





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



@dataclass
class    _a     :






							"""simple docstring"""

							snake_case_							 =						42
							snake_case_							 =						field(default_factory=lowercase__							)
							snake_case_							 =						field(default_factory=lowercase__							)

							def      A_  (      self				:     Tuple							,	a				:     Dict							,	a				:     Tensor							,	a				:     Tensor    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					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				:     List[str]							,	a				:     Tensor    )	->Any:
													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				:     List[str]    )	->Optional[int]:
													# check the len of the state_dict keys to see if we have learnable params
													return list(filter(lambda a    : len(list(x.state_dict().keys()    )    ) > 0							,	self.traced    )    )







@dataclass
class    _a     :






							"""simple docstring"""

							snake_case_							 =						42
							snake_case_							 =						42
							snake_case_							 =						1
							snake_case_							 =						field(default_factory=lowercase__							)
							snake_case_							 =						field(default_factory=lowercase__							)
							snake_case_							 =						True

							def __call__(      self				:     int							,	a				:     Tensor    )	->Optional[int]:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					Tracker(self.dest    )(a    ).parametrized
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					Tracker(self.src    )(a    ).parametrized

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					list(filter(lambda a    : type(a    ) not in self.src_skip							,	a    )    )
													SCREAMING_SNAKE_CASE__     :				Dict   =					list(filter(lambda a    : 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    _a     (				nn.Module							):






							"""simple docstring"""

							def __init__(      self				:     Optional[int]							,	a				:     nn.Module    )	->int:
													super().__init__()

													SCREAMING_SNAKE_CASE__     :				List[Tuple[str, nn.Module]]   =					[]
													# - 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}"""
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					len(a    ) + 1
																			feature_blocks.append((f"""res{block_index}""", v)    )

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					nn.ModuleDict(a    )






							def      A_  (      self				:     List[Any]							,	a				:     Tensor    )	->Optional[Any]:
													return get_trunk_forward_outputs(
													    a							,	out_feat_keys=a							,	feature_blocks=self._feature_blocks							,	)







class    _a     (				lowercase__							):






							"""simple docstring"""

							def      A_  (      self				:     Tuple							,	a				:     str    )	->str:
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					x.split("-"    )
													return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]    )






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

													else:
																			SCREAMING_SNAKE_CASE__     :				Dict   =					super().__getitem__(a    )

													return val







class    _a     (				lowercase__							):






							"""simple docstring"""

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







def       UpperCAmelCase							(     _lowerCamelCase      :  Union[str, Any]			,   _lowerCamelCase      :  int			,   _lowerCamelCase      :  List[Tuple[str, str]]     ):





						'''simple docstring'''


						for from_key, to_key in keys:
												SCREAMING_SNAKE_CASE__     :				Tuple   =					from_state_dict[from_key].clone()
												print(f"""Copied key={from_key} to={to_key}"""     )
						return to_state_dict







def       UpperCAmelCase							(     _lowerCamelCase      :  str			,   _lowerCamelCase      :  Callable[[], nn.Module]			,   _lowerCamelCase      :  Callable[[], nn.Module]			,   _lowerCamelCase      :  RegNetConfig			,   _lowerCamelCase      :  Path			,   _lowerCamelCase      :  bool = True			,   ):





						'''simple docstring'''


						print(f"""Converting {name}..."""     )
						with torch.no_grad():
												SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[Any]   =					from_model_func()
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					our_model_func(_lowerCamelCase     ).eval()
												SCREAMING_SNAKE_CASE__     :				int   =					ModuleTransfer(src=_lowerCamelCase			,   dest=_lowerCamelCase			,   raise_if_mismatch=_lowerCamelCase     )
												SCREAMING_SNAKE_CASE__     :				Any   =					torch.randn((1, 3, 224, 224)     )
												module_transfer(_lowerCamelCase     )

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

						SCREAMING_SNAKE_CASE__     :				Any   =					our_model(_lowerCamelCase			,   output_hidden_states=_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					(
						    our_outputs.logits if isinstance(_lowerCamelCase			,   _lowerCamelCase     ) else our_outputs.last_hidden_state
						)

