


'''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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


A				:					int    						=			logging.get_logger(__name__)


if is_vision_available():
   import PIL


class 			__lowerCamelCase			(	a_	):
    """simple docstring"""
    a													=   ["pixel_values"]


    def __init__(  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							bool = True				,							SCREAMING_SNAKE_CASE      :							Dict[str, int] = None				,							SCREAMING_SNAKE_CASE      :							PILImageResampling = PILImageResampling.BICUBIC				,							SCREAMING_SNAKE_CASE      :							bool = True				,							SCREAMING_SNAKE_CASE      :							Dict[str, int] = None				,							SCREAMING_SNAKE_CASE      :							bool = True				,							SCREAMING_SNAKE_CASE      :							Union[int, float] = 1 / 255				,							SCREAMING_SNAKE_CASE      :							bool = True				,							SCREAMING_SNAKE_CASE      :							Optional[Union[float, List[float]]] = None				,							SCREAMING_SNAKE_CASE      :							Optional[Union[float, List[float]]] = None				,							SCREAMING_SNAKE_CASE      :							bool = True				,							**SCREAMING_SNAKE_CASE      :							List[str]				,							):
           super().__init__(**SCREAMING_SNAKE_CASE)
           _A					:     Dict     = size if size is not None else {'shortest_edge': 224}
           _A					:     List[str]     = get_size_dict(SCREAMING_SNAKE_CASE				,							default_to_square=SCREAMING_SNAKE_CASE)
           _A					:     int     = crop_size if crop_size is not None else {'height': 224, 'width': 224}
           _A					:     Dict     = get_size_dict(SCREAMING_SNAKE_CASE				,							default_to_square=SCREAMING_SNAKE_CASE				,							param_name='crop_size')

           _A					:     Any     = do_resize
           _A					:     Dict     = size
           _A					:     Optional[Any]     = resample
           _A					:     Tuple     = do_center_crop
           _A					:     List[Any]     = crop_size
           _A					:     Tuple     = do_rescale
           _A					:     List[str]     = rescale_factor
           _A					:     Union[str, Any]     = do_normalize
           _A					:     int     = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
           _A					:     int     = image_std if image_std is not None else OPENAI_CLIP_STD
           _A					:     Union[str, Any]     = do_convert_rgb


    def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							np.ndarray				,							SCREAMING_SNAKE_CASE      :							Dict[str, int]				,							SCREAMING_SNAKE_CASE      :							PILImageResampling = PILImageResampling.BICUBIC				,							SCREAMING_SNAKE_CASE      :							Optional[Union[str, ChannelDimension]] = None				,							**SCREAMING_SNAKE_CASE      :							List[Any]				,							):
           _A					:     List[Any]     = get_size_dict(SCREAMING_SNAKE_CASE				,							default_to_square=SCREAMING_SNAKE_CASE)
           if "shortest_edge" not in size:
                  raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}')
           _A					:     Dict     = get_resize_output_image_size(SCREAMING_SNAKE_CASE				,							size=size['shortest_edge']				,							default_to_square=SCREAMING_SNAKE_CASE)
           return resize(SCREAMING_SNAKE_CASE				,							size=SCREAMING_SNAKE_CASE				,							resample=SCREAMING_SNAKE_CASE				,							data_format=SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							np.ndarray				,							SCREAMING_SNAKE_CASE      :							Dict[str, int]				,							SCREAMING_SNAKE_CASE      :							Optional[Union[str, ChannelDimension]] = None				,							**SCREAMING_SNAKE_CASE      :							Optional[Any]				,							):
           _A					:     List[str]     = get_size_dict(SCREAMING_SNAKE_CASE)
           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(SCREAMING_SNAKE_CASE				,							size=(size['height'], size['width'])				,							data_format=SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)


