



"""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()
snake_case_				     =       logging.get_logger()







@dataclass
class       A_			:



						"""simple docstring"""
						__UpperCamelCase						=							42
						__UpperCamelCase						=							field(default_factory=SCREAMING_SNAKE_CASE_				)
						__UpperCamelCase						=							field(default_factory=SCREAMING_SNAKE_CASE_				)






						def 	UpperCAmelCase__     (					self :str   ,  lowercase_ :Any   ,  lowercase_ :Tensor   ,  lowercase_ :Tensor					)				->			Union[str, Any]:
												UpperCAmelCase             = len(list(m.modules()					)					) == 1 or isinstance(lowercase_   ,  nn.Convad					) or isinstance(lowercase_   ,  nn.BatchNormad					)
												if has_not_submodules:
																		self.traced.append(lowercase_					)






						def __call__(					self :Dict   ,  lowercase_ :Tensor					)				->			List[Any]:
												for m in self.module.modules():
																		self.handles.append(m.register_forward_hook(self._forward_hook					)					)
												self.module(lowercase_					)
												[x.remove() for x in self.handles]
												return self






						@property
						def 	UpperCAmelCase__     (					self :Optional[int]					)				->			Any:
												# check the len of the state_dict keys to see if we have learnable params
												return list(filter(lambda lowercase_					: len(list(x.state_dict().keys()					)					) > 0   ,  self.traced					)					)







@dataclass
class       A_			:



						"""simple docstring"""
						__UpperCamelCase						=							42
						__UpperCamelCase						=							42
						__UpperCamelCase						=							1
						__UpperCamelCase						=							field(default_factory=SCREAMING_SNAKE_CASE_				)
						__UpperCamelCase						=							field(default_factory=SCREAMING_SNAKE_CASE_				)
						__UpperCamelCase						=							True






						def __call__(					self :Tuple   ,  lowercase_ :Tensor					)				->			Dict:
												UpperCAmelCase             = Tracker(self.dest					)(lowercase_					).parametrized
												UpperCAmelCase             = Tracker(self.src					)(lowercase_					).parametrized

												UpperCAmelCase             = list(filter(lambda lowercase_					: type(lowercase_					) not in self.src_skip   ,  lowercase_					)					)
												UpperCAmelCase             = list(filter(lambda lowercase_					: type(lowercase_					) not in self.dest_skip   ,  lowercase_					)					)

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

												for dest_m, src_m in zip(lowercase_   ,  lowercase_					):
																		dest_m.load_state_dict(src_m.state_dict()					)
																		if self.verbose == 1:
																								print(f"""Transfered from={src_m} to={dest_m}"""					)







class       A_			(     nn.Module				):



						"""simple docstring"""






						def __init__(					self :List[Any]   ,  lowercase_ :nn.Module					)				->			Union[str, Any]:
												super().__init__()

												UpperCAmelCase             = []
												# - 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}"""
																		UpperCAmelCase             = len(lowercase_					) + 1
																		feature_blocks.append((f"""res{block_index}""", v)					)

												UpperCAmelCase             = nn.ModuleDict(lowercase_					)






						def 	UpperCAmelCase__     (					self :str   ,  lowercase_ :Tensor					)				->			Tuple:
												return get_trunk_forward_outputs(
												    lowercase_   ,  out_feat_keys=lowercase_   ,  feature_blocks=self._feature_blocks   ,  )







class       A_			(     SCREAMING_SNAKE_CASE_				):



						"""simple docstring"""






						def 	UpperCAmelCase__     (					self :List[Any]   ,  lowercase_ :str					)				->			str:
												UpperCAmelCase             = x.split('-'					)
												return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]					)






						def __getitem__(					self :Optional[int]   ,  lowercase_ :str					)				->			Callable[[], Tuple[nn.Module, Dict]]:
												# default to timm!
												if x not in self:
																		UpperCAmelCase             = self.convert_name_to_timm(lowercase_					)
																		UpperCAmelCase             = partial(lambda: (timm.create_model(lowercase_   ,  pretrained=lowercase_					).eval(), None)					)

												else:
																		UpperCAmelCase             = super().__getitem__(lowercase_					)

												return val







class       A_			(     SCREAMING_SNAKE_CASE_				):



