

import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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


enable_full_determinism()

@skip_mps
class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   Dict       				=  StableDiffusionPanoramaPipeline
	_lowercase  :   int       				=  TEXT_TO_IMAGE_PARAMS
	_lowercase  :   int       				=  TEXT_TO_IMAGE_BATCH_PARAMS
	_lowercase  :   int       				=  TEXT_TO_IMAGE_IMAGE_PARAMS
	_lowercase  :   int       				=  TEXT_TO_IMAGE_IMAGE_PARAMS







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						torch.manual_seed(0      )
						__magic_name__							=    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  , )
						__magic_name__							=    DDIMScheduler()
						torch.manual_seed(0      )
						__magic_name__							=    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  , )
						torch.manual_seed(0      )
						__magic_name__							=    CLIPTextConfig(
						    bos_token_id=0  , eos_token_id=2  , hidden_size=32  , intermediate_size=37  , layer_norm_eps=1E-05  , num_attention_heads=4  , num_hidden_layers=5  , pad_token_id=1  , vocab_size=10_00  , )
						__magic_name__							=    CLIPTextModel(__UpperCamelCase      )
						__magic_name__							=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

						__magic_name__							=    {
						    'unet': unet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}
						return components







	def 	_SCREAMING_SNAKE_CASE							(   self: int  , __UpperCamelCase: List[str]  , __UpperCamelCase: Optional[int]=0      ):

						'''simple docstring'''





						__magic_name__							=    torch.manual_seed(__UpperCamelCase      )
						__magic_name__							=    {
						    'prompt': 'a photo of the dolomites',
						    'generator': generator,
						    # Setting height and width to None to prevent OOMs on CPU.
						    'height': None,
						    'width': None,
						    'num_inference_steps': 1,
						    'guidance_scale': 6.0,
						    'output_type': 'numpy',
						}
						return inputs







	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						__magic_name__							=    'cpu'  # ensure determinism for the device-dependent torch.Generator
						__magic_name__							=    self.get_dummy_components()
						__magic_name__							=    StableDiffusionPanoramaPipeline(**__UpperCamelCase      )
						__magic_name__							=    sd_pipe.to(__UpperCamelCase      )
						sd_pipe.set_progress_bar_config(disable=__UpperCamelCase      )

						__magic_name__							=    self.get_dummy_inputs(__UpperCamelCase      )
						__magic_name__							=    sd_pipe(**__UpperCamelCase      ).images
						__magic_name__							=    image[0, -3:, -3:, -1]
						assert image.shape == (1, 64, 64, 3)

						__magic_name__							=    np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757]      )

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







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						super().test_inference_batch_consistent(batch_sizes=[1, 2]      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						super().test_inference_batch_single_identical(batch_size=2  , expected_max_diff=3.25E-3      )







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

						'''simple docstring'''





						__magic_name__							=    'cpu'  # ensure determinism for the device-dependent torch.Generator
						__magic_name__							=    self.get_dummy_components()
						__magic_name__							=    StableDiffusionPanoramaPipeline(**__UpperCamelCase      )
						__magic_name__							=    sd_pipe.to(__UpperCamelCase      )
						sd_pipe.set_progress_bar_config(disable=__UpperCamelCase      )

						__magic_name__							=    self.get_dummy_inputs(__UpperCamelCase      )
						__magic_name__							=    'french fries'
						__magic_name__							=    sd_pipe(**__UpperCamelCase  , negative_prompt=__UpperCamelCase      )
						__magic_name__							=    output.images
						__magic_name__							=    image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)

						__magic_name__							=    np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]      )

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







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						__magic_name__							=    'cpu'  # ensure determinism for the device-dependent torch.Generator
						__magic_name__							=    self.get_dummy_components()
						__magic_name__							=    StableDiffusionPanoramaPipeline(**__UpperCamelCase      )
						__magic_name__							=    sd_pipe.to(__UpperCamelCase      )
						sd_pipe.set_progress_bar_config(disable=__UpperCamelCase      )

						__magic_name__							=    self.get_dummy_inputs(__UpperCamelCase      )
						__magic_name__							=    sd_pipe(**__UpperCamelCase  , view_batch_size=2      )
						__magic_name__							=    output.images
						__magic_name__							=    image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)

						__magic_name__							=    np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]      )

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







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

						'''simple docstring'''





