



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 		snake_case    (						__UpperCamelCase       ,__UpperCamelCase       ,unittest.TestCase	):



   '''simple docstring'''






   snake_case_       :			Optional[int]        =		StableDiffusionSAGPipeline
   snake_case_       :			Union[str, Any]        =		TEXT_TO_IMAGE_PARAMS
   snake_case_       :			int        =		TEXT_TO_IMAGE_BATCH_PARAMS
   snake_case_       :			List[str]        =		TEXT_TO_IMAGE_IMAGE_PARAMS
   snake_case_       :			Dict        =		TEXT_TO_IMAGE_IMAGE_PARAMS
   snake_case_       :			Optional[Any]        =		False

   def   UpperCamelCase_	(       self				:      List[Any])					->					Dict:

    """simple docstring"""







    torch.manual_seed(0)
    _snake_case     :	List[str]											=       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					,		)
    _snake_case     :	Optional[Any]											=       DDIMScheduler(
        beta_start=0.00_085					,		beta_end=0.012					,		beta_schedule="""scaled_linear"""					,		clip_sample=__UpperCAmelCase					,		set_alpha_to_one=__UpperCAmelCase					,		)
    torch.manual_seed(0)
    _snake_case     :	Dict											=       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)
    _snake_case     :	List[Any]											=       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=1000					,		)
    _snake_case     :	Any											=       CLIPTextModel(__UpperCAmelCase)
    _snake_case     :	Tuple											=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")

    _snake_case     :	Optional[Any]											=       {
        "unet": unet,
        "scheduler": scheduler,
        "vae": vae,
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "safety_checker": None,
        "feature_extractor": None,
    }
    return components

   def   UpperCamelCase_	(       self				:      Any					,		lowerCAmelCase				:      Optional[int]					,		lowerCAmelCase				:      int=0)					->					Tuple:

    """simple docstring"""







    if str(__UpperCAmelCase).startswith("""mps"""):
     _snake_case     :	int											=       torch.manual_seed(__UpperCAmelCase)
    else:
     _snake_case     :	List[str]											=       torch.Generator(device=__UpperCAmelCase).manual_seed(__UpperCAmelCase)
    _snake_case     :	List[str]											=       {
        "prompt": ".",
        "generator": generator,
        "num_inference_steps": 2,
        "guidance_scale": 1.0,
        "sag_scale": 1.0,
        "output_type": "numpy",
    }
    return inputs


   def   UpperCamelCase_	(       self				:      Dict)					->					Union[str, Any]:

    """simple docstring"""







    super().test_inference_batch_single_identical(expected_max_diff=3E-3)







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



   '''simple docstring'''






   def   UpperCamelCase_	(       self				:      Tuple)					->					Optional[int]:

    """simple docstring"""







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

   def   UpperCamelCase_	(       self				:      List[Any])					->					Tuple:

    """simple docstring"""







    _snake_case     :	List[str]											=       StableDiffusionSAGPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4""")
    _snake_case     :	int											=       sag_pipe.to(__UpperCAmelCase)
    sag_pipe.set_progress_bar_config(disable=__UpperCAmelCase)

    _snake_case     :	Dict											=       "."
    _snake_case     :	int											=       torch.manual_seed(0)
    _snake_case     :	int											=       sag_pipe(
        [prompt]					,		generator=__UpperCAmelCase					,		guidance_scale=7.5					,		sag_scale=1.0					,		num_inference_steps=20					,		output_type="""np""")

    _snake_case     :	Tuple											=       output.images

    _snake_case     :	Any											=       image[0, -3:, -3:, -1]

    assert image.shape == (1, 512, 512, 3)
    _snake_case     :	Optional[Any]											=       np.array([0.1_568, 0.1_738, 0.1_695, 0.1_693, 0.1_507, 0.1_705, 0.1_547, 0.1_751, 0.1_949])

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

   def   UpperCamelCase_	(       self				:      List[Any])					->					str:

    """simple docstring"""







    _snake_case     :	Union[str, Any]											=       StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""")
    _snake_case     :	Optional[Any]											=       sag_pipe.to(__UpperCAmelCase)
    sag_pipe.set_progress_bar_config(disable=__UpperCAmelCase)

