





"""simple docstring"""





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCamelCase					      =   logging.get_logger(__name__)



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Optional[Any] ):


							'''simple docstring'''







							__lowerCamelCase   :   List[Any]       =SwinConfig(
							    embed_dim=192     ,    depths=(2, 2, 18, 2)     ,    num_heads=(6, 12, 24, 48)     ,    window_size=12     ,    out_features=['''stage2''', '''stage3''', '''stage4''']     ,    )

							__lowerCamelCase   :   List[str]       =DetaConfig(
							    backbone_config=SCREAMING_SNAKE_CASE     ,    num_queries=900     ,    encoder_ffn_dim=2048     ,    decoder_ffn_dim=2048     ,    num_feature_levels=5     ,    assign_first_stage=SCREAMING_SNAKE_CASE     ,    with_box_refine=SCREAMING_SNAKE_CASE     ,    two_stage=SCREAMING_SNAKE_CASE     ,    )

							# set labels
							__lowerCamelCase   :   Optional[int]       ='''huggingface/label-files'''
							if "o365" in model_name:
														__lowerCamelCase   :   Optional[int]       =366
														__lowerCamelCase   :   int       ='''object365-id2label.json'''
							else:
														__lowerCamelCase   :   Optional[Any]       =91
														__lowerCamelCase   :   List[Any]       ='''coco-detection-id2label.json'''

							__lowerCamelCase   :   List[str]       =num_labels
							__lowerCamelCase   :   Tuple       =json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    repo_type='''dataset''' ) )     ,    '''r''' ) )
							__lowerCamelCase   :   Tuple       ={int(SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
							__lowerCamelCase   :   str       =idalabel
							__lowerCamelCase   :   Union[str, Any]       ={v: k for k, v in idalabel.items()}

							return config



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str ):


							'''simple docstring'''







							__lowerCamelCase   :   Optional[int]       =[]

							# stem
							# fmt: off
							rename_keys.append(('''backbone.0.body.patch_embed.proj.weight''', '''model.backbone.model.embeddings.patch_embeddings.projection.weight''') )
							rename_keys.append(('''backbone.0.body.patch_embed.proj.bias''', '''model.backbone.model.embeddings.patch_embeddings.projection.bias''') )
							rename_keys.append(('''backbone.0.body.patch_embed.norm.weight''', '''model.backbone.model.embeddings.norm.weight''') )
							rename_keys.append(('''backbone.0.body.patch_embed.norm.bias''', '''model.backbone.model.embeddings.norm.bias''') )
							# stages
							for i in range(len(config.backbone_config.depths ) ):
														for j in range(config.backbone_config.depths[i] ):
																					rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm1.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight') )
																					rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm1.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias') )
																					rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table') )
																					rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index') )
																					rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight') )
																					rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias') )
																					rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm2.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight') )
																					rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm2.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias') )
																					rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight') )
																					rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias') )
																					rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight') )
																					rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias') )

														if i < 3:
																					rename_keys.append((F'backbone.0.body.layers.{i}.downsample.reduction.weight', F'model.backbone.model.encoder.layers.{i}.downsample.reduction.weight') )
																					rename_keys.append((F'backbone.0.body.layers.{i}.downsample.norm.weight', F'model.backbone.model.encoder.layers.{i}.downsample.norm.weight') )
																					rename_keys.append((F'backbone.0.body.layers.{i}.downsample.norm.bias', F'model.backbone.model.encoder.layers.{i}.downsample.norm.bias') )

							rename_keys.append(('''backbone.0.body.norm1.weight''', '''model.backbone.model.hidden_states_norms.stage2.weight''') )
							rename_keys.append(('''backbone.0.body.norm1.bias''', '''model.backbone.model.hidden_states_norms.stage2.bias''') )
							rename_keys.append(('''backbone.0.body.norm2.weight''', '''model.backbone.model.hidden_states_norms.stage3.weight''') )
							rename_keys.append(('''backbone.0.body.norm2.bias''', '''model.backbone.model.hidden_states_norms.stage3.bias''') )
							rename_keys.append(('''backbone.0.body.norm3.weight''', '''model.backbone.model.hidden_states_norms.stage4.weight''') )
							rename_keys.append(('''backbone.0.body.norm3.bias''', '''model.backbone.model.hidden_states_norms.stage4.bias''') )

