



'''simple docstring'''

import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


a							   =				random.Random()







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase=1.0				,							__UpperCAmelCase=None				,							__UpperCAmelCase=None					)				->					List[str]:





			"""simple docstring"""







			if rng is None:
						snake_case:    Dict      							=global_rng

			snake_case:    Any      							=[]
			for batch_idx in range(shape[0]					):
						values.append([]					)
						for _ in range(shape[1]					):
									values[-1].append(rng.random() * scale					)

			return values






class 						a_		(     unittest.TestCase    ):


							def __init__(				self   :						Union[str, Any]      ,						a_   :						List[Any]      ,						a_   :						Any=7      ,						a_   :						str=4_0_0      ,						a_   :						Dict=2_0_0_0      ,						a_   :						Dict=1      ,						a_   :						str=0.0      ,						a_   :						Optional[int]=1_6_0_0_0      ,						a_   :						Dict=True      ,						a_   :						Union[str, Any]=True      ,						)			->					Union[str, Any]:
										snake_case:    str      							=parent
										snake_case:    Union[str, Any]      							=batch_size
										snake_case:    Dict      							=min_seq_length
										snake_case:    Union[str, Any]      							=max_seq_length
										snake_case:    Any      							=(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
										snake_case:    List[str]      							=feature_size
										snake_case:    Tuple      							=padding_value
										snake_case:    List[Any]      							=sampling_rate
										snake_case:    str      							=return_attention_mask
										snake_case:    List[str]      							=do_normalize


							def    UpperCamelCase     (				self   :						List[str]			)			->					Dict:
										return {
										    "feature_size": self.feature_size,
										    "padding_value": self.padding_value,
										    "sampling_rate": self.sampling_rate,
										    "return_attention_mask": self.return_attention_mask,
										    "do_normalize": self.do_normalize,
										}




							def    UpperCamelCase     (				self   :						int      ,						a_   :						int=False      ,						a_   :						Any=False			)			->					Optional[Any]:
										def _flatten(a_   :						Union[str, Any]			):
													return list(itertools.chain(*a_			)			)

										if equal_length:
													snake_case:    Tuple      							=floats_list((self.batch_size, self.max_seq_length)			)
										else:
													# make sure that inputs increase in size
													snake_case:    Tuple      							=[
													    _flatten(floats_list((x, self.feature_size)			)			)
													    for x in range(self.min_seq_length      ,						self.max_seq_length      ,						self.seq_length_diff			)
													]

										if numpify:
													snake_case:    Optional[int]      							=[np.asarray(a_			) for x in speech_inputs]

										return speech_inputs






class 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		Tuple        =				WavaVecaFeatureExtractor


							def    UpperCamelCase     (				self   :						Tuple			)			->					List[str]:
										snake_case:    Optional[Any]      							=WavaVecaFeatureExtractionTester(self			)


							def    UpperCamelCase     (				self   :						List[Any]      ,						a_   :						Any			)			->					List[str]:
										self.assertTrue(np.all(np.mean(a_      ,						axis=0			) < 1E-3			)			)
										self.assertTrue(np.all(np.abs(np.var(a_      ,						axis=0			) - 1			) < 1E-3			)			)


							def    UpperCamelCase     (				self   :						Any			)			->					Optional[Any]:
										# Tests that all call wrap to encode_plus and batch_encode_plus
										snake_case:    Union[str, Any]      							=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
										# create three inputs of length 800, 1000, and 1200
										snake_case:    Union[str, Any]      							=[floats_list((1, x)			)[0] for x in range(8_0_0      ,						1_4_0_0      ,						2_0_0			)]
										snake_case:    Tuple      							=[np.asarray(a_			) for speech_input in speech_inputs]

										# Test not batched input
										snake_case:    str      							=feat_extract(speech_inputs[0]      ,						return_tensors='np'			).input_values
										snake_case:    Tuple      							=feat_extract(np_speech_inputs[0]      ,						return_tensors='np'			).input_values
										self.assertTrue(np.allclose(a_      ,						a_      ,						atol=1E-3			)			)

