






'''simple docstring'''
import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, 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 PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class    __SCREAMING_SNAKE_CASE					(	snake_case_		, snake_case_		, unittest.TestCase ):
 snake_case_     							=							StableDiffusionSAGPipeline
 snake_case_     							=							TEXT_TO_IMAGE_PARAMS
 snake_case_     							=							TEXT_TO_IMAGE_BATCH_PARAMS
 snake_case_     							=							TEXT_TO_IMAGE_IMAGE_PARAMS
 snake_case_     							=							TEXT_TO_IMAGE_IMAGE_PARAMS
 snake_case_     							=							False




 def    __magic_name__		(					self		:							List[str]					)		->  List[str]:
       torch.manual_seed(0					)
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=UNetaDConditionModel(
           block_out_channels=(32, 64)    ,					layers_per_block=2    ,					sample_size=32    ,					in_channels=4    ,					out_channels=4    ,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')    ,					up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')    ,					cross_attention_dim=32    ,					)
       SCREAMING_SNAKE_CASE__   : Dict													=DDIMScheduler(
           beta_start=0.00085    ,					beta_end=0.012    ,					beta_schedule='''scaled_linear'''    ,					clip_sample=__lowercase    ,					set_alpha_to_one=__lowercase    ,					)
       torch.manual_seed(0					)
       SCREAMING_SNAKE_CASE__   : Optional[int]													=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					)
       SCREAMING_SNAKE_CASE__   : int													=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    ,					)
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=CLIPTextModel(__lowercase					)
       SCREAMING_SNAKE_CASE__   : Optional[int]													=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

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




 def    __magic_name__		(					self		:							Optional[int]    ,					__lowercase		:							Any    ,					__lowercase		:							Any=0					)		->  List[str]:
       if str(__lowercase					).startswith('''mps'''					):
             SCREAMING_SNAKE_CASE__   : str													=torch.manual_seed(__lowercase					)
       else:
             SCREAMING_SNAKE_CASE__   : Optional[Any]													=torch.Generator(device=__lowercase					).manual_seed(__lowercase					)
       SCREAMING_SNAKE_CASE__   : List[Any]													={
           '''prompt''': '''.''',
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 1.0,
           '''sag_scale''': 1.0,
           '''output_type''': '''numpy''',
       }
       return inputs




 def    __magic_name__		(					self		:							Tuple					)		->  Optional[Any]:
       super().test_inference_batch_single_identical(expected_max_diff=3e-3					)






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




 def    __magic_name__		(					self		:							str					)		->  Optional[int]:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def    __magic_name__		(					self		:							Optional[Any]					)		->  Tuple:
       SCREAMING_SNAKE_CASE__   : Optional[int]													=StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''					)
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=sag_pipe.to(__lowercase					)
       sag_pipe.set_progress_bar_config(disable=__lowercase					)

       SCREAMING_SNAKE_CASE__   : Any													='''.'''
       SCREAMING_SNAKE_CASE__   : Optional[int]													=torch.manual_seed(0					)
       SCREAMING_SNAKE_CASE__   : int													=sag_pipe(
           [prompt]    ,					generator=__lowercase    ,					guidance_scale=7.5    ,					sag_scale=1.0    ,					num_inference_steps=20    ,					output_type='''np'''					)

       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=output.images

       SCREAMING_SNAKE_CASE__   : Optional[Any]													=image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 5e-2




 def    __magic_name__		(					self		:							Any					)		->  Tuple:
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''					)
       SCREAMING_SNAKE_CASE__   : List[str]													=sag_pipe.to(__lowercase					)
       sag_pipe.set_progress_bar_config(disable=__lowercase					)

       SCREAMING_SNAKE_CASE__   : Any													='''.'''
       SCREAMING_SNAKE_CASE__   : Optional[int]													=torch.manual_seed(0					)
       SCREAMING_SNAKE_CASE__   : Any													=sag_pipe(
           [prompt]    ,					generator=__lowercase    ,					guidance_scale=7.5    ,					sag_scale=1.0    ,					num_inference_steps=20    ,					output_type='''np'''					)

       SCREAMING_SNAKE_CASE__   : Dict													=output.images

       SCREAMING_SNAKE_CASE__   : List[Any]													=image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 5e-2




 def    __magic_name__		(					self		:							List[Any]					)		->  str:
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''					)
       SCREAMING_SNAKE_CASE__   : str													=sag_pipe.to(__lowercase					)
       sag_pipe.set_progress_bar_config(disable=__lowercase					)

       SCREAMING_SNAKE_CASE__   : List[str]													='''.'''
       SCREAMING_SNAKE_CASE__   : Optional[int]													=torch.manual_seed(0					)
       SCREAMING_SNAKE_CASE__   : int													=sag_pipe(
           [prompt]    ,					width=7_68    ,					height=5_12    ,					generator=__lowercase    ,					guidance_scale=7.5    ,					sag_scale=1.0    ,					num_inference_steps=20    ,					output_type='''np'''    ,					)

       SCREAMING_SNAKE_CASE__   : Dict													=output.images

       assert image.shape == (1, 5_12, 7_68, 3)

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A				={'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''WavLMForAudioFrameClassification''',
			    '''WavLMForCTC''',
			    '''WavLMForSequenceClassification''',
			    '''WavLMForXVector''',
			    '''WavLMModel''',
			    '''WavLMPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_wavlm import (
						    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    WavLMForAudioFrameClassification,
						    WavLMForCTC,
						    WavLMForSequenceClassification,
						    WavLMForXVector,
						    WavLMModel,
						    WavLMPreTrainedModel,
						)

else:
			import sys

			__A				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