							# transformer encoder
							for i in range(config.encoder_layers ):
														rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight', F'model.encoder.layers.{i}.self_attn.sampling_offsets.weight') )
														rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias', F'model.encoder.layers.{i}.self_attn.sampling_offsets.bias') )
														rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.attention_weights.weight', F'model.encoder.layers.{i}.self_attn.attention_weights.weight') )
														rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.attention_weights.bias', F'model.encoder.layers.{i}.self_attn.attention_weights.bias') )
														rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.value_proj.weight', F'model.encoder.layers.{i}.self_attn.value_proj.weight') )
														rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.value_proj.bias', F'model.encoder.layers.{i}.self_attn.value_proj.bias') )
														rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.output_proj.weight', F'model.encoder.layers.{i}.self_attn.output_proj.weight') )
														rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.output_proj.bias', F'model.encoder.layers.{i}.self_attn.output_proj.bias') )
														rename_keys.append((F'transformer.encoder.layers.{i}.norm1.weight', F'model.encoder.layers.{i}.self_attn_layer_norm.weight') )
														rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'model.encoder.layers.{i}.self_attn_layer_norm.bias') )
														rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'model.encoder.layers.{i}.fc1.weight') )
														rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'model.encoder.layers.{i}.fc1.bias') )
														rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'model.encoder.layers.{i}.fc2.weight') )
														rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'model.encoder.layers.{i}.fc2.bias') )
														rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'model.encoder.layers.{i}.final_layer_norm.weight') )
														rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'model.encoder.layers.{i}.final_layer_norm.bias') )

							# transformer decoder
							for i in range(config.decoder_layers ):
														rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight', F'model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight') )
														rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias', F'model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias') )
														rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.attention_weights.weight', F'model.decoder.layers.{i}.encoder_attn.attention_weights.weight') )
														rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.attention_weights.bias', F'model.decoder.layers.{i}.encoder_attn.attention_weights.bias') )
														rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.value_proj.weight', F'model.decoder.layers.{i}.encoder_attn.value_proj.weight') )
														rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.value_proj.bias', F'model.decoder.layers.{i}.encoder_attn.value_proj.bias') )
														rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.output_proj.weight', F'model.decoder.layers.{i}.encoder_attn.output_proj.weight') )
														rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.output_proj.bias', F'model.decoder.layers.{i}.encoder_attn.output_proj.bias') )
														rename_keys.append((F'transformer.decoder.layers.{i}.norm1.weight', F'model.decoder.layers.{i}.encoder_attn_layer_norm.weight') )
														rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'model.decoder.layers.{i}.encoder_attn_layer_norm.bias') )
														rename_keys.append((F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'model.decoder.layers.{i}.self_attn.out_proj.weight') )
														rename_keys.append((F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'model.decoder.layers.{i}.self_attn.out_proj.bias') )
														rename_keys.append((F'transformer.decoder.layers.{i}.norm2.weight', F'model.decoder.layers.{i}.self_attn_layer_norm.weight') )
														rename_keys.append((F'transformer.decoder.layers.{i}.norm2.bias', F'model.decoder.layers.{i}.self_attn_layer_norm.bias') )
														rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'model.decoder.layers.{i}.fc1.weight') )
														rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'model.decoder.layers.{i}.fc1.bias') )
														rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'model.decoder.layers.{i}.fc2.weight') )
														rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'model.decoder.layers.{i}.fc2.bias') )
														rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'model.decoder.layers.{i}.final_layer_norm.weight') )
														rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'model.decoder.layers.{i}.final_layer_norm.bias') )

							# fmt: on

							return rename_keys



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Union[str, Any]     ,    SCREAMING_SNAKE_CASE							:				Union[str, Any]     ,    SCREAMING_SNAKE_CASE							:				str ):


							'''simple docstring'''







							__lowerCamelCase   :   List[str]       =dct.pop(SCREAMING_SNAKE_CASE )
							__lowerCamelCase   :   int       =val



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				Any ):


							'''simple docstring'''







