



import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
						import jax
						import jax.numpy as jnp
						from flax.jax_utils import replicate
						from flax.training.common_utils import shard



@slow
@require_flax
class     UpperCAmelCase_       (			unittest.TestCase):







							def   snake_case__ (  self):

														'''simple docstring'''

														super().tearDown()
														gc.collect()







							def   snake_case__ (  self):

														'''simple docstring'''

														_lowerCAmelCase  ,       _lowerCAmelCase	:				List[Any]					  =       FlaxStableDiffusionPipeline.from_pretrained(
														    "stabilityai/stable-diffusion-2",     revision="bf16",     dtype=jnp.bfloataa,     )

														_lowerCAmelCase	:				Dict					  =       "A painting of a squirrel eating a burger"
														_lowerCAmelCase	:				int					  =       jax.device_count()
														_lowerCAmelCase	:				List[Any]					  =       num_samples * [prompt]
														_lowerCAmelCase	:				List[str]					  =       sd_pipe.prepare_inputs(__UpperCamelCase)

														_lowerCAmelCase	:				int					  =       replicate(__UpperCamelCase)
														_lowerCAmelCase	:				Optional[Any]					  =       shard(__UpperCamelCase)

														_lowerCAmelCase	:				str					  =       jax.random.PRNGKey(0)
														_lowerCAmelCase	:				List[str]					  =       jax.random.split(__UpperCamelCase,     jax.device_count())

														_lowerCAmelCase	:				Tuple					  =       sd_pipe(__UpperCamelCase,     __UpperCamelCase,     __UpperCamelCase,     num_inference_steps=25,     jit=__UpperCamelCase)[0]
														assert images.shape == (jax.device_count(), 1, 768, 768, 3)

														_lowerCAmelCase	:				Optional[int]					  =       images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
														_lowerCAmelCase	:				Any					  =       images[0, 253:256, 253:256, -1]

														_lowerCAmelCase	:				Optional[Any]					  =       jnp.asarray(jax.device_get(image_slice.flatten()))
														_lowerCAmelCase	:				List[str]					  =       jnp.array([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512])
														print(f"output_slice: {output_slice}")
														assert jnp.abs(output_slice - expected_slice).max() < 1E-2







							def   snake_case__ (  self):

														'''simple docstring'''

														_lowerCAmelCase	:				int					  =       "stabilityai/stable-diffusion-2"
														_lowerCAmelCase  ,       _lowerCAmelCase	:				Dict					  =       FlaxDPMSolverMultistepScheduler.from_pretrained(__UpperCamelCase,     subfolder="scheduler")
														_lowerCAmelCase  ,       _lowerCAmelCase	:				int					  =       FlaxStableDiffusionPipeline.from_pretrained(
														    __UpperCamelCase,     scheduler=__UpperCamelCase,     revision="bf16",     dtype=jnp.bfloataa,     )
														_lowerCAmelCase	:				str					  =       scheduler_params

														_lowerCAmelCase	:				int					  =       "A painting of a squirrel eating a burger"
														_lowerCAmelCase	:				Any					  =       jax.device_count()
														_lowerCAmelCase	:				str					  =       num_samples * [prompt]
														_lowerCAmelCase	:				str					  =       sd_pipe.prepare_inputs(__UpperCamelCase)

														_lowerCAmelCase	:				Optional[int]					  =       replicate(__UpperCamelCase)
														_lowerCAmelCase	:				Optional[Any]					  =       shard(__UpperCamelCase)

														_lowerCAmelCase	:				int					  =       jax.random.PRNGKey(0)
														_lowerCAmelCase	:				Any					  =       jax.random.split(__UpperCamelCase,     jax.device_count())

														_lowerCAmelCase	:				List[str]					  =       sd_pipe(__UpperCamelCase,     __UpperCamelCase,     __UpperCamelCase,     num_inference_steps=25,     jit=__UpperCamelCase)[0]
														assert images.shape == (jax.device_count(), 1, 768, 768, 3)

														_lowerCAmelCase	:				str					  =       images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
														_lowerCAmelCase	:				Optional[Any]					  =       images[0, 253:256, 253:256, -1]

														_lowerCAmelCase	:				Dict					  =       jnp.asarray(jax.device_get(image_slice.flatten()))
														_lowerCAmelCase	:				int					  =       jnp.array([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297])
														print(f"output_slice: {output_slice}")
														assert jnp.abs(output_slice - expected_slice).max() < 1E-2








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 				_UpperCamelCase      (unittest.TestCase      ):


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           __lowerCAmelCase             =       "laion/clap-htsat-unfused"
           __lowerCAmelCase             =       tempfile.mkdtemp()


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


      def 			__UpperCAmelCase		(						self       ,    **__UpperCamelCase						)->						List[Any]:
           return ClapFeatureExtractor.from_pretrained(self.checkpoint       ,    **__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           shutil.rmtree(self.tmpdirname						)


      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.get_tokenizer()
           __lowerCAmelCase             =       self.get_feature_extractor()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

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

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

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


      def 			__UpperCAmelCase		(						self						)->						List[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=__UpperCamelCase       ,    padding_value=1.0						)

           __lowerCAmelCase             =       ClapProcessor.from_pretrained(
               self.tmpdirname       ,    bos_token="(BOS)"       ,    eos_token="(EOS)"       ,    do_normalize=__UpperCamelCase       ,    padding_value=1.0						)

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

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


      def 			__UpperCAmelCase		(						self						)->						List[str]:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

           __lowerCAmelCase             =       floats_list((3, 1_0_0_0)						)

           __lowerCAmelCase             =       feature_extractor(__UpperCamelCase       ,    return_tensors="np"						)
           __lowerCAmelCase             =       processor(audios=__UpperCamelCase       ,    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 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

           __lowerCAmelCase             =       "This is a test string"

           __lowerCAmelCase             =       processor(text=__UpperCamelCase						)

           __lowerCAmelCase             =       tokenizer(__UpperCamelCase						)

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


      def 			__UpperCAmelCase		(						self						)->						int:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

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

           __lowerCAmelCase             =       processor.batch_decode(__UpperCamelCase						)
           __lowerCAmelCase             =       tokenizer.batch_decode(__UpperCamelCase						)

           self.assertListEqual(__UpperCamelCase       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

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






