

"""simple docstring"""





import warnings
from typing import List, Optional, Union

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

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate




class       _lowerCAmelCase						(			lowercase     ):







 """simple docstring"""







 __UpperCAmelCase			:      int     	=			CONFIG_NAME


 @register_to_config
 def __init__(    self							:    List[Any],      UpperCAmelCase__							:    bool = True,      UpperCAmelCase__							:    int = 8,      UpperCAmelCase__							:    str = "lanczos",      UpperCAmelCase__							:    bool = True,      UpperCAmelCase__							:    bool = False,      ):
    super().__init__()


 @staticmethod
 def       _lowercase				(    UpperCAmelCase__							:    np.ndarray			):
    if images.ndim == 3:
       __lowercase			=		images[None, ...]
    __lowercase			=		(images * 2_5_5).round().astype("uint8"			)
    if images.shape[-1] == 1:
       # special case for grayscale (single channel) images
       __lowercase			=		[Image.fromarray(image.squeeze(),      mode="L"			) for image in images]
    else:
       __lowercase			=		[Image.fromarray(UpperCAmelCase__			) for image in images]

    return pil_images


 @staticmethod
 def       _lowercase				(    UpperCAmelCase__							:    Union[List[PIL.Image.Image], PIL.Image.Image]			):
    if not isinstance(UpperCAmelCase__,      UpperCAmelCase__			):
       __lowercase			=		[images]
    __lowercase			=		[np.array(UpperCAmelCase__			).astype(np.floataa			) / 255.0 for image in images]
    __lowercase			=		np.stack(UpperCAmelCase__,      axis=0			)

    return images


 @staticmethod
 def       _lowercase				(    UpperCAmelCase__							:    np.ndarray			):
    if images.ndim == 3:
       __lowercase			=		images[..., None]

    __lowercase			=		torch.from_numpy(images.transpose(0,      3,      1,      2			)			)
    return images


 @staticmethod
 def       _lowercase				(    UpperCAmelCase__							:    torch.FloatTensor			):
    __lowercase			=		images.cpu().permute(0,      2,      3,      1			).float().numpy()
    return images


 @staticmethod
 def       _lowercase				(    UpperCAmelCase__							:    Optional[Any]			):
    return 2.0 * images - 1.0


 @staticmethod
 def       _lowercase				(    UpperCAmelCase__							:    Optional[Any]			):
    return (images / 2 + 0.5).clamp(0,      1			)


 @staticmethod
 def       _lowercase				(    UpperCAmelCase__							:    PIL.Image.Image			):
    __lowercase			=		image.convert("RGB"			)
    return image


 def       _lowercase				(    self							:    List[Any],      UpperCAmelCase__							:    PIL.Image.Image,      UpperCAmelCase__							:    Optional[int] = None,      UpperCAmelCase__							:    Optional[int] = None,      ):
    if height is None:
       __lowercase			=		image.height
    if width is None:
       __lowercase			=		image.width

    __lowercase	,__lowercase			=		(
        x - x % self.config.vae_scale_factor for x in (width, height)
    )  # resize to integer multiple of vae_scale_factor
    __lowercase			=		image.resize((width, height),      resample=PIL_INTERPOLATION[self.config.resample]			)
    return image


 def       _lowercase				(    self							:    Union[str, Any],      UpperCAmelCase__							:    Union[torch.FloatTensor, PIL.Image.Image, np.ndarray],      UpperCAmelCase__							:    Optional[int] = None,      UpperCAmelCase__							:    Optional[int] = None,      ):
    __lowercase			=		(PIL.Image.Image, np.ndarray, torch.Tensor)
    if isinstance(UpperCAmelCase__,      UpperCAmelCase__			):
       __lowercase			=		[image]
    elif not (isinstance(UpperCAmelCase__,      UpperCAmelCase__			) and all(isinstance(UpperCAmelCase__,      UpperCAmelCase__			) for i in image			)):
       raise ValueError(
           F"""Input is in incorrect format: {[type(UpperCAmelCase__			) for i in image]}. Currently, we only support {", ".join(UpperCAmelCase__			)}"""			)

