'''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


__lowercase          =  random.Random()






def 				snake_case__							(			_A:					Dict      ,   _A:					Optional[Any]=1.0      ,   _A:					str=None      ,   _A:					Optional[Any]=None )    ->	str:

		'''simple docstring'''


		if rng is None:
				lowerCAmelCase   					=  global_rng

		lowerCAmelCase   					=  []
		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	):


			'''simple docstring'''






			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase=7       ,    __lowerCAmelCase=400       ,    __lowerCAmelCase=2000       ,    __lowerCAmelCase=1       ,    __lowerCAmelCase=0.0       ,    __lowerCAmelCase=16000       ,    __lowerCAmelCase=True       ,    __lowerCAmelCase=True       ,    ):




					"""simple docstring"""


					lowerCAmelCase   					=  parent
					lowerCAmelCase   					=  batch_size
					lowerCAmelCase   					=  min_seq_length
					lowerCAmelCase   					=  max_seq_length
					lowerCAmelCase   					=  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					lowerCAmelCase   					=  feature_size
					lowerCAmelCase   					=  padding_value
					lowerCAmelCase   					=  sampling_rate
					lowerCAmelCase   					=  return_attention_mask
					lowerCAmelCase   					=  do_normalize



			def 							a_       (   self):




					"""simple docstring"""


					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 							a_       (   self       ,    __lowerCAmelCase=False       ,    __lowerCAmelCase=False):




					"""simple docstring"""


					def _flatten(__lowerCAmelCase):
							return list(itertools.chain(*__lowerCAmelCase))

					if equal_length:
							lowerCAmelCase   					=  floats_list((self.batch_size, self.max_seq_length))
					else:
							# make sure that inputs increase in size
							lowerCAmelCase   					=  [
							    _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:
							lowerCAmelCase   					=  [np.asarray(__lowerCAmelCase) for x in speech_inputs]

					return speech_inputs







class 				a__(					lowerCAmelCase__ ,						unittest.TestCase	):


			'''simple docstring'''






			UpperCAmelCase_			:     Dict      =       WavaVecaFeatureExtractor



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  WavaVecaFeatureExtractionTester(self)



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					self.assertTrue(np.all(np.mean(__lowerCAmelCase       ,    axis=0) < 1E-3))
					self.assertTrue(np.all(np.abs(np.var(__lowerCAmelCase       ,    axis=0) - 1) < 1E-3))



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
					# create three inputs of length 800, 1000, and 1200
					lowerCAmelCase   					=  [floats_list((1, x))[0] for x in range(800       ,    1400       ,    200)]
					lowerCAmelCase   					=  [np.asarray(__lowerCAmelCase) for speech_input in speech_inputs]

					# Test not batched input
					lowerCAmelCase   					=  feat_extract(speech_inputs[0]       ,    return_tensors="""np""").input_values
					lowerCAmelCase   					=  feat_extract(np_speech_inputs[0]       ,    return_tensors="""np""").input_values
					self.assertTrue(np.allclose(__lowerCAmelCase       ,    __lowerCAmelCase       ,    atol=1E-3))

					# Test batched
					lowerCAmelCase   					=  feat_extract(__lowerCAmelCase       ,    return_tensors="""np""").input_values
					lowerCAmelCase   					=  feat_extract(__lowerCAmelCase       ,    return_tensors="""np""").input_values
					for enc_seq_a, enc_seq_a in zip(__lowerCAmelCase       ,    __lowerCAmelCase):
							self.assertTrue(np.allclose(__lowerCAmelCase       ,    __lowerCAmelCase       ,    atol=1E-3))

					# Test 2-D numpy arrays are batched.
					lowerCAmelCase   					=  [floats_list((1, x))[0] for x in (800, 800, 800)]
					lowerCAmelCase   					=  np.asarray(__lowerCAmelCase)
					lowerCAmelCase   					=  feat_extract(__lowerCAmelCase       ,    return_tensors="""np""").input_values
					lowerCAmelCase   					=  feat_extract(__lowerCAmelCase       ,    return_tensors="""np""").input_values
					for enc_seq_a, enc_seq_a in zip(__lowerCAmelCase       ,    __lowerCAmelCase):
							self.assertTrue(np.allclose(__lowerCAmelCase       ,    __lowerCAmelCase       ,    atol=1E-3))



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
					lowerCAmelCase   					=  [floats_list((1, x))[0] for x in range(800       ,    1400       ,    200)]

					lowerCAmelCase   					=  ["""longest""", """max_length""", """do_not_pad"""]
					lowerCAmelCase   					=  [None, 1600, None]
					for max_length, padding in zip(__lowerCAmelCase       ,    __lowerCAmelCase):
							lowerCAmelCase   					=  feat_extract(__lowerCAmelCase       ,    padding=__lowerCAmelCase       ,    max_length=__lowerCAmelCase       ,    return_tensors="""np""")
							lowerCAmelCase   					=  processed.input_values

							self._check_zero_mean_unit_variance(input_values[0][:800])
							self.assertTrue(input_values[0][800:].sum() < 1E-6)
							self._check_zero_mean_unit_variance(input_values[1][:1000])
							self.assertTrue(input_values[0][1000:].sum() < 1E-6)
							self._check_zero_mean_unit_variance(input_values[2][:1200])



