


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        __snake_case   (				self			:    List[str]							)      ->       Optional[int]:
														__snake_case						:     List[str]									=  "laion/clap-htsat-unfused"
														__snake_case						:     int									=  tempfile.mkdtemp()

							def        __snake_case   (				self			:    List[Any]       ,      **lowerCamelCase			:    List[str]							)      ->       Optional[int]:
														return RobertaTokenizer.from_pretrained(self.checkpoint       ,      **lowerCamelCase							)

							def        __snake_case   (				self			:    Union[str, Any]       ,      **lowerCamelCase			:    Tuple							)      ->       Optional[Any]:
														return ClapFeatureExtractor.from_pretrained(self.checkpoint       ,      **lowerCamelCase							)

							def        __snake_case   (				self			:    Any							)      ->       Any:
														shutil.rmtree(self.tmpdirname							)

							def        __snake_case   (				self			:    Union[str, Any]							)      ->       str:
														__snake_case						:     List[Any]									=  self.get_tokenizer()
														__snake_case						:     Any									=  self.get_feature_extractor()

														__snake_case						:     str									=  ClapProcessor(tokenizer=lowerCamelCase       ,      feature_extractor=lowerCamelCase							)

														processor.save_pretrained(self.tmpdirname							)
														__snake_case						:     List[Any]									=  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        __snake_case   (				self			:    List[str]							)      ->       Union[str, Any]:
														__snake_case						:     List[str]									=  ClapProcessor(tokenizer=self.get_tokenizer()       ,      feature_extractor=self.get_feature_extractor()							)
														processor.save_pretrained(self.tmpdirname							)

														__snake_case						:     Union[str, Any]									=  self.get_tokenizer(bos_token="(BOS)"       ,      eos_token="(EOS)"							)
														__snake_case						:     Union[str, Any]									=  self.get_feature_extractor(do_normalize=lowerCamelCase       ,      padding_value=1.0							)

														__snake_case						:     Any									=  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        __snake_case   (				self			:    Optional[Any]							)      ->       Dict:
														__snake_case						:     Optional[Any]									=  self.get_feature_extractor()
														__snake_case						:     Dict									=  self.get_tokenizer()

														__snake_case						:     Any									=  ClapProcessor(tokenizer=lowerCamelCase       ,      feature_extractor=lowerCamelCase							)

														__snake_case						:     List[str]									=  floats_list((3, 1000)							)

														__snake_case						:     List[str]									=  feature_extractor(lowerCamelCase       ,      return_tensors="np"							)
														__snake_case						:     Dict									=  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        __snake_case   (				self			:    Any							)      ->       Optional[Any]:
														__snake_case						:     Dict									=  self.get_feature_extractor()
														__snake_case						:     Optional[Any]									=  self.get_tokenizer()

														__snake_case						:     str									=  ClapProcessor(tokenizer=lowerCamelCase       ,      feature_extractor=lowerCamelCase							)

														__snake_case						:     Dict									=  "This is a test string"

														__snake_case						:     List[Any]									=  processor(text=lowerCamelCase							)

														__snake_case						:     Dict									=  tokenizer(lowerCamelCase							)

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

							def        __snake_case   (				self			:    List[Any]							)      ->       Union[str, Any]:
														__snake_case						:     Dict									=  self.get_feature_extractor()
														__snake_case						:     Union[str, Any]									=  self.get_tokenizer()

														__snake_case						:     int									=  ClapProcessor(tokenizer=lowerCamelCase       ,      feature_extractor=lowerCamelCase							)

														__snake_case						:     Optional[Any]									=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

														__snake_case						:     Tuple									=  processor.batch_decode(lowerCamelCase							)
														__snake_case						:     int									=  tokenizer.batch_decode(lowerCamelCase							)

														self.assertListEqual(lowerCamelCase       ,      lowerCamelCase							)






							def        __snake_case   (				self			:    Any							)      ->       int:
														__snake_case						:     Dict									=  self.get_feature_extractor()
														__snake_case						:     Tuple									=  self.get_tokenizer()

														__snake_case						:     List[str]									=  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 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        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




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



     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




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

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

        A_       :					List[Any]        =      replicate(snake_case	)
        A_       :					str        =      shard(snake_case	)

        A_       :					Any        =      jax.random.PRNGKey(0	)
        A_       :					Dict        =      jax.random.split(snake_case      ,   jax.device_count()	)

        A_       :					int        =      sd_pipe(snake_case      ,   snake_case      ,   snake_case      ,   num_inference_steps=25      ,   jit=snake_case	)[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

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

        A_       :					Dict        =      jnp.asarray(jax.device_get(image_slice.flatten()	)	)
        A_       :					List[str]        =      jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512]	)
        print(f"output_slice: {output_slice}"	)
        assert jnp.abs(output_slice - expected_slice	).max() < 1e-2





     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]	):





        '''simple docstring'''




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

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

        A_       :					Optional[Any]        =      replicate(snake_case	)
        A_       :					Dict        =      shard(snake_case	)

        A_       :					Any        =      jax.random.PRNGKey(0	)
        A_       :					Union[str, Any]        =      jax.random.split(snake_case      ,   jax.device_count()	)

        A_       :					Union[str, Any]        =      sd_pipe(snake_case      ,   snake_case      ,   snake_case      ,   num_inference_steps=25      ,   jit=snake_case	)[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

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

        A_       :					Tuple        =      jnp.asarray(jax.device_get(image_slice.flatten()	)	)
        A_       :					str        =      jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]	)
        print(f"output_slice: {output_slice}"	)
        assert jnp.abs(output_slice - expected_slice	).max() < 1e-2
