




'''simple docstring'''





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      _a       (		lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      unittest.TestCase							):

     '''simple docstring'''





     SCREAMING_SNAKE_CASE_							:					List[Any]					       =		StableDiffusionModelEditingPipeline
     SCREAMING_SNAKE_CASE_							:					Union[str, Any]					       =		TEXT_TO_IMAGE_PARAMS
     SCREAMING_SNAKE_CASE_							:					List[Any]					       =		TEXT_TO_IMAGE_BATCH_PARAMS
     SCREAMING_SNAKE_CASE_							:					List[str]					       =		TEXT_TO_IMAGE_IMAGE_PARAMS
     SCREAMING_SNAKE_CASE_							:					Tuple					       =		TEXT_TO_IMAGE_IMAGE_PARAMS






     def        _lowercase			(				self					)							->    List[str]:
          torch.manual_seed(0					)
          _snake_case							=   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							=   DDIMScheduler()
          torch.manual_seed(0					)
          _snake_case							=   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							=   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=1_000      ,)
          _snake_case							=   CLIPTextModel(lowerCamelCase_					)
          _snake_case							=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)

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






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE=0					)							->    List[str]:
          _snake_case							=   torch.manual_seed(lowerCamelCase_					)
          _snake_case							=   {
              "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        _lowercase			(				self					)							->    Tuple:
          _snake_case							=   "cpu"  # ensure determinism for the device-dependent torch.Generator
          _snake_case							=   self.get_dummy_components()
          _snake_case							=   StableDiffusionModelEditingPipeline(**lowerCamelCase_					)
          _snake_case							=   sd_pipe.to(lowerCamelCase_					)
          sd_pipe.set_progress_bar_config(disable=lowerCamelCase_					)

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

          _snake_case							=   np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9]					)

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






     def        _lowercase			(				self					)							->    Optional[int]:
          _snake_case							=   "cpu"  # ensure determinism for the device-dependent torch.Generator
          _snake_case							=   self.get_dummy_components()
          _snake_case							=   StableDiffusionModelEditingPipeline(**lowerCamelCase_					)
          _snake_case							=   sd_pipe.to(lowerCamelCase_					)
          sd_pipe.set_progress_bar_config(disable=lowerCamelCase_					)

          _snake_case							=   self.get_dummy_inputs(lowerCamelCase_					)
          _snake_case							=   "french fries"
          _snake_case							=   sd_pipe(**lowerCamelCase_      ,negative_prompt=lowerCamelCase_					)
          _snake_case							=   output.images
          _snake_case							=   image[0, -3:, -3:, -1]

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

          _snake_case							=   np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3]					)

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






     def        _lowercase			(				self					)							->    Dict:
          _snake_case							=   "cpu"  # ensure determinism for the device-dependent torch.Generator
          _snake_case							=   self.get_dummy_components()
          _snake_case							=   EulerAncestralDiscreteScheduler(
              beta_start=0.0_0_0_8_5      ,beta_end=0.0_1_2      ,beta_schedule="scaled_linear"					)
          _snake_case							=   StableDiffusionModelEditingPipeline(**lowerCamelCase_					)
          _snake_case							=   sd_pipe.to(lowerCamelCase_					)
          sd_pipe.set_progress_bar_config(disable=lowerCamelCase_					)

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

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

          _snake_case							=   np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7]					)

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






     def        _lowercase			(				self					)							->    Union[str, Any]:
          _snake_case							=   "cpu"  # ensure determinism for the device-dependent torch.Generator
          _snake_case							=   self.get_dummy_components()
          _snake_case							=   PNDMScheduler()
          _snake_case							=   StableDiffusionModelEditingPipeline(**lowerCamelCase_					)
          _snake_case							=   sd_pipe.to(lowerCamelCase_					)
          sd_pipe.set_progress_bar_config(disable=lowerCamelCase_					)

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






     def        _lowercase			(				self					)							->    Dict:
          super().test_inference_batch_single_identical(expected_max_diff=5e-3					)






     def        _lowercase			(				self					)							->    Dict:
          super().test_attention_slicing_forward_pass(expected_max_diff=5e-3					)







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

     '''simple docstring'''





     def        _lowercase			(				self					)							->    str:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE=0					)							->    Any:
          _snake_case							=   torch.manual_seed(lowerCamelCase_					)
          _snake_case							=   {
              "prompt": "A field of roses",
              "generator": generator,
              "num_inference_steps": 3,
              "guidance_scale": 7.5,
              "output_type": "numpy",
          }
          return inputs






     def        _lowercase			(				self					)							->    Union[str, Any]:
          _snake_case							=   "CompVis/stable-diffusion-v1-4"
          _snake_case							=   StableDiffusionModelEditingPipeline.from_pretrained(lowerCamelCase_      ,safety_checker=lowerCamelCase_					)
          pipe.to(lowerCamelCase_					)
          pipe.set_progress_bar_config(disable=lowerCamelCase_					)
          pipe.enable_attention_slicing()

