



'''simple docstring'''




import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase							:Union[str, Any]       = logging.get_logger()







@dataclass
class      _lowerCAmelCase							:
	__SCREAMING_SNAKE_CASE       :		nn.Module
	__SCREAMING_SNAKE_CASE       :		List[nn.Module]		      = field(default_factory=snake_case__			)
	__SCREAMING_SNAKE_CASE       :		list		      = field(default_factory=snake_case__			)


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase							):
				A_     :						str     =					len(list(m.modules()							)							) == 1 or isinstance(_A	,				nn.Convad							) or isinstance(_A	,				nn.BatchNormad							)
				if has_not_submodules:
							self.traced.append(_A							)


	def __call__(self	,				lowercase							):



				for m in self.module.modules():
							self.handles.append(m.register_forward_hook(self._forward_hook							)							)
				self.module(_A							)
				[x.remove() for x in self.handles]
				return self


	@property
	def    _a     (self							):
				return list(filter(lambda lowercase							: len(list(x.state_dict().keys()							)							) > 0	,				self.traced							)							)







@dataclass
class      _lowerCAmelCase							:
	__SCREAMING_SNAKE_CASE       :		nn.Module
	__SCREAMING_SNAKE_CASE       :		nn.Module
	__SCREAMING_SNAKE_CASE       :		int		      = 1
	__SCREAMING_SNAKE_CASE       :		List		      = field(default_factory=snake_case__			)
	__SCREAMING_SNAKE_CASE       :		List		      = field(default_factory=snake_case__			)
	__SCREAMING_SNAKE_CASE       :		bool		      = True


	def __call__(self	,				lowercase							):
				A_     :						int     =					Tracker(self.dest							)(_A							).parametrized
				A_     :						Tuple     =					Tracker(self.src							)(_A							).parametrized

				A_     :						Dict     =					list(filter(lambda lowercase							: type(_A							) not in self.src_skip	,				_A							)							)
				A_     :						Union[str, Any]     =					list(filter(lambda lowercase							: type(_A							) not in self.dest_skip	,				_A							)							)

				if len(_A							) != len(_A							) and self.raise_if_mismatch:
							raise Exception(
							    F'Numbers of operations are different. Source module has {len(_A							)} operations while'
							    F' destination module has {len(_A							)}.'							)

				for dest_m, src_m in zip(_A	,				_A							):
							dest_m.load_state_dict(src_m.state_dict()							)
							if self.verbose == 1:
										print(F'Transfered from={src_m} to={dest_m}'							)







class      _lowerCAmelCase							(      nn.Module			):


	def __init__(self	,				lowercase							):
				super().__init__()

				A_     :						List[str]     =					[]
				# - get the stem
				feature_blocks.append(("""conv1""", model.stem)							)
				# - get all the feature blocks
				for k, v in model.trunk_output.named_children():
							assert k.startswith("""block"""							), F'Unexpected layer name {k}'
							A_     :						Optional[int]     =					len(_A							) + 1
							feature_blocks.append((F'res{block_index}', v)							)

				A_     :						Dict     =					nn.ModuleDict(_A							)


	def    _a     (self	,				lowercase							):
				return get_trunk_forward_outputs(
				    _A	,				out_feat_keys=_A	,				feature_blocks=self._feature_blocks	,				)







class      _lowerCAmelCase							(      snake_case__			):


	def    _a     (self	,				lowercase							):
				A_     :						List[Any]     =					x.split("""-"""							)
				return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]							)


	def __getitem__(self	,				lowercase							):



				if x not in self:
							A_     :						Optional[int]     =					self.convert_name_to_timm(_A							)
							A_     :						Union[str, Any]     =					partial(lambda: (timm.create_model(_A	,				pretrained=_A							).eval(), None)							)

				else:
							A_     :						List[str]     =					super().__getitem__(_A							)

				return val







class      _lowerCAmelCase							(      snake_case__			):


	def __getitem__(self	,				lowercase							):



				if "seer" in x and "in1k" not in x:
							A_     :						Union[str, Any]     =					RegNetModel
				else:
							A_     :						Optional[int]     =					RegNetForImageClassification
				return val






def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			for from_key, to_key in keys:
						A_     :						Optional[int]     =					from_state_dict[from_key].clone()
						print(f'Copied key={from_key} to={to_key}'   )
			return to_state_dict