							__lowerCamelCase   :   str       =[int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
							for i in range(len(backbone_config.depths ) ):
														__lowerCamelCase   :   List[str]       =num_features[i]
														for j in range(backbone_config.depths[i] ):
																					# fmt: off
																					# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
																					__lowerCamelCase   :   Any       =state_dict.pop(F'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight' )
																					__lowerCamelCase   :   Any       =state_dict.pop(F'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias' )
																					# next, add query, keys and values (in that order) to the state dict
																					__lowerCamelCase   :   Optional[int]       =in_proj_weight[:dim, :]
																					__lowerCamelCase   :   Any       =in_proj_bias[: dim]
																					__lowerCamelCase   :   List[str]       =in_proj_weight[
																					    dim : dim * 2, :
																					]
																					__lowerCamelCase   :   str       =in_proj_bias[
																					    dim : dim * 2
																					]
																					__lowerCamelCase   :   Tuple       =in_proj_weight[
																					    -dim :, :
																					]
																					__lowerCamelCase   :   List[str]       =in_proj_bias[-dim :]
																					# fmt: on



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Tuple     ,    SCREAMING_SNAKE_CASE							:				Union[str, Any] ):


							'''simple docstring'''







							__lowerCamelCase   :   List[Any]       =config.d_model
							for i in range(config.decoder_layers ):
														# read in weights + bias of input projection layer of self-attention
														__lowerCamelCase   :   List[Any]       =state_dict.pop(F'transformer.decoder.layers.{i}.self_attn.in_proj_weight' )
														__lowerCamelCase   :   int       =state_dict.pop(F'transformer.decoder.layers.{i}.self_attn.in_proj_bias' )
														# next, add query, keys and values (in that order) to the state dict
														__lowerCamelCase   :   List[str]       =in_proj_weight[:hidden_size, :]
														__lowerCamelCase   :   Optional[int]       =in_proj_bias[:hidden_size]
														__lowerCamelCase   :   Union[str, Any]       =in_proj_weight[
														    hidden_size : hidden_size * 2, :
														]
														__lowerCamelCase   :   Any       =in_proj_bias[hidden_size : hidden_size * 2]
														__lowerCamelCase   :   str       =in_proj_weight[-hidden_size:, :]
														__lowerCamelCase   :   Tuple       =in_proj_bias[-hidden_size:]



def 					lowerCAmelCase_      (   ):


							'''simple docstring'''







							__lowerCamelCase   :   Optional[Any]       ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
							__lowerCamelCase   :   Dict       =Image.open(requests.get(SCREAMING_SNAKE_CASE     ,    stream=SCREAMING_SNAKE_CASE ).raw )

							return im



@torch.no_grad()
def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				List[Any]     ,    SCREAMING_SNAKE_CASE							:				Tuple     ,    SCREAMING_SNAKE_CASE							:				Optional[Any] ):


							'''simple docstring'''







							__lowerCamelCase   :   int       =get_deta_config(SCREAMING_SNAKE_CASE )

							# load original state dict
							if model_name == "deta-swin-large":
														__lowerCamelCase   :   Tuple       =hf_hub_download(repo_id='''nielsr/deta-checkpoints'''     ,    filename='''adet_swin_ft.pth''' )
							elif model_name == "deta-swin-large-o365":
														__lowerCamelCase   :   List[str]       =hf_hub_download(repo_id='''jozhang97/deta-swin-l-o365'''     ,    filename='''deta_swin_pt_o365.pth''' )
							else:
														raise ValueError(F'Model name {model_name} not supported' )

							__lowerCamelCase   :   int       =torch.load(SCREAMING_SNAKE_CASE     ,    map_location='''cpu''' )['''model''']

							# original state dict
							for name, param in state_dict.items():
														print(SCREAMING_SNAKE_CASE     ,    param.shape )

							# rename keys
							__lowerCamelCase   :   Any       =create_rename_keys(SCREAMING_SNAKE_CASE )
							for src, dest in rename_keys:
														rename_key(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )
							read_in_swin_q_k_v(SCREAMING_SNAKE_CASE     ,    config.backbone_config )
							read_in_decoder_q_k_v(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )

							# fix some prefixes
							for key in state_dict.copy().keys():
														if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
																					__lowerCamelCase   :   Union[str, Any]       =state_dict.pop(SCREAMING_SNAKE_CASE )
																					__lowerCamelCase   :   List[Any]       =val
														if "input_proj" in key:
																					__lowerCamelCase   :   Dict       =state_dict.pop(SCREAMING_SNAKE_CASE )
																					__lowerCamelCase   :   Union[str, Any]       =val
														if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
																					__lowerCamelCase   :   Optional[Any]       =state_dict.pop(SCREAMING_SNAKE_CASE )
																					__lowerCamelCase   :   List[str]       =val

