

import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    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 PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


@skip_mps
class     lowerCAmelCase				(    __UpperCamelCase   ,				__UpperCamelCase   ,				__UpperCamelCase   ,				unittest.TestCase				):


       '''simple docstring'''







       snake_case    							=	StableDiffusionModelEditingPipeline
       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

       def   lowerCamelCase__						(	self					:  Optional[int]     )   -> Union[str, Any]:







        '''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')						,	cross_attention_dim=32						,	)
        lowerCamelCase      						=    DDIMScheduler()
        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						,	)
        torch.manual_seed(0     )
        lowerCamelCase      						=    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						,	)
        lowerCamelCase      						=    CLIPTextModel(__snake_case     )
        lowerCamelCase      						=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

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

       def   lowerCamelCase__						(	self					:  Optional[int]						,	__snake_case					:  str						,	__snake_case					:  Any=0     )   -> Optional[int]:







        '''simple docstring'''

        lowerCamelCase      						=    torch.manual_seed(__snake_case     )
        lowerCamelCase      						=    {
            'prompt': 'A field of roses',
            'generator': generator,
            # Setting height and width to None to prevent OOMs on CPU.
            'height': None,
            'width': None,
            'num_inference_steps': 2,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
        }
        return inputs

       def   lowerCamelCase__						(	self					:  Tuple     )   -> Dict:







        '''simple docstring'''

        lowerCamelCase      						=    'cpu'  # ensure determinism for the device-dependent torch.Generator
        lowerCamelCase      						=    self.get_dummy_components()
        lowerCamelCase      						=    StableDiffusionModelEditingPipeline(**__snake_case     )
        lowerCamelCase      						=    sd_pipe.to(__snake_case     )
        sd_pipe.set_progress_bar_config(disable=__snake_case     )

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

        lowerCamelCase      						=    np.array([0.4755, 0.5132, 0.4976, 0.3904, 0.3554, 0.4765, 0.5139, 0.5158, 0.4889]     )

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

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







        '''simple docstring'''

        lowerCamelCase      						=    'cpu'  # ensure determinism for the device-dependent torch.Generator
        lowerCamelCase      						=    self.get_dummy_components()
        lowerCamelCase      						=    StableDiffusionModelEditingPipeline(**__snake_case     )
        lowerCamelCase      						=    sd_pipe.to(__snake_case     )
        sd_pipe.set_progress_bar_config(disable=__snake_case     )

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

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

        lowerCamelCase      						=    np.array([0.4992, 0.5101, 0.5004, 0.3949, 0.3604, 0.4735, 0.5216, 0.5204, 0.4913]     )

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

       def   lowerCamelCase__						(	self					:  Optional[int]     )   -> Any:







        '''simple docstring'''

        lowerCamelCase      						=    'cpu'  # ensure determinism for the device-dependent torch.Generator
        lowerCamelCase      						=    self.get_dummy_components()
        lowerCamelCase      						=    EulerAncestralDiscreteScheduler(
            beta_start=0.0_0085						,	beta_end=0.012						,	beta_schedule='scaled_linear'     )
        lowerCamelCase      						=    StableDiffusionModelEditingPipeline(**__snake_case     )
        lowerCamelCase      						=    sd_pipe.to(__snake_case     )
        sd_pipe.set_progress_bar_config(disable=__snake_case     )

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

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

        lowerCamelCase      						=    np.array([0.4747, 0.5372, 0.4779, 0.4982, 0.5543, 0.4816, 0.5238, 0.4904, 0.5027]     )

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

       def   lowerCamelCase__						(	self					:  str     )   -> Tuple:







        '''simple docstring'''

        lowerCamelCase      						=    'cpu'  # ensure determinism for the device-dependent torch.Generator
        lowerCamelCase      						=    self.get_dummy_components()
        lowerCamelCase      						=    PNDMScheduler()
        lowerCamelCase      						=    StableDiffusionModelEditingPipeline(**__snake_case     )
        lowerCamelCase      						=    sd_pipe.to(__snake_case     )
        sd_pipe.set_progress_bar_config(disable=__snake_case     )

        lowerCamelCase      						=    self.get_dummy_inputs(__snake_case     )
        # the pipeline does not expect pndm so test if it raises error.
        with self.assertRaises(__snake_case     ):
         lowerCamelCase      						=    sd_pipe(**__snake_case     ).images

       def   lowerCamelCase__						(	self					:  Optional[int]     )   -> List[str]:







