





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


UpperCamelCase__           =			logging.get_logger(__name__)

class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   str      =							["""pixel_values"""]






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

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






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

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






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase = None	,			**__lowerCAmelCase	,			):
														return rescale(__lowerCAmelCase	,			scale=__lowerCAmelCase	,			data_format=__lowerCAmelCase	,			**__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase = None	,			**__lowerCAmelCase	,			):
														return normalize(__lowerCAmelCase	,			mean=__lowerCAmelCase	,			std=__lowerCAmelCase	,			data_format=__lowerCAmelCase	,			**__lowerCAmelCase	)







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = ChannelDimension.FIRST	,			**__lowerCAmelCase	,			):
														UpperCamelCase__					  =      do_resize if do_resize is not None else self.do_resize
														UpperCamelCase__					  =      crop_pct if crop_pct is not None else self.crop_pct
														UpperCamelCase__					  =      resample if resample is not None else self.resample
														UpperCamelCase__					  =      do_rescale if do_rescale is not None else self.do_rescale
														UpperCamelCase__					  =      rescale_factor if rescale_factor is not None else self.rescale_factor
														UpperCamelCase__					  =      do_normalize if do_normalize is not None else self.do_normalize
														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(__lowerCAmelCase	,			default_to_square=__lowerCAmelCase	)

														UpperCamelCase__					  =      make_list_of_images(__lowerCAmelCase	)

														if not valid_images(__lowerCAmelCase	):
																					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.
														UpperCamelCase__					  =      [to_numpy_array(__lowerCAmelCase	) for image in images]

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

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

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

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

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











from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
					import timm


if is_torch_available():
					from torch import Tensor

class 							__SCREAMING_SNAKE_CASE       (  _a ,		_a							):
							snake_case  :   int      =							"""pixel_values"""
							snake_case  :   List[Any]      =							False
							snake_case  :   str      =							TimmBackboneConfig






							def __init__(    self	,			__lowerCAmelCase	,			**__lowerCAmelCase	):
														requires_backends(self	,			"""timm"""	)
														super().__init__(__lowerCAmelCase	)
														UpperCamelCase__					  =      config

														if config.backbone is None:
																					raise ValueError("""backbone is not set in the config. Please set it to a timm model name."""	)

														if config.backbone not in timm.list_models():
																					raise ValueError(f"""backbone {config.backbone} is not supported by timm."""	)

														if hasattr(__lowerCAmelCase	,			"""out_features"""	) and config.out_features is not None:
																					raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead."""	)

														UpperCamelCase__					  =      getattr(__lowerCAmelCase	,			"""use_pretrained_backbone"""	,			__lowerCAmelCase	)
														if pretrained is None:
																					raise ValueError("""use_pretrained_backbone is not set in the config. Please set it to True or False."""	)

														# We just take the final layer by default. This matches the default for the transformers models.
														UpperCamelCase__					  =      config.out_indices if getattr(__lowerCAmelCase	,			"""out_indices"""	,			__lowerCAmelCase	) is not None else (-1,)

														UpperCamelCase__					  =      timm.create_model(
														    config.backbone	,			pretrained=__lowerCAmelCase	,			features_only=config.features_only	,			in_chans=config.num_channels	,			out_indices=__lowerCAmelCase	,			**__lowerCAmelCase	,			)
														# These are used to control the output of the model when called. If output_hidden_states is True, then
														# return_layers is modified to include all layers.
														UpperCamelCase__					  =      self._backbone.return_layers
														UpperCamelCase__					  =      {layer["""module"""]: str(__lowerCAmelCase	) for i, layer in enumerate(self._backbone.feature_info.info	)}
														super()._init_backbone(__lowerCAmelCase	)






							@classmethod
							def 					_lowerCamelCase		(    cls	,			__lowerCAmelCase	,			*__lowerCAmelCase	,			**__lowerCAmelCase	):
														requires_backends(cls	,			["""vision""", """timm"""]	)
														from ...models.timm_backbone import TimmBackboneConfig

														UpperCamelCase__					  =      kwargs.pop("""config"""	,			TimmBackboneConfig()	)

														UpperCamelCase__					  =      kwargs.pop("""use_timm_backbone"""	,			__lowerCAmelCase	)
														if not use_timm:
																					raise ValueError("""use_timm_backbone must be True for timm backbones"""	)

														UpperCamelCase__					  =      kwargs.pop("""num_channels"""	,			config.num_channels	)
														UpperCamelCase__					  =      kwargs.pop("""features_only"""	,			config.features_only	)
														UpperCamelCase__					  =      kwargs.pop("""use_pretrained_backbone"""	,			config.use_pretrained_backbone	)
														UpperCamelCase__					  =      kwargs.pop("""out_indices"""	,			config.out_indices	)
														UpperCamelCase__					  =      TimmBackboneConfig(
														    backbone=__lowerCAmelCase	,			num_channels=__lowerCAmelCase	,			features_only=__lowerCAmelCase	,			use_pretrained_backbone=__lowerCAmelCase	,			out_indices=__lowerCAmelCase	,			)
														return super()._from_config(__lowerCAmelCase	,			**__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														pass







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase=None	,			__lowerCAmelCase=None	,			__lowerCAmelCase=None	,			**__lowerCAmelCase	):
														UpperCamelCase__					  =      return_dict if return_dict is not None else self.config.use_return_dict
														UpperCamelCase__					  =      (
														    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
														)
														UpperCamelCase__					  =      output_attentions if output_attentions is not None else self.config.output_attentions

														if output_attentions:
																					raise ValueError("""Cannot output attentions for timm backbones at the moment"""	)

														if output_hidden_states:
																					# We modify the return layers to include all the stages of the backbone
																					UpperCamelCase__					  =      self._all_layers
																					UpperCamelCase__					  =      self._backbone(__lowerCAmelCase	,			**__lowerCAmelCase	)
																					UpperCamelCase__					  =      self._return_layers
																					UpperCamelCase__					  =      tuple(hidden_states[i] for i in self.out_indices	)
														else:
																					UpperCamelCase__					  =      self._backbone(__lowerCAmelCase	,			**__lowerCAmelCase	)
																					UpperCamelCase__					  =      None

														UpperCamelCase__					  =      tuple(__lowerCAmelCase	)
														UpperCamelCase__					  =      tuple(__lowerCAmelCase	) if hidden_states is not None else None

														if not return_dict:
																					UpperCamelCase__					  =      (feature_maps,)
																					if output_hidden_states:
																												UpperCamelCase__					  =      output + (hidden_states,)
																					return output

														return BackboneOutput(feature_maps=__lowerCAmelCase	,			hidden_states=__lowerCAmelCase	,			attentions=__lowerCAmelCase	)