										# Test batched
										snake_case:    List[Any]      							=feat_extract(a_      ,						return_tensors='np'			).input_values
										snake_case:    Optional[Any]      							=feat_extract(a_      ,						return_tensors='np'			).input_values
										for enc_seq_a, enc_seq_a in zip(a_      ,						a_			):
													self.assertTrue(np.allclose(a_      ,						a_      ,						atol=1E-3			)			)

										# Test 2-D numpy arrays are batched.
										snake_case:    Optional[Any]      							=[floats_list((1, x)			)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
										snake_case:    Union[str, Any]      							=np.asarray(a_			)
										snake_case:    Dict      							=feat_extract(a_      ,						return_tensors='np'			).input_values
										snake_case:    Tuple      							=feat_extract(a_      ,						return_tensors='np'			).input_values
										for enc_seq_a, enc_seq_a in zip(a_      ,						a_			):
													self.assertTrue(np.allclose(a_      ,						a_      ,						atol=1E-3			)			)


							def    UpperCamelCase     (				self   :						Tuple			)			->					str:
										snake_case:    Dict      							=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
										snake_case:    Optional[int]      							=[floats_list((1, x)			)[0] for x in range(8_0_0      ,						1_4_0_0      ,						2_0_0			)]

										snake_case:    str      							=['longest', 'max_length', 'do_not_pad']
										snake_case:    Tuple      							=[None, 1_6_0_0, None]
										for max_length, padding in zip(a_      ,						a_			):
													snake_case:    Dict      							=feat_extract(a_      ,						padding=a_      ,						max_length=a_      ,						return_tensors='np'			)
													snake_case:    Optional[int]      							=processed.input_values

													self._check_zero_mean_unit_variance(input_values[0][:8_0_0]			)
													self.assertTrue(input_values[0][8_0_0:].sum() < 1E-6			)
													self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]			)
													self.assertTrue(input_values[0][1_0_0_0:].sum() < 1E-6			)
													self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]			)


							def    UpperCamelCase     (				self   :						Tuple			)			->					Any:
										snake_case:    Union[str, Any]      							=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
										snake_case:    Any      							=range(8_0_0      ,						1_4_0_0      ,						2_0_0			)
										snake_case:    List[Any]      							=[floats_list((1, x)			)[0] for x in lengths]

										snake_case:    Optional[Any]      							=['longest', 'max_length', 'do_not_pad']
										snake_case:    List[Any]      							=[None, 1_6_0_0, None]

										for max_length, padding in zip(a_      ,						a_			):
													snake_case:    Union[str, Any]      							=feat_extract(a_      ,						max_length=a_      ,						padding=a_			)
													snake_case:    str      							=processed.input_values

													self._check_zero_mean_unit_variance(input_values[0][:8_0_0]			)
													self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]			)
													self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]			)


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					int:
										snake_case:    int      							=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
										snake_case:    Union[str, Any]      							=[floats_list((1, x)			)[0] for x in range(8_0_0      ,						1_4_0_0      ,						2_0_0			)]
										snake_case:    Any      							=feat_extract(
										    a_      ,						truncation=a_      ,						max_length=1_0_0_0      ,						padding='max_length'      ,						return_tensors='np'			)
										snake_case:    Union[str, Any]      							=processed.input_values

										self._check_zero_mean_unit_variance(input_values[0, :8_0_0]			)
										self._check_zero_mean_unit_variance(input_values[1]			)
										self._check_zero_mean_unit_variance(input_values[2]			)


							def    UpperCamelCase     (				self   :						List[Any]			)			->					str:
										snake_case:    Union[str, Any]      							=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
										snake_case:    Tuple      							=[floats_list((1, x)			)[0] for x in range(8_0_0      ,						1_4_0_0      ,						2_0_0			)]
										snake_case:    List[Any]      							=feat_extract(
										    a_      ,						truncation=a_      ,						max_length=1_0_0_0      ,						padding='longest'      ,						return_tensors='np'			)
										snake_case:    Union[str, Any]      							=processed.input_values

										self._check_zero_mean_unit_variance(input_values[0, :8_0_0]			)
										self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]			)
										self._check_zero_mean_unit_variance(input_values[2]			)