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


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


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							ImageInput				,							SCREAMING_SNAKE_CASE      :							bool = None				,							SCREAMING_SNAKE_CASE      :							Dict[str, int] = None				,							SCREAMING_SNAKE_CASE      :							PILImageResampling = None				,							SCREAMING_SNAKE_CASE      :							bool = None				,							SCREAMING_SNAKE_CASE      :							int = None				,							SCREAMING_SNAKE_CASE      :							bool = None				,							SCREAMING_SNAKE_CASE      :							float = None				,							SCREAMING_SNAKE_CASE      :							bool = None				,							SCREAMING_SNAKE_CASE      :							Optional[Union[float, List[float]]] = None				,							SCREAMING_SNAKE_CASE      :							Optional[Union[float, List[float]]] = None				,							SCREAMING_SNAKE_CASE      :							bool = None				,							SCREAMING_SNAKE_CASE      :							Optional[Union[str, TensorType]] = None				,							SCREAMING_SNAKE_CASE      :							Optional[ChannelDimension] = ChannelDimension.FIRST				,							**SCREAMING_SNAKE_CASE      :							Optional[Any]				,							):
           _A					:     List[Any]     = do_resize if do_resize is not None else self.do_resize
           _A					:     Tuple     = size if size is not None else self.size
           _A					:     Tuple     = get_size_dict(SCREAMING_SNAKE_CASE				,							param_name='size'				,							default_to_square=SCREAMING_SNAKE_CASE)
           _A					:     int     = resample if resample is not None else self.resample
           _A					:     Optional[Any]     = do_center_crop if do_center_crop is not None else self.do_center_crop
           _A					:     str     = crop_size if crop_size is not None else self.crop_size
           _A					:     List[Any]     = get_size_dict(SCREAMING_SNAKE_CASE				,							param_name='crop_size'				,							default_to_square=SCREAMING_SNAKE_CASE)
           _A					:     int     = do_rescale if do_rescale is not None else self.do_rescale
           _A					:     Dict     = rescale_factor if rescale_factor is not None else self.rescale_factor
           _A					:     Optional[int]     = do_normalize if do_normalize is not None else self.do_normalize
           _A					:     Union[str, Any]     = image_mean if image_mean is not None else self.image_mean
           _A					:     Tuple     = image_std if image_std is not None else self.image_std
           _A					:     Union[str, Any]     = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

           _A					:     Optional[Any]     = make_list_of_images(SCREAMING_SNAKE_CASE)

           if not valid_images(SCREAMING_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:
                  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.')

           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.')

           # PIL RGBA images are converted to RGB
           if do_convert_rgb:
                  _A					:     Tuple     = [convert_to_rgb(SCREAMING_SNAKE_CASE) for image in images]

           # All transformations expect numpy arrays.
           _A					:     Optional[Any]     = [to_numpy_array(SCREAMING_SNAKE_CASE) for image in images]

           if do_resize:
                  _A					:     str     = [self.resize(image=SCREAMING_SNAKE_CASE				,							size=SCREAMING_SNAKE_CASE				,							resample=SCREAMING_SNAKE_CASE) for image in images]

           if do_center_crop:
                  _A					:     List[Any]     = [self.center_crop(image=SCREAMING_SNAKE_CASE				,							size=SCREAMING_SNAKE_CASE) for image in images]

           if do_rescale:
                  _A					:     str     = [self.rescale(image=SCREAMING_SNAKE_CASE				,							scale=SCREAMING_SNAKE_CASE) for image in images]

           if do_normalize:
                  _A					:     str     = [self.normalize(image=SCREAMING_SNAKE_CASE				,							mean=SCREAMING_SNAKE_CASE				,							std=SCREAMING_SNAKE_CASE) for image in images]

           _A					:     Tuple     = [to_channel_dimension_format(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE) for image in images]

           _A					:     Optional[Any]     = {'pixel_values': images}
           return BatchFeature(data=SCREAMING_SNAKE_CASE				,							tensor_type=SCREAMING_SNAKE_CASE)










'''simple docstring'''







import operator as op


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

A				:					List[Any]    						=			{'''>''': op.gt, '''>=''': op.ge, '''==''': op.eq, '''!=''': op.ne, '''<=''': op.le, '''<''': op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
A				:					Tuple    						=			[
    '''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''',
]

A				:					List[str]    						=			['''DEEPSPEED''', '''MULTI_GPU''', '''FSDP''', '''MEGATRON_LM''']
A				:					Any    						=			['''DEEPSPEED''', '''MULTI_XPU''', '''FSDP''']






