
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


_lowercase			:  List[str]   							=	get_tests_dir("fixtures")
_lowercase			:  Optional[Any]   							=	get_tests_dir("fixtures/dummy_feature_extractor_config.json")
_lowercase			:  Union[str, Any]   							=	get_tests_dir("fixtures/dummy-config.json")






class 			lowerCAmelCase__       (  unittest.TestCase					):






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	0






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Dict													=	AutoFeatureExtractor.from_pretrained('''facebook/wav2vec2-base-960h'''		)
											self.assertIsInstance(_A    ,							_A		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[str]													=	AutoFeatureExtractor.from_pretrained(_A		)
											self.assertIsInstance(_A    ,							_A		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											with tempfile.TemporaryDirectory() as tmpdirname:
																	lowercase_     :					Dict													=	WavaVecaConfig()

																	# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
																	lowercase_     :					Optional[Any]													=	AutoFeatureExtractor.from_pretrained(_A		).to_dict()

																	config_dict.pop('''feature_extractor_type'''		)
																	lowercase_     :					List[str]													=	WavaVecaFeatureExtractor(**_A		)

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

																	lowercase_     :					Union[str, Any]													=	AutoFeatureExtractor.from_pretrained(_A		)

																	# make sure private variable is not incorrectly saved
																	lowercase_     :					Optional[Any]													=	json.loads(config.to_json_string()		)
																	self.assertTrue('''_processor_class''' not in dict_as_saved		)

											self.assertIsInstance(_A    ,							_A		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Any													=	AutoFeatureExtractor.from_pretrained(_A		)
											self.assertIsInstance(_A    ,							_A		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											with self.assertRaisesRegex(
											    _A    ,							'''bert-base is not a local folder and is not a valid model identifier'''		):
																	lowercase_     :					Union[str, Any]													=	AutoFeatureExtractor.from_pretrained('''bert-base'''		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											with self.assertRaisesRegex(
											    _A    ,							R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''		):
																	lowercase_     :					Optional[int]													=	AutoFeatureExtractor.from_pretrained(_A    ,							revision='''aaaaaa'''		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											with self.assertRaisesRegex(
											    _A    ,							'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''    ,							):
																	lowercase_     :					int													=	AutoFeatureExtractor.from_pretrained('''hf-internal-testing/config-no-model'''		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											with self.assertRaises(_A		):
																	lowercase_     :					Any													=	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		):
																	lowercase_     :					Tuple													=	AutoFeatureExtractor.from_pretrained(
																	    '''hf-internal-testing/test_dynamic_feature_extractor'''    ,							trust_remote_code=_A		)

											lowercase_     :					str													=	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		)
																	lowercase_     :					List[str]													=	AutoFeatureExtractor.from_pretrained(_A    ,							trust_remote_code=_A		)
											self.assertEqual(reloaded_feature_extractor.__class__.__name__    ,							'''NewFeatureExtractor'''		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											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
																	lowercase_     :					Tuple													=	CustomFeatureExtractor.from_pretrained(_A		)
																	with tempfile.TemporaryDirectory() as tmp_dir:
																							feature_extractor.save_pretrained(_A		)
																							lowercase_     :					Tuple													=	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      _snake_case   (       self		):


											"""simple docstring"""





											class 			lowerCAmelCase__       (  snake_case_					):
																lowerCAmelCase_					       =					True

											try:
																	AutoConfig.register('''custom'''    ,							_A		)
																	AutoFeatureExtractor.register(_A    ,							_A		)
																	# If remote code is not set, the default is to use local
																	lowercase_     :					Any													=	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.
																	lowercase_     :					Any													=	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
																	lowercase_     :					Tuple													=	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]





'''simple docstring'''







import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					(DDPMScheduler,)






					def      _snake_case   (       self    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	{
											    '''num_train_timesteps''': 10_00,
											    '''beta_start''': 0.0_001,
											    '''beta_end''': 0.02,
											    '''beta_schedule''': '''linear''',
											    '''variance_type''': '''fixed_small''',
											    '''clip_sample''': True,
											}

											config.update(**__SCREAMING_SNAKE_CASE		)
											return config






					def      _snake_case   (       self		):


											"""simple docstring"""





											for timesteps in [1, 5, 1_00, 10_00]:
																	self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]    ,							[0.002, 0.02, 0.2, 2]		):
																	self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE    ,							beta_end=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for schedule in ["linear", "squaredcos_cap_v2"]:
																	self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for variance in ["fixed_small", "fixed_large", "other"]:
																	self.check_over_configs(variance_type=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for clip_sample in [True, False]:
																	self.check_over_configs(clip_sample=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE		)
											for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "sample", "v_prediction"]:
																							self.check_over_configs(
																							    thresholding=__SCREAMING_SNAKE_CASE    ,							prediction_type=__SCREAMING_SNAKE_CASE    ,							sample_max_value=__SCREAMING_SNAKE_CASE    ,							)






					def      _snake_case   (       self		):