										# make sure that if max_length < longest -> then pad to max_length
										self.assertTrue(input_values.shape == (3, 1_0_0_0)			)

										snake_case:    Optional[int]      							=[floats_list((1, x)			)[0] for x in range(8_0_0      ,						1_4_0_0      ,						2_0_0			)]
										snake_case:    Optional[Any]      							=feat_extract(
										    a_      ,						truncation=a_      ,						max_length=2_0_0_0      ,						padding='longest'      ,						return_tensors='np'			)
										snake_case:    Union[str, Any]      							=processed.input_values

										self._check_zero_mean_unit_variance(input_values[0, :8_0_0]			)
										self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]			)
										self._check_zero_mean_unit_variance(input_values[2]			)

										# make sure that if max_length > longest -> then pad to longest
										self.assertTrue(input_values.shape == (3, 1_2_0_0)			)


							@require_torch
							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					int:
										import torch

										snake_case:    Union[str, Any]      							=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
										snake_case:    Optional[int]      							=np.random.rand(1_0_0			).astype(np.floataa			)
										snake_case:    Union[str, Any]      							=np_speech_inputs.tolist()

										for inputs in [py_speech_inputs, np_speech_inputs]:
													snake_case:    List[Any]      							=feature_extractor.pad([{'input_values': inputs}]      ,						return_tensors='np'			)
													self.assertTrue(np_processed.input_values.dtype == np.floataa			)
													snake_case:    Dict      							=feature_extractor.pad([{'input_values': inputs}]      ,						return_tensors='pt'			)
													self.assertTrue(pt_processed.input_values.dtype == torch.floataa			)




							@slow
							@require_torch
							def    UpperCamelCase     (				self   :						List[str]			)			->					str:
										# this test makes sure that models that are using
										# group norm don't have their feature extractor return the
										# attention_mask
										for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
													snake_case:    List[str]      							=WavaVecaConfig.from_pretrained(a_			)
													snake_case:    List[str]      							=WavaVecaFeatureExtractor.from_pretrained(a_			)

													# only "layer" feature extraction norm should make use of
													# attention_mask
													self.assertEqual(feat_extract.return_attention_mask      ,						config.feat_extract_norm == 'layer'			)









'''simple docstring'''

import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class 						a_		(     snake_case    ):
							UpperCAmelCase			:		str        =				(CMStochasticIterativeScheduler,)
							UpperCAmelCase			:		int        =				10


							def    UpperCamelCase     (				self   :						Dict      ,						**a_   :						List[str]			)			->					Any:
										snake_case:    Any      							={
										    'num_train_timesteps': 2_0_1,
										    'sigma_min': 0.0_0_2,
										    'sigma_max': 8_0.0,
										}

										config.update(**a_			)
										return config


							def    UpperCamelCase     (				self   :						List[Any]			)			->					List[Any]:
										snake_case:    Any      							=1_0

										snake_case:    List[str]      							=self.get_scheduler_config()
										snake_case:    List[Any]      							=self.scheduler_classes[0](**a_			)

										scheduler.set_timesteps(a_			)

										snake_case:    Dict      							=scheduler.timesteps[0]
										snake_case:    Union[str, Any]      							=scheduler.timesteps[1]

										snake_case:    List[str]      							=self.dummy_sample
										snake_case:    List[str]      							=0.1 * sample

										snake_case:    int      							=scheduler.step(a_      ,						a_      ,						a_			).prev_sample
										snake_case:    Optional[Any]      							=scheduler.step(a_      ,						a_      ,						a_			).prev_sample

										self.assertEqual(output_a.shape      ,						sample.shape			)
										self.assertEqual(output_a.shape      ,						output_a.shape			)


							def    UpperCamelCase     (				self   :						int			)			->					int:
										for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=a_			)


