


'''simple docstring'''






import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list




@require_torchaudio
@require_sentencepiece
class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														lowerCAmelCase__			  =      '''laion/clap-htsat-unfused'''
														lowerCAmelCase__			  =      tempfile.mkdtemp()







							def     __SCREAMING_SNAKE_CASE				(						self   ,       **lowerCamelCase_  )			->				Optional[int]:
														return RobertaTokenizer.from_pretrained(self.checkpoint   ,       **lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       **lowerCamelCase_  )			->				Optional[int]:
														return ClapFeatureExtractor.from_pretrained(self.checkpoint   ,       **lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														shutil.rmtree(self.tmpdirname  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														lowerCAmelCase__			  =      self.get_tokenizer()
														lowerCAmelCase__			  =      self.get_feature_extractor()

														lowerCAmelCase__			  =      ClapProcessor(tokenizer=lowerCamelCase_   ,       feature_extractor=lowerCamelCase_  )

														processor.save_pretrained(self.tmpdirname  )
														lowerCAmelCase__			  =      ClapProcessor.from_pretrained(self.tmpdirname  )

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

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







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														lowerCAmelCase__			  =      ClapProcessor(tokenizer=self.get_tokenizer()   ,       feature_extractor=self.get_feature_extractor()  )
														processor.save_pretrained(self.tmpdirname  )

														lowerCAmelCase__			  =      self.get_tokenizer(bos_token='''(BOS)'''   ,       eos_token='''(EOS)'''  )
														lowerCAmelCase__			  =      self.get_feature_extractor(do_normalize=lowerCamelCase_   ,       padding_value=1.0  )

														lowerCAmelCase__			  =      ClapProcessor.from_pretrained(
														    self.tmpdirname   ,       bos_token='''(BOS)'''   ,       eos_token='''(EOS)'''   ,       do_normalize=lowerCamelCase_   ,       padding_value=1.0  )

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

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







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__			  =      self.get_feature_extractor()
														lowerCAmelCase__			  =      self.get_tokenizer()

														lowerCAmelCase__			  =      ClapProcessor(tokenizer=lowerCamelCase_   ,       feature_extractor=lowerCamelCase_  )

														lowerCAmelCase__			  =      floats_list((3, 10_00)  )

														lowerCAmelCase__			  =      feature_extractor(lowerCamelCase_   ,       return_tensors='''np'''  )
														lowerCAmelCase__			  =      processor(audios=lowerCamelCase_   ,       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  )			->				int:
														lowerCAmelCase__			  =      self.get_feature_extractor()
														lowerCAmelCase__			  =      self.get_tokenizer()

														lowerCAmelCase__			  =      ClapProcessor(tokenizer=lowerCamelCase_   ,       feature_extractor=lowerCamelCase_  )

														lowerCAmelCase__			  =      '''This is a test string'''

														lowerCAmelCase__			  =      processor(text=lowerCamelCase_  )

														lowerCAmelCase__			  =      tokenizer(lowerCamelCase_  )

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







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[str]:
														lowerCAmelCase__			  =      self.get_feature_extractor()
														lowerCAmelCase__			  =      self.get_tokenizer()

														lowerCAmelCase__			  =      ClapProcessor(tokenizer=lowerCamelCase_   ,       feature_extractor=lowerCamelCase_  )

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

														lowerCAmelCase__			  =      processor.batch_decode(lowerCamelCase_  )
														lowerCAmelCase__			  =      tokenizer.batch_decode(lowerCamelCase_  )

														self.assertListEqual(lowerCamelCase_   ,       lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														lowerCAmelCase__			  =      self.get_feature_extractor()
														lowerCAmelCase__			  =      self.get_tokenizer()

														lowerCAmelCase__			  =      ClapProcessor(tokenizer=lowerCamelCase_   ,       feature_extractor=lowerCamelCase_  )

														self.assertListEqual(
														    processor.model_input_names[2:]   ,       feature_extractor.model_input_names   ,       msg='''`processor` and `feature_extractor` model input names do not match'''   ,       )

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 							lowerCAmelCase__   ( unittest.TestCase						):

						@property
						def 			A_					(      self					)       ->   int:


								'''simple docstring'''

								torch.manual_seed(0					)
								_UpperCamelCase         =						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					)       ->   Any:


								'''simple docstring'''

								_UpperCamelCase         =						self.dummy_uncond_unet
								_UpperCamelCase         =						PNDMScheduler()

								_UpperCamelCase         =						PNDMPipeline(unet=a							,				scheduler=a					)
								pndm.to(a					)
								pndm.set_progress_bar_config(disable=a					)

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

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

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

								assert image.shape == (1, 32, 32, 3)
								_UpperCamelCase         =						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 							lowerCAmelCase__   ( unittest.TestCase						):

						def 			A_					(      self					)       ->   Optional[Any]:


								'''simple docstring'''

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

								_UpperCamelCase         =						UNetaDModel.from_pretrained(a					)
								_UpperCamelCase         =						PNDMScheduler()

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

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

								assert image.shape == (1, 32, 32, 3)
								_UpperCamelCase         =						np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]					)

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