    _snake_case     :	Tuple											=       "."
    _snake_case     :	str											=       torch.manual_seed(0)
    _snake_case     :	str											=       sag_pipe(
        [prompt]					,		generator=__UpperCAmelCase					,		guidance_scale=7.5					,		sag_scale=1.0					,		num_inference_steps=20					,		output_type="""np""")

    _snake_case     :	int											=       output.images

    _snake_case     :	Tuple											=       image[0, -3:, -3:, -1]

    assert image.shape == (1, 512, 512, 3)
    _snake_case     :	Tuple											=       np.array([0.3_459, 0.2_876, 0.2_537, 0.3_002, 0.2_671, 0.2_160, 0.3_026, 0.2_262, 0.2_371])

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


   def   UpperCamelCase_	(       self				:      List[Any])					->					Union[str, Any]:

    """simple docstring"""







    _snake_case     :	Optional[int]											=       StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""")
    _snake_case     :	str											=       sag_pipe.to(__UpperCAmelCase)
    sag_pipe.set_progress_bar_config(disable=__UpperCAmelCase)

    _snake_case     :	Tuple											=       "."
    _snake_case     :	Any											=       torch.manual_seed(0)
    _snake_case     :	str											=       sag_pipe(
        [prompt]					,		width=768					,		height=512					,		generator=__UpperCAmelCase					,		guidance_scale=7.5					,		sag_scale=1.0					,		num_inference_steps=20					,		output_type="""np"""					,		)

    _snake_case     :	List[str]											=       output.images

    assert image.shape == (1, 512, 768, 3)








from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__      			=						logging.get_logger(__name__)

UpperCAmelCase__      			=						{
    '''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						snake_case__        =      """glpn"""



						def __init__(self:					Tuple ,					__UpperCAmelCase:					Tuple=3 ,					__UpperCAmelCase:					Dict=4 ,					__UpperCAmelCase:					Any=[2, 2, 2, 2] ,					__UpperCAmelCase:					Optional[int]=[8, 4, 2, 1] ,					__UpperCAmelCase:					Dict=[32, 64, 160, 256] ,					__UpperCAmelCase:					List[str]=[7, 3, 3, 3] ,					__UpperCAmelCase:					Dict=[4, 2, 2, 2] ,					__UpperCAmelCase:					Optional[int]=[1, 2, 5, 8] ,					__UpperCAmelCase:					Dict=[4, 4, 4, 4] ,					__UpperCAmelCase:					List[str]="gelu" ,					__UpperCAmelCase:					str=0.0 ,					__UpperCAmelCase:					List[Any]=0.0 ,					__UpperCAmelCase:					Dict=0.02 ,					__UpperCAmelCase:					List[str]=0.1 ,					__UpperCAmelCase:					Union[str, Any]=1E-6 ,					__UpperCAmelCase:					Dict=64 ,					__UpperCAmelCase:					Dict=10 ,					__UpperCAmelCase:					Union[str, Any]=-1 ,					**__UpperCAmelCase:					Dict ,					)    ->     str:




											'''simple docstring'''



											super().__init__(**__UpperCAmelCase   )

											__a					:						Optional[Any]      =					num_channels
											__a					:						Tuple      =					num_encoder_blocks
											__a					:						int      =					depths
											__a					:						Union[str, Any]      =					sr_ratios
											__a					:						str      =					hidden_sizes
											__a					:						List[Any]      =					patch_sizes
											__a					:						Optional[Any]      =					strides
											__a					:						Any      =					mlp_ratios
											__a					:						Any      =					num_attention_heads
											__a					:						Any      =					hidden_act
											__a					:						List[Any]      =					hidden_dropout_prob
											__a					:						int      =					attention_probs_dropout_prob
											__a					:						List[str]      =					initializer_range
											__a					:						Optional[int]      =					drop_path_rate
											__a					:						Any      =					layer_norm_eps
											__a					:						List[Any]      =					decoder_hidden_size
											__a					:						Any      =					max_depth
											__a					:						int      =					head_in_index





