


'''simple docstring'''



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


__lowercase     :						Tuple	=     False





@skip_mps
class 							__UpperCamelCase    (		lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				unittest.TestCase ):
	A_           =   StableDiffusionAttendAndExcitePipeline
	A_           =   False
	A_           =   TEXT_TO_IMAGE_PARAMS
	A_           =   TEXT_TO_IMAGE_BATCH_PARAMS.union({"token_indices"} )
	A_           =   TEXT_TO_IMAGE_IMAGE_PARAMS
	A_           =   TEXT_TO_IMAGE_IMAGE_PARAMS




	@classmethod
	def 				__UpperCAmelCase							(       cls							):

						'''simple docstring'''





						super().setUpClass()
						torch.use_deterministic_algorithms(__lowerCAmelCase							)




	@classmethod
	def 				__UpperCAmelCase							(       cls							):

						'''simple docstring'''





						super().tearDownClass()
						torch.use_deterministic_algorithms(__lowerCAmelCase							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						torch.manual_seed(0							)
						__a      :	List[str]    =  UNetaDConditionModel(
						    block_out_channels=(32, 64)						,    layers_per_block=1						,    sample_size=32						,    in_channels=4						,    out_channels=4						,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')						,    up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')						,    cross_attention_dim=32						,    attention_head_dim=(2, 4)						,    use_linear_projection=__lowerCAmelCase						,    )
						__a      :	Dict    =  DDIMScheduler(
						    beta_start=0.00085						,    beta_end=0.012						,    beta_schedule='scaled_linear'						,    clip_sample=__lowerCAmelCase						,    set_alpha_to_one=__lowerCAmelCase						,    )
						torch.manual_seed(0							)
						__a      :	Tuple    =  AutoencoderKL(
						    block_out_channels=[32, 64]						,    in_channels=3						,    out_channels=3						,    down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']						,    up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']						,    latent_channels=4						,    sample_size=128						,    )
						torch.manual_seed(0							)
						__a      :	List[Any]    =  CLIPTextConfig(
						    bos_token_id=0						,    eos_token_id=2						,    hidden_size=32						,    intermediate_size=37						,    layer_norm_eps=1E-0_5						,    num_attention_heads=4						,    num_hidden_layers=5						,    pad_token_id=1						,    vocab_size=1000						,    hidden_act='gelu'						,    projection_dim=512						,    )
						__a      :	List[str]    =  CLIPTextModel(__lowerCAmelCase							)
						__a      :	Dict    =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

						__a      :	Dict    =  {
						    'unet': unet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}

						return components




	def 				__UpperCAmelCase							(       self						,    __a						,    __a=0							):

						'''simple docstring'''





						if str(__lowerCAmelCase							).startswith('mps'							):
											__a      :	Union[str, Any]    =  torch.manual_seed(__lowerCAmelCase							)
						else:
											__a      :	Optional[int]    =  torch.Generator(device=__lowerCAmelCase							).manual_seed(__lowerCAmelCase							)
						__a      :	List[Any]    =  {
						    'prompt': 'a cat and a frog',
						    'token_indices': [2, 5],
						    'generator': generator,
						    'num_inference_steps': 1,
						    'guidance_scale': 6.0,
						    'output_type': 'numpy',
						    'max_iter_to_alter': 2,
						    'thresholds': {0: 0.7},
						}
						return inputs




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  'cpu'

						__a      :	str    =  self.get_dummy_components()
						__a      :	Optional[Any]    =  self.pipeline_class(**__lowerCAmelCase							)
						pipe.to(__lowerCAmelCase							)
						pipe.set_progress_bar_config(disable=__lowerCAmelCase							)

						__a      :	List[Any]    =  self.get_dummy_inputs(__lowerCAmelCase							)
						__a      :	List[str]    =  pipe(**__lowerCAmelCase							).images
						__a      :	List[str]    =  image[0, -3:, -3:, -1]

						self.assertEqual(image.shape						,    (1, 64, 64, 3)							)
						__a      :	int    =  np.array(
						    [0.63905364, 0.62897307, 0.48599017, 0.5133624, 0.5550048, 0.45769516, 0.50326973, 0.5023139, 0.45384496]							)
						__a      :	List[str]    =  np.abs(image_slice.flatten() - expected_slice							).max()
						self.assertLessEqual(__lowerCAmelCase						,    1E-3							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						super().test_cpu_offload_forward_pass(expected_max_diff=5E-4							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						self._test_inference_batch_consistent(batch_sizes=[1, 2]							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						self._test_inference_batch_single_identical(batch_size=2						,    expected_max_diff=7E-4							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						super().test_save_load_local(expected_max_difference=5E-4							)





