



"""simple docstring"""






import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
     from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm




def 			snake_case		(		lowerCAmelCase_	)						->				Optional[Any]:
  if is_torch_version('''<'''  ,						'''2.0.0'''	) or not hasattr(lowerCAmelCase_  ,						'''_dynamo'''	):
    return False
  return isinstance(lowerCAmelCase_  ,						torch._dynamo.eval_frame.OptimizedModule	)




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_ = True	)						->				Dict:
  _snake_case   	=      (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

  _snake_case   	=      is_compiled_module(lowerCAmelCase_	)
  if is_compiled:
    _snake_case   	=      model
    _snake_case   	=      model._orig_mod

  if is_deepspeed_available():
    options += (DeepSpeedEngine,)

  while isinstance(lowerCAmelCase_  ,						lowerCAmelCase_	):
    _snake_case   	=      model.module

  if not keep_fpaa_wrapper:
    _snake_case   	=      getattr(lowerCAmelCase_  ,						'''forward'''	)
    _snake_case   	=      model.__dict__.pop('''_original_forward'''  ,						lowerCAmelCase_	)
    if original_forward is not None:
      while hasattr(lowerCAmelCase_  ,						'''__wrapped__'''	):
        _snake_case   	=      forward.__wrapped__
        if forward == original_forward:
          break
      _snake_case   	=      forward
    if getattr(lowerCAmelCase_  ,						'''_converted_to_transformer_engine'''  ,						lowerCAmelCase_	):
      convert_model(lowerCAmelCase_  ,						to_transformer_engine=lowerCAmelCase_	)

  if is_compiled:
    _snake_case   	=      model
    _snake_case   	=      compiled_model

  return model




def 			snake_case		(		)						->				str:
  PartialState().wait_for_everyone()




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_	)						->				Dict:
  if PartialState().distributed_type == DistributedType.TPU:
    xm.save(lowerCAmelCase_  ,						lowerCAmelCase_	)
  elif PartialState().local_process_index == 0:
    torch.save(lowerCAmelCase_  ,						lowerCAmelCase_	)




@contextmanager
def 			snake_case		(		**lowerCAmelCase_	)						->				str:
  for key, value in kwargs.items():
    _snake_case   	=      str(lowerCAmelCase_	)

  yield

  for key in kwargs:
    if key.upper() in os.environ:
      del os.environ[key.upper()]




def 			snake_case		(		lowerCAmelCase_	)						->				Tuple:
  if not hasattr(lowerCAmelCase_  ,						'''__qualname__'''	) and not hasattr(lowerCAmelCase_  ,						'''__name__'''	):
    _snake_case   	=      getattr(lowerCAmelCase_  ,						'''__class__'''  ,						lowerCAmelCase_	)
  if hasattr(lowerCAmelCase_  ,						'''__qualname__'''	):
    return obj.__qualname__
  if hasattr(lowerCAmelCase_  ,						'''__name__'''	):
    return obj.__name__
  return str(lowerCAmelCase_	)




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_	)						->				Tuple:
  for key, value in source.items():
    if isinstance(lowerCAmelCase_  ,						lowerCAmelCase_	):
      _snake_case   	=      destination.setdefault(lowerCAmelCase_  ,						{}	)
      merge_dicts(lowerCAmelCase_  ,						lowerCAmelCase_	)
    else:
      _snake_case   	=      value

  return destination







def 			snake_case		(		lowerCAmelCase_ = None	)						->				bool:
  if port is None:
    _snake_case   	=      29500
  with socket.socket(socket.AF_INET  ,						socket.SOCK_STREAM	) as s:
    return s.connect_ex(('''localhost''', port)	) == 0












from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_           =	logging.get_logger(__name__)




class 			__UpperCamelCase    (							A__    ):
     __A	:  int							  =   ["""pixel_values"""]





     def __init__(     self				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							_UpperCamelCase = PILImageResampling.BICUBIC				,							_UpperCamelCase = True				,							_UpperCamelCase = True				,							_UpperCamelCase = 1 / 255				,							_UpperCamelCase = None				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        super().__init__(**_UpperCamelCase					)
        _UpperCAmelCase           =   size if size is not None else {'''height''': 224, '''width''': 224}
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)
        _UpperCAmelCase           =   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase				,							default_to_square=_UpperCamelCase				,							param_name='''crop_size'''					)