def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__ = True						,				):
			'''simple docstring'''





			print(f'Converting {name}...'   )
			with torch.no_grad():
						A_,   A_     :						Tuple     =					from_model_func()
						A_     :						List[str]     =					our_model_func(SCREAMING_SNAKE_CASE_   ).eval()
						A_     :						List[Any]     =					ModuleTransfer(src=SCREAMING_SNAKE_CASE_						,				dest=SCREAMING_SNAKE_CASE_						,				raise_if_mismatch=SCREAMING_SNAKE_CASE_   )
						A_     :						Optional[int]     =					torch.randn((1, 3, 2_24, 2_24)   )
						module_transfer(SCREAMING_SNAKE_CASE_   )

			if from_state_dict is not None:
						A_     :						Tuple     =					[]
						# for seer - in1k finetuned we have to manually copy the head
						if "seer" in name and "in1k" in name:
									A_     :						Tuple     =					[("""0.clf.0.weight""", """classifier.1.weight"""), ("""0.clf.0.bias""", """classifier.1.bias""")]
						A_     :						Optional[Any]     =					manually_copy_vissl_head(SCREAMING_SNAKE_CASE_						,				our_model.state_dict()						,				SCREAMING_SNAKE_CASE_   )
						our_model.load_state_dict(SCREAMING_SNAKE_CASE_   )

			A_     :						Optional[int]     =					our_model(SCREAMING_SNAKE_CASE_						,				output_hidden_states=SCREAMING_SNAKE_CASE_   )
			A_     :						Optional[int]     =					(
			    our_outputs.logits if isinstance(SCREAMING_SNAKE_CASE_						,				SCREAMING_SNAKE_CASE_   ) else our_outputs.last_hidden_state
			)

			A_     :						Dict     =					from_model(SCREAMING_SNAKE_CASE_   )
			A_     :						str     =					from_output[-1] if type(SCREAMING_SNAKE_CASE_   ) is list else from_output

			# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
			if "seer" in name and "in1k" in name:
						A_     :						Tuple     =					our_outputs.hidden_states[-1]

			assert torch.allclose(SCREAMING_SNAKE_CASE_						,				SCREAMING_SNAKE_CASE_   ), "The model logits don't match the original one."

			if push_to_hub:
						our_model.push_to_hub(
						    repo_path_or_name=save_directory / name						,				commit_message="""Add model"""						,				use_temp_dir=SCREAMING_SNAKE_CASE_						,				)

						A_     :						Optional[Any]     =					2_24 if """seer""" not in name else 3_84
						# we can use the convnext one
						A_     :						str     =					AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""						,				size=SCREAMING_SNAKE_CASE_   )
						image_processor.push_to_hub(
						    repo_path_or_name=save_directory / name						,				commit_message="""Add image processor"""						,				use_temp_dir=SCREAMING_SNAKE_CASE_						,				)

						print(f'Pushed {name}'   )




def      a      (			lowerCamelCase__						,				lowerCamelCase__ = None						,				lowerCamelCase__ = True   ):
			'''simple docstring'''





			A_     :						List[str]     =					"""imagenet-1k-id2label.json"""
			A_     :						Tuple     =					10_00
			A_     :						Union[str, Any]     =					(1, num_labels)

			A_     :						Dict     =					"""huggingface/label-files"""
			A_     :						Optional[Any]     =					num_labels
			A_     :						Union[str, Any]     =					json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE_						,				SCREAMING_SNAKE_CASE_						,				repo_type="""dataset"""   )   )						,				"""r"""   )   )
			A_     :						Any     =					{int(SCREAMING_SNAKE_CASE_   ): v for k, v in idalabel.items()}

			A_     :						List[Any]     =					idalabel
			A_     :						List[Any]     =					{v: k for k, v in idalabel.items()}

			A_     :						Any     =					partial(SCREAMING_SNAKE_CASE_						,				num_labels=SCREAMING_SNAKE_CASE_						,				idalabel=SCREAMING_SNAKE_CASE_						,				labelaid=SCREAMING_SNAKE_CASE_   )

