.assertEqual(__UpperCamelCase       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Optional[Any]:
           __lowerCAmelCase             =       self.scheduler_classes[0]
           __lowerCAmelCase             =       self.get_scheduler_config()
           __lowerCAmelCase             =       scheduler_class(**__UpperCamelCase						)

           __lowerCAmelCase             =       [1_0_0, 8_7, 5_0, 5_1, 0]

           with self.assertRaises(__UpperCamelCase       ,    msg="`custom_timesteps` must be in descending order."						):
                scheduler.set_timesteps(timesteps=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						int:
           __lowerCAmelCase             =       self.scheduler_classes[0]
           __lowerCAmelCase             =       self.get_scheduler_config()
           __lowerCAmelCase             =       scheduler_class(**__UpperCamelCase						)

           __lowerCAmelCase             =       [1_0_0, 8_7, 5_0, 1, 0]
           __lowerCAmelCase             =       len(__UpperCamelCase						)

           with self.assertRaises(__UpperCamelCase       ,    msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."						):
                scheduler.set_timesteps(num_inference_steps=__UpperCamelCase       ,    timesteps=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Any:
           __lowerCAmelCase             =       self.scheduler_classes[0]
           __lowerCAmelCase             =       self.get_scheduler_config()
           __lowerCAmelCase             =       scheduler_class(**__UpperCamelCase						)

           __lowerCAmelCase             =       [scheduler.config.num_train_timesteps]

           with self.assertRaises(
               __UpperCamelCase       ,    msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"       ,    ):
                scheduler.set_timesteps(timesteps=__UpperCamelCase						)














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						)->						Optional[int]:
           __lowerCAmelCase             =       "laion/clap-htsat-unfused"
           __lowerCAmelCase             =       tempfile.mkdtemp()


      def 			__UpperCAmelCase		(						self       ,    **__UpperCamelCase						)->						Optional[int]:
           return RobertaTokenizer.from_pretrained(self.checkpoint       ,    **__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self       ,    **__UpperCamelCase						)->						List[Any]:
           return ClapFeatureExtractor.from_pretrained(self.checkpoint       ,    **__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           shutil.rmtree(self.tmpdirname						)


      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.get_tokenizer()
           __lowerCAmelCase             =       self.get_feature_extractor()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

           processor.save_pretrained(self.tmpdirname						)
           __lowerCAmelCase             =       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 			__UpperCAmelCase		(						self						)->						List[str]:
           __lowerCAmelCase             =       ClapProcessor(tokenizer=self.get_tokenizer()       ,    feature_extractor=self.get_feature_extractor()						)
           processor.save_pretrained(self.tmpdirname						)

           __lowerCAmelCase             =       self.get_tokenizer(bos_token="(BOS)"       ,    eos_token="(EOS)"						)
           __lowerCAmelCase             =       self.get_feature_extractor(do_normalize=__UpperCamelCase       ,    padding_value=1.0						)

           __lowerCAmelCase             =       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 			__UpperCAmelCase		(						self						)->						List[str]:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

           __lowerCAmelCase             =       floats_list((3, 1_0_0_0)						)

           __lowerCAmelCase             =       feature_extractor(__UpperCamelCase       ,    return_tensors="np"						)
           __lowerCAmelCase             =       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 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

           __lowerCAmelCase             =       "This is a test string"

           __lowerCAmelCase             =       processor(text=__UpperCamelCase						)

           __lowerCAmelCase             =       tokenizer(__UpperCamelCase						)

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


      def 			__UpperCAmelCase		(						self						)->						int:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

           __lowerCAmelCase             =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           __lowerCAmelCase             =       processor.batch_decode(__UpperCamelCase						)
           __lowerCAmelCase             =       tokenizer.batch_decode(__UpperCamelCase						)

           self.assertListEqual(__UpperCamelCase       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       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"       ,    )