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Dict:
										for clip_denoised in [True, False]:
													self.check_over_configs(clip_denoised=a_			)


							def    UpperCamelCase     (				self   :						Tuple			)			->					List[str]:
										snake_case:    List[Any]      							=self.scheduler_classes[0]
										snake_case:    List[Any]      							=self.get_scheduler_config()
										snake_case:    Any      							=scheduler_class(**a_			)

										snake_case:    Dict      							=1
										scheduler.set_timesteps(a_			)
										snake_case:    List[Any]      							=scheduler.timesteps

										snake_case:    Optional[Any]      							=torch.manual_seed(0			)

										snake_case:    Optional[Any]      							=self.dummy_model()
										snake_case:    List[Any]      							=self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(a_			):
													# 1. scale model input
													snake_case:    Any      							=scheduler.scale_model_input(a_      ,						a_			)

													# 2. predict noise residual
													snake_case:    List[str]      							=model(a_      ,						a_			)

													# 3. predict previous sample x_t-1
													snake_case:    Dict      							=scheduler.step(a_      ,						a_      ,						a_      ,						generator=a_			).prev_sample

													snake_case:    List[Any]      							=pred_prev_sample

										snake_case:    Optional[Any]      							=torch.sum(torch.abs(a_			)			)
										snake_case:    Optional[Any]      							=torch.mean(torch.abs(a_			)			)

										assert abs(result_sum.item() - 1_9_2.7_6_1_4			) < 1E-2
										assert abs(result_mean.item() - 0.2_5_1_0			) < 1E-3


							def    UpperCamelCase     (				self   :						Dict			)			->					Union[str, Any]:
										snake_case:    Dict      							=self.scheduler_classes[0]
										snake_case:    Tuple      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    List[Any]      							=[1_0_6, 0]
										scheduler.set_timesteps(timesteps=a_			)
										snake_case:    Optional[Any]      							=scheduler.timesteps

										snake_case:    Dict      							=torch.manual_seed(0			)

										snake_case:    Optional[int]      							=self.dummy_model()
										snake_case:    Any      							=self.dummy_sample_deter * scheduler.init_noise_sigma

										for t in timesteps:
													# 1. scale model input
													snake_case:    List[Any]      							=scheduler.scale_model_input(a_      ,						a_			)

													# 2. predict noise residual
													snake_case:    Any      							=model(a_      ,						a_			)

													# 3. predict previous sample x_t-1
													snake_case:    List[str]      							=scheduler.step(a_      ,						a_      ,						a_      ,						generator=a_			).prev_sample

													snake_case:    Optional[Any]      							=pred_prev_sample

										snake_case:    Union[str, Any]      							=torch.sum(torch.abs(a_			)			)
										snake_case:    Tuple      							=torch.mean(torch.abs(a_			)			)

										assert abs(result_sum.item() - 3_4_7.6_3_5_7			) < 1E-2
										assert abs(result_mean.item() - 0.4_5_2_7			) < 1E-3


							def    UpperCamelCase     (				self   :						int			)			->					Tuple:
										snake_case:    List[Any]      							=self.scheduler_classes[0]
										snake_case:    Union[str, Any]      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    str      							=[3_9, 3_0, 1_2, 1_5, 0]

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


							def    UpperCamelCase     (				self   :						Dict			)			->					Optional[int]:
										snake_case:    Optional[Any]      							=self.scheduler_classes[0]
										snake_case:    Dict      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    Any      							=[3_9, 3_0, 1_2, 1, 0]
										snake_case:    List[Any]      							=len(a_			)

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




							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Tuple:
										snake_case:    Any      							=self.scheduler_classes[0]
										snake_case:    int      							=self.get_scheduler_config()
										snake_case:    Optional[Any]      							=scheduler_class(**a_			)

										snake_case:    List[Any]      							=[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_			)