			A_     :						Optional[int]     =					{
			    """regnet-x-002""": ImageNetPreTrainedConfig(
			        depths=[1, 1, 4, 7]						,				hidden_sizes=[24, 56, 1_52, 3_68]						,				groups_width=8						,				layer_type="""x"""   ),
			    """regnet-x-004""": ImageNetPreTrainedConfig(
			        depths=[1, 2, 7, 12]						,				hidden_sizes=[32, 64, 1_60, 3_84]						,				groups_width=16						,				layer_type="""x"""   ),
			    """regnet-x-006""": ImageNetPreTrainedConfig(
			        depths=[1, 3, 5, 7]						,				hidden_sizes=[48, 96, 2_40, 5_28]						,				groups_width=24						,				layer_type="""x"""   ),
			    """regnet-x-008""": ImageNetPreTrainedConfig(
			        depths=[1, 3, 7, 5]						,				hidden_sizes=[64, 1_28, 2_88, 6_72]						,				groups_width=16						,				layer_type="""x"""   ),
			    """regnet-x-016""": ImageNetPreTrainedConfig(
			        depths=[2, 4, 10, 2]						,				hidden_sizes=[72, 1_68, 4_08, 9_12]						,				groups_width=24						,				layer_type="""x"""   ),
			    """regnet-x-032""": ImageNetPreTrainedConfig(
			        depths=[2, 6, 15, 2]						,				hidden_sizes=[96, 1_92, 4_32, 10_08]						,				groups_width=48						,				layer_type="""x"""   ),
			    """regnet-x-040""": ImageNetPreTrainedConfig(
			        depths=[2, 5, 14, 2]						,				hidden_sizes=[80, 2_40, 5_60, 13_60]						,				groups_width=40						,				layer_type="""x"""   ),
			    """regnet-x-064""": ImageNetPreTrainedConfig(
			        depths=[2, 4, 10, 1]						,				hidden_sizes=[1_68, 3_92, 7_84, 16_24]						,				groups_width=56						,				layer_type="""x"""   ),
			    """regnet-x-080""": ImageNetPreTrainedConfig(
			        depths=[2, 5, 15, 1]						,				hidden_sizes=[80, 2_40, 7_20, 19_20]						,				groups_width=1_20						,				layer_type="""x"""   ),
			    """regnet-x-120""": ImageNetPreTrainedConfig(
			        depths=[2, 5, 11, 1]						,				hidden_sizes=[2_24, 4_48, 8_96, 22_40]						,				groups_width=1_12						,				layer_type="""x"""   ),
			    """regnet-x-160""": ImageNetPreTrainedConfig(
			        depths=[2, 6, 13, 1]						,				hidden_sizes=[2_56, 5_12, 8_96, 20_48]						,				groups_width=1_28						,				layer_type="""x"""   ),
			    """regnet-x-320""": ImageNetPreTrainedConfig(
			        depths=[2, 7, 13, 1]						,				hidden_sizes=[3_36, 6_72, 13_44, 25_20]						,				groups_width=1_68						,				layer_type="""x"""   ),
			    # y variant
			    """regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]						,				hidden_sizes=[24, 56, 1_52, 3_68]						,				groups_width=8   ),
			    """regnet-y-004""": ImageNetPreTrainedConfig(
			        depths=[1, 3, 6, 6]						,				hidden_sizes=[48, 1_04, 2_08, 4_40]						,				groups_width=8   ),
			    """regnet-y-006""": ImageNetPreTrainedConfig(
			        depths=[1, 3, 7, 4]						,				hidden_sizes=[48, 1_12, 2_56, 6_08]						,				groups_width=16   ),
			    """regnet-y-008""": ImageNetPreTrainedConfig(
			        depths=[1, 3, 8, 2]						,				hidden_sizes=[64, 1_28, 3_20, 7_68]						,				groups_width=16   ),
			    """regnet-y-016""": ImageNetPreTrainedConfig(
