
"""simple docstring"""
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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		['''pixel_values''']




							def __init__( self    ,				snake_case_ = True    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = PILImageResampling.BILINEAR    ,				snake_case_ = True    ,				snake_case_ = 1 / 255    ,				snake_case_ = True    ,				snake_case_ = None    ,				snake_case_ = None    ,				**snake_case_    ,				)       ->  None:
													super().__init__(**snake_case_				)
													__lowerCAmelCase     =  size if size is not None else {"""shortest_edge""": 384}
													__lowerCAmelCase     =  get_size_dict(snake_case_    ,				default_to_square=snake_case_				)

													__lowerCAmelCase     =  do_resize
													__lowerCAmelCase     =  size
													# Default value set here for backwards compatibility where the value in config is None
													__lowerCAmelCase     =  crop_pct if crop_pct is not None else 224 / 256
													__lowerCAmelCase     =  resample
													__lowerCAmelCase     =  do_rescale
													__lowerCAmelCase     =  rescale_factor
													__lowerCAmelCase     =  do_normalize
													__lowerCAmelCase     =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													__lowerCAmelCase     =  image_std if image_std is not None else IMAGENET_STANDARD_STD




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_ = PILImageResampling.BICUBIC    ,				snake_case_ = None    ,				**snake_case_    ,				)       ->  np.ndarray:
													__lowerCAmelCase     =  get_size_dict(snake_case_    ,				default_to_square=snake_case_				)
													if "shortest_edge" not in size:
																			raise ValueError(f"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""				)
													__lowerCAmelCase     =  size["""shortest_edge"""]

													if shortest_edge < 384:
																			# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																			__lowerCAmelCase     =  int(shortest_edge / crop_pct				)
																			__lowerCAmelCase     =  get_resize_output_image_size(snake_case_    ,				size=snake_case_    ,				default_to_square=snake_case_				)
																			__lowerCAmelCase     =  resize(image=snake_case_    ,				size=snake_case_    ,				resample=snake_case_    ,				data_format=snake_case_    ,				**snake_case_				)
																			# then crop to (shortest_edge, shortest_edge)
																			return center_crop(image=snake_case_    ,				size=(shortest_edge, shortest_edge)    ,				data_format=snake_case_    ,				**snake_case_				)
													else:
																			# warping (no cropping) when evaluated at 384 or larger
																			return resize(
																			    snake_case_    ,				size=(shortest_edge, shortest_edge)    ,				resample=snake_case_    ,				data_format=snake_case_    ,				**snake_case_				)




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_ = None    ,				**snake_case_    ,				)       ->  List[str]:
													return rescale(snake_case_    ,				scale=snake_case_    ,				data_format=snake_case_    ,				**snake_case_				)




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_ = None    ,				**snake_case_    ,				)       ->  np.ndarray:
													return normalize(snake_case_    ,				mean=snake_case_    ,				std=snake_case_    ,				data_format=snake_case_    ,				**snake_case_				)




							def    A__						( self    ,				snake_case_    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = ChannelDimension.FIRST    ,				**snake_case_    ,				)       ->  PIL.Image.Image:
													__lowerCAmelCase     =  do_resize if do_resize is not None else self.do_resize
													__lowerCAmelCase     =  crop_pct if crop_pct is not None else self.crop_pct
													__lowerCAmelCase     =  resample if resample is not None else self.resample
													__lowerCAmelCase     =  do_rescale if do_rescale is not None else self.do_rescale
													__lowerCAmelCase     =  rescale_factor if rescale_factor is not None else self.rescale_factor
													__lowerCAmelCase     =  do_normalize if do_normalize is not None else self.do_normalize
													__lowerCAmelCase     =  image_mean if image_mean is not None else self.image_mean
													__lowerCAmelCase     =  image_std if image_std is not None else self.image_std

													__lowerCAmelCase     =  size if size is not None else self.size
													__lowerCAmelCase     =  get_size_dict(snake_case_    ,				default_to_square=snake_case_				)

													__lowerCAmelCase     =  make_list_of_images(snake_case_				)

													if not valid_images(snake_case_				):
																			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 or resample is None:
																			raise ValueError("""Size and resample must be specified if do_resize is True."""				)

													if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
																			raise ValueError("""crop_pct must be specified if size < 384."""				)

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

													if do_normalize and (image_mean is None or image_std is None):
																			raise ValueError("""Image mean and std must be specified if do_normalize is True."""				)

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

													if do_resize:
																			__lowerCAmelCase     =  [self.resize(image=snake_case_    ,				size=snake_case_    ,				crop_pct=snake_case_    ,				resample=snake_case_				) for image in images]

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

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

													__lowerCAmelCase     =  [to_channel_dimension_format(snake_case_    ,				snake_case_				) for image in images]

													__lowerCAmelCase     =  {"""pixel_values""": images}
													return BatchFeature(data=snake_case_    ,				tensor_type=snake_case_				)


"""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  lowercase     (_lowerCAmelCase						):
						if is_torch_version("""<""" ,    """2.0.0"""						) or not hasattr(_lowerCAmelCase ,    """_dynamo"""						):
												return False
						return isinstance(_lowerCAmelCase ,    torch._dynamo.eval_frame.OptimizedModule						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase = True						):
						__lowerCAmelCase     =  (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

						__lowerCAmelCase     =  is_compiled_module(_lowerCAmelCase						)
						if is_compiled:
												__lowerCAmelCase     =  model
												__lowerCAmelCase     =  model._orig_mod

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

						while isinstance(_lowerCAmelCase ,    _lowerCAmelCase						):
												__lowerCAmelCase     =  model.module

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

						if is_compiled:
												__lowerCAmelCase     =  model
												__lowerCAmelCase     =  compiled_model

						return model

def  lowercase     ():
						PartialState().wait_for_everyone()

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						if PartialState().distributed_type == DistributedType.TPU:
												xm.save(_lowerCAmelCase ,    _lowerCAmelCase						)
						elif PartialState().local_process_index == 0:
												torch.save(_lowerCAmelCase ,    _lowerCAmelCase						)

@contextmanager
def  lowercase     (**_lowerCAmelCase						):
						for key, value in kwargs.items():
												__lowerCAmelCase     =  str(_lowerCAmelCase						)

						yield

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

def  lowercase     (_lowerCAmelCase						):
						if not hasattr(_lowerCAmelCase ,    """__qualname__"""						) and not hasattr(_lowerCAmelCase ,    """__name__"""						):
												__lowerCAmelCase     =  getattr(_lowerCAmelCase ,    """__class__""" ,    _lowerCAmelCase						)
						if hasattr(_lowerCAmelCase ,    """__qualname__"""						):
												return obj.__qualname__
						if hasattr(_lowerCAmelCase ,    """__name__"""						):
												return obj.__name__
						return str(_lowerCAmelCase						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						for key, value in source.items():
												if isinstance(_lowerCAmelCase ,    _lowerCAmelCase						):
																		__lowerCAmelCase     =  destination.setdefault(_lowerCAmelCase ,    {}						)
																		merge_dicts(_lowerCAmelCase ,    _lowerCAmelCase						)
												else:
																		__lowerCAmelCase     =  value

						return destination



def  lowercase     (_lowerCAmelCase = None						):
						if port is None:
												__lowerCAmelCase     =  2_9500
						with socket.socket(socket.AF_INET ,    socket.SOCK_STREAM						) as s:
												return s.connect_ex(("""localhost""", port)						) == 0