						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					from_model(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					from_output[-1] if type(_lowerCamelCase     ) 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:
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					our_outputs.hidden_states[-1]

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

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

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







def       UpperCAmelCase							(     _lowerCamelCase      :  Path			,   _lowerCamelCase      :  str = None			,   _lowerCamelCase      :  bool = True     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					"imagenet-1k-id2label.json"
						SCREAMING_SNAKE_CASE__     :				int   =					1_000
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					(1, num_labels)

						SCREAMING_SNAKE_CASE__     :				Dict   =					"huggingface/label-files"
						SCREAMING_SNAKE_CASE__     :				Tuple   =					num_labels
						SCREAMING_SNAKE_CASE__     :				Dict   =					json.load(open(cached_download(hf_hub_url(_lowerCamelCase			,   _lowerCamelCase			,   repo_type="dataset"     )     )			,   "r"     )     )
						SCREAMING_SNAKE_CASE__     :				Any   =					{int(_lowerCamelCase     ): v for k, v in idalabel.items()}

						SCREAMING_SNAKE_CASE__     :				str   =					idalabel
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					{v: k for k, v in idalabel.items()}

						SCREAMING_SNAKE_CASE__     :				Dict   =					partial(_lowerCamelCase			,   num_labels=_lowerCamelCase			,   idalabel=_lowerCamelCase			,   labelaid=_lowerCamelCase     )

						SCREAMING_SNAKE_CASE__     :				Tuple   =					{
						    "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, 1_008]			,   groups_width=48			,   layer_type="x"     ),
						    "regnet-x-040": ImageNetPreTrainedConfig(
						        depths=[2, 5, 14, 2]			,   hidden_sizes=[80, 240, 560, 1_360]			,   groups_width=40			,   layer_type="x"     ),
						    "regnet-x-064": ImageNetPreTrainedConfig(
						        depths=[2, 4, 10, 1]			,   hidden_sizes=[168, 392, 784, 1_624]			,   groups_width=56			,   layer_type="x"     ),
						    "regnet-x-080": ImageNetPreTrainedConfig(
						        depths=[2, 5, 15, 1]			,   hidden_sizes=[80, 240, 720, 1_920]			,   groups_width=120			,   layer_type="x"     ),
						    "regnet-x-120": ImageNetPreTrainedConfig(
						        depths=[2, 5, 11, 1]			,   hidden_sizes=[224, 448, 896, 2_240]			,   groups_width=112			,   layer_type="x"     ),
						    "regnet-x-160": ImageNetPreTrainedConfig(
						        depths=[2, 6, 13, 1]			,   hidden_sizes=[256, 512, 896, 2_048]			,   groups_width=128			,   layer_type="x"     ),
						    "regnet-x-320": ImageNetPreTrainedConfig(
						        depths=[2, 7, 13, 1]			,   hidden_sizes=[336, 672, 1_344, 2_520]			,   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, 1_512]			,   groups_width=24     ),
						    "regnet-y-040": ImageNetPreTrainedConfig(
						        depths=[2, 6, 12, 2]			,   hidden_sizes=[128, 192, 512, 1_088]			,   groups_width=64     ),
						    "regnet-y-064": ImageNetPreTrainedConfig(
						        depths=[2, 7, 14, 2]			,   hidden_sizes=[144, 288, 576, 1_296]			,   groups_width=72     ),
						    "regnet-y-080": ImageNetPreTrainedConfig(
						        depths=[2, 4, 10, 1]			,   hidden_sizes=[168, 448, 896, 2_016]			,   groups_width=56     ),
						    "regnet-y-120": ImageNetPreTrainedConfig(
						        depths=[2, 5, 11, 1]			,   hidden_sizes=[224, 448, 896, 2_240]			,   groups_width=112     ),
						    "regnet-y-160": ImageNetPreTrainedConfig(
						        depths=[2, 4, 11, 1]			,   hidden_sizes=[224, 448, 1_232, 3_024]			,   groups_width=112     ),
						    "regnet-y-320": ImageNetPreTrainedConfig(
						        depths=[2, 5, 12, 1]			,   hidden_sizes=[232, 696, 1_392, 3_712]			,   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, 1_392, 3_712]			,   groups_width=232     ),
						    "regnet-y-640-seer": RegNetConfig(depths=[2, 5, 12, 1]			,   hidden_sizes=[328, 984, 1_968, 4_920]			,   groups_width=328     ),
						    "regnet-y-1280-seer": RegNetConfig(
						        depths=[2, 7, 17, 1]			,   hidden_sizes=[528, 1_056, 2_904, 7_392]			,   groups_width=264     ),
						    "regnet-y-2560-seer": RegNetConfig(
						        depths=[3, 7, 16, 1]			,   hidden_sizes=[640, 1_696, 2_544, 5_088]			,   groups_width=640     ),
						    "regnet-y-10b-seer": ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]			,   hidden_sizes=[2_020, 4_040, 11_110, 28_280]			,   groups_width=1_010     ),
						    # finetuned on imagenet
						    "regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(
						        depths=[2, 5, 12, 1]			,   hidden_sizes=[232, 696, 1_392, 3_712]			,   groups_width=232     ),
						    "regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
						        depths=[2, 5, 12, 1]			,   hidden_sizes=[328, 984, 1_968, 4_920]			,   groups_width=328     ),
						    "regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]			,   hidden_sizes=[528, 1_056, 2_904, 7_392]			,   groups_width=264     ),
						    "regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
						        depths=[3, 7, 16, 1]			,   hidden_sizes=[640, 1_696, 2_544, 5_088]			,   groups_width=640     ),
						    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]			,   hidden_sizes=[2_020, 4_040, 11_110, 28_280]			,   groups_width=1_010     ),
						}