			        depths=[2, 6, 17, 2]						,				hidden_sizes=[48, 1_20, 3_36, 8_88]						,				groups_width=24   ),
			    """regnet-y-032""": ImageNetPreTrainedConfig(
			        depths=[2, 5, 13, 1]						,				hidden_sizes=[72, 2_16, 5_76, 15_12]						,				groups_width=24   ),
			    """regnet-y-040""": ImageNetPreTrainedConfig(
			        depths=[2, 6, 12, 2]						,				hidden_sizes=[1_28, 1_92, 5_12, 10_88]						,				groups_width=64   ),
			    """regnet-y-064""": ImageNetPreTrainedConfig(
			        depths=[2, 7, 14, 2]						,				hidden_sizes=[1_44, 2_88, 5_76, 12_96]						,				groups_width=72   ),
			    """regnet-y-080""": ImageNetPreTrainedConfig(
			        depths=[2, 4, 10, 1]						,				hidden_sizes=[1_68, 4_48, 8_96, 20_16]						,				groups_width=56   ),
			    """regnet-y-120""": ImageNetPreTrainedConfig(
			        depths=[2, 5, 11, 1]						,				hidden_sizes=[2_24, 4_48, 8_96, 22_40]						,				groups_width=1_12   ),
			    """regnet-y-160""": ImageNetPreTrainedConfig(
			        depths=[2, 4, 11, 1]						,				hidden_sizes=[2_24, 4_48, 12_32, 30_24]						,				groups_width=1_12   ),
			    """regnet-y-320""": ImageNetPreTrainedConfig(
			        depths=[2, 5, 12, 1]						,				hidden_sizes=[2_32, 6_96, 13_92, 37_12]						,				groups_width=2_32   ),
			    # models created by SEER -> https://arxiv.org/abs/2202.08360
			    """regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 12, 1]						,				hidden_sizes=[2_32, 6_96, 13_92, 37_12]						,				groups_width=2_32   ),
			    """regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 12, 1]						,				hidden_sizes=[3_28, 9_84, 19_68, 49_20]						,				groups_width=3_28   ),
			    """regnet-y-1280-seer""": RegNetConfig(
			        depths=[2, 7, 17, 1]						,				hidden_sizes=[5_28, 10_56, 29_04, 73_92]						,				groups_width=2_64   ),
			    """regnet-y-2560-seer""": RegNetConfig(
			        depths=[3, 7, 16, 1]						,				hidden_sizes=[6_40, 16_96, 25_44, 50_88]						,				groups_width=6_40   ),
			    """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
			        depths=[2, 7, 17, 1]						,				hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]						,				groups_width=10_10   ),
			    # finetuned on imagenet
			    """regnet-y-320-seer-in1k""": ImageNetPreTrainedConfig(
			        depths=[2, 5, 12, 1]						,				hidden_sizes=[2_32, 6_96, 13_92, 37_12]						,				groups_width=2_32   ),
			    """regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
			        depths=[2, 5, 12, 1]						,				hidden_sizes=[3_28, 9_84, 19_68, 49_20]						,				groups_width=3_28   ),
			    """regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
			        depths=[2, 7, 17, 1]						,				hidden_sizes=[5_28, 10_56, 29_04, 73_92]						,				groups_width=2_64   ),
			    """regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
			        depths=[3, 7, 16, 1]						,				hidden_sizes=[6_40, 16_96, 25_44, 50_88]						,				groups_width=6_40   ),
			    """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
			        depths=[2, 7, 17, 1]						,				hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]						,				groups_width=10_10   ),
			}

