from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


UpperCAmelCase_								=							logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCAmelCase_								=							'''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to("cuda")

        >>> prompt = "A red cartoon frog, 4k"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
        ... )
        >>> pipe.to("cuda")

        >>> init_image = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/frog.png"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save("red_frog.png")
        ```
'''

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_=8   )		->						List[Any]:




       '''simple docstring'''






       _A=	height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       _A=	width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_=5_12       ,					lowerCAmelCase_=5_12   )		->						Optional[int]:




       '''simple docstring'''






       _A=	pil_image.resize((w, h)       ,					resample=Image.BICUBIC       ,					reducing_gap=1   )
       _A=	np.array(pil_image.convert('RGB'   )   )
       _A=	arr.astype(np.floataa   ) / 127.5 - 1
       _A=	np.transpose(lowerCAmelCase_       ,					[2, 0, 1]   )
       _A=	torch.from_numpy(lowerCAmelCase_   ).unsqueeze(0   )
       return image





class      lowerCAmelCase						(					_a			):



     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      ):
            super().__init__()

            self.register_modules(
                unet=lowerCAmelCase__				,      scheduler=lowerCAmelCase__				,      movq=lowerCAmelCase__				,      )
            _A=	2 ** (len(self.movq.config.block_out_channels       ) - 1)



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            # get the original timestep using init_timestep
            _A=	min(int(num_inference_steps * strength       )				,      lowerCAmelCase__       )

            _A=	max(num_inference_steps - init_timestep				,      0       )
            _A=	self.scheduler.timesteps[t_start:]

            return timesteps, num_inference_steps - t_start



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__=None       ):
            if not isinstance(lowerCAmelCase__				,      (torch.Tensor, PIL.Image.Image, list)       ):
                   raise ValueError(
                       f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowerCAmelCase__       )}"       )

            _A=	image.to(device=lowerCAmelCase__				,      dtype=lowerCAmelCase__       )

            _A=	batch_size * num_images_per_prompt

            if image.shape[1] == 4:
                   _A=	image

            else:
                   if isinstance(lowerCAmelCase__				,      lowerCAmelCase__       ) and len(lowerCAmelCase__       ) != batch_size:
                          raise ValueError(
                              f"You have passed a list of generators of length {len(lowerCAmelCase__       )}, but requested an effective batch"
                              f" size of {batch_size}. Make sure the batch size matches the length of the generators."       )

                   elif isinstance(lowerCAmelCase__				,      lowerCAmelCase__       ):
                          _A=	[
                              self.movq.encode(image[i : i + 1]       ).latent_dist.sample(generator[i]       ) for i in range(lowerCAmelCase__       )
                          ]
                          _A=	torch.cat(lowerCAmelCase__				,      dim=0       )
                   else:
                          _A=	self.movq.encode(lowerCAmelCase__       ).latent_dist.sample(lowerCAmelCase__       )

                   _A=	self.movq.config.scaling_factor * init_latents

            _A=	torch.cat([init_latents]				,      dim=0       )

            _A=	init_latents.shape
            _A=	randn_tensor(lowerCAmelCase__				,      generator=lowerCAmelCase__				,      device=lowerCAmelCase__				,      dtype=lowerCAmelCase__       )

            # get latents
            _A=	self.scheduler.add_noise(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       )

            _A=	init_latents

            return latents



     def 			a__  (					self				,      lowerCAmelCase__=0       ):
            if is_accelerate_available():
                   from accelerate import cpu_offload
            else:
                   raise ImportError('Please install accelerate via `pip install accelerate`'       )

            _A=	torch.device(f"cuda:{gpu_id}"       )

            _A=	[
                self.unet,
                self.movq,
            ]
            for cpu_offloaded_model in models:
                   if cpu_offloaded_model is not None:
                          cpu_offload(lowerCAmelCase__				,      lowerCAmelCase__       )



     def 			a__  (					self				,      lowerCAmelCase__=0       ):
            if is_accelerate_available() and is_accelerate_version('>='				,      '0.17.0.dev0'       ):
                   from accelerate import cpu_offload_with_hook
            else:
                   raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'       )

            _A=	torch.device(f"cuda:{gpu_id}"       )

            if self.device.type != "cpu":
                   self.to('cpu'				,      silence_dtype_warnings=lowerCAmelCase__       )
                   torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

            _A=	None
            for cpu_offloaded_model in [self.unet, self.movq]:
                   _A,			_A=	cpu_offload_with_hook(lowerCAmelCase__				,      lowerCAmelCase__				,      prev_module_hook=lowerCAmelCase__       )

            # We'll offload the last model manually.
            _A=	hook



     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 			a__  (					self       ):
            if not hasattr(self.unet				,      '_hf_hook'       ):
                   return self.device
            for module in self.unet.modules():
                   if (
                       hasattr(lowerCAmelCase__				,      '_hf_hook'       )
                       and hasattr(module._hf_hook				,      'execution_device'       )
                       and module._hf_hook.execution_device is not None
                   ):
                          return torch.device(module._hf_hook.execution_device       )
            return self.device





     @torch.no_grad()
     @replace_example_docstring(lowerCAmelCase__       )
     def __call__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__ = 512				,      lowerCAmelCase__ = 512				,      lowerCAmelCase__ = 100				,      lowerCAmelCase__ = 4.0				,      lowerCAmelCase__ = 0.3				,      lowerCAmelCase__ = 1				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = "pil"				,      lowerCAmelCase__ = True				,      ):
            _A=	self._execution_device

