
import operator as op


lowercase        =  '''scaler.pt'''
lowercase        =  '''pytorch_model'''
lowercase        =  '''random_states'''
lowercase        =  '''optimizer'''
lowercase        =  '''scheduler'''
lowercase        =  '''pytorch_model.bin'''
lowercase        =  '''pytorch_model.bin.index.json'''
lowercase        =  '''model.safetensors'''
lowercase        =  '''model.safetensors.index.json'''
lowercase        =  '''1.10.2'''
lowercase        =  '''py38'''
lowercase        =  '''4.17.0'''
lowercase        =  ['''ml.p3.16xlarge''', '''ml.p3dn.24xlarge''', '''ml.p4dn.24xlarge''']
lowercase        =  ['''FULL_SHARD''', '''SHARD_GRAD_OP''', '''NO_SHARD''', '''HYBRID_SHARD''', '''HYBRID_SHARD_ZERO2''']
lowercase        =  ['''TRANSFORMER_BASED_WRAP''', '''SIZE_BASED_WRAP''', '''NO_WRAP''']
lowercase        =  ['''BACKWARD_PRE''', '''BACKWARD_POST''', '''NO_PREFETCH''']
lowercase        =  ['''FULL_STATE_DICT''', '''LOCAL_STATE_DICT''', '''SHARDED_STATE_DICT''']
lowercase        =  '''2.0.1'''
lowercase        =  ['''pdsh''', '''standard''', '''openmpi''', '''mvapich''']
lowercase        =  ['''default''', '''reduce-overhead''', '''max-autotune''']

lowercase        =  {'''>''': op.gt, '''>=''': op.ge, '''==''': op.eq, '''!=''': op.ne, '''<=''': op.le, '''<''': op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
lowercase        =  [
    '''nnodes''',
    '''nproc_per_node''',
    '''rdzv_backend''',
    '''rdzv_endpoint''',
    '''rdzv_id''',
    '''rdzv_conf''',
    '''standalone''',
    '''max_restarts''',
    '''monitor_interval''',
    '''start_method''',
    '''role''',
    '''module''',
    '''m''',
    '''no_python''',
    '''run_path''',
    '''log_dir''',
    '''r''',
    '''redirects''',
    '''t''',
    '''tee''',
    '''node_rank''',
    '''master_addr''',
    '''master_port''',
]

lowercase        =  ['''DEEPSPEED''', '''MULTI_GPU''', '''FSDP''', '''MEGATRON_LM''']
lowercase        =  ['''DEEPSPEED''', '''MULTI_XPU''', '''FSDP''']







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


lowercase        =  logging.get_logger(__name__)





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    ['''pixel_values''']

					def __init__(	self					:     Any   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Dict[str, int] = None   ,					__UpperCamelCase					:     float = None   ,					__UpperCamelCase					:     PILImageResampling = PILImageResampling.BILINEAR   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Union[int, float] = 1 / 2_5_5   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Optional[Union[float, List[float]]] = None   ,					__UpperCamelCase					:     Optional[Union[float, List[float]]] = None   ,					**__UpperCamelCase					:     Optional[int]   ,					):
												super().__init__(**__UpperCamelCase   )
												lowerCamelCase_							  =					size if size is not None else {"""shortest_edge""": 3_8_4}
												lowerCamelCase_							  =					get_size_dict(__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )

												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 2_2_4 / 2_5_6
												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    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     np.ndarray   ,					__UpperCamelCase					:     Dict[str, int]   ,					__UpperCamelCase					:     float   ,					__UpperCamelCase					:     PILImageResampling = PILImageResampling.BICUBIC   ,					__UpperCamelCase					:     Optional[Union[str, ChannelDimension]] = None   ,					**__UpperCamelCase					:     Tuple   ,					):
												lowerCamelCase_							  =					get_size_dict(__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )
												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 < 3_8_4:
																			# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																			lowerCamelCase_							  =					int(shortest_edge / crop_pct   )
																			lowerCamelCase_							  =					get_resize_output_image_size(__UpperCamelCase   ,					size=__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )
																			lowerCamelCase_							  =					resize(image=__UpperCamelCase   ,					size=__UpperCamelCase   ,					resample=__UpperCamelCase   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )
																			# then crop to (shortest_edge, shortest_edge)
																			return center_crop(image=__UpperCamelCase   ,					size=(shortest_edge, shortest_edge)   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )
												else:
																			# warping (no cropping) when evaluated at 384 or larger
																			return resize(
																			    __UpperCamelCase   ,					size=(shortest_edge, shortest_edge)   ,					resample=__UpperCamelCase   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     np.ndarray   ,					__UpperCamelCase					:     Union[int, float]   ,					__UpperCamelCase					:     Optional[Union[str, ChannelDimension]] = None   ,					**__UpperCamelCase					:     int   ,					):
												return rescale(__UpperCamelCase   ,					scale=__UpperCamelCase   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     np.ndarray   ,					__UpperCamelCase					:     Union[float, List[float]]   ,					__UpperCamelCase					:     Union[float, List[float]]   ,					__UpperCamelCase					:     Optional[Union[str, ChannelDimension]] = None   ,					**__UpperCamelCase					:     List[str]   ,					):
												return normalize(__UpperCamelCase   ,					mean=__UpperCamelCase   ,					std=__UpperCamelCase   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )



					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     ImageInput   ,					__UpperCamelCase					:     bool = None   ,					__UpperCamelCase					:     Dict[str, int] = None   ,					__UpperCamelCase					:     float = None   ,					__UpperCamelCase					:     PILImageResampling = None   ,					__UpperCamelCase					:     bool = None   ,					__UpperCamelCase					:     float = None   ,					__UpperCamelCase					:     bool = None   ,					__UpperCamelCase					:     Optional[Union[float, List[float]]] = None   ,					__UpperCamelCase					:     Optional[Union[float, List[float]]] = None   ,					__UpperCamelCase					:     Optional[Union[str, TensorType]] = None   ,					__UpperCamelCase					:     ChannelDimension = ChannelDimension.FIRST   ,					**__UpperCamelCase					:     List[str]   ,					):
												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(__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )

												lowerCamelCase_							  =					make_list_of_images(__UpperCamelCase   )

												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 or resample is None:
																			raise ValueError("""Size and resample must be specified if do_resize is True."""   )

												if do_resize and size["shortest_edge"] < 3_8_4 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(__UpperCamelCase   ) for image in images]

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

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

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

												lowerCamelCase_							  =					[to_channel_dimension_format(__UpperCamelCase   ,					__UpperCamelCase   ) for image in images]

												lowerCamelCase_							  =					{"""pixel_values""": images}
												return BatchFeature(data=__UpperCamelCase   ,					tensor_type=__UpperCamelCase   )