											"""simple docstring"""





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






					def      _snake_case   (       self		):


											"""simple docstring"""





											for t in [0, 5_00, 9_99]:
																	self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[str]													=	self.scheduler_classes[0]
											lowercase_     :					int													=	self.get_scheduler_config()
											lowercase_     :					str													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(4_87		) - 0.00_979		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(9_99		) - 0.02		)		) < 1E-5






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Union[str, Any]													=	self.scheduler_classes[0]
											lowercase_     :					Any													=	self.get_scheduler_config()
											lowercase_     :					Optional[int]													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Any													=	len(__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Optional[int]													=	self.dummy_model()
											lowercase_     :					Any													=	self.dummy_sample_deter
											lowercase_     :					List[str]													=	torch.manual_seed(0		)

											for t in reversed(range(__SCREAMING_SNAKE_CASE		)		):
																	# 1. predict noise residual
																	lowercase_     :					Optional[int]													=	model(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)

																	# 2. predict previous mean of sample x_t-1
																	lowercase_     :					Union[str, Any]													=	scheduler.step(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							generator=__SCREAMING_SNAKE_CASE		).prev_sample

																	# if t > 0:
																	#     noise = self.dummy_sample_deter
																	#     variance = scheduler.get_variance(t) ** (0.5) * noise
																	#
																	# sample = pred_prev_sample + variance
																	lowercase_     :					List[str]													=	pred_prev_sample

											lowercase_     :					str													=	torch.sum(torch.abs(__SCREAMING_SNAKE_CASE		)		)
											lowercase_     :					Tuple													=	torch.mean(torch.abs(__SCREAMING_SNAKE_CASE		)		)

											assert abs(result_sum.item() - 258.9_606		) < 1E-2
											assert abs(result_mean.item() - 0.3_372		) < 1E-3






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	self.scheduler_classes[0]
											lowercase_     :					Tuple													=	self.get_scheduler_config(prediction_type='''v_prediction'''		)
											lowercase_     :					Any													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Optional[int]													=	len(__SCREAMING_SNAKE_CASE		)

											lowercase_     :					List[str]													=	self.dummy_model()
											lowercase_     :					int													=	self.dummy_sample_deter
											lowercase_     :					str													=	torch.manual_seed(0		)

											for t in reversed(range(__SCREAMING_SNAKE_CASE		)		):
																	# 1. predict noise residual
																	lowercase_     :					Optional[Any]													=	model(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)

																	# 2. predict previous mean of sample x_t-1
																	lowercase_     :					Optional[Any]													=	scheduler.step(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							generator=__SCREAMING_SNAKE_CASE		).prev_sample

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

											lowercase_     :					str													=	torch.sum(torch.abs(__SCREAMING_SNAKE_CASE		)		)
											lowercase_     :					Tuple													=	torch.mean(torch.abs(__SCREAMING_SNAKE_CASE		)		)

											assert abs(result_sum.item() - 202.0_296		) < 1E-2
											assert abs(result_mean.item() - 0.2_631		) < 1E-3






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[int]													=	self.scheduler_classes[0]
											lowercase_     :					int													=	self.get_scheduler_config()
											lowercase_     :					int													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					int													=	[1_00, 87, 50, 1, 0]

											scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE		)

											lowercase_     :					List[str]													=	scheduler.timesteps

											for i, timestep in enumerate(__SCREAMING_SNAKE_CASE		):
																	if i == len(__SCREAMING_SNAKE_CASE		) - 1:
																							lowercase_     :					str													=	-1
																	else:
																							lowercase_     :					Any													=	timesteps[i + 1]

																	lowercase_     :					Optional[Any]													=	scheduler.previous_timestep(__SCREAMING_SNAKE_CASE		)
																	lowercase_     :					int													=	prev_t.item()

																	self.assertEqual(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Tuple													=	self.scheduler_classes[0]
											lowercase_     :					List[Any]													=	self.get_scheduler_config()
											lowercase_     :					str													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Optional[int]													=	[1_00, 87, 50, 51, 0]

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






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	self.scheduler_classes[0]
											lowercase_     :					str													=	self.get_scheduler_config()
											lowercase_     :					Tuple													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Tuple													=	[1_00, 87, 50, 1, 0]
											lowercase_     :					List[str]													=	len(__SCREAMING_SNAKE_CASE		)

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






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	self.scheduler_classes[0]
											lowercase_     :					Optional[int]													=	self.get_scheduler_config()
											lowercase_     :					Any													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					str													=	[scheduler.config.num_train_timesteps]

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