			A_     :						Tuple     =					NameToOurModelFuncMap()
			A_     :						Tuple     =					NameToFromModelFuncMap()
			# add seer weights logic

			def load_using_classy_vision(lowerCamelCase__						,				lowerCamelCase__   ) -> Tuple[nn.Module, Dict]:
						A_     :						Dict     =					torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_						,				model_dir=str(SCREAMING_SNAKE_CASE_   )						,				map_location="""cpu"""   )
						A_     :						Optional[Any]     =					model_func()
						# check if we have a head, if yes add it
						A_     :						Dict     =					files["""classy_state_dict"""]["""base_model"""]["""model"""]
						A_     :						Optional[int]     =					model_state_dict["""trunk"""]
						model.load_state_dict(SCREAMING_SNAKE_CASE_   )
						return model.eval(), model_state_dict["heads"]

			# pretrained
			A_     :						List[str]     =					partial(
			    SCREAMING_SNAKE_CASE_						,				"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"""						,				lambda: FakeRegNetVisslWrapper(RegNetYaagf()   )						,				)

			A_     :						List[str]     =					partial(
			    SCREAMING_SNAKE_CASE_						,				"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"""						,				lambda: FakeRegNetVisslWrapper(RegNetYaagf()   )						,				)

			A_     :						Tuple     =					partial(
			    SCREAMING_SNAKE_CASE_						,				"""https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"""						,				lambda: FakeRegNetVisslWrapper(RegNetYaaagf()   )						,				)

			A_     :						Optional[int]     =					partial(
			    SCREAMING_SNAKE_CASE_						,				"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""						,				lambda: FakeRegNetVisslWrapper(
			        RegNet(RegNetParams(depth=27						,				group_width=10_10						,				w_a=17_44						,				w_a=6_20.83						,				w_m=2.52   )   )   )						,				)

			# IN1K finetuned
			A_     :						Union[str, Any]     =					partial(
			    SCREAMING_SNAKE_CASE_						,				"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"""						,				lambda: FakeRegNetVisslWrapper(RegNetYaagf()   )						,				)

			A_     :						Union[str, Any]     =					partial(
			    SCREAMING_SNAKE_CASE_						,				"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"""						,				lambda: FakeRegNetVisslWrapper(RegNetYaagf()   )						,				)

			A_     :						Any     =					partial(
			    SCREAMING_SNAKE_CASE_						,				"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"""						,				lambda: FakeRegNetVisslWrapper(RegNetYaaagf()   )						,				)

			A_     :						Any     =					partial(
			    SCREAMING_SNAKE_CASE_						,				"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""						,				lambda: FakeRegNetVisslWrapper(
			        RegNet(RegNetParams(depth=27						,				group_width=10_10						,				w_a=17_44						,				w_a=6_20.83						,				w_m=2.52   )   )   )						,				)

			if model_name:
						convert_weight_and_push(
						    SCREAMING_SNAKE_CASE_						,				names_to_from_model_map[model_name]						,				names_to_ours_model_map[model_name]						,				names_to_config[model_name]						,				SCREAMING_SNAKE_CASE_						,				SCREAMING_SNAKE_CASE_						,				)
			else:
						for model_name, config in names_to_config.items():
									convert_weight_and_push(
									    SCREAMING_SNAKE_CASE_						,				names_to_from_model_map[model_name]						,				names_to_ours_model_map[model_name]						,				SCREAMING_SNAKE_CASE_						,				SCREAMING_SNAKE_CASE_						,				SCREAMING_SNAKE_CASE_						,				)
			return config, expected_shape


if __name__ == "__main__":
						lowerCamelCase							:Tuple       = argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--model_name''',
						    default=None,
						    type=str,
						    help=(
						        '''The name of the model you wish to convert, it must be one of the supported regnet* architecture,'''
						        ''' currently: regnetx-*, regnety-*. If `None`, all of them will the converted.'''
						    ),
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''',
						    default=None,
						    type=Path,
						    required=True,
						    help='''Path to the output PyTorch model directory.''',
						)
						parser.add_argument(
						    '''--push_to_hub''',
						    default=True,
						    type=bool,
						    required=False,
						    help='''If True, push model and image processor to the hub.''',
						)

						lowerCamelCase							:Tuple       = parser.parse_args()

						lowerCamelCase							:Dict       = args.pytorch_dump_folder_path
						pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
						convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




'''simple docstring'''




from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
						import PIL

if is_torch_available():
						import torch


lowerCamelCase							:str       = logging.get_logger(__name__)







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		Tuple		      = ['pixel_values']


	def __init__(self	,				lowercase = True	,				lowercase = None	,				lowercase = PILImageResampling.BILINEAR	,				lowercase = True	,				lowercase = 1 / 255	,				lowercase = True	,				lowercase = None	,				lowercase = True	,				**lowercase	,				):
				super().__init__(**lowercase							)
				A_     :						Union[str, Any]     =					size if size is not None else {"""shortest_edge""": 224}
				A_     :						Union[str, Any]     =					get_size_dict(lowercase	,				default_to_square=lowercase							)
				A_     :						Optional[Any]     =					crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
				A_     :						Tuple     =					get_size_dict(lowercase	,				param_name="""crop_size"""							)