						"""simple docstring"""






						def __getitem__(					self :Tuple   ,  lowercase_ :str					)				->			Callable[[], nn.Module]:
												if "seer" in x and "in1k" not in x:
																		UpperCAmelCase             = RegNetModel
												else:
																		UpperCAmelCase             = RegNetForImageClassification
												return val


def  _lowerCAmelCase      (   lowercase_		,	lowercase_		,	lowercase_						):
						for from_key, to_key in keys:
												UpperCAmelCase             = from_state_dict[from_key].clone()
												print(F"""Copied key={from_key} to={to_key}"""						)
						return to_state_dict

def  _lowerCAmelCase      (   lowercase_		,	lowercase_		,	lowercase_		,	lowercase_		,	lowercase_		,	lowercase_ = True		,	):
						print(F"""Converting {name}..."""						)
						with torch.no_grad():
												UpperCAmelCase      ,    UpperCAmelCase             = from_model_func()
												UpperCAmelCase             = our_model_func(lowercase_						).eval()
												UpperCAmelCase             = ModuleTransfer(src=lowercase_		,	dest=lowercase_		,	raise_if_mismatch=lowercase_						)
												UpperCAmelCase             = torch.randn((1, 3, 224, 224)						)
												module_transfer(lowercase_						)

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

						UpperCAmelCase             = our_model(lowercase_		,	output_hidden_states=lowercase_						)
						UpperCAmelCase             = (
						    our_outputs.logits if isinstance(lowercase_		,	lowercase_						) else our_outputs.last_hidden_state
						)

						UpperCAmelCase             = from_model(lowercase_						)
						UpperCAmelCase             = from_output[-1] if type(lowercase_						) 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:
												UpperCAmelCase             = our_outputs.hidden_states[-1]

						assert torch.allclose(lowercase_		,	lowercase_						), "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=lowercase_		,	)

												UpperCAmelCase             = 224 if 'seer' not in name else 384
												# we can use the convnext one
												UpperCAmelCase             = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'		,	size=lowercase_						)
												image_processor.push_to_hub(
												    repo_path_or_name=save_directory / name		,	commit_message='Add image processor'		,	use_temp_dir=lowercase_		,	)

												print(F"""Pushed {name}"""						)

def  _lowerCAmelCase      (   lowercase_		,	lowercase_ = None		,	lowercase_ = True						):
						UpperCAmelCase             = 'imagenet-1k-id2label.json'
						UpperCAmelCase             = 1000
						UpperCAmelCase             = (1, num_labels)

						UpperCAmelCase             = 'huggingface/label-files'
						UpperCAmelCase             = num_labels
						UpperCAmelCase             = json.load(open(cached_download(hf_hub_url(lowercase_		,	lowercase_		,	repo_type='dataset'						)						)		,	'r'						)						)
						UpperCAmelCase             = {int(lowercase_						): v for k, v in idalabel.items()}

						UpperCAmelCase             = idalabel
						UpperCAmelCase             = {v: k for k, v in idalabel.items()}

						UpperCAmelCase             = partial(lowercase_		,	num_labels=lowercase_		,	idalabel=lowercase_		,	labelaid=lowercase_						)