        '''simple docstring'''

        super().test_inference_batch_single_identical(expected_max_diff=5e-3     )





       def   lowerCamelCase__						(	self					:  int     )   -> List[Any]:







        '''simple docstring'''

        super().test_attention_slicing_forward_pass(expected_max_diff=5e-3     )


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


       '''simple docstring'''







       def   lowerCamelCase__						(	self					:  Tuple     )   -> str:







        '''simple docstring'''

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

       def   lowerCamelCase__						(	self					:  Tuple						,	__snake_case					:  Tuple=0     )   -> Any:







        '''simple docstring'''

        lowerCamelCase      						=    torch.manual_seed(__snake_case     )
        lowerCamelCase      						=    {
            'prompt': 'A field of roses',
            'generator': generator,
            'num_inference_steps': 3,
            'guidance_scale': 7.5,
            'output_type': 'numpy',
        }
        return inputs

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







        '''simple docstring'''

        lowerCamelCase      						=    'CompVis/stable-diffusion-v1-4'
        lowerCamelCase      						=    StableDiffusionModelEditingPipeline.from_pretrained(__snake_case						,	safety_checker=__snake_case     )
        pipe.to(__snake_case     )
        pipe.set_progress_bar_config(disable=__snake_case     )
        pipe.enable_attention_slicing()

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

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

        lowerCamelCase      						=    np.array(
            [0.674_9496, 0.638_6453, 0.5144_3267, 0.6609_4905, 0.6192_1215, 0.549_1332, 0.574_4417, 0.5807_5106, 0.517_4658]     )

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

        # make sure image changes after editing
        pipe.edit_model('A pack of roses'						,	'A pack of blue roses'     )

        lowerCamelCase      						=    pipe(**__snake_case     ).images
        lowerCamelCase      						=    image[0, -3:, -3:, -1].flatten()

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

        assert np.abs(expected_slice - image_slice     ).max() > 1e-1





       def   lowerCamelCase__						(	self					:  Optional[Any]     )   -> Optional[int]:







        '''simple docstring'''

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

        lowerCamelCase      						=    'CompVis/stable-diffusion-v1-4'
        lowerCamelCase      						=    DDIMScheduler.from_pretrained(__snake_case						,	subfolder='scheduler'     )
        lowerCamelCase      						=    StableDiffusionModelEditingPipeline.from_pretrained(
            __snake_case						,	scheduler=__snake_case						,	safety_checker=__snake_case     )
        lowerCamelCase      						=    pipe.to(__snake_case     )
        pipe.set_progress_bar_config(disable=__snake_case     )
        pipe.enable_attention_slicing(1     )
        pipe.enable_sequential_cpu_offload()

        lowerCamelCase      						=    self.get_inputs()
        lowerCamelCase      						=    pipe(**__snake_case     )

        lowerCamelCase      						=    torch.cuda.max_memory_allocated()
        # make sure that less than 4.4 GB is allocated
        assert mem_bytes < 4.4 * 10**9







def a_		(       UpperCamelCase_   :							int   ,					UpperCamelCase_   :							list  )							->   list:







 """simple docstring"""

 lowerCamelCase      						=    sorted(UpperCamelCase_   ,					key=lambda UpperCamelCase_  : value[2]   ,					reverse=UpperCamelCase_  )

 # Create a list of size equal to the maximum deadline
 # and initialize it with -1
 lowerCamelCase      						=    max(UpperCamelCase_   ,					key=lambda UpperCamelCase_  : value[1]  )[1]
 lowerCamelCase      						=    [-1] * max_deadline

 # Finding the maximum profit and the count of jobs
 lowerCamelCase      						=    0
 lowerCamelCase      						=    0
 for job in jobs:
  # Find a free time slot for this job
  # (Note that we start from the last possible slot)
  for i in range(job[1] - 1   ,					-1   ,					-1  ):
   if time_slots[i] == -1:
    lowerCamelCase      						=    job[0]
    count += 1
    max_profit += job[2]
    break
 return [count, max_profit]


if __name__ == "__main__":
     import doctest

     doctest.testmod()




