





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,
)


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

_A				:							Optional[Any]							= """
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")

        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_=8  )							->	int:
    SCREAMING_SNAKE_CASE__       							= height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    SCREAMING_SNAKE_CASE__       							= width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_=5_1_2   ,       lowerCAmelCase_=5_1_2  )							->	List[str]:
    SCREAMING_SNAKE_CASE__       							= pil_image.resize((w, h)   ,       resample=Image.BICUBIC   ,       reducing_gap=1  )
    SCREAMING_SNAKE_CASE__       							= np.array(pil_image.convert('''RGB'''  )  )
    SCREAMING_SNAKE_CASE__       							= arr.astype(np.floataa  ) / 1_27.5 - 1
    SCREAMING_SNAKE_CASE__       							= np.transpose(lowerCAmelCase_   ,       [2, 0, 1]  )
    SCREAMING_SNAKE_CASE__       							= torch.from_numpy(lowerCAmelCase_  ).unsqueeze(0  )
    return image



class 				__snake_case  (     __SCREAMING_SNAKE_CASE						):


    '''simple docstring'''







    def __init__(					self      ,							A_      ,							A_      ,							A_      ,							):





        '''simple docstring'''

        super().__init__()

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



    def    lowercase_				(					self      ,							A_      ,							A_      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= min(int(num_inference_steps * strength    )      ,							A_    )

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

        return timesteps, num_inference_steps - t_start



    def    lowercase_				(					self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_=None    ):





        '''simple docstring'''

        if not isinstance(A_      ,							(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(A_    )}'''    )

        SCREAMING_SNAKE_CASE__       							= image.to(device=A_      ,							dtype=A_    )

        SCREAMING_SNAKE_CASE__       							= batch_size * num_images_per_prompt

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

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

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

            SCREAMING_SNAKE_CASE__       							= self.movq.config.scaling_factor * init_latents

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

        SCREAMING_SNAKE_CASE__       							= init_latents.shape
        SCREAMING_SNAKE_CASE__       							= randn_tensor(A_      ,							generator=A_      ,							device=A_      ,							dtype=A_    )

        # get latents
        SCREAMING_SNAKE_CASE__       							= self.scheduler.add_noise(A_      ,							A_      ,							A_    )

        SCREAMING_SNAKE_CASE__       							= init_latents

        return latents



    def    lowercase_				(					self      ,							A_=0    ):





        '''simple docstring'''

        if is_accelerate_available():
            from accelerate import cpu_offload
        else:
            raise ImportError('''Please install accelerate via `pip install accelerate`'''    )

        SCREAMING_SNAKE_CASE__       							= torch.device(f'''cuda:{gpu_id}'''    )

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



    def    lowercase_				(					self      ,							A_=0    ):





        '''simple docstring'''

        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.'''    )

        SCREAMING_SNAKE_CASE__       							= torch.device(f'''cuda:{gpu_id}'''    )

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

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

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



    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def    lowercase_				(					self    ):





        '''simple docstring'''

        if not hasattr(self.unet      ,							'''_hf_hook'''    ):
            return self.device
        for module in self.unet.modules():
            if (
                hasattr(A_      ,							'''_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(A_    )
    def __call__(					self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_ = 5_12      ,							A_ = 5_12      ,							A_ = 1_00      ,							A_ = 4.0      ,							A_ = 0.3      ,							A_ = 1      ,							A_ = None      ,							A_ = "pil"      ,							A_ = True      ,							):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self._execution_device

        SCREAMING_SNAKE_CASE__       							= guidance_scale > 1.0

        if isinstance(A_      ,							A_    ):
            SCREAMING_SNAKE_CASE__       							= torch.cat(A_      ,							dim=0    )
        if isinstance(A_      ,							A_    ):
            SCREAMING_SNAKE_CASE__       							= torch.cat(A_      ,							dim=0    )
        if isinstance(A_      ,							A_    ):
            SCREAMING_SNAKE_CASE__       							= torch.cat(A_      ,							dim=0    )

        SCREAMING_SNAKE_CASE__       							= image_embeds.shape[0]

        if do_classifier_free_guidance:
            SCREAMING_SNAKE_CASE__       							= image_embeds.repeat_interleave(A_      ,							dim=0    )
            SCREAMING_SNAKE_CASE__       							= negative_image_embeds.repeat_interleave(A_      ,							dim=0    )
            SCREAMING_SNAKE_CASE__       							= hint.repeat_interleave(A_      ,							dim=0    )