						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					NameToOurModelFuncMap()
						SCREAMING_SNAKE_CASE__     :				Any   =					NameToFromModelFuncMap()
						# add seer weights logic

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

						# pretrained
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					partial(
						    _lowerCamelCase			,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"			,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )			,   )

						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					partial(
						    _lowerCamelCase			,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"			,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )			,   )

						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					partial(
						    _lowerCamelCase			,   "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()     )			,   )

						SCREAMING_SNAKE_CASE__     :				Dict   =					partial(
						    _lowerCamelCase			,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"			,   lambda: FakeRegNetVisslWrapper(
						        RegNet(RegNetParams(depth=27			,   group_width=1_010			,   w_a=1_744			,   w_a=6_2_0.8_3			,   w_m=2.5_2     )     )     )			,   )

						# IN1K finetuned
						SCREAMING_SNAKE_CASE__     :				Any   =					partial(
						    _lowerCamelCase			,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"			,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )			,   )

						SCREAMING_SNAKE_CASE__     :				List[Any]   =					partial(
						    _lowerCamelCase			,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"			,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )			,   )

						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					partial(
						    _lowerCamelCase			,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"			,   lambda: FakeRegNetVisslWrapper(RegNetYaaagf()     )			,   )

						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					partial(
						    _lowerCamelCase			,   "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=1_010			,   w_a=1_744			,   w_a=6_2_0.8_3			,   w_m=2.5_2     )     )     )			,   )

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


if __name__ == "__main__":
				__lowercase       :List[Any]       =  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.",
				)

				__lowercase       :Optional[int]       =  parser.parse_args()

				__lowercase       :Path       =  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)






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


__lowercase       :int       =  logging.get_logger(__name__)



class    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						["pixel_values"]

							def __init__(      self				:     int							,	a				:     bool = True							,	a				:     Optional[Dict[str, int]] = None							,	a				:     PILImageResampling = PILImageResampling.BILINEAR							,	a				:     bool = True							,	a				:     Dict[str, int] = None							,	a				:     bool = True							,	a				:     Union[int, float] = 1 / 2_55							,	a				:     bool = True							,	a				:     Optional[Union[float, List[float]]] = None							,	a				:     Optional[Union[float, List[float]]] = None							,	**a				:     List[str]							,	)	->None:
													super().__init__(**a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					size if size is not None else {"shortest_edge": 2_56}
													SCREAMING_SNAKE_CASE__     :				Any   =					get_size_dict(a							,	default_to_square=a    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
													SCREAMING_SNAKE_CASE__     :				Dict   =					get_size_dict(a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					do_resize
													SCREAMING_SNAKE_CASE__     :				List[str]   =					size
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					resample
													SCREAMING_SNAKE_CASE__     :				int   =					do_center_crop
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					crop_size
													SCREAMING_SNAKE_CASE__     :				Any   =					do_rescale
													SCREAMING_SNAKE_CASE__     :				Any   =					rescale_factor
													SCREAMING_SNAKE_CASE__     :				int   =					do_normalize
													SCREAMING_SNAKE_CASE__     :				str   =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													SCREAMING_SNAKE_CASE__     :				int   =					image_std if image_std is not None else IMAGENET_STANDARD_STD