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						super().test_save_load_optional_components(expected_max_difference=4E-4							)





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




	@classmethod
	def 				__UpperCAmelCase							(       cls							):

						'''simple docstring'''





						super().setUpClass()
						torch.use_deterministic_algorithms(__lowerCAmelCase							)




	@classmethod
	def 				__UpperCAmelCase							(       cls							):

						'''simple docstring'''





						super().tearDownClass()
						torch.use_deterministic_algorithms(__lowerCAmelCase							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Tuple    =  torch.manual_seed(51							)

						__a      :	List[Any]    =  StableDiffusionAttendAndExcitePipeline.from_pretrained(
						    'CompVis/stable-diffusion-v1-4'						,    safety_checker=__lowerCAmelCase						,    torch_dtype=torch.floataa							)
						pipe.to('cuda'							)

						__a      :	int    =  'a painting of an elephant with glasses'
						__a      :	Optional[Any]    =  [5, 7]

						__a      :	List[Any]    =  pipe(
						    prompt=__lowerCAmelCase						,    token_indices=__lowerCAmelCase						,    guidance_scale=7.5						,    generator=__lowerCAmelCase						,    num_inference_steps=5						,    max_iter_to_alter=5						,    output_type='numpy'						,    ).images[0]

						__a      :	List[str]    =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'							)
						assert np.abs((expected_image - image).max()							) < 5E-1







'''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 							__UpperCamelCase    (		unittest.TestCase ):




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  'laion/clap-htsat-unfused'
						__a      :	Optional[Any]    =  tempfile.mkdtemp()




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





						return RobertaTokenizer.from_pretrained(self.checkpoint						,    **__a							)




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





						return ClapFeatureExtractor.from_pretrained(self.checkpoint						,    **__a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						shutil.rmtree(self.tmpdirname							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  self.get_tokenizer()
						__a      :	List[str]    =  self.get_feature_extractor()

						__a      :	Any    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						processor.save_pretrained(self.tmpdirname							)
						__a      :	Tuple    =  ClapProcessor.from_pretrained(self.tmpdirname							)

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

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  ClapProcessor(tokenizer=self.get_tokenizer()						,    feature_extractor=self.get_feature_extractor()							)
						processor.save_pretrained(self.tmpdirname							)

						__a      :	int    =  self.get_tokenizer(bos_token='(BOS)'						,    eos_token='(EOS)'							)
						__a      :	List[str]    =  self.get_feature_extractor(do_normalize=__a						,    padding_value=1.0							)

						__a      :	Tuple    =  ClapProcessor.from_pretrained(
						    self.tmpdirname						,    bos_token='(BOS)'						,    eos_token='(EOS)'						,    do_normalize=__a						,    padding_value=1.0							)

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

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  self.get_feature_extractor()
						__a      :	int    =  self.get_tokenizer()

						__a      :	str    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						__a      :	int    =  floats_list((3, 1000)							)

						__a      :	str    =  feature_extractor(__a						,    return_tensors='np'							)
						__a      :	int    =  processor(audios=__a						,    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							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  self.get_feature_extractor()
						__a      :	Any    =  self.get_tokenizer()

						__a      :	Any    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						__a      :	Union[str, Any]    =  'This is a test string'

						__a      :	Union[str, Any]    =  processor(text=__a							)

						__a      :	Tuple    =  tokenizer(__a							)

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  self.get_feature_extractor()
						__a      :	str    =  self.get_tokenizer()

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

						__a      :	Dict    =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						__a      :	Optional[int]    =  processor.batch_decode(__a							)
						__a      :	Optional[Any]    =  tokenizer.batch_decode(__a							)

						self.assertListEqual(__a						,    __a							)





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  self.get_feature_extractor()
						__a      :	Optional[int]    =  self.get_tokenizer()

						__a      :	int    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

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



