'''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, logging


lowerCAmelCase_	:		Optional[Any]			=     logging.get_logger(__name__)




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							['''pixel_values''']





				def __init__(						self					:						int       ,		lowercase__					:						bool = True       ,		lowercase__					:						Optional[Dict[str, int]] = None       ,		lowercase__					:						PILImageResampling = PILImageResampling.BILINEAR       ,		lowercase__					:						bool = True       ,		lowercase__					:						Dict[str, int] = None       ,		lowercase__					:						bool = True       ,		lowercase__					:						Union[int, float] = 1 / 255       ,		lowercase__					:						bool = True       ,		lowercase__					:						Optional[Union[float, List[float]]] = None       ,		lowercase__					:						Optional[Union[float, List[float]]] = None       ,		**lowercase__					:						Optional[int]       ,		)							->None:


										'''simple docstring'''




										super().__init__(**lowercase__					)
										_UpperCamelCase			:			List[Any]   			=						size if size is not None else {"shortest_edge": 256}
										_UpperCamelCase			:			Optional[Any]   			=						get_size_dict(lowercase__       ,		default_to_square=lowercase__					)
										_UpperCamelCase			:			Any   			=						crop_size if crop_size is not None else {"height": 224, "width": 224}
										_UpperCamelCase			:			Any   			=						get_size_dict(lowercase__					)
										_UpperCamelCase			:			Tuple   			=						do_resize
										_UpperCamelCase			:			Tuple   			=						size
										_UpperCamelCase			:			Union[str, Any]   			=						resample
										_UpperCamelCase			:			Optional[Any]   			=						do_center_crop
										_UpperCamelCase			:			Tuple   			=						crop_size
										_UpperCamelCase			:			Union[str, Any]   			=						do_rescale
										_UpperCamelCase			:			int   			=						rescale_factor
										_UpperCamelCase			:			Optional[int]   			=						do_normalize
										_UpperCamelCase			:			Optional[int]   			=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										_UpperCamelCase			:			List[str]   			=						image_std if image_std is not None else IMAGENET_STANDARD_STD





				def  snake_case__				(						self					:						str       ,		lowercase__					:						np.ndarray       ,		lowercase__					:						Dict[str, int]       ,		lowercase__					:						PILImageResampling = PILImageResampling.BICUBIC       ,		lowercase__					:						Optional[Union[str, ChannelDimension]] = None       ,		**lowercase__					:						int       ,		)							->np.ndarray:


										'''simple docstring'''




										_UpperCamelCase			:			List[str]   			=						get_size_dict(lowercase__       ,		default_to_square=lowercase__					)
										if "shortest_edge" not in size:
																raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''					)
										_UpperCamelCase			:			Optional[int]   			=						get_resize_output_image_size(lowercase__       ,		size=size["shortest_edge"]       ,		default_to_square=lowercase__					)
										return resize(lowercase__       ,		size=lowercase__       ,		resample=lowercase__       ,		data_format=lowercase__       ,		**lowercase__					)





				def  snake_case__				(						self					:						Any       ,		lowercase__					:						np.ndarray       ,		lowercase__					:						Dict[str, int]       ,		lowercase__					:						Optional[Union[str, ChannelDimension]] = None       ,		**lowercase__					:						Dict       ,		)							->np.ndarray:


										'''simple docstring'''




										_UpperCamelCase			:			Union[str, Any]   			=						get_size_dict(lowercase__					)
										return center_crop(lowercase__       ,		size=(size["height"], size["width"])       ,		data_format=lowercase__       ,		**lowercase__					)





				def  snake_case__				(						self					:						List[Any]       ,		lowercase__					:						np.ndarray       ,		lowercase__					:						float       ,		lowercase__					:						Optional[Union[str, ChannelDimension]] = None       ,		**lowercase__					:						List[Any]					)							->np.ndarray:


										'''simple docstring'''




										return rescale(lowercase__       ,		scale=lowercase__       ,		data_format=lowercase__       ,		**lowercase__					)





				def  snake_case__				(						self					:						List[Any]       ,		lowercase__					:						np.ndarray       ,		lowercase__					:						Union[float, List[float]]       ,		lowercase__					:						Union[float, List[float]]       ,		lowercase__					:						Optional[Union[str, ChannelDimension]] = None       ,		**lowercase__					:						List[str]       ,		)							->np.ndarray:


										'''simple docstring'''




										return normalize(lowercase__       ,		mean=lowercase__       ,		std=lowercase__       ,		data_format=lowercase__       ,		**lowercase__					)







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


										'''simple docstring'''