    if isinstance(image[0],      PIL.Image.Image			):
       if self.config.do_convert_rgb:
          __lowercase			=		[self.convert_to_rgb(UpperCAmelCase__			) for i in image]
       if self.config.do_resize:
          __lowercase			=		[self.resize(UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__			) for i in image]
       __lowercase			=		self.pil_to_numpy(UpperCAmelCase__			)  # to np
       __lowercase			=		self.numpy_to_pt(UpperCAmelCase__			)  # to pt

    elif isinstance(image[0],      np.ndarray			):
       __lowercase			=		np.concatenate(UpperCAmelCase__,      axis=0			) if image[0].ndim == 4 else np.stack(UpperCAmelCase__,      axis=0			)
       __lowercase			=		self.numpy_to_pt(UpperCAmelCase__			)
       __lowercase	,__lowercase	,__lowercase	,__lowercase			=		image.shape
       if self.config.do_resize and (
           height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
       ):
          raise ValueError(
              F"""Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"""
              F"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""			)

    elif isinstance(image[0],      torch.Tensor			):
       __lowercase			=		torch.cat(UpperCAmelCase__,      axis=0			) if image[0].ndim == 4 else torch.stack(UpperCAmelCase__,      axis=0			)
       __lowercase	,__lowercase	,__lowercase	,__lowercase			=		image.shape

       # don't need any preprocess if the image is latents
       if channel == 4:
          return image

       if self.config.do_resize and (
           height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
       ):
          raise ValueError(
              F"""Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"""
              F"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""			)

        # expected range [0,1], normalize to [-1,1]
    __lowercase			=		self.config.do_normalize
    if image.min() < 0:
       warnings.warn(
           "Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "
           F"""when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]""",      UpperCAmelCase__,      )
       __lowercase			=		False

    if do_normalize:
       __lowercase			=		self.normalize(UpperCAmelCase__			)

    return image



 def       _lowercase				(    self							:    Tuple,      UpperCAmelCase__							:    torch.FloatTensor,      UpperCAmelCase__							:    str = "pil",      UpperCAmelCase__							:    Optional[List[bool]] = None,      ):
    if not isinstance(UpperCAmelCase__,      torch.Tensor			):
       raise ValueError(
           F"""Input for postprocessing is in incorrect format: {type(UpperCAmelCase__			)}. We only support pytorch tensor"""			)
    if output_type not in ["latent", "pt", "np", "pil"]:
       __lowercase			=		(
           F"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
           "`pil`, `np`, `pt`, `latent`"
       )
       deprecate("Unsupported output_type",      "1.0.0",      UpperCAmelCase__,      standard_warn=UpperCAmelCase__			)
       __lowercase			=		"np"

    if output_type == "latent":
       return image

    if do_denormalize is None:
       __lowercase			=		[self.config.do_normalize] * image.shape[0]

    __lowercase			=		torch.stack(
        [self.denormalize(image[i]			) if do_denormalize[i] else image[i] for i in range(image.shape[0]			)]			)

    if output_type == "pt":
       return image

    __lowercase			=		self.pt_to_numpy(UpperCAmelCase__			)

    if output_type == "np":
       return image

    if output_type == "pil":
       return self.numpy_to_pil(UpperCAmelCase__			)







class       _lowerCAmelCase						(			lowercase     ):







 """simple docstring"""







 __UpperCAmelCase			:      Dict     	=			CONFIG_NAME


 @register_to_config
 def __init__(    self							:    int,      UpperCAmelCase__							:    bool = True,      UpperCAmelCase__							:    int = 8,      UpperCAmelCase__							:    str = "lanczos",      UpperCAmelCase__							:    bool = True,      ):
    super().__init__()


