

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__lowerCamelCase						    =      False


class    A__ ( unittest.TestCase     ):
    pass


@nightly
@require_torch_gpu
class    A__ ( unittest.TestCase     ):



    def    snake_case_     (   self    )  -> Dict:





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



    def    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           A_		   = VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""						,			torch_dtype=torch.floataa    )
           pipe.to(UpperCamelCase__    )
           pipe.set_progress_bar_config(disable=UpperCamelCase__    )

           A_		   = load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""    )

           A_		   = torch.manual_seed(0    )
           A_		   = pipe.dual_guided(
               prompt="""first prompt"""						,			image=UpperCamelCase__						,			text_to_image_strength=0.75						,			generator=UpperCamelCase__						,			guidance_scale=7.5						,			num_inference_steps=2						,			output_type="""numpy"""						,			).images

           with tempfile.TemporaryDirectory() as tmpdirname:
                  pipe.save_pretrained(UpperCamelCase__    )
                  A_		   = VersatileDiffusionPipeline.from_pretrained(UpperCamelCase__						,			torch_dtype=torch.floataa    )
           pipe.to(UpperCamelCase__    )
           pipe.set_progress_bar_config(disable=UpperCamelCase__    )

           A_		   = generator.manual_seed(0    )
           A_		   = pipe.dual_guided(
               prompt="""first prompt"""						,			image=UpperCamelCase__						,			text_to_image_strength=0.75						,			generator=UpperCamelCase__						,			guidance_scale=7.5						,			num_inference_steps=2						,			output_type="""numpy"""						,			).images

           assert np.abs(image - new_image    ).sum() < 1e-5, "Models don't have the same forward pass"







    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           A_		   = VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""						,			torch_dtype=torch.floataa    )
           pipe.to(UpperCamelCase__    )
           pipe.set_progress_bar_config(disable=UpperCamelCase__    )

           A_		   = """cyberpunk 2077"""
           A_		   = load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""    )
           A_		   = torch.manual_seed(0    )
           A_		   = pipe.dual_guided(
               prompt=UpperCamelCase__						,			image=UpperCamelCase__						,			text_to_image_strength=0.75						,			generator=UpperCamelCase__						,			guidance_scale=7.5						,			num_inference_steps=50						,			output_type="""numpy"""						,			).images

           A_		   = image[0, 253:256, 253:256, -1]

           assert image.shape == (1, 512, 512, 3)
           A_		   = np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]    )

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

           A_		   = """A painting of a squirrel eating a burger """
           A_		   = torch.manual_seed(0    )
           A_		   = pipe.text_to_image(
               prompt=UpperCamelCase__						,			generator=UpperCamelCase__						,			guidance_scale=7.5						,			num_inference_steps=50						,			output_type="""numpy"""    ).images

           A_		   = image[0, 253:256, 253:256, -1]

           assert image.shape == (1, 512, 512, 3)
           A_		   = np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]    )

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

           A_		   = pipe.image_variation(UpperCamelCase__						,			generator=UpperCamelCase__						,			output_type="""numpy"""    ).images

           A_		   = image[0, 253:256, 253:256, -1]

           assert image.shape == (1, 512, 512, 3)
           A_		   = np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]    )

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



'''simple docstring'''







import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)
__lowerCamelCase						    =      logging.getLogger(__name__)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     int:
       A_		   = git.Repo(search_parent_directories=UpperCAmelCase__			)
       A_		   = {
           """repo_id""": str(UpperCAmelCase__			),
           """repo_sha""": str(repo.head.object.hexsha			),
           """repo_branch""": str(repo.active_branch			),
       }

       with open(os.path.join(UpperCAmelCase__,    """git_log.json"""			),    """w"""			) as f:
              json.dump(UpperCAmelCase__,    UpperCAmelCase__,    indent=4			)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Dict:
       if params.n_gpu <= 0:
              A_		   = 0
              A_		   = -1
              A_		   = True
              A_		   = False
              return

       assert torch.cuda.is_available()

       logger.info("""Initializing GPUs"""			)
       if params.n_gpu > 1:
              assert params.local_rank != -1

              A_		   = int(os.environ["""WORLD_SIZE"""]			)
              A_		   = int(os.environ["""N_GPU_NODE"""]			)
              A_		   = int(os.environ["""RANK"""]			)

              # number of nodes / node ID
              A_		   = params.world_size // params.n_gpu_per_node
              A_		   = params.global_rank // params.n_gpu_per_node
              A_		   = True

              assert params.n_nodes == int(os.environ["""N_NODES"""]			)
              assert params.node_id == int(os.environ["""NODE_RANK"""]			)

       # local job (single GPU)
       else:
              assert params.local_rank == -1

              A_		   = 1
              A_		   = 0
              A_		   = 0
              A_		   = 0
              A_		   = 1
              A_		   = 1
              A_		   = False

       # sanity checks
       assert params.n_nodes >= 1
       assert 0 <= params.node_id < params.n_nodes
       assert 0 <= params.local_rank <= params.global_rank < params.world_size
       assert params.world_size == params.n_nodes * params.n_gpu_per_node

       # define whether this is the master process / if we are in multi-node distributed mode
       A_		   = params.node_id == 0 and params.local_rank == 0
       A_		   = params.n_nodes > 1

       # summary
       A_		   = F'''--- Global rank: {params.global_rank} - '''
       logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes			)
       logger.info(PREFIX + """Node ID        : %i""" % params.node_id			)
       logger.info(PREFIX + """Local rank     : %i""" % params.local_rank			)
       logger.info(PREFIX + """World size     : %i""" % params.world_size			)
       logger.info(PREFIX + """GPUs per node  : %i""" % params.n_gpu_per_node			)
       logger.info(PREFIX + """Master         : %s""" % str(params.is_master			)			)
       logger.info(PREFIX + """Multi-node     : %s""" % str(params.multi_node			)			)
       logger.info(PREFIX + """Multi-GPU      : %s""" % str(params.multi_gpu			)			)
       logger.info(PREFIX + """Hostname       : %s""" % socket.gethostname()			)

       # set GPU device
       torch.cuda.set_device(params.local_rank			)

       # initialize multi-GPU
       if params.multi_gpu:
              logger.info("""Initializing PyTorch distributed"""			)
              torch.distributed.init_process_group(
                  init_method="""env://""",    backend="""nccl""",    )







def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Tuple:
       np.random.seed(args.seed			)
       torch.manual_seed(args.seed			)
       if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed			)