    # finally, create HuggingFace model and load state dict
							__lowerCamelCase   :   List[Any]       =DetaForObjectDetection(SCREAMING_SNAKE_CASE )
							model.load_state_dict(SCREAMING_SNAKE_CASE )
							model.eval()

							__lowerCamelCase   :   Tuple       ='''cuda''' if torch.cuda.is_available() else '''cpu'''
							model.to(SCREAMING_SNAKE_CASE )

							# load image processor
							__lowerCamelCase   :   Union[str, Any]       =DetaImageProcessor(format='''coco_detection''' )

							# verify our conversion on image
							__lowerCamelCase   :   Union[str, Any]       =prepare_img()
							__lowerCamelCase   :   List[str]       =processor(images=SCREAMING_SNAKE_CASE     ,    return_tensors='''pt''' )
							__lowerCamelCase   :   Union[str, Any]       =encoding['''pixel_values''']
							__lowerCamelCase   :   int       =model(pixel_values.to(SCREAMING_SNAKE_CASE ) )

							# verify logits
							print('''Logits:'''     ,    outputs.logits[0, :3, :3] )
							print('''Boxes:'''     ,    outputs.pred_boxes[0, :3, :3] )
							if model_name == "deta-swin-large":
														__lowerCamelCase   :   Union[str, Any]       =torch.tensor(
														    [[-7.6_308, -2.8_485, -5.3_737], [-7.2_037, -4.5_505, -4.8_027], [-7.2_943, -4.2_611, -4.6_617]] )
														__lowerCamelCase   :   Dict       =torch.tensor([[0.4_987, 0.4_969, 0.9_999], [0.2_549, 0.5_498, 0.4_805], [0.5_498, 0.2_757, 0.0_569]] )
							elif model_name == "deta-swin-large-o365":
														__lowerCamelCase   :   str       =torch.tensor(
														    [[-8.0_122, -3.5_720, -4.9_717], [-8.1_547, -3.6_886, -4.6_389], [-7.6_610, -3.6_194, -5.0_134]] )
														__lowerCamelCase   :   int       =torch.tensor([[0.2_523, 0.5_549, 0.4_881], [0.7_715, 0.4_149, 0.4_601], [0.5_503, 0.2_753, 0.0_575]] )
							assert torch.allclose(outputs.logits[0, :3, :3]     ,    expected_logits.to(SCREAMING_SNAKE_CASE )     ,    atol=1E-4 )
							assert torch.allclose(outputs.pred_boxes[0, :3, :3]     ,    expected_boxes.to(SCREAMING_SNAKE_CASE )     ,    atol=1E-4 )
							print('''Everything ok!''' )

							if pytorch_dump_folder_path:
														# Save model and processor
														logger.info(F'Saving PyTorch model and processor to {pytorch_dump_folder_path}...' )
														Path(SCREAMING_SNAKE_CASE ).mkdir(exist_ok=SCREAMING_SNAKE_CASE )
														model.save_pretrained(SCREAMING_SNAKE_CASE )
														processor.save_pretrained(SCREAMING_SNAKE_CASE )

							# Push to hub
							if push_to_hub:
														print('''Pushing model and processor to hub...''' )
														model.push_to_hub(F'jozhang97/{model_name}' )
														processor.push_to_hub(F'jozhang97/{model_name}' )


if __name__ == "__main__":
							_UpperCamelCase					      =   argparse.ArgumentParser()

							parser.add_argument(
							    '--model_name',
							    type=str,
							    default='deta-swin-large',
							    choices=['deta-swin-large', 'deta-swin-large-o365'],
							    help='Name of the model you\'d like to convert.',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path',
							    default=None,
							    type=str,
							    help='Path to the folder to output PyTorch model.',
							)
							parser.add_argument(
							    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
							)
							_UpperCamelCase					      =   parser.parse_args()
							convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