        _UpperCAmelCase           =   do_resize
        _UpperCAmelCase           =   do_rescale
        _UpperCAmelCase           =   do_normalize
        _UpperCAmelCase           =   do_center_crop
        _UpperCAmelCase           =   crop_size
        _UpperCAmelCase           =   size
        _UpperCAmelCase           =   resample
        _UpperCAmelCase           =   rescale_factor
        _UpperCAmelCase           =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        _UpperCAmelCase           =   image_std if image_std is not None else IMAGENET_DEFAULT_STD





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = PILImageResampling.BILINEAR				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)

        if "shortest_edge" in size:
           _UpperCAmelCase           =   get_resize_output_image_size(_UpperCamelCase				,							size=size['''shortest_edge''']				,							default_to_square=_UpperCamelCase					)
           # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
        elif "height" in size and "width" in size:
           _UpperCAmelCase           =   (size['''height'''], size['''width'''])
        else:
           raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''					)
        return resize(_UpperCamelCase				,							size=_UpperCamelCase				,							resample=_UpperCamelCase				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)
        if "height" not in size or "width" not in size:
           raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''					)
        return center_crop(_UpperCamelCase				,							size=(size['''height'''], size['''width'''])				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							**_UpperCamelCase					):
        return rescale(_UpperCamelCase				,							scale=_UpperCamelCase				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        return normalize(_UpperCamelCase				,							mean=_UpperCamelCase				,							std=_UpperCamelCase				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = ChannelDimension.FIRST				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   do_resize if do_resize is not None else self.do_resize
        _UpperCAmelCase           =   do_rescale if do_rescale is not None else self.do_rescale
        _UpperCAmelCase           =   do_normalize if do_normalize is not None else self.do_normalize
        _UpperCAmelCase           =   do_center_crop if do_center_crop is not None else self.do_center_crop
        _UpperCAmelCase           =   crop_size if crop_size is not None else self.crop_size
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase				,							param_name='''crop_size'''				,							default_to_square=_UpperCamelCase					)
        _UpperCAmelCase           =   resample if resample is not None else self.resample
        _UpperCAmelCase           =   rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCAmelCase           =   image_mean if image_mean is not None else self.image_mean
        _UpperCAmelCase           =   image_std if image_std is not None else self.image_std

        _UpperCAmelCase           =   size if size is not None else self.size
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)

        if not is_batched(_UpperCamelCase					):
           _UpperCAmelCase           =   [images]

        if not valid_images(_UpperCamelCase					):
           raise ValueError(
               '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
               '''torch.Tensor, tf.Tensor or jax.ndarray.'''					)

        if do_resize and size is None:
           raise ValueError('''Size must be specified if do_resize is True.'''					)

        if do_center_crop and crop_size is None:
           raise ValueError('''Crop size must be specified if do_center_crop is True.'''					)

        if do_rescale and rescale_factor is None:
           raise ValueError('''Rescale factor must be specified if do_rescale is True.'''					)

        # All transformations expect numpy arrays.
        _UpperCAmelCase           =   [to_numpy_array(_UpperCamelCase					) for image in images]

        if do_resize:
           _UpperCAmelCase           =   [self.resize(image=_UpperCamelCase				,							size=_UpperCamelCase				,							resample=_UpperCamelCase					) for image in images]

        if do_center_crop:
           _UpperCAmelCase           =   [self.center_crop(image=_UpperCamelCase				,							size=_UpperCamelCase					) for image in images]

        if do_rescale:
           _UpperCAmelCase           =   [self.rescale(image=_UpperCamelCase				,							scale=_UpperCamelCase					) for image in images]

        if do_normalize:
           _UpperCAmelCase           =   [self.normalize(image=_UpperCamelCase				,							mean=_UpperCamelCase				,							std=_UpperCamelCase					) for image in images]

        _UpperCAmelCase           =   [to_channel_dimension_format(_UpperCamelCase				,							_UpperCamelCase					) for image in images]

        _UpperCAmelCase           =   {'''pixel_values''': images}
        return BatchFeature(data=_UpperCamelCase				,							tensor_type=_UpperCamelCase					)