 @staticmethod
 def       _lowercase				(    UpperCAmelCase__							:    Dict			):
    if images.ndim == 3:
       __lowercase			=		images[None, ...]
    __lowercase			=		(images * 2_5_5).round().astype("uint8"			)
    if images.shape[-1] == 1:
       # special case for grayscale (single channel) images
       __lowercase			=		[Image.fromarray(image.squeeze(),      mode="L"			) for image in images]
    else:
       __lowercase			=		[Image.fromarray(image[:, :, :3]			) for image in images]

    return pil_images


 @staticmethod
 def       _lowercase				(    UpperCAmelCase__							:    List[Any]			):
    return image[:, :, 1] * 2**8 + image[:, :, 2]


 def       _lowercase				(    self							:    Union[str, Any],      UpperCAmelCase__							:    int			):
    if images.ndim == 3:
       __lowercase			=		images[None, ...]
    __lowercase			=		images[:, :, :, 3:]
    if images.shape[-1] == 6:
       __lowercase			=		(images_depth * 2_5_5).round().astype("uint8"			)
       __lowercase			=		[
           Image.fromarray(self.rgblike_to_depthmap(UpperCAmelCase__			),      mode="I;16"			) for image_depth in images_depth
       ]
    elif images.shape[-1] == 4:
       __lowercase			=		(images_depth * 65_535.0).astype(np.uintaa			)
       __lowercase			=		[Image.fromarray(UpperCAmelCase__,      mode="I;16"			) for image_depth in images_depth]
    else:
       raise Exception("Not supported"			)

    return pil_images



 def       _lowercase				(    self							:    List[str],      UpperCAmelCase__							:    torch.FloatTensor,      UpperCAmelCase__							:    str = "pil",      UpperCAmelCase__							:    Optional[List[bool]] = None,      ):
    if not isinstance(UpperCAmelCase__,      torch.Tensor			):
       raise ValueError(
           F"""Input for postprocessing is in incorrect format: {type(UpperCAmelCase__			)}. We only support pytorch tensor"""			)
    if output_type not in ["latent", "pt", "np", "pil"]:
       __lowercase			=		(
           F"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
           "`pil`, `np`, `pt`, `latent`"
       )
       deprecate("Unsupported output_type",      "1.0.0",      UpperCAmelCase__,      standard_warn=UpperCAmelCase__			)
       __lowercase			=		"np"

    if do_denormalize is None:
       __lowercase			=		[self.config.do_normalize] * image.shape[0]

    __lowercase			=		torch.stack(
        [self.denormalize(image[i]			) if do_denormalize[i] else image[i] for i in range(image.shape[0]			)]			)

    __lowercase			=		self.pt_to_numpy(UpperCAmelCase__			)

    if output_type == "np":
       if image.shape[-1] == 6:
          __lowercase			=		np.stack([self.rgblike_to_depthmap(im[:, :, 3:]			) for im in image],      axis=0			)
       else:
          __lowercase			=		image[:, :, :, 3:]
       return image[:, :, :, :3], image_depth

    if output_type == "pil":
       return self.numpy_to_pil(UpperCAmelCase__			), self.numpy_to_depth(UpperCAmelCase__			)
    else:
       raise Exception(F"""This type {output_type} is not supported"""			)







"""simple docstring"""





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				   =							Args()
   _a				   =							wandb.init(project='bigbird-natural-questions', config=args.__dict__)
   _a				   =							dict(logger.config)
   del wandb_args["batch_size"]
   _a				   =							replace(args, **wandb_args)
   _a				   =							args.base_dir + '-' + wandb.run.id
   _a				   =							replace(args, base_dir=base_dir)
   print(args)

   _a				   =							load_dataset('json', data_files=args.tr_data_path)['train']
   _a				   =							load_dataset('json', data_files=args.val_data_path)['train']

   # drop extra batch for now
   _a				   =							range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
   _a				   =							tr_dataset.shuffle().select(indices)
   _a				   =							range(len(val_dataset) - len(val_dataset) % args.batch_size)
   _a				   =							val_dataset.shuffle().select(indices)

   if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
      _a				   =							tr_dataset.shuffle().select(range(8_00_00))
      _a				   =							val_dataset.shuffle().select(range(80_00))

   print(tr_dataset)
   print(val_dataset)

   _a				   =							FlaxBigBirdForNaturalQuestions.from_pretrained(
       args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
   )
   _a				   =							BigBirdTokenizerFast.from_pretrained(args.model_id)
   _a				   =							DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

   _a				   =							{
       '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				   =							build_tx(**tx_args)

   _a				   =							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				   =							None
   _a				   =							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				   =							jax_utils.unreplicate(state.params)
   model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)




