





'''simple docstring'''







import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            =(DDPMScheduler,)




				def   UpperCamelCase__   (  self     :					List[str]					,				**__a     :					List[str]					):
								_a								= {
								    "num_train_timesteps": 10_00,
								    "beta_start": 0.0001,
								    "beta_end": 0.02,
								    "beta_schedule": "linear",
								    "variance_type": "fixed_small",
								    "clip_sample": True,
								}

								config.update(**__a					)
								return config




				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								for timesteps in [1, 5, 1_00, 10_00]:
												self.check_over_configs(num_train_timesteps=__a					)




				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]					,				[0.002, 0.02, 0.2, 2]					):
												self.check_over_configs(beta_start=__a					,				beta_end=__a					)




				def   UpperCamelCase__   (  self     :					int					):
								for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=__a					)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								for variance in ["fixed_small", "fixed_large", "other"]:
												self.check_over_configs(variance_type=__a					)




				def   UpperCamelCase__   (  self     :					Any					):
								for clip_sample in [True, False]:
												self.check_over_configs(clip_sample=__a					)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								self.check_over_configs(thresholding=__a					)
								for threshold in [0.5, 1.0, 2.0]:
												for prediction_type in ["epsilon", "sample", "v_prediction"]:
																self.check_over_configs(
																    thresholding=__a					,				prediction_type=__a					,				sample_max_value=__a					,				)




				def   UpperCamelCase__   (  self     :					Tuple					):
								for prediction_type in ["epsilon", "sample", "v_prediction"]:
												self.check_over_configs(prediction_type=__a					)




				def   UpperCamelCase__   (  self     :					str					):
								for t in [0, 5_00, 9_99]:
												self.check_over_forward(time_step=__a					)




				def   UpperCamelCase__   (  self     :					Any					):
								_a								= self.scheduler_classes[0]
								_a								= self.get_scheduler_config()
								_a								= scheduler_class(**__a					)

								assert torch.sum(torch.abs(scheduler._get_variance(0					) - 0.0					)					) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_87					) - 0.00979					)					) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_99					) - 0.02					)					) < 1e-5




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= self.scheduler_classes[0]
								_a								= self.get_scheduler_config()
								_a								= scheduler_class(**__a					)

								_a								= len(__a					)

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

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

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

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

								_a								= torch.sum(torch.abs(__a					)					)
								_a								= torch.mean(torch.abs(__a					)					)

								assert abs(result_sum.item() - 258.9606					) < 1e-2
								assert abs(result_mean.item() - 0.3372					) < 1e-3




				def   UpperCamelCase__   (  self     :					List[str]					):
								_a								= self.scheduler_classes[0]
								_a								= self.get_scheduler_config(prediction_type="v_prediction"					)
								_a								= scheduler_class(**__a					)

								_a								= len(__a					)

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

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

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

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

								_a								= torch.sum(torch.abs(__a					)					)
								_a								= torch.mean(torch.abs(__a					)					)

								assert abs(result_sum.item() - 202.0296					) < 1e-2
								assert abs(result_mean.item() - 0.2631					) < 1e-3




				def   UpperCamelCase__   (  self     :					Dict					):
								_a								= self.scheduler_classes[0]
								_a								= self.get_scheduler_config()
								_a								= scheduler_class(**__a					)

								_a								= [1_00, 87, 50, 1, 0]

								scheduler.set_timesteps(timesteps=__a					)

								_a								= scheduler.timesteps

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

												_a								= scheduler.previous_timestep(__a					)
												_a								= prev_t.item()

												self.assertEqual(__a					,				__a					)




				def   UpperCamelCase__   (  self     :					str					):
								_a								= self.scheduler_classes[0]
								_a								= self.get_scheduler_config()
								_a								= scheduler_class(**__a					)

								_a								= [1_00, 87, 50, 51, 0]

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




				def   UpperCamelCase__   (  self     :					str					):
								_a								= self.scheduler_classes[0]
								_a								= self.get_scheduler_config()
								_a								= scheduler_class(**__a					)

								_a								= [1_00, 87, 50, 1, 0]
								_a								= len(__a					)

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




				def   UpperCamelCase__   (  self     :					Dict					):
								_a								= self.scheduler_classes[0]
								_a								= self.get_scheduler_config()
								_a								= scheduler_class(**__a					)

								_a								= [scheduler.config.num_train_timesteps]

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







'''simple docstring'''







import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


lowerCAmelCase_					:    Any      	=			get_tests_dir('fixtures')
lowerCAmelCase_					:    Union[str, Any]      	=			get_tests_dir('fixtures/dummy_feature_extractor_config.json')
lowerCAmelCase_					:    Dict      	=			get_tests_dir('fixtures/dummy-config.json')
class 				__SCREAMING_SNAKE_CASE  (unittest.TestCase      ):






				"""simple docstring"""







				def   UpperCamelCase__   (  self     :					Optional[int]					):
								_a								= 0




				def   UpperCamelCase__   (  self     :					str					):
								_a								= AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h"					)
								self.assertIsInstance(__a					,				__a					)