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

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

          _snake_case							=   np.array(
              [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8]					)

          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"					)

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

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

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






     def        _lowercase			(				self					)							->    Dict:
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()

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

          _snake_case							=   self.get_inputs()
          _snake_case							=   pipe(**lowerCamelCase_					)

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








'''simple docstring'''





import argparse
import os


UpperCamelCase_		:    Union[str, Any]									=		{
    '''pytorch''': {
        '''1.13''': {
            '''torch''': '''1.13.1''',
            '''torchvision''': '''0.14.1''',
            '''torchaudio''': '''0.13.1''',
            '''python''': 3.9,
            '''cuda''': '''cu116''',
            '''install''': (
                '''python3 -m pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1'''
                ''' --extra-index-url https://download.pytorch.org/whl/cu116'''
            ),
            '''base_image''': '''nvidia/cuda:11.6.2-cudnn8-devel-ubuntu20.04''',
        },
        '''1.12''': {
            '''torch''': '''1.12.1''',
            '''torchvision''': '''0.13.1''',
            '''torchaudio''': '''0.12.1''',
            '''python''': 3.9,
            '''cuda''': '''cu113''',
            '''install''': (
                '''python3 -m pip install --no-cache-dir -U torch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1'''
                ''' --extra-index-url https://download.pytorch.org/whl/cu113'''
            ),
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''1.11''': {
            '''torch''': '''1.11.0''',
            '''torchvision''': '''0.12.0''',
            '''torchaudio''': '''0.11.0''',
            '''python''': 3.9,
            '''cuda''': '''cu113''',
            '''install''': (
                '''python3 -m pip install --no-cache-dir -U torch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0'''
                ''' --extra-index-url https://download.pytorch.org/whl/cu113'''
            ),
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''1.10''': {
            '''torch''': '''1.10.2''',
            '''torchvision''': '''0.11.3''',
            '''torchaudio''': '''0.10.2''',
            '''python''': 3.9,
            '''cuda''': '''cu113''',
            '''install''': (
                '''python3 -m pip install --no-cache-dir -U torch==1.10.2 torchvision==0.11.3 torchaudio==0.10.2'''
                ''' --extra-index-url https://download.pytorch.org/whl/cu113'''
            ),
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        # torchaudio < 0.10 has no CUDA-enabled binary distributions
        '''1.9''': {
            '''torch''': '''1.9.1''',
            '''torchvision''': '''0.10.1''',
            '''torchaudio''': '''0.9.1''',
            '''python''': 3.9,
            '''cuda''': '''cu111''',
            '''install''': (
                '''python3 -m pip install --no-cache-dir -U torch==1.9.1 torchvision==0.10.1 torchaudio==0.9.1'''
                ''' --extra-index-url https://download.pytorch.org/whl/cu111'''
            ),
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
    },
    '''tensorflow''': {
        '''2.11''': {
            '''tensorflow''': '''2.11.1''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.11.1''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.10''': {
            '''tensorflow''': '''2.10.1''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.10.1''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.9''': {
            '''tensorflow''': '''2.9.3''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.9.3''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.8''': {
            '''tensorflow''': '''2.8.2''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.8.2''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.7''': {
            '''tensorflow''': '''2.7.3''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.7.3''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.6''': {
            '''tensorflow''': '''2.6.5''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.6.5''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
        '''2.5''': {
            '''tensorflow''': '''2.5.3''',
            '''install''': '''python3 -m pip install --no-cache-dir -U tensorflow==2.5.3''',
            '''base_image''': '''nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04''',
        },
    },
}


if __name__ == "__main__":
   UpperCamelCase_		:    Optional[Any]									=		argparse.ArgumentParser('''Choose the framework and version to install''')
   parser.add_argument(
       '''--framework''', help='''The framework to install. Should be `torch` or `tensorflow`''', type=str, required=True
   )
   parser.add_argument('''--version''', help='''The version of the framework to install.''', type=str, required=True)
   UpperCamelCase_		:    Tuple									=		parser.parse_args()

   UpperCamelCase_		:    Optional[int]									=		past_versions_testing[args.framework][args.version]

   os.system(F'echo "export INSTALL_CMD=\'{info["install"]}\'" >> ~/.profile')
   print(F'echo "export INSTALL_CMD=\'{info["install"]}\'" >> ~/.profile')

   UpperCamelCase_		:    List[Any]									=		''''''
   if args.framework == "pytorch":
      UpperCamelCase_		:    str									=		info['''cuda''']
   os.system(F'echo \"export CUDA=\'{cuda}\'\" >> ~/.profile')
   print(F'echo \"export CUDA=\'{cuda}\'\" >> ~/.profile')