						UpperCAmelCase             = {
						    'regnet-x-002': ImageNetPreTrainedConfig(
						        depths=[1, 1, 4, 7]		,	hidden_sizes=[24, 56, 152, 368]		,	groups_width=8		,	layer_type='x'						),
						    'regnet-x-004': ImageNetPreTrainedConfig(
						        depths=[1, 2, 7, 12]		,	hidden_sizes=[32, 64, 160, 384]		,	groups_width=16		,	layer_type='x'						),
						    'regnet-x-006': ImageNetPreTrainedConfig(
						        depths=[1, 3, 5, 7]		,	hidden_sizes=[48, 96, 240, 528]		,	groups_width=24		,	layer_type='x'						),
						    'regnet-x-008': ImageNetPreTrainedConfig(
						        depths=[1, 3, 7, 5]		,	hidden_sizes=[64, 128, 288, 672]		,	groups_width=16		,	layer_type='x'						),
						    'regnet-x-016': ImageNetPreTrainedConfig(
						        depths=[2, 4, 10, 2]		,	hidden_sizes=[72, 168, 408, 912]		,	groups_width=24		,	layer_type='x'						),
						    'regnet-x-032': ImageNetPreTrainedConfig(
						        depths=[2, 6, 15, 2]		,	hidden_sizes=[96, 192, 432, 1008]		,	groups_width=48		,	layer_type='x'						),
						    'regnet-x-040': ImageNetPreTrainedConfig(
						        depths=[2, 5, 14, 2]		,	hidden_sizes=[80, 240, 560, 1360]		,	groups_width=40		,	layer_type='x'						),
						    'regnet-x-064': ImageNetPreTrainedConfig(
						        depths=[2, 4, 10, 1]		,	hidden_sizes=[168, 392, 784, 1624]		,	groups_width=56		,	layer_type='x'						),
						    'regnet-x-080': ImageNetPreTrainedConfig(
						        depths=[2, 5, 15, 1]		,	hidden_sizes=[80, 240, 720, 1920]		,	groups_width=120		,	layer_type='x'						),
						    'regnet-x-120': ImageNetPreTrainedConfig(
						        depths=[2, 5, 11, 1]		,	hidden_sizes=[224, 448, 896, 2240]		,	groups_width=112		,	layer_type='x'						),
						    'regnet-x-160': ImageNetPreTrainedConfig(
						        depths=[2, 6, 13, 1]		,	hidden_sizes=[256, 512, 896, 2048]		,	groups_width=128		,	layer_type='x'						),
						    'regnet-x-320': ImageNetPreTrainedConfig(
						        depths=[2, 7, 13, 1]		,	hidden_sizes=[336, 672, 1344, 2520]		,	groups_width=168		,	layer_type='x'						),
						    # y variant
						    'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]		,	hidden_sizes=[24, 56, 152, 368]		,	groups_width=8						),
						    'regnet-y-004': ImageNetPreTrainedConfig(
						        depths=[1, 3, 6, 6]		,	hidden_sizes=[48, 104, 208, 440]		,	groups_width=8						),
						    'regnet-y-006': ImageNetPreTrainedConfig(
						        depths=[1, 3, 7, 4]		,	hidden_sizes=[48, 112, 256, 608]		,	groups_width=16						),
						    'regnet-y-008': ImageNetPreTrainedConfig(
						        depths=[1, 3, 8, 2]		,	hidden_sizes=[64, 128, 320, 768]		,	groups_width=16						),
						    'regnet-y-016': ImageNetPreTrainedConfig(
						        depths=[2, 6, 17, 2]		,	hidden_sizes=[48, 120, 336, 888]		,	groups_width=24						),
						    'regnet-y-032': ImageNetPreTrainedConfig(
						        depths=[2, 5, 13, 1]		,	hidden_sizes=[72, 216, 576, 1512]		,	groups_width=24						),
						    'regnet-y-040': ImageNetPreTrainedConfig(
						        depths=[2, 6, 12, 2]		,	hidden_sizes=[128, 192, 512, 1088]		,	groups_width=64						),
						    'regnet-y-064': ImageNetPreTrainedConfig(
						        depths=[2, 7, 14, 2]		,	hidden_sizes=[144, 288, 576, 1296]		,	groups_width=72						),
						    'regnet-y-080': ImageNetPreTrainedConfig(
						        depths=[2, 4, 10, 1]		,	hidden_sizes=[168, 448, 896, 2016]		,	groups_width=56						),
						    'regnet-y-120': ImageNetPreTrainedConfig(
						        depths=[2, 5, 11, 1]		,	hidden_sizes=[224, 448, 896, 2240]		,	groups_width=112						),
						    'regnet-y-160': ImageNetPreTrainedConfig(
						        depths=[2, 4, 11, 1]		,	hidden_sizes=[224, 448, 1232, 3024]		,	groups_width=112						),
						    'regnet-y-320': ImageNetPreTrainedConfig(
						        depths=[2, 5, 12, 1]		,	hidden_sizes=[232, 696, 1392, 3712]		,	groups_width=232						),
						    # models created by SEER -> https://arxiv.org/abs/2202.08360
						    'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 12, 1]		,	hidden_sizes=[232, 696, 1392, 3712]		,	groups_width=232						),
						    'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1]		,	hidden_sizes=[328, 984, 1968, 4920]		,	groups_width=328						),
						    'regnet-y-1280-seer': RegNetConfig(
						        depths=[2, 7, 17, 1]		,	hidden_sizes=[528, 1056, 2904, 7392]		,	groups_width=264						),
						    'regnet-y-2560-seer': RegNetConfig(
						        depths=[3, 7, 16, 1]		,	hidden_sizes=[640, 1696, 2544, 5088]		,	groups_width=640						),
						    'regnet-y-10b-seer': ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]		,	hidden_sizes=[2020, 4040, 11110, 28280]		,	groups_width=1010						),
						    # finetuned on imagenet
						    'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
						        depths=[2, 5, 12, 1]		,	hidden_sizes=[232, 696, 1392, 3712]		,	groups_width=232						),
						    'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
						        depths=[2, 5, 12, 1]		,	hidden_sizes=[328, 984, 1968, 4920]		,	groups_width=328						),
						    'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]		,	hidden_sizes=[528, 1056, 2904, 7392]		,	groups_width=264						),
						    'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
						        depths=[3, 7, 16, 1]		,	hidden_sizes=[640, 1696, 2544, 5088]		,	groups_width=640						),
						    'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]		,	hidden_sizes=[2020, 4040, 11110, 28280]		,	groups_width=1010						),
						}