            _A=	guidance_scale > 1.0

            if isinstance(lowerCAmelCase__				,      lowerCAmelCase__       ):
                   _A=	torch.cat(lowerCAmelCase__				,      dim=0       )
            _A=	image_embeds.shape[0]
            if isinstance(lowerCAmelCase__				,      lowerCAmelCase__       ):
                   _A=	torch.cat(lowerCAmelCase__				,      dim=0       )

            if do_classifier_free_guidance:
                   _A=	image_embeds.repeat_interleave(lowerCAmelCase__				,      dim=0       )
                   _A=	negative_image_embeds.repeat_interleave(lowerCAmelCase__				,      dim=0       )

            _A=	torch.cat([negative_image_embeds, image_embeds]				,      dim=0       ).to(dtype=self.unet.dtype				,      device=lowerCAmelCase__       )

            if not isinstance(lowerCAmelCase__				,      lowerCAmelCase__       ):
                   _A=	[image]
            if not all(isinstance(lowerCAmelCase__				,      (PIL.Image.Image, torch.Tensor)       ) for i in image       ):
                   raise ValueError(
                       f"Input is in incorrect format: {[type(lowerCAmelCase__       ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"       )

            _A=	torch.cat([prepare_image(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ) for i in image]				,      dim=0       )
            _A=	image.to(dtype=image_embeds.dtype				,      device=lowerCAmelCase__       )

            _A=	self.movq.encode(lowerCAmelCase__       )['latents']
            _A=	latents.repeat_interleave(lowerCAmelCase__				,      dim=0       )
            self.scheduler.set_timesteps(lowerCAmelCase__				,      device=lowerCAmelCase__       )
            _A,			_A=	self.get_timesteps(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       )
            _A=	timesteps[:1].repeat(batch_size * num_images_per_prompt       )
            _A,			_A=	downscale_height_and_width(lowerCAmelCase__				,      lowerCAmelCase__				,      self.movq_scale_factor       )
            _A=	self.prepare_latents(
                lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      image_embeds.dtype				,      lowerCAmelCase__				,      lowerCAmelCase__       )
            for i, t in enumerate(self.progress_bar(lowerCAmelCase__       )       ):
                   # expand the latents if we are doing classifier free guidance
                   _A=	torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

                   _A=	{'image_embeds': image_embeds}
                   _A=	self.unet(
                       sample=lowerCAmelCase__				,      timestep=lowerCAmelCase__				,      encoder_hidden_states=lowerCAmelCase__				,      added_cond_kwargs=lowerCAmelCase__				,      return_dict=lowerCAmelCase__				,      )[0]

                   if do_classifier_free_guidance:
                          _A,			_A=	noise_pred.split(latents.shape[1]				,      dim=1       )
                          _A,			_A=	noise_pred.chunk(2       )
                          _A,			_A=	variance_pred.chunk(2       )
                          _A=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                          _A=	torch.cat([noise_pred, variance_pred_text]				,      dim=1       )

                   if not (
                       hasattr(self.scheduler.config				,      'variance_type'       )
                       and self.scheduler.config.variance_type in ["learned", "learned_range"]
                   ):
                          _A,			_A=	noise_pred.split(latents.shape[1]				,      dim=1       )

                   # compute the previous noisy sample x_t -> x_t-1
                   _A=	self.scheduler.step(
                       lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      generator=lowerCAmelCase__				,      )[0]

            # post-processing
            _A=	self.movq.decode(lowerCAmelCase__				,      force_not_quantize=lowerCAmelCase__       )['sample']

            if output_type not in ["pt", "np", "pil"]:
                   raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"       )

            if output_type in ["np", "pil"]:
                   _A=	image * 0.5 + 0.5
                   _A=	image.clamp(0				,      1       )
                   _A=	image.cpu().permute(0				,      2				,      3				,      1       ).float().numpy()

            if output_type == "pil":
                   _A=	self.numpy_to_pil(lowerCAmelCase__       )

            if not return_dict:
                   return (image,)

            return ImagePipelineOutput(images=lowerCAmelCase__       )
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist

def 							UpperCamelCase				(    *lowerCAmelCase_   )		->						Dict:




       '''simple docstring'''






       with open(lowerCAmelCase_       ,					'r'   ) as fh:
              fcntl.flock(lowerCAmelCase_       ,					fcntl.LOCK_EX   )
              try:
                     print(*lowerCAmelCase_   )
              finally:
                     fcntl.flock(lowerCAmelCase_       ,					fcntl.LOCK_UN   )


UpperCAmelCase_								=							int(os.environ['''LOCAL_RANK'''])
torch.cuda.set_device(local_rank)
UpperCAmelCase_								=							torch.device('''cuda''', local_rank)
UpperCAmelCase_								=							socket.gethostname()

UpperCAmelCase_								=							F"[{hostname}-{local_rank}]"

try:
      # test distributed
      dist.init_process_group('''nccl''')
      dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
      dist.barrier()

      # test cuda is available and can allocate memory
      torch.cuda.is_available()
      torch.ones(1).cuda(local_rank)

      # global rank
      UpperCAmelCase_								=							dist.get_rank()
      UpperCAmelCase_								=							dist.get_world_size()

      printflock(F"{gpu} is OK (global rank: {rank}/{world_size})")

      dist.barrier()
      if rank == 0:
            printflock(F"pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}")

except Exception:
      printflock(F"{gpu} is broken")
      raise