				A_     :						List[Any]     =					do_resize
				A_     :						List[str]     =					size
				A_     :						Dict     =					resample
				A_     :						int     =					do_rescale
				A_     :						str     =					rescale_factor
				A_     :						Tuple     =					do_center_crop
				A_     :						Tuple     =					crop_size
				A_     :						List[str]     =					do_flip_channel_order


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase = PIL.Image.BILINEAR	,				lowercase = None	,				**lowercase	,				):
				A_     :						Any     =					get_size_dict(lowercase	,				default_to_square=lowercase							)
				if "shortest_edge" not in size:
							raise ValueError(F'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'							)
				A_     :						Any     =					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    _a     (self	,				lowercase	,				lowercase	,				lowercase = None	,				**lowercase	,				):
				A_     :						Tuple     =					get_size_dict(lowercase							)
				if "height" not in size or "width" not in size:
							raise ValueError(F'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'							)
				return center_crop(lowercase	,				size=(size["""height"""], size["""width"""])	,				data_format=lowercase	,				**lowercase							)


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase = None	,				**lowercase	,				):
				return rescale(lowercase	,				scale=lowercase	,				data_format=lowercase	,				**lowercase							)


	def    _a     (self	,				lowercase	,				lowercase = None							):
				return flip_channel_order(lowercase	,				data_format=lowercase							)


	def    _a     (self	,				lowercase	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = ChannelDimension.FIRST	,				**lowercase	,				):
				A_     :						str     =					do_resize if do_resize is not None else self.do_resize
				A_     :						Optional[int]     =					resample if resample is not None else self.resample
				A_     :						str     =					do_rescale if do_rescale is not None else self.do_rescale
				A_     :						str     =					rescale_factor if rescale_factor is not None else self.rescale_factor
				A_     :						str     =					do_center_crop if do_center_crop is not None else self.do_center_crop
				A_     :						Dict     =					(
				    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
				)

				A_     :						Union[str, Any]     =					size if size is not None else self.size
				A_     :						Dict     =					get_size_dict(lowercase	,				default_to_square=lowercase							)
				A_     :						Any     =					crop_size if crop_size is not None else self.crop_size
				A_     :						Union[str, Any]     =					get_size_dict(lowercase	,				param_name="""crop_size"""							)

				A_     :						Union[str, Any]     =					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_rescale and rescale_factor is None:
							raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

				if do_center_crop and crop_size is None:
							raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

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

				if do_resize:
							A_     :						List[Any]     =					[self.resize(image=lowercase	,				size=lowercase	,				resample=lowercase							) for image in images]

				if do_center_crop:
							A_     :						str     =					[self.center_crop(image=lowercase	,				size=lowercase							) for image in images]

				if do_rescale:
							A_     :						Dict     =					[self.rescale(image=lowercase	,				scale=lowercase							) for image in images]

				# the pretrained checkpoints assume images are BGR, not RGB
				if do_flip_channel_order:
							A_     :						List[str]     =					[self.flip_channel_order(image=lowercase							) for image in images]

				A_     :						List[str]     =					[to_channel_dimension_format(lowercase	,				lowercase							) for image in images]

				A_     :						str     =					{"""pixel_values""": images}
				return BatchFeature(data=lowercase	,				tensor_type=lowercase							)


	def    _a     (self	,				lowercase	,				lowercase = None							):
				A_     :						Optional[Any]     =					outputs.logits

				# Resize logits and compute semantic segmentation maps
				if target_sizes is not None:
							if len(lowercase							) != len(lowercase							):
										raise ValueError(
										    """Make sure that you pass in as many target sizes as the batch dimension of the logits"""							)

							if is_torch_tensor(lowercase							):
										A_     :						Dict     =					target_sizes.numpy()

							A_     :						Union[str, Any]     =					[]

							for idx in range(len(lowercase							)							):
										A_     :						Union[str, Any]     =					torch.nn.functional.interpolate(
										    logits[idx].unsqueeze(dim=0							)	,				size=target_sizes[idx]	,				mode="""bilinear"""	,				align_corners=lowercase							)
										A_     :						Any     =					resized_logits[0].argmax(dim=0							)
										semantic_segmentation.append(lowercase							)
				else:
							A_     :						str     =					logits.argmax(dim=1							)
							A_     :						Optional[Any]     =					[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]							)]

				return semantic_segmentation