






"""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  UpperCamelCase    (			snake_case_   ):
 UpperCamelCase      : Union[str, Any]    =      CONFIG_NAME


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


 @staticmethod
 def _lowercase			(  UpperCAmelCase__	:		np.ndarray							)      -> PIL.Image.Image:
   if images.ndim == 3:
     _a						: Tuple    =			images[None, ...]
   _a						: Optional[Any]    =			(images * 255).round().astype("""uint8"""							)
   if images.shape[-1] == 1:
     # special case for grayscale (single channel) images
     _a						: Union[str, Any]    =			[Image.fromarray(image.squeeze()							,		mode="""L"""							) for image in images]
   else:
     _a						: int    =			[Image.fromarray(UpperCAmelCase__							) for image in images]

   return pil_images


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

   return images


 @staticmethod
 def _lowercase			(  UpperCAmelCase__	:		np.ndarray							)      -> torch.FloatTensor:
   if images.ndim == 3:
     _a						: int    =			images[..., None]

   _a						: Any    =			torch.from_numpy(images.transpose(0							,		3							,		1							,		2							)							)
   return images


 @staticmethod
 def _lowercase			(  UpperCAmelCase__	:		torch.FloatTensor							)      -> np.ndarray:
   _a						: Any    =			images.cpu().permute(0							,		2							,		3							,		1							).float().numpy()
   return images


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


 @staticmethod
 def _lowercase			(  UpperCAmelCase__	:		Tuple							)      -> Union[str, Any]:
   return (images / 2 + 0.5).clamp(0							,		1							)


 @staticmethod
 def _lowercase			(  UpperCAmelCase__	:		PIL.Image.Image							)      -> PIL.Image.Image:
   _a						: int    =			image.convert("""RGB"""							)
   return image


 def _lowercase			(  self	:		int							,		UpperCAmelCase__	:		PIL.Image.Image							,		UpperCAmelCase__	:		Optional[int] = None							,		UpperCAmelCase__	:		Optional[int] = None							,		)      -> PIL.Image.Image:
   if height is None:
     _a						: Union[str, Any]    =			image.height
   if width is None:
     _a						: Optional[Any]    =			image.width

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


 def _lowercase			(  self	:		int							,		UpperCAmelCase__	:		Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]							,		UpperCAmelCase__	:		Optional[int] = None							,		UpperCAmelCase__	:		Optional[int] = None							,		)      -> torch.Tensor:
   _a						: Dict    =			(PIL.Image.Image, np.ndarray, torch.Tensor)
   if isinstance(UpperCAmelCase__							,		UpperCAmelCase__							):
     _a						: str    =			[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:
       _a						: Dict    =			[self.convert_to_rgb(UpperCAmelCase__							) for i in image]
     if self.config.do_resize:
       _a						: List[Any]    =			[self.resize(UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							) for i in image]
     _a						: Tuple    =			self.pil_to_numpy(UpperCAmelCase__							)  # to np
     _a						: Optional[int]    =			self.numpy_to_pt(UpperCAmelCase__							)  # to pt

   elif isinstance(image[0]							,		np.ndarray							):
     _a						: Tuple    =			np.concatenate(UpperCAmelCase__							,		axis=0							) if image[0].ndim == 4 else np.stack(UpperCAmelCase__							,		axis=0							)
     _a						: Any    =			self.numpy_to_pt(UpperCAmelCase__							)
     _a						: Optional[Any]    =			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							):
     _a						: Tuple    =			torch.cat(UpperCAmelCase__							,		axis=0							) if image[0].ndim == 4 else torch.stack(UpperCAmelCase__							,		axis=0							)
     _a						: Dict    =			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]
   _a						: List[str]    =			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__							,		)
     _a						: Dict    =			False

   if do_normalize:
     _a						: Any    =			self.normalize(UpperCAmelCase__							)

   return image







 def _lowercase			(  self	:		List[Any]							,		UpperCAmelCase__	:		torch.FloatTensor							,		UpperCAmelCase__	:		str = "pil"							,		UpperCAmelCase__	:		Optional[List[bool]] = None							,		)      -> Tuple:
   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"]:
     _a						: Optional[Any]    =			(
         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__							)
     _a						: Union[str, Any]    =			"""np"""

   if output_type == "latent":
     return image

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

   _a						: List[Any]    =			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

   _a						: Tuple    =			self.pt_to_numpy(UpperCAmelCase__							)

   if output_type == "np":
     return image

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







class  UpperCamelCase    (			snake_case_   ):
 UpperCamelCase      : str    =      CONFIG_NAME