							def      A_  (      self				:     Tuple							,	a				:     np.ndarray							,	a				:     Dict[str, int]							,	a				:     PILImageResampling = PILImageResampling.BICUBIC							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     Optional[int]							,	)	->np.ndarray:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					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()}"""    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					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      A_  (      self				:     List[Any]							,	a				:     np.ndarray							,	a				:     Dict[str, int]							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     List[Any]							,	)	->np.ndarray:
													SCREAMING_SNAKE_CASE__     :				Tuple   =					get_size_dict(a    )
													return center_crop(a							,	size=(size["height"], size["width"])							,	data_format=a							,	**a    )

							def      A_  (      self				:     Optional[int]							,	a				:     np.ndarray							,	a				:     float							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     Dict    )	->np.ndarray:
													return rescale(a							,	scale=a							,	data_format=a							,	**a    )

							def      A_  (      self				:     Union[str, Any]							,	a				:     np.ndarray							,	a				:     Union[float, List[float]]							,	a				:     Union[float, List[float]]							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     Union[str, Any]							,	)	->np.ndarray:
													return normalize(a							,	mean=a							,	std=a							,	data_format=a							,	**a    )






							def      A_  (      self				:     Tuple							,	a				:     ImageInput							,	a				:     Optional[bool] = None							,	a				:     Dict[str, int] = None							,	a				:     PILImageResampling = None							,	a				:     bool = None							,	a				:     Dict[str, int] = None							,	a				:     Optional[bool] = None							,	a				:     Optional[float] = None							,	a				:     Optional[bool] = None							,	a				:     Optional[Union[float, List[float]]] = None							,	a				:     Optional[Union[float, List[float]]] = None							,	a				:     Optional[Union[str, TensorType]] = None							,	a				:     Union[str, ChannelDimension] = ChannelDimension.FIRST							,	**a				:     Any							,	)	->Optional[int]:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					do_resize if do_resize is not None else self.do_resize
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					size if size is not None else self.size
													SCREAMING_SNAKE_CASE__     :				Dict   =					get_size_dict(a							,	default_to_square=a    )
													SCREAMING_SNAKE_CASE__     :				str   =					resample if resample is not None else self.resample
													SCREAMING_SNAKE_CASE__     :				List[str]   =					do_center_crop if do_center_crop is not None else self.do_center_crop
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					crop_size if crop_size is not None else self.crop_size
													SCREAMING_SNAKE_CASE__     :				Dict   =					get_size_dict(a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					do_rescale if do_rescale is not None else self.do_rescale
													SCREAMING_SNAKE_CASE__     :				int   =					rescale_factor if rescale_factor is not None else self.rescale_factor
													SCREAMING_SNAKE_CASE__     :				Dict   =					do_normalize if do_normalize is not None else self.do_normalize
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					image_mean if image_mean is not None else self.image_mean
													SCREAMING_SNAKE_CASE__     :				Tuple   =					image_std if image_std is not None else self.image_std

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					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.
													SCREAMING_SNAKE_CASE__     :				List[str]   =					[to_numpy_array(a    ) for image in images]

													if do_resize:
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					[self.resize(image=a							,	size=a							,	resample=a    ) for image in images]

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

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

													if do_normalize:
																			SCREAMING_SNAKE_CASE__     :				Dict   =					[self.normalize(image=a							,	mean=a							,	std=a    ) for image in images]

													SCREAMING_SNAKE_CASE__     :				Dict   =					[to_channel_dimension_format(a							,	a    ) for image in images]

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					{"pixel_values": images}
													return BatchFeature(data=a							,	tensor_type=a    )