



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


a_			=					logging.get_logger(__name__)



class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  """upernet"""




 def __init__(						self	,				__UpperCAmelCase=None	,				__UpperCAmelCase=512	,				__UpperCAmelCase=0.02	,				__UpperCAmelCase=[1, 2, 3, 6]	,				__UpperCAmelCase=True	,				__UpperCAmelCase=0.4	,				__UpperCAmelCase=384	,				__UpperCAmelCase=256	,				__UpperCAmelCase=1	,				__UpperCAmelCase=False	,				__UpperCAmelCase=255	,				**__UpperCAmelCase	,				):



    '''simple docstring'''
    super().__init__(**__UpperCAmelCase				)

    if backbone_config is None:
       logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''				)
       __lowerCamelCase													=    CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']				)
    elif isinstance(__UpperCAmelCase	,				__UpperCAmelCase				):
       __lowerCamelCase													=    backbone_config.get('''model_type'''				)
       __lowerCamelCase													=    CONFIG_MAPPING[backbone_model_type]
       __lowerCamelCase													=    config_class.from_dict(__UpperCAmelCase				)

    __lowerCamelCase													=    backbone_config
    __lowerCamelCase													=    hidden_size
    __lowerCamelCase													=    initializer_range
    __lowerCamelCase													=    pool_scales
    __lowerCamelCase													=    use_auxiliary_head
    __lowerCamelCase													=    auxiliary_loss_weight
    __lowerCamelCase													=    auxiliary_in_channels
    __lowerCamelCase													=    auxiliary_channels
    __lowerCamelCase													=    auxiliary_num_convs
    __lowerCamelCase													=    auxiliary_concat_input
    __lowerCamelCase													=    loss_ignore_index




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    copy.deepcopy(self.__dict__				)
    __lowerCamelCase													=    self.backbone_config.to_dict()
    __lowerCamelCase													=    self.__class__.model_type
    return output











import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class  __lowerCAmelCase     (	lowerCAmelCase__  ,	lowerCAmelCase__  ,	unittest.TestCase   ):
 lowerCAmelCase__       					=  StableDiffusionXLImgaImgPipeline
 lowerCAmelCase__       					=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
 lowerCAmelCase__       					=  PipelineTesterMixin.required_optional_params - {"""latents"""}
 lowerCAmelCase__       					=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 lowerCAmelCase__       					=  IMAGE_TO_IMAGE_IMAGE_PARAMS
 lowerCAmelCase__       					=  IMAGE_TO_IMAGE_IMAGE_PARAMS




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    torch.manual_seed(0				)
    __lowerCamelCase													=    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''')	,				attention_head_dim=(2, 4)	,				use_linear_projection=__UpperCAmelCase	,				addition_embed_type='''text_time'''	,				addition_time_embed_dim=8	,				transformer_layers_per_block=(1, 2)	,				projection_class_embeddings_input_dim=80	,				cross_attention_dim=64	,				)
    __lowerCamelCase													=    EulerDiscreteScheduler(
        beta_start=0.00_085	,				beta_end=0.012	,				steps_offset=1	,				beta_schedule='''scaled_linear'''	,				timestep_spacing='''leading'''	,				)
    torch.manual_seed(0				)
    __lowerCamelCase													=    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				)
    __lowerCamelCase													=    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=32	,				)
    __lowerCamelCase													=    CLIPTextModel(__UpperCAmelCase				)
    __lowerCamelCase													=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	,				local_files_only=__UpperCAmelCase				)

    __lowerCamelCase													=    CLIPTextModelWithProjection(__UpperCAmelCase				)
    __lowerCamelCase													=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	,				local_files_only=__UpperCAmelCase				)

    __lowerCamelCase													=    {
        '''unet''': unet,
        '''scheduler''': scheduler,
        '''vae''': vae,
        '''text_encoder''': text_encoder,
        '''tokenizer''': tokenizer,
        '''text_encoder_2''': text_encoder_a,
        '''tokenizer_2''': tokenizer_a,
        # "safety_checker": None,
        # "feature_extractor": None,
    }
    return components