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


 @staticmethod
 def _lowercase			(  UpperCAmelCase__	:		Union[str, Any]							)      -> Any:
   if images.ndim == 3:
     _a						: Optional[Any]    =			images[None, ...]
   _a						: Tuple    =			(images * 255).round().astype("""uint8"""							)
   if images.shape[-1] == 1:
     # special case for grayscale (single channel) images
     _a						: Tuple    =			[Image.fromarray(image.squeeze()							,		mode="""L"""							) for image in images]
   else:
     _a						: List[Any]    =			[Image.fromarray(image[:, :, :3]							) for image in images]

   return pil_images


 @staticmethod
 def _lowercase			(  UpperCAmelCase__	:		Union[str, Any]							)      -> Optional[Any]:
   return image[:, :, 1] * 2**8 + image[:, :, 2]


 def _lowercase			(  self	:		Any							,		UpperCAmelCase__	:		Optional[int]							)      -> List[str]:
   if images.ndim == 3:
     _a						: Optional[int]    =			images[None, ...]
   _a						: str    =			images[:, :, :, 3:]
   if images.shape[-1] == 6:
     _a						: Optional[Any]    =			(images_depth * 255).round().astype("""uint8"""							)
     _a						: Dict    =			[
         Image.fromarray(self.rgblike_to_depthmap(UpperCAmelCase__							)							,		mode="""I;16"""							) for image_depth in images_depth
     ]
   elif images.shape[-1] == 4:
     _a						: str    =			(images_depth * 65535.0).astype(np.uintaa							)
     _a						: str    =			[Image.fromarray(UpperCAmelCase__							,		mode="""I;16"""							) for image_depth in images_depth]
   else:
     raise Exception("""Not supported"""							)

   return pil_images







 def _lowercase			(  self	:		List[Any]							,		UpperCAmelCase__	:		torch.FloatTensor							,		UpperCAmelCase__	:		str = "pil"							,		UpperCAmelCase__	:		Optional[List[bool]] = None							,		)      -> Dict:
   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"]:
     _a						: Tuple    =			(
         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__							)
     _a						: Dict    =			"""np"""

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

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

   _a						: Dict    =			self.pt_to_numpy(UpperCAmelCase__							)

   if output_type == "np":
     if image.shape[-1] == 6:
       _a						: int    =			np.stack([self.rgblike_to_depthmap(im[:, :, 3:]							) for im in image]							,		axis=0							)
     else:
       _a						: Any    =			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 json
import logging
import os
import subprocess
from argparse import ArgumentParser


_snake_case					   =     logging.getLogger(__name__)

def     lowerCAmelCase__  (  ):



  '''simple docstring'''
  _a						: Any    =			ArgumentParser()
  _a   ,						_a						: str    =			parser.parse_known_args()
  for arg in unknown:
    if arg.startswith(("""-""", """--""")       ):
      parser.add_argument(arg.split("""="""       )[0]       )

  return parser.parse_args()

def     lowerCAmelCase__  (  ):



  '''simple docstring'''
  _a						: Optional[Any]    =			parse_args()
  _a						: Any    =			8_8_8_8
  _a						: List[str]    =			int(os.environ["""SM_NUM_GPUS"""]       )
  _a						: Union[str, Any]    =			json.loads(os.environ["""SM_HOSTS"""]       )
  _a						: Tuple    =			len(UpperCamelCase__       )
  _a						: Optional[int]    =			os.environ["""SM_CURRENT_HOST"""]
  _a						: Optional[Any]    =			hosts.index(UpperCamelCase__       )
  _a						: List[str]    =			"""INFO"""

  if num_nodes > 1:
    _a						: int    =			F"""python -m torch.distributed.launch \
                --nnodes={num_nodes}  \
                --node_rank={rank}  \
                --nproc_per_node={num_gpus}  \
                --master_addr={hosts[0]}  \
                --master_port={port} \
                ./run_glue.py \
                {''.join(['nested fstring' for parameter,value in args.__dict__.items()]       )}"""
  else:
    _a						: Any    =			F"""python -m torch.distributed.launch \
            --nproc_per_node={num_gpus}  \
            ./run_glue.py \
            {''.join(['nested fstring' for parameter,value in args.__dict__.items()]       )}"""
  try:
    subprocess.run(UpperCamelCase__  ,     shell=UpperCamelCase__       )
  except Exception as e:
    logger.info(UpperCamelCase__       )


if __name__ == "__main__":
 main()