						__magic_name__							=    'cpu'  # ensure determinism for the device-dependent torch.Generator
						__magic_name__							=    self.get_dummy_components()
						__magic_name__							=    EulerAncestralDiscreteScheduler(
						    beta_start=0.00085  , beta_end=0.012  , beta_schedule='scaled_linear'      )
						__magic_name__							=    StableDiffusionPanoramaPipeline(**__UpperCamelCase      )
						__magic_name__							=    sd_pipe.to(__UpperCamelCase      )
						sd_pipe.set_progress_bar_config(disable=__UpperCamelCase      )

						__magic_name__							=    self.get_dummy_inputs(__UpperCamelCase      )
						__magic_name__							=    sd_pipe(**__UpperCamelCase      ).images
						__magic_name__							=    image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)

						__magic_name__							=    np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952]      )

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







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						__magic_name__							=    'cpu'  # ensure determinism for the device-dependent torch.Generator
						__magic_name__							=    self.get_dummy_components()
						__magic_name__							=    PNDMScheduler(
						    beta_start=0.00085  , beta_end=0.012  , beta_schedule='scaled_linear'  , skip_prk_steps=__UpperCamelCase      )
						__magic_name__							=    StableDiffusionPanoramaPipeline(**__UpperCamelCase      )
						__magic_name__							=    sd_pipe.to(__UpperCamelCase      )
						sd_pipe.set_progress_bar_config(disable=__UpperCamelCase      )

						__magic_name__							=    self.get_dummy_inputs(__UpperCamelCase      )
						__magic_name__							=    sd_pipe(**__UpperCamelCase      ).images
						__magic_name__							=    image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)

						__magic_name__							=    np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539]      )

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




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







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , __UpperCamelCase: Optional[Any]=0      ):

						'''simple docstring'''





						__magic_name__							=    torch.manual_seed(__UpperCamelCase      )
						__magic_name__							=    {
						    'prompt': 'a photo of the dolomites',
						    'generator': generator,
						    'num_inference_steps': 3,
						    'guidance_scale': 7.5,
						    'output_type': 'numpy',
						}
						return inputs







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						__magic_name__							=    'stabilityai/stable-diffusion-2-base'
						__magic_name__							=    DDIMScheduler.from_pretrained(__UpperCamelCase  , subfolder='scheduler'      )
						__magic_name__							=    StableDiffusionPanoramaPipeline.from_pretrained(__UpperCamelCase  , scheduler=__UpperCamelCase  , safety_checker=__UpperCamelCase      )
						pipe.to(__UpperCamelCase      )
						pipe.set_progress_bar_config(disable=__UpperCamelCase      )
						pipe.enable_attention_slicing()

						__magic_name__							=    self.get_inputs()
						__magic_name__							=    pipe(**__UpperCamelCase      ).images
						__magic_name__							=    image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 5_12, 20_48, 3)

						__magic_name__							=    np.array(
						    [
						        0.36968392,
						        0.27025372,
						        0.32446766,
						        0.28379387,
						        0.36363274,
						        0.30733347,
						        0.27100027,
						        0.27054125,
						        0.25536096,
						    ]      )

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







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						__magic_name__							=    StableDiffusionPanoramaPipeline.from_pretrained(
						    'stabilityai/stable-diffusion-2-base'  , safety_checker=__UpperCamelCase      )
						__magic_name__							=    LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
						pipe.to(__UpperCamelCase      )
						pipe.set_progress_bar_config(disable=__UpperCamelCase      )
						pipe.enable_attention_slicing()

						__magic_name__							=    self.get_inputs()
						__magic_name__							=    pipe(**__UpperCamelCase      ).images
						__magic_name__							=    image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 5_12, 20_48, 3)

						__magic_name__							=    np.array(
						    [
						        [
						            0.0,
						            0.0,
						            0.0,
						            0.0,
						            0.0,
						            0.0,
						            0.0,
						            0.0,
						            0.0,
						        ]
						    ]      )

						assert np.abs(expected_slice - image_slice      ).max() < 1E-3







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    0

						def callback_fn(__UpperCamelCase: int  , __UpperCamelCase: int  , __UpperCamelCase: torch.FloatTensor      ) -> None:
											__magic_name__							=    True
											nonlocal number_of_steps
											number_of_steps += 1
											if step == 1:
																__magic_name__							=    latents.detach().cpu().numpy()
																assert latents.shape == (1, 4, 64, 2_56)
																__magic_name__							=    latents[0, -3:, -3:, -1]