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



    '''simple docstring'''
    __lowerCamelCase													=    floats_tensor((1, 3, 32, 32)	,				rng=random.Random(__UpperCAmelCase				)				).to(__UpperCAmelCase				)
    __lowerCamelCase													=    image / 2 + 0.5
    if str(__UpperCAmelCase				).startswith('''mps'''				):
       __lowerCamelCase													=    torch.manual_seed(__UpperCAmelCase				)
    else:
       __lowerCamelCase													=    torch.Generator(device=__UpperCAmelCase				).manual_seed(__UpperCAmelCase				)
    __lowerCamelCase													=    {
        '''prompt''': '''A painting of a squirrel eating a burger''',
        '''image''': image,
        '''generator''': generator,
        '''num_inference_steps''': 2,
        '''guidance_scale''': 5.0,
        '''output_type''': '''numpy''',
        '''strength''': 0.75,
    }
    return inputs




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
    __lowerCamelCase													=    self.get_dummy_components()
    __lowerCamelCase													=    StableDiffusionXLImgaImgPipeline(**__UpperCAmelCase				)
    __lowerCamelCase													=    sd_pipe.to(__UpperCAmelCase				)
    sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

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

    assert image.shape == (1, 32, 32, 3)

    __lowerCamelCase													=    np.array([0.4_656, 0.4_840, 0.4_439, 0.6_698, 0.5_574, 0.4_524, 0.5_799, 0.5_943, 0.5_165]				)

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




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    super().test_attention_slicing_forward_pass(expected_max_diff=3E-3				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    super().test_inference_batch_single_identical(expected_max_diff=3E-3				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_dummy_components()
    __lowerCamelCase													=    StableDiffusionXLImgaImgPipeline(**__UpperCAmelCase				)
    __lowerCamelCase													=    sd_pipe.to(__UpperCAmelCase				)
    __lowerCamelCase													=    sd_pipe.to(__UpperCAmelCase				)
    sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    # forward without prompt embeds
    __lowerCamelCase													=    self.get_dummy_inputs(__UpperCAmelCase				)
    __lowerCamelCase													=    3 * ['''this is a negative prompt''']
    __lowerCamelCase													=    negative_prompt
    __lowerCamelCase													=    3 * [inputs['''prompt''']]

    __lowerCamelCase													=    sd_pipe(**__UpperCAmelCase				)
    __lowerCamelCase													=    output.images[0, -3:, -3:, -1]

    # forward with prompt embeds
    __lowerCamelCase													=    self.get_dummy_inputs(__UpperCAmelCase				)
    __lowerCamelCase													=    3 * ['''this is a negative prompt''']
    __lowerCamelCase													=    3 * [inputs.pop('''prompt'''				)]

    (
        (
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,
    )													=    sd_pipe.encode_prompt(__UpperCAmelCase	,				negative_prompt=__UpperCAmelCase				)

    __lowerCamelCase													=    sd_pipe(
        **__UpperCAmelCase	,				prompt_embeds=__UpperCAmelCase	,				negative_prompt_embeds=__UpperCAmelCase	,				pooled_prompt_embeds=__UpperCAmelCase	,				negative_pooled_prompt_embeds=__UpperCAmelCase	,				)
    __lowerCamelCase													=    output.images[0, -3:, -3:, -1]

    # make sure that it's equal
    assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()				).max() < 1E-4



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




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase="cpu"	,				__UpperCAmelCase=torch.floataa	,				__UpperCAmelCase=0				):



    '''simple docstring'''
    __lowerCamelCase													=    torch.Generator(device=__UpperCAmelCase				).manual_seed(__UpperCAmelCase				)
    __lowerCamelCase													=    np.random.RandomState(__UpperCAmelCase				).standard_normal((1, 4, 64, 64)				)
    __lowerCamelCase													=    torch.from_numpy(__UpperCAmelCase				).to(device=__UpperCAmelCase	,				dtype=__UpperCAmelCase				)
    __lowerCamelCase													=    {
        '''prompt''': '''a photograph of an astronaut riding a horse''',
        '''latents''': latents,
        '''generator''': generator,
        '''num_inference_steps''': 3,
        '''guidance_scale''': 7.5,
        '''output_type''': '''numpy''',
    }
    return inputs




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''				)
    pipe.to(__UpperCAmelCase				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

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

    assert image.shape == (1, 512, 512, 3)
    __lowerCamelCase													=    np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506]				)
    assert np.abs(image_slice - expected_slice				).max() < 7E-3






