

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   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_: int  		=       "laion/clap-htsat-unfused"
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       tempfile.mkdtemp()



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						**lowerCAmelCase__  :     Optional[Any]):
									return RobertaTokenizer.from_pretrained(self.checkpoint   ,						**lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple   ,						**lowerCAmelCase__  :     List[Any]):
									return ClapFeatureExtractor.from_pretrained(self.checkpoint   ,						**lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									shutil.rmtree(self.tmpdirname)



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.get_tokenizer()
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self.get_feature_extractor()

									SCREAMING_SNAKE_CASE_: Tuple  		=       ClapProcessor(tokenizer=lowerCAmelCase__   ,						feature_extractor=lowerCAmelCase__)

									processor.save_pretrained(self.tmpdirname)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       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  :     List[str]):
									SCREAMING_SNAKE_CASE_: int  		=       ClapProcessor(tokenizer=self.get_tokenizer()   ,						feature_extractor=self.get_feature_extractor())
									processor.save_pretrained(self.tmpdirname)

									SCREAMING_SNAKE_CASE_: Dict  		=       self.get_tokenizer(bos_token="(BOS)"   ,						eos_token="(EOS)")
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.get_feature_extractor(do_normalize=lowerCAmelCase__   ,						padding_value=1.0)

									SCREAMING_SNAKE_CASE_: Tuple  		=       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  :     str):
									SCREAMING_SNAKE_CASE_: Tuple  		=       self.get_feature_extractor()
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.get_tokenizer()

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       ClapProcessor(tokenizer=lowerCAmelCase__   ,						feature_extractor=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       floats_list((3, 1000))

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       feature_extractor(lowerCAmelCase__   ,						return_tensors="np")
									SCREAMING_SNAKE_CASE_: List[Any]  		=       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  :     List[str]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.get_feature_extractor()
									SCREAMING_SNAKE_CASE_: int  		=       self.get_tokenizer()

									SCREAMING_SNAKE_CASE_: Tuple  		=       ClapProcessor(tokenizer=lowerCAmelCase__   ,						feature_extractor=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Tuple  		=       "This is a test string"

									SCREAMING_SNAKE_CASE_: int  		=       processor(text=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       tokenizer(lowerCAmelCase__)

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



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.get_feature_extractor()
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.get_tokenizer()

									SCREAMING_SNAKE_CASE_: str  		=       ClapProcessor(tokenizer=lowerCAmelCase__   ,						feature_extractor=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: List[str]  		=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       processor.batch_decode(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Dict  		=       tokenizer.batch_decode(lowerCAmelCase__)

									self.assertListEqual(lowerCAmelCase__   ,						lowerCAmelCase__)




			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: str  		=       self.get_feature_extractor()
									SCREAMING_SNAKE_CASE_: List[Any]  		=       self.get_tokenizer()

									SCREAMING_SNAKE_CASE_: Dict  		=       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"   ,						)












"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class lowerCAmelCase__	(     lowercase,	unittest.TestCase						):






			'''simple docstring'''



			lowerCamelCase__      =			DDIMPipeline
			lowerCamelCase__      =			UNCONDITIONAL_IMAGE_GENERATION_PARAMS
			lowerCamelCase__      =			PipelineTesterMixin.required_optional_params - {
			    """num_images_per_prompt""",
			    """latents""",
			    """callback""",
			    """callback_steps""",
			}
			lowerCamelCase__      =			UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
			lowerCamelCase__      =			False
			def 					A_    (  self  ):
							torch.manual_seed(0  )
							_lowerCamelCase     :			List[Any]    =    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')  ,      )
							_lowerCamelCase     :			List[str]    =    DDIMScheduler()
							_lowerCamelCase     :			Optional[int]    =    {'unet': unet, 'scheduler': scheduler}
							return components
			def 					A_    (  self  ,      lowercase  ,      lowercase=0  ):
							if str(lowercase  ).startswith('mps'  ):
											_lowerCamelCase     :			Dict    =    torch.manual_seed(lowercase  )
							else:
											_lowerCamelCase     :			List[str]    =    torch.Generator(device=lowercase  ).manual_seed(lowercase  )
							_lowerCamelCase     :			Tuple    =    {
							    'batch_size': 1,
							    'generator': generator,
							    'num_inference_steps': 2,
							    'output_type': 'numpy',
							}
							return inputs
			def 					A_    (  self  ):
							_lowerCamelCase     :			Any    =    'cpu'

							_lowerCamelCase     :			Tuple    =    self.get_dummy_components()
							_lowerCamelCase     :			Optional[Any]    =    self.pipeline_class(**lowercase  )
							pipe.to(lowercase  )
							pipe.set_progress_bar_config(disable=lowercase  )

							_lowerCamelCase     :			str    =    self.get_dummy_inputs(lowercase  )
							_lowerCamelCase     :			int    =    pipe(**lowercase  ).images
							_lowerCamelCase     :			Any    =    image[0, -3:, -3:, -1]

							self.assertEqual(image.shape  ,      (1, 32, 32, 3)  )
							_lowerCamelCase     :			Tuple    =    np.array(
							    [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]  )
							_lowerCamelCase     :			str    =    np.abs(image_slice.flatten() - expected_slice  ).max()
							self.assertLessEqual(lowercase  ,      1E-3  )
			def 					A_    (  self  ):
							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3  )
			def 					A_    (  self  ):
							super().test_save_load_local(expected_max_difference=3E-3  )
			def 					A_    (  self  ):
							super().test_save_load_optional_components(expected_max_difference=3E-3  )






			def 					A_    (  self  ):
							super().test_inference_batch_single_identical(expected_max_diff=3E-3  )






@slow
@require_torch_gpu
class lowerCAmelCase__	(     unittest.TestCase						):






			'''simple docstring'''



			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[Any]    =    'google/ddpm-cifar10-32'

							_lowerCamelCase     :			Optional[Any]    =    UNetaDModel.from_pretrained(lowercase  )
							_lowerCamelCase     :			Dict    =    DDIMScheduler()

							_lowerCamelCase     :			Dict    =    DDIMPipeline(unet=lowercase  ,      scheduler=lowercase  )
							ddim.to(lowercase  )
							ddim.set_progress_bar_config(disable=lowercase  )

							_lowerCamelCase     :			List[str]    =    torch.manual_seed(0  )
							_lowerCamelCase     :			str    =    ddim(generator=lowercase  ,      eta=0.0  ,      output_type='numpy'  ).images

							_lowerCamelCase     :			Dict    =    image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							_lowerCamelCase     :			List[Any]    =    np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53]  )

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






			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[int]    =    'google/ddpm-ema-bedroom-256'

							_lowerCamelCase     :			str    =    UNetaDModel.from_pretrained(lowercase  )
							_lowerCamelCase     :			str    =    DDIMScheduler.from_pretrained(lowercase  )

							_lowerCamelCase     :			Optional[int]    =    DDIMPipeline(unet=lowercase  ,      scheduler=lowercase  )
							ddpm.to(lowercase  )
							ddpm.set_progress_bar_config(disable=lowercase  )

							_lowerCamelCase     :			Tuple    =    torch.manual_seed(0  )
							_lowerCamelCase     :			int    =    ddpm(generator=lowercase  ,      output_type='numpy'  ).images

							_lowerCamelCase     :			Dict    =    image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)
							_lowerCamelCase     :			str    =    np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69]  )

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