																__magic_name__							=    np.array(
																    [
																        0.18681869,
																        0.33907816,
																        0.5361276,
																        0.14432865,
																        -0.02856611,
																        -0.73941123,
																        0.23397987,
																        0.47322682,
																        -0.37823164,
																    ]      )
																assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5E-2
											elif step == 2:
																__magic_name__							=    latents.detach().cpu().numpy()
																assert latents.shape == (1, 4, 64, 2_56)
																__magic_name__							=    latents[0, -3:, -3:, -1]

																__magic_name__							=    np.array(
																    [
																        0.18539645,
																        0.33987248,
																        0.5378559,
																        0.14437142,
																        -0.02455261,
																        -0.7338317,
																        0.23990755,
																        0.47356272,
																        -0.3786505,
																    ]      )

																assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5E-2

						__magic_name__							=    False

						__magic_name__							=    'stabilityai/stable-diffusion-2-base'
						__magic_name__							=    DDIMScheduler.from_pretrained(__UpperCamelCase  , subfolder='scheduler'      )
						__magic_name__							=    StableDiffusionPanoramaPipeline.from_pretrained(__UpperCamelCase  , scheduler=__UpperCamelCase  , safety_checker=__UpperCamelCase      )
						__magic_name__							=    pipe.to(__UpperCamelCase      )
						pipe.set_progress_bar_config(disable=__UpperCamelCase      )
						pipe.enable_attention_slicing()

						__magic_name__							=    self.get_inputs()
						pipe(**__UpperCamelCase  , callback=__UpperCamelCase  , callback_steps=1      )
						assert callback_fn.has_been_called
						assert number_of_steps == 3







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

						__magic_name__							=    'stabilityai/stable-diffusion-2-base'
						__magic_name__							=    DDIMScheduler.from_pretrained(__UpperCamelCase  , subfolder='scheduler'      )
						__magic_name__							=    StableDiffusionPanoramaPipeline.from_pretrained(__UpperCamelCase  , scheduler=__UpperCamelCase  , safety_checker=__UpperCamelCase      )
						__magic_name__							=    pipe.to(__UpperCamelCase      )
						pipe.set_progress_bar_config(disable=__UpperCamelCase      )
						pipe.enable_attention_slicing(1      )
						pipe.enable_sequential_cpu_offload()

						__magic_name__							=    self.get_inputs()
						__magic_name__							=    pipe(**__UpperCamelCase      )

						__magic_name__							=    torch.cuda.max_memory_allocated()
						# make sure that less than 5.2 GB is allocated
						assert mem_bytes < 5.5 * 10**9







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 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    'laion/clap-htsat-unfused'
						__magic_name__							=    tempfile.mkdtemp()







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , **__UpperCamelCase: List[Any]      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: int  , **__UpperCamelCase: Dict      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						shutil.rmtree(self.tmpdirname      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.get_tokenizer()
						__magic_name__							=    self.get_feature_extractor()

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

						processor.save_pretrained(self.tmpdirname      )
						__magic_name__							=    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 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





						__magic_name__							=    ClapProcessor(tokenizer=self.get_tokenizer()  , feature_extractor=self.get_feature_extractor()      )
						processor.save_pretrained(self.tmpdirname      )

						__magic_name__							=    self.get_tokenizer(bos_token='(BOS)'  , eos_token='(EOS)'      )
						__magic_name__							=    self.get_feature_extractor(do_normalize=__UpperCamelCase  , padding_value=1.0      )

						__magic_name__							=    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 	_SCREAMING_SNAKE_CASE							(   self: List[str]      ):

						'''simple docstring'''





						__magic_name__							=    self.get_feature_extractor()
						__magic_name__							=    self.get_tokenizer()

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

						__magic_name__							=    floats_list((3, 10_00)      )

						__magic_name__							=    feature_extractor(__UpperCamelCase  , return_tensors='np'      )
						__magic_name__							=    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 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.get_feature_extractor()
						__magic_name__							=    self.get_tokenizer()

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

						__magic_name__							=    'This is a test string'

						__magic_name__							=    processor(text=__UpperCamelCase      )

						__magic_name__							=    tokenizer(__UpperCamelCase      )

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







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.get_feature_extractor()
						__magic_name__							=    self.get_tokenizer()

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

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

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

						self.assertListEqual(__UpperCamelCase  , __UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						__magic_name__							=    self.get_feature_extractor()
						__magic_name__							=    self.get_tokenizer()

						__magic_name__							=    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'  , )