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= AutoFeatureExtractor.from_pretrained(__a					)
								self.assertIsInstance(__a					,				__a					)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								with tempfile.TemporaryDirectory() as tmpdirname:
												_a								= WavaVecaConfig()

												# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
												_a								= AutoFeatureExtractor.from_pretrained(__a					).to_dict()

												config_dict.pop("feature_extractor_type"					)
												_a								= WavaVecaFeatureExtractor(**__a					)

												# save in new folder
												model_config.save_pretrained(__a					)
												config.save_pretrained(__a					)

												_a								= AutoFeatureExtractor.from_pretrained(__a					)

												# make sure private variable is not incorrectly saved
												_a								= json.loads(config.to_json_string()					)
												self.assertTrue("_processor_class" not in dict_as_saved					)

								self.assertIsInstance(__a					,				__a					)




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= AutoFeatureExtractor.from_pretrained(__a					)
								self.assertIsInstance(__a					,				__a					)




				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								with self.assertRaisesRegex(
								    __a					,				"bert-base is not a local folder and is not a valid model identifier"					):
												_a								= AutoFeatureExtractor.from_pretrained("bert-base"					)




				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								with self.assertRaisesRegex(
								    __a					,				r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"					):
												_a								= AutoFeatureExtractor.from_pretrained(__a					,				revision="aaaaaa"					)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								with self.assertRaisesRegex(
								    __a					,				"hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."					,				):
												_a								= AutoFeatureExtractor.from_pretrained("hf-internal-testing/config-no-model"					)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								# If remote code is not set, we will time out when asking whether to load the model.
								with self.assertRaises(__a					):
												_a								= AutoFeatureExtractor.from_pretrained(
												    "hf-internal-testing/test_dynamic_feature_extractor"					)
								# If remote code is disabled, we can't load this config.
								with self.assertRaises(__a					):
												_a								= AutoFeatureExtractor.from_pretrained(
												    "hf-internal-testing/test_dynamic_feature_extractor"					,				trust_remote_code=__a					)

								_a								= AutoFeatureExtractor.from_pretrained(
								    "hf-internal-testing/test_dynamic_feature_extractor"					,				trust_remote_code=__a					)
								self.assertEqual(feature_extractor.__class__.__name__					,				"NewFeatureExtractor"					)

								# Test feature extractor can be reloaded.
								with tempfile.TemporaryDirectory() as tmp_dir:
												feature_extractor.save_pretrained(__a					)
												_a								= AutoFeatureExtractor.from_pretrained(__a					,				trust_remote_code=__a					)
								self.assertEqual(reloaded_feature_extractor.__class__.__name__					,				"NewFeatureExtractor"					)




				def   UpperCamelCase__   (  self     :					Any					):
								try:
												AutoConfig.register("custom"					,				__a					)
												AutoFeatureExtractor.register(__a					,				__a					)
												# Trying to register something existing in the Transformers library will raise an error
												with self.assertRaises(__a					):
																AutoFeatureExtractor.register(__a					,				__a					)

												# Now that the config is registered, it can be used as any other config with the auto-API
												_a								= CustomFeatureExtractor.from_pretrained(__a					)
												with tempfile.TemporaryDirectory() as tmp_dir:
																feature_extractor.save_pretrained(__a					)
																_a								= AutoFeatureExtractor.from_pretrained(__a					)
																self.assertIsInstance(__a					,				__a					)

								finally:
												if "custom" in CONFIG_MAPPING._extra_content:
																del CONFIG_MAPPING._extra_content["custom"]
												if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]




				def   UpperCamelCase__   (  self     :					Tuple					):
								class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






												"""simple docstring"""







												__a            =True

								try:
												AutoConfig.register("custom"					,				__a					)
												AutoFeatureExtractor.register(__a					,				__a					)
												# If remote code is not set, the default is to use local
												_a								= AutoFeatureExtractor.from_pretrained(
												    "hf-internal-testing/test_dynamic_feature_extractor"					)
												self.assertEqual(feature_extractor.__class__.__name__					,				"NewFeatureExtractor"					)
												self.assertTrue(feature_extractor.is_local					)

												# If remote code is disabled, we load the local one.
												_a								= AutoFeatureExtractor.from_pretrained(
												    "hf-internal-testing/test_dynamic_feature_extractor"					,				trust_remote_code=__a					)
												self.assertEqual(feature_extractor.__class__.__name__					,				"NewFeatureExtractor"					)
												self.assertTrue(feature_extractor.is_local					)

												# If remote is enabled, we load from the Hub
												_a								= AutoFeatureExtractor.from_pretrained(
												    "hf-internal-testing/test_dynamic_feature_extractor"					,				trust_remote_code=__a					)
												self.assertEqual(feature_extractor.__class__.__name__					,				"NewFeatureExtractor"					)
												self.assertTrue(not hasattr(__a					,				"is_local"					)					)

								finally:
												if "custom" in CONFIG_MAPPING._extra_content:
																del CONFIG_MAPPING._extra_content["custom"]
												if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