										_UpperCamelCase			:			str   			=						do_resize if do_resize is not None else self.do_resize
										_UpperCamelCase			:			List[str]   			=						size if size is not None else self.size
										_UpperCamelCase			:			List[str]   			=						get_size_dict(lowercase__       ,		default_to_square=lowercase__					)
										_UpperCamelCase			:			Optional[Any]   			=						resample if resample is not None else self.resample
										_UpperCamelCase			:			List[Any]   			=						do_center_crop if do_center_crop is not None else self.do_center_crop
										_UpperCamelCase			:			Union[str, Any]   			=						crop_size if crop_size is not None else self.crop_size
										_UpperCamelCase			:			int   			=						get_size_dict(lowercase__					)
										_UpperCamelCase			:			Optional[Any]   			=						do_rescale if do_rescale is not None else self.do_rescale
										_UpperCamelCase			:			Any   			=						rescale_factor if rescale_factor is not None else self.rescale_factor
										_UpperCamelCase			:			Tuple   			=						do_normalize if do_normalize is not None else self.do_normalize
										_UpperCamelCase			:			Optional[Any]   			=						image_mean if image_mean is not None else self.image_mean
										_UpperCamelCase			:			List[str]   			=						image_std if image_std is not None else self.image_std

										_UpperCamelCase			:			int   			=						make_list_of_images(lowercase__					)

										if not valid_images(lowercase__					):
																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."					)

										# All transformations expect numpy arrays.
										_UpperCamelCase			:			List[Any]   			=						[to_numpy_array(lowercase__					) for image in images]

										if do_resize:
																_UpperCamelCase			:			Union[str, Any]   			=						[self.resize(image=lowercase__       ,		size=lowercase__       ,		resample=lowercase__					) for image in images]

										if do_center_crop:
																_UpperCamelCase			:			Union[str, Any]   			=						[self.center_crop(image=lowercase__       ,		size=lowercase__					) for image in images]

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

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

										_UpperCamelCase			:			Optional[Any]   			=						[to_channel_dimension_format(lowercase__       ,		lowercase__					) for image in images]

										_UpperCamelCase			:			Union[str, Any]   			=						{"pixel_values": images}
										return BatchFeature(data=lowercase__       ,		tensor_type=lowercase__					)



'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_	:		Union[str, Any]			=     logging.get_logger(__name__)

lowerCAmelCase_	:		List[str]			=     {
    """hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							'''yolos'''





				def __init__(						self					:						int       ,		lowercase__					:						List[str]=768       ,		lowercase__					:						Optional[Any]=12       ,		lowercase__					:						Union[str, Any]=12       ,		lowercase__					:						Any=3_072       ,		lowercase__					:						List[Any]="gelu"       ,		lowercase__					:						Dict=0.0       ,		lowercase__					:						Any=0.0       ,		lowercase__					:						Dict=0.0_2       ,		lowercase__					:						Tuple=1e-12       ,		lowercase__					:						str=[512, 864]       ,		lowercase__					:						Dict=16       ,		lowercase__					:						int=3       ,		lowercase__					:						Optional[Any]=True       ,		lowercase__					:						List[Any]=100       ,		lowercase__					:						str=True       ,		lowercase__					:						str=False       ,		lowercase__					:						List[str]=1       ,		lowercase__					:						Dict=5       ,		lowercase__					:						str=2       ,		lowercase__					:						Optional[int]=5       ,		lowercase__					:						Optional[int]=2       ,		lowercase__					:						Optional[Any]=0.1       ,		**lowercase__					:						Union[str, Any]       ,		)							->Tuple:


										'''simple docstring'''




										super().__init__(**lowercase__					)

										_UpperCamelCase			:			Optional[int]   			=						hidden_size
										_UpperCamelCase			:			str   			=						num_hidden_layers
										_UpperCamelCase			:			Optional[Any]   			=						num_attention_heads
										_UpperCamelCase			:			Optional[int]   			=						intermediate_size
										_UpperCamelCase			:			Union[str, Any]   			=						hidden_act
										_UpperCamelCase			:			Tuple   			=						hidden_dropout_prob
										_UpperCamelCase			:			Any   			=						attention_probs_dropout_prob
										_UpperCamelCase			:			Optional[int]   			=						initializer_range
										_UpperCamelCase			:			str   			=						layer_norm_eps
										_UpperCamelCase			:			Optional[int]   			=						image_size
										_UpperCamelCase			:			int   			=						patch_size
										_UpperCamelCase			:			Dict   			=						num_channels
										_UpperCamelCase			:			List[str]   			=						qkv_bias
										_UpperCamelCase			:			Dict   			=						num_detection_tokens
										_UpperCamelCase			:			int   			=						use_mid_position_embeddings
										_UpperCamelCase			:			int   			=						auxiliary_loss
										# Hungarian matcher
										_UpperCamelCase			:			Tuple   			=						class_cost
										_UpperCamelCase			:			str   			=						bbox_cost
										_UpperCamelCase			:			str   			=						giou_cost
										# Loss coefficients
										_UpperCamelCase			:			List[str]   			=						bbox_loss_coefficient
										_UpperCamelCase			:			str   			=						giou_loss_coefficient
										_UpperCamelCase			:			str   			=						eos_coefficient





class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							version.parse('''1.11'''	)





				@property
				def  snake_case__				(						self					:						Union[str, Any]					)							->Mapping[str, Mapping[int, str]]:


										'''simple docstring'''




										return OrderedDict(
										    [
										        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
										    ]					)





				@property
				def  snake_case__				(						self					:						Any					)							->float:


										'''simple docstring'''




										return 1e-4







				@property
				def  snake_case__				(						self					:						List[str]					)							->int:


										'''simple docstring'''




										return 12