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
					lowerCAmelCase   					=  range(800       ,    1400       ,    200)
					lowerCAmelCase   					=  [floats_list((1, x))[0] for x in lengths]

					lowerCAmelCase   					=  ["""longest""", """max_length""", """do_not_pad"""]
					lowerCAmelCase   					=  [None, 1600, None]

					for max_length, padding in zip(__lowerCAmelCase       ,    __lowerCAmelCase):
							lowerCAmelCase   					=  feat_extract(__lowerCAmelCase       ,    max_length=__lowerCAmelCase       ,    padding=__lowerCAmelCase)
							lowerCAmelCase   					=  processed.input_values

							self._check_zero_mean_unit_variance(input_values[0][:800])
							self._check_zero_mean_unit_variance(input_values[1][:1000])
							self._check_zero_mean_unit_variance(input_values[2][:1200])



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
					lowerCAmelCase   					=  [floats_list((1, x))[0] for x in range(800       ,    1400       ,    200)]
					lowerCAmelCase   					=  feat_extract(
					    __lowerCAmelCase       ,    truncation=__lowerCAmelCase       ,    max_length=1000       ,    padding="""max_length"""       ,    return_tensors="""np""")
					lowerCAmelCase   					=  processed.input_values

					self._check_zero_mean_unit_variance(input_values[0, :800])
					self._check_zero_mean_unit_variance(input_values[1])
					self._check_zero_mean_unit_variance(input_values[2])



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
					lowerCAmelCase   					=  [floats_list((1, x))[0] for x in range(800       ,    1400       ,    200)]
					lowerCAmelCase   					=  feat_extract(
					    __lowerCAmelCase       ,    truncation=__lowerCAmelCase       ,    max_length=1000       ,    padding="""longest"""       ,    return_tensors="""np""")
					lowerCAmelCase   					=  processed.input_values

					self._check_zero_mean_unit_variance(input_values[0, :800])
					self._check_zero_mean_unit_variance(input_values[1, :1000])
					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, 1000))

					lowerCAmelCase   					=  [floats_list((1, x))[0] for x in range(800       ,    1400       ,    200)]
					lowerCAmelCase   					=  feat_extract(
					    __lowerCAmelCase       ,    truncation=__lowerCAmelCase       ,    max_length=2000       ,    padding="""longest"""       ,    return_tensors="""np""")
					lowerCAmelCase   					=  processed.input_values

					self._check_zero_mean_unit_variance(input_values[0, :800])
					self._check_zero_mean_unit_variance(input_values[1, :1000])
					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, 1200))



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					import torch

					lowerCAmelCase   					=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
					lowerCAmelCase   					=  np.random.rand(100).astype(np.floataa)
					lowerCAmelCase   					=  np_speech_inputs.tolist()

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







			@slow
			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
							lowerCAmelCase   					=  WavaVecaConfig.from_pretrained(__lowerCAmelCase)
							lowerCAmelCase   					=  WavaVecaFeatureExtractor.from_pretrained(__lowerCAmelCase)

							# 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 unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 				a__(					unittest.TestCase	):


			'''simple docstring'''






			@property
			def 							a_       (   self):




					"""simple docstring"""


					torch.manual_seed(0)
					lowerCAmelCase   					=  UNetaDModel(
					    block_out_channels=(32, 64)       ,    layers_per_block=2       ,    sample_size=32       ,    in_channels=3       ,    out_channels=3       ,    down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")       ,    up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")       ,    )
					return model







			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.dummy_uncond_unet
					lowerCAmelCase   					=  PNDMScheduler()

					lowerCAmelCase   					=  PNDMPipeline(unet=__lowerCAmelCase       ,    scheduler=__lowerCAmelCase)
					pndm.to(__lowerCAmelCase)
					pndm.set_progress_bar_config(disable=__lowerCAmelCase)

					lowerCAmelCase   					=  torch.manual_seed(0)
					lowerCAmelCase   					=  pndm(generator=__lowerCAmelCase       ,    num_inference_steps=20       ,    output_type="""numpy""").images

					lowerCAmelCase   					=  torch.manual_seed(0)
					lowerCAmelCase   					=  pndm(generator=__lowerCAmelCase       ,    num_inference_steps=20       ,    output_type="""numpy"""       ,    return_dict=__lowerCAmelCase)[0]

					lowerCAmelCase   					=  image[0, -3:, -3:, -1]
					lowerCAmelCase   					=  image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					lowerCAmelCase   					=  np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0])

					assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2







@slow
@require_torch
class 				a__(					unittest.TestCase	):


			'''simple docstring'''






			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  """google/ddpm-cifar10-32"""

					lowerCAmelCase   					=  UNetaDModel.from_pretrained(__lowerCAmelCase)
					lowerCAmelCase   					=  PNDMScheduler()

					lowerCAmelCase   					=  PNDMPipeline(unet=__lowerCAmelCase       ,    scheduler=__lowerCAmelCase)
					pndm.to(__lowerCAmelCase)
					pndm.set_progress_bar_config(disable=__lowerCAmelCase)
					lowerCAmelCase   					=  torch.manual_seed(0)
					lowerCAmelCase   					=  pndm(generator=__lowerCAmelCase       ,    output_type="""numpy""").images

					lowerCAmelCase   					=  image[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					lowerCAmelCase   					=  np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125])

					assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2