						UpperCAmelCase             = NameToOurModelFuncMap()
						UpperCAmelCase             = NameToFromModelFuncMap()
						# add seer weights logic

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

						# pretrained
						UpperCAmelCase             = partial(
						    lowercase_		,	'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'		,	lambda: FakeRegNetVisslWrapper(RegNetYaagf()						)		,	)

						UpperCAmelCase             = partial(
						    lowercase_		,	'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'		,	lambda: FakeRegNetVisslWrapper(RegNetYaagf()						)		,	)

						UpperCAmelCase             = partial(
						    lowercase_		,	'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()						)		,	)

						UpperCAmelCase             = partial(
						    lowercase_		,	'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'		,	lambda: FakeRegNetVisslWrapper(
						        RegNet(RegNetParams(depth=27		,	group_width=1010		,	w_a=1744		,	w_a=6_2_0.8_3		,	w_m=2.5_2						)						)						)		,	)

						# IN1K finetuned
						UpperCAmelCase             = partial(
						    lowercase_		,	'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'		,	lambda: FakeRegNetVisslWrapper(RegNetYaagf()						)		,	)

						UpperCAmelCase             = partial(
						    lowercase_		,	'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'		,	lambda: FakeRegNetVisslWrapper(RegNetYaagf()						)		,	)

						UpperCAmelCase             = partial(
						    lowercase_		,	'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'		,	lambda: FakeRegNetVisslWrapper(RegNetYaaagf()						)		,	)

						UpperCAmelCase             = partial(
						    lowercase_		,	'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=1010		,	w_a=1744		,	w_a=6_2_0.8_3		,	w_m=2.5_2						)						)						)		,	)

