





"""simple docstring"""


import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list



class        lowercase_			(       unittest.TestCase							):

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			"<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z".split(" "       )
        snake_case__      							=			dict(zip(__SCREAMING_SNAKE_CASE				,   range(len(__SCREAMING_SNAKE_CASE       )       )       )       )

        snake_case__      							=			{
            "pad_token": "<pad>",
            "unk_token": "<unk>",
            "bos_token": "<s>",
            "eos_token": "</s>",
        }
        snake_case__      							=			{
            "feature_size": 1,
            "padding_value": 0.0,
            "sampling_rate": 1_6000,
            "return_attention_mask": False,
            "do_normalize": True,
        }

        snake_case__      							=			tempfile.mkdtemp()
        snake_case__      							=			os.path.join(self.tmpdirname				,   VOCAB_FILES_NAMES["vocab_file"]       )
        snake_case__      							=			os.path.join(self.tmpdirname				,   __SCREAMING_SNAKE_CASE       )
        with open(self.vocab_file				,   "w"				,   encoding="utf-8"       ) as fp:
         fp.write(json.dumps(__SCREAMING_SNAKE_CASE       ) + "\n"       )

        with open(self.feature_extraction_file				,   "w"				,   encoding="utf-8"       ) as fp:
         fp.write(json.dumps(__SCREAMING_SNAKE_CASE       ) + "\n"       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   **__SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			self.add_kwargs_tokens_map.copy()
        kwargs.update(__SCREAMING_SNAKE_CASE       )
        return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname				,   **__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   **__SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname				,   **__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        shutil.rmtree(self.tmpdirname       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.get_tokenizer()
        snake_case__      							=			self.get_feature_extractor()

        snake_case__      							=			WavaVecaProcessor(tokenizer=__SCREAMING_SNAKE_CASE				,   feature_extractor=__SCREAMING_SNAKE_CASE       )

        processor.save_pretrained(self.tmpdirname       )
        snake_case__      							=			WavaVecaProcessor.from_pretrained(self.tmpdirname       )

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

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			WavaVecaProcessor(tokenizer=self.get_tokenizer()				,   feature_extractor=self.get_feature_extractor()       )
        processor.save_pretrained(self.tmpdirname       )

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

        snake_case__      							=			WavaVecaProcessor.from_pretrained(
            self.tmpdirname				,   bos_token="(BOS)"				,   eos_token="(EOS)"				,   do_normalize=__SCREAMING_SNAKE_CASE				,   padding_value=1.0       )

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

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.get_feature_extractor()
        snake_case__      							=			self.get_tokenizer()

        snake_case__      							=			WavaVecaProcessor(tokenizer=__SCREAMING_SNAKE_CASE				,   feature_extractor=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			floats_list((3, 1000)       )

        snake_case__      							=			feature_extractor(__SCREAMING_SNAKE_CASE				,   return_tensors="np"       )
        snake_case__      							=			processor(__SCREAMING_SNAKE_CASE				,   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 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.get_feature_extractor()
        snake_case__      							=			self.get_tokenizer()

        snake_case__      							=			WavaVecaProcessor(tokenizer=__SCREAMING_SNAKE_CASE				,   feature_extractor=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			"This is a test string"

        snake_case__      							=			processor(text=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			tokenizer(__SCREAMING_SNAKE_CASE       )

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.get_feature_extractor()
        snake_case__      							=			self.get_tokenizer()

        snake_case__      							=			WavaVecaProcessor(tokenizer=__SCREAMING_SNAKE_CASE				,   feature_extractor=__SCREAMING_SNAKE_CASE       )

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

        snake_case__      							=			processor.batch_decode(__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			tokenizer.batch_decode(__SCREAMING_SNAKE_CASE       )

        self.assertListEqual(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.get_feature_extractor()
        snake_case__      							=			self.get_tokenizer()

        snake_case__      							=			WavaVecaProcessor(tokenizer=__SCREAMING_SNAKE_CASE				,   feature_extractor=__SCREAMING_SNAKE_CASE       )

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








"""simple docstring"""


import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							Optional[int]										=       (LMSDiscreteScheduler,)
       snake_case__			:							int										=       10

       def 						_SCREAMING_SNAKE_CASE(     self				,   **__SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

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

        config.update(**__SCREAMING_SNAKE_CASE       )
        return config

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]				,   [0.0002, 0.002, 0.02]       ):
         self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE				,   beta_end=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        for t in [0, 500, 800]:
         self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.scheduler_classes[0]
        snake_case__      							=			self.get_scheduler_config()
        snake_case__      							=			scheduler_class(**__SCREAMING_SNAKE_CASE       )

        scheduler.set_timesteps(self.num_inference_steps       )

        snake_case__      							=			self.dummy_model()
        snake_case__      							=			self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps       ):
         snake_case__      							=			scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			scheduler.step(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )
         snake_case__      							=			output.prev_sample

        snake_case__      							=			torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
        snake_case__      							=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

        assert abs(result_sum.item() - 1006.388       ) < 1e-2
        assert abs(result_mean.item() - 1.31       ) < 1e-3

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.scheduler_classes[0]
        snake_case__      							=			self.get_scheduler_config(prediction_type="v_prediction"       )
        snake_case__      							=			scheduler_class(**__SCREAMING_SNAKE_CASE       )

        scheduler.set_timesteps(self.num_inference_steps       )

        snake_case__      							=			self.dummy_model()
        snake_case__      							=			self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps       ):
         snake_case__      							=			scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			scheduler.step(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )
         snake_case__      							=			output.prev_sample

        snake_case__      							=			torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
        snake_case__      							=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

        assert abs(result_sum.item() - 0.0017       ) < 1e-2
        assert abs(result_mean.item() - 2.2676e-06       ) < 1e-3

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.scheduler_classes[0]
        snake_case__      							=			self.get_scheduler_config()
        snake_case__      							=			scheduler_class(**__SCREAMING_SNAKE_CASE       )

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

        snake_case__      							=			self.dummy_model()
        snake_case__      							=			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        snake_case__      							=			sample.to(__SCREAMING_SNAKE_CASE       )

        for i, t in enumerate(scheduler.timesteps       ):
         snake_case__      							=			scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			scheduler.step(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )
         snake_case__      							=			output.prev_sample

        snake_case__      							=			torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
        snake_case__      							=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

        assert abs(result_sum.item() - 1006.388       ) < 1e-2
        assert abs(result_mean.item() - 1.31       ) < 1e-3

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.scheduler_classes[0]
        snake_case__      							=			self.get_scheduler_config()
        snake_case__      							=			scheduler_class(**__SCREAMING_SNAKE_CASE				,   use_karras_sigmas=__SCREAMING_SNAKE_CASE       )

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

        snake_case__      							=			self.dummy_model()
        snake_case__      							=			self.dummy_sample_deter.to(__SCREAMING_SNAKE_CASE       ) * scheduler.init_noise_sigma
        snake_case__      							=			sample.to(__SCREAMING_SNAKE_CASE       )

        for t in scheduler.timesteps:
         snake_case__      							=			scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			scheduler.step(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )
         snake_case__      							=			output.prev_sample

        snake_case__      							=			torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
        snake_case__      							=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

        assert abs(result_sum.item() - 3812.9927       ) < 2e-2
        assert abs(result_mean.item() - 4.9648       ) < 1e-3

