





'''simple docstring'''



import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
							import jax
							import jax.numpy as jnp
@slow
@require_flax
class 	lowerCamelCase_							(					unittest.TestCase			):







						def 					__lowercase    (  self   :      List[Any]				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      Any				):

													"""simple docstring"""
													return F"""gaussian_noise_s={seed}_shape={"_".join([str(lowerCAmelCase__				) for s in shape]				)}.npy"""







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													super().tearDown()
													gc.collect()







						def 					__lowercase    (  self   :      Optional[int]				,    lowerCAmelCase__   :      Dict=0				,    lowerCAmelCase__   :      List[str]=(4, 4, 64, 64)				,    lowerCAmelCase__   :      List[str]=False				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			jnp.bfloataa if fpaa else jnp.floataa
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			jnp.array(load_hf_numpy(self.get_file_format(lowerCAmelCase__				,    lowerCAmelCase__				)				)				,    dtype=lowerCAmelCase__				)
													return image







						def 					__lowercase    (  self   :      Optional[int]				,    lowerCAmelCase__   :      Union[str, Any]=False				,    lowerCAmelCase__   :      Dict="CompVis/stable-diffusion-v1-4"				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			jnp.bfloataa if fpaa else jnp.floataa
													SCREAMING_SNAKE_CASE					:      List[str]     					=			'''bf16''' if fpaa else None

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			FlaxUNetaDConditionModel.from_pretrained(
													    lowerCAmelCase__				,    subfolder='''unet'''				,    dtype=lowerCAmelCase__				,    revision=lowerCAmelCase__				)
													return model, params







						def 					__lowercase    (  self   :      List[Any]				,    lowerCAmelCase__   :      List[Any]=0				,    lowerCAmelCase__   :      str=(4, 77, 7_68)				,    lowerCAmelCase__   :      List[str]=False				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			jnp.bfloataa if fpaa else jnp.floataa
													SCREAMING_SNAKE_CASE					:      str     					=			jnp.array(load_hf_numpy(self.get_file_format(lowerCAmelCase__				,    lowerCAmelCase__				)				)				,    dtype=lowerCAmelCase__				)
													return hidden_states







						@parameterized.expand(
						    [
						        # fmt: off
						        [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
						        [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
						        [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
						        [3, 10_00, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
						        # fmt: on
						    ]				)
						def 					__lowercase    (  self   :      Union[str, Any]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      List[Any]				,    lowerCAmelCase__   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4'''				,    fpaa=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      str     					=			self.get_latents(lowerCAmelCase__				,    fpaa=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.get_encoder_hidden_states(lowerCAmelCase__				,    fpaa=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Any     					=			model.apply(
													    {'''params''': params}				,    lowerCAmelCase__				,    jnp.array(lowerCAmelCase__				,    dtype=jnp.intaa				)				,    encoder_hidden_states=lowerCAmelCase__				,    ).sample

													assert sample.shape == latents.shape

													SCREAMING_SNAKE_CASE					:      int     					=			jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())				)				,    dtype=jnp.floataa				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			jnp.array(lowerCAmelCase__				,    dtype=jnp.floataa				)

													# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
													assert jnp.allclose(lowerCAmelCase__				,    lowerCAmelCase__				,    atol=1e-2				)







						@parameterized.expand(
						    [
						        # fmt: off
						        [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
						        [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
						        [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
						        [3, 10_00, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
						        # fmt: on
						    ]				)
						def 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.get_unet_model(model_id='''stabilityai/stable-diffusion-2'''				,    fpaa=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.get_latents(lowerCAmelCase__				,    shape=(4, 4, 96, 96)				,    fpaa=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_encoder_hidden_states(lowerCAmelCase__				,    shape=(4, 77, 10_24)				,    fpaa=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			model.apply(
													    {'''params''': params}				,    lowerCAmelCase__				,    jnp.array(lowerCAmelCase__				,    dtype=jnp.intaa				)				,    encoder_hidden_states=lowerCAmelCase__				,    ).sample

													assert sample.shape == latents.shape

													SCREAMING_SNAKE_CASE					:      Tuple     					=			jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())				)				,    dtype=jnp.floataa				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			jnp.array(lowerCAmelCase__				,    dtype=jnp.floataa				)

													# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
													assert jnp.allclose(lowerCAmelCase__				,    lowerCAmelCase__				,    atol=1e-2				)









'''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 	lowerCamelCase_							(					unittest.TestCase			):







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

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			'''laion/clap-htsat-unfused'''
													SCREAMING_SNAKE_CASE					:      Tuple     					=			tempfile.mkdtemp()







						def 					__lowercase    (  self   :      Tuple				,    **lowerCAmelCase__   :      Dict				):

													"""simple docstring"""
													return RobertaTokenizer.from_pretrained(self.checkpoint				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      int				,    **lowerCAmelCase__   :      str				):

													"""simple docstring"""
													return ClapFeatureExtractor.from_pretrained(self.checkpoint				,    **lowerCAmelCase__				)







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

													"""simple docstring"""
													shutil.rmtree(self.tmpdirname				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.get_tokenizer()
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self.get_feature_extractor()

													SCREAMING_SNAKE_CASE					:      List[str]     					=			ClapProcessor(tokenizer=lowerCAmelCase__				,    feature_extractor=lowerCAmelCase__				)

													processor.save_pretrained(self.tmpdirname				)
													SCREAMING_SNAKE_CASE					:      Optional[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 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			ClapProcessor(tokenizer=self.get_tokenizer()				,    feature_extractor=self.get_feature_extractor()				)
													processor.save_pretrained(self.tmpdirname				)

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_tokenizer(bos_token='''(BOS)'''				,    eos_token='''(EOS)'''				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_feature_extractor(do_normalize=lowerCAmelCase__				,    padding_value=1.0				)

													SCREAMING_SNAKE_CASE					:      str     					=			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 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_feature_extractor()
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.get_tokenizer()

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

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			floats_list((3, 10_00)				)

													SCREAMING_SNAKE_CASE					:      Any     					=			feature_extractor(lowerCAmelCase__				,    return_tensors='''np'''				)
													SCREAMING_SNAKE_CASE					:      Union[str, 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 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.get_feature_extractor()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			ClapProcessor(tokenizer=lowerCAmelCase__				,    feature_extractor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      int     					=			'''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 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			self.get_feature_extractor()
													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Any     					=			ClapProcessor(tokenizer=lowerCAmelCase__				,    feature_extractor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													SCREAMING_SNAKE_CASE					:      Tuple     					=			processor.batch_decode(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			tokenizer.batch_decode(lowerCAmelCase__				)

													self.assertListEqual(lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.get_feature_extractor()
													SCREAMING_SNAKE_CASE					:      str     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			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'''				,    )