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


if __name__ == "__main__":
				snake_case_				     =       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.""",
				)

				snake_case_				     =       parser.parse_args()

				snake_case_				     =       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,
    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_torch_available, is_torch_tensor, logging


if is_torch_available():
					import torch


lowerCAmelCase							:			int     							=							logging.get_logger(__name__)






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				__UpperCamelCase			  =						["pixel_values"]





				def __init__(       self		, _a = True		, _a = None		, _a = PILImageResampling.BILINEAR		, _a = True		, _a = None		, _a = True		, _a = 1 / 255		, _a = True		, _a = None		, _a = None		, **_a		, ):





							"""simple docstring"""
							super().__init__(**_a )
							lowerCamelCase			      =					size if size is not None else {"""shortest_edge""": 256}
							lowerCamelCase			      =					get_size_dict(_a		, default_to_square=_a )
							lowerCamelCase			      =					crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
							lowerCamelCase			      =					get_size_dict(_a		, param_name="""crop_size""" )
							lowerCamelCase			      =					do_resize
							lowerCamelCase			      =					size
							lowerCamelCase			      =					resample
							lowerCamelCase			      =					do_center_crop
							lowerCamelCase			      =					crop_size
							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    _lowerCAmelCase				(       self		, _a		, _a		, _a = PILImageResampling.BICUBIC		, _a = None		, **_a		, ):





							"""simple docstring"""
							lowerCamelCase			      =					get_size_dict(_a		, default_to_square=_a )
							if "shortest_edge" not in size:
										raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
							lowerCamelCase			      =					get_resize_output_image_size(_a		, size=size["""shortest_edge"""]		, default_to_square=_a )
							return resize(_a		, size=_a		, resample=_a		, data_format=_a		, **_a )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a = None		, **_a		, ):





							"""simple docstring"""
							lowerCamelCase			      =					get_size_dict(_a )
							if "height" not in size or "width" not in size:
										raise ValueError(f'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}' )
							return center_crop(_a		, size=(size["""height"""], size["""width"""])		, data_format=_a		, **_a )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a = None		, **_a ):





							"""simple docstring"""
							return rescale(_a		, scale=_a		, data_format=_a		, **_a )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a		, _a = None		, **_a		, ):





							"""simple docstring"""
							return normalize(_a		, mean=_a		, std=_a		, data_format=_a		, **_a )





				def    _lowerCAmelCase				(       self		, _a		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = None		, _a = ChannelDimension.FIRST		, **_a		, ):





							"""simple docstring"""
							lowerCamelCase			      =					do_resize if do_resize is not None else self.do_resize
							lowerCamelCase			      =					size if size is not None else self.size
							lowerCamelCase			      =					get_size_dict(_a		, default_to_square=_a )
							lowerCamelCase			      =					resample if resample is not None else self.resample
							lowerCamelCase			      =					do_center_crop if do_center_crop is not None else self.do_center_crop
							lowerCamelCase			      =					crop_size if crop_size is not None else self.crop_size
							lowerCamelCase			      =					get_size_dict(_a		, param_name="""crop_size""" )
							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			      =					make_list_of_images(_a )

							if not valid_images(_a ):
										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.
							lowerCamelCase			      =					[to_numpy_array(_a ) for image in images]

							if do_resize:
										lowerCamelCase			      =					[self.resize(image=_a		, size=_a		, resample=_a ) for image in images]

							if do_center_crop:
										lowerCamelCase			      =					[self.center_crop(image=_a		, size=_a ) for image in images]

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

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

							lowerCamelCase			      =					[to_channel_dimension_format(_a		, _a ) for image in images]

							lowerCamelCase			      =					{"""pixel_values""": images}
							return BatchFeature(data=_a		, tensor_type=_a )





				def    _lowerCAmelCase				(       self		, _a		, _a = None ):





							"""simple docstring"""
							lowerCamelCase			      =					outputs.logits

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

										if is_torch_tensor(_a ):
													lowerCamelCase			      =					target_sizes.numpy()

										lowerCamelCase			      =					[]

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

							return semantic_segmentation