        SCREAMING_SNAKE_CASE__       							= torch.cat([negative_image_embeds, image_embeds]      ,							dim=0    ).to(dtype=self.unet.dtype      ,							device=A_    )
        SCREAMING_SNAKE_CASE__       							= torch.cat([hint, hint]      ,							dim=0    ).to(dtype=self.unet.dtype      ,							device=A_    )

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

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

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

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

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

            # compute the previous noisy sample x_t -> x_t-1

            SCREAMING_SNAKE_CASE__       							= self.scheduler.step(
                A_      ,							A_      ,							A_      ,							generator=A_      ,							)[0]

        # post-processing
        SCREAMING_SNAKE_CASE__       							= self.movq.decode(A_      ,							force_not_quantize=A_    )['''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"]:
            SCREAMING_SNAKE_CASE__       							= image * 0.5 + 0.5
            SCREAMING_SNAKE_CASE__       							= image.clamp(0      ,							1    )
            SCREAMING_SNAKE_CASE__       							= image.cpu().permute(0      ,							2      ,							3      ,							1    ).float().numpy()

        if output_type == "pil":
            SCREAMING_SNAKE_CASE__       							= self.numpy_to_pil(A_    )

        if not return_dict:
            return (image,)

        return ImagePipelineOutput(images=A_    )







import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
  print("""#################### AVAILABLE DEVICES ####################""")
  print(jax.devices())
  print("""###########################################################""")

  # setup for wandb sweep
  _A				:							Any							= Args()
  _A				:							Union[str, Any]							= wandb.init(project="""bigbird-natural-questions""", config=args.__dict__)
  _A				:							Optional[Any]							= dict(logger.config)
  del wandb_args["batch_size"]
  _A				:							Any							= replace(args, **wandb_args)
  _A				:							Dict							= args.base_dir + """-""" + wandb.run.id
  _A				:							Dict							= replace(args, base_dir=base_dir)
  print(args)

  _A				:							Tuple							= load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
  _A				:							Optional[Any]							= load_dataset("""json""", data_files=args.val_data_path)["""train"""]

  # drop extra batch for now
  _A				:							Dict							= range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
  _A				:							int							= tr_dataset.shuffle().select(indices)
  _A				:							Optional[int]							= range(len(val_dataset) - len(val_dataset) % args.batch_size)
  _A				:							Dict							= val_dataset.shuffle().select(indices)

  if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
    _A				:							Optional[Any]							= tr_dataset.shuffle().select(range(8_00_00))
    _A				:							Tuple							= val_dataset.shuffle().select(range(80_00))

  print(tr_dataset)
  print(val_dataset)

  _A				:							int							= FlaxBigBirdForNaturalQuestions.from_pretrained(
      args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
  )
  _A				:							Any							= BigBirdTokenizerFast.from_pretrained(args.model_id)
  _A				:							Optional[Any]							= DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

  _A				:							int							= {
      """lr""": args.lr,
      """init_lr""": args.init_lr,
      """warmup_steps""": args.warmup_steps,
      """num_train_steps""": args.max_epochs * (len(tr_dataset) // args.batch_size),
      """weight_decay""": args.weight_decay,
  }
  _A,					_A				:							Union[str, Any]							= build_tx(**tx_args)

  _A				:							str							= Trainer(
      args=args,
      data_collator=data_collator,
      model_save_fn=model.save_pretrained,
      train_step_fn=train_step,
      val_step_fn=val_step,
      logger=logger,
      scheduler_fn=lr,
  )

  _A				:							Tuple							= None
  _A				:							Dict							= trainer.create_state(model, tx, num_train_steps=tx_args["""num_train_steps"""], ckpt_dir=ckpt_dir)
  try:
    trainer.train(state, tr_dataset, val_dataset)
  except KeyboardInterrupt:
    print("""Oooops; TRAINING STOPPED UNFORTUNATELY""")

  print("""SAVING WEIGHTS IN `final-weights`""")
  _A				:							Tuple							= jax_utils.unreplicate(state.params)
  model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)