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


if is_vision_available():
							import PIL


_UpperCamelCase					      =   logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							str									=		["""pixel_values"""]




				def __init__(  self	:str    ,     __lowercase	:bool = True    ,     __lowercase	:Dict[str, int] = None    ,     __lowercase	:float = None    ,     __lowercase	:PILImageResampling = PILImageResampling.BILINEAR    ,     __lowercase	:bool = True    ,     __lowercase	:Union[int, float] = 1 / 255    ,     __lowercase	:bool = True    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     **__lowercase	:Tuple    ,     ):
											super().__init__(**__lowercase      )
											__lowerCamelCase   :   int       =size if size is not None else {'''shortest_edge''': 384}
											__lowerCamelCase   :   List[str]       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )

											__lowerCamelCase   :   Any       =do_resize
											__lowerCamelCase   :   List[str]       =size
											# Default value set here for backwards compatibility where the value in config is None
											__lowerCamelCase   :   Tuple       =crop_pct if crop_pct is not None else 224 / 256
											__lowerCamelCase   :   Any       =resample
											__lowerCamelCase   :   List[str]       =do_rescale
											__lowerCamelCase   :   Dict       =rescale_factor
											__lowerCamelCase   :   Any       =do_normalize
											__lowerCamelCase   :   int       =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__lowerCamelCase   :   List[Any]       =image_std if image_std is not None else IMAGENET_STANDARD_STD




				def        __lowercase	(  self	:Any    ,     __lowercase	:np.ndarray    ,     __lowercase	:Dict[str, int]    ,     __lowercase	:float    ,     __lowercase	:PILImageResampling = PILImageResampling.BICUBIC    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:int    ,     ):
											__lowerCamelCase   :   int       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )
											if "shortest_edge" not in size:
																		raise ValueError(f'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'      )
											__lowerCamelCase   :   Optional[int]       =size['''shortest_edge''']

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




				def        __lowercase	(  self	:int    ,     __lowercase	:np.ndarray    ,     __lowercase	:Union[int, float]    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:List[Any]    ,     ):
											return rescale(__lowercase    ,     scale=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )




				def        __lowercase	(  self	:str    ,     __lowercase	:np.ndarray    ,     __lowercase	:Union[float, List[float]]    ,     __lowercase	:Union[float, List[float]]    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:Optional[int]    ,     ):
											return normalize(__lowercase    ,     mean=__lowercase    ,     std=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )




				def        __lowercase	(  self	:int    ,     __lowercase	:ImageInput    ,     __lowercase	:bool = None    ,     __lowercase	:Dict[str, int] = None    ,     __lowercase	:float = None    ,     __lowercase	:PILImageResampling = None    ,     __lowercase	:bool = None    ,     __lowercase	:float = None    ,     __lowercase	:bool = None    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     __lowercase	:Optional[Union[str, TensorType]] = None    ,     __lowercase	:ChannelDimension = ChannelDimension.FIRST    ,     **__lowercase	:Optional[Any]    ,     ):
											__lowerCamelCase   :   Optional[Any]       =do_resize if do_resize is not None else self.do_resize
											__lowerCamelCase   :   int       =crop_pct if crop_pct is not None else self.crop_pct
											__lowerCamelCase   :   List[Any]       =resample if resample is not None else self.resample
											__lowerCamelCase   :   List[Any]       =do_rescale if do_rescale is not None else self.do_rescale
											__lowerCamelCase   :   List[Any]       =rescale_factor if rescale_factor is not None else self.rescale_factor
											__lowerCamelCase   :   List[Any]       =do_normalize if do_normalize is not None else self.do_normalize
											__lowerCamelCase   :   Optional[Any]       =image_mean if image_mean is not None else self.image_mean
											__lowerCamelCase   :   str       =image_std if image_std is not None else self.image_std

											__lowerCamelCase   :   str       =size if size is not None else self.size
											__lowerCamelCase   :   Optional[int]       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )

											__lowerCamelCase   :   Optional[int]       =make_list_of_images(__lowercase      )

											if not valid_images(__lowercase      ):
																		raise ValueError(
																		    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																		    '''torch.Tensor, tf.Tensor or jax.ndarray.'''      )

											if do_resize and size is None 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.
											__lowerCamelCase   :   List[Any]       =[to_numpy_array(__lowercase      ) for image in images]

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

											if do_rescale:
																		__lowerCamelCase   :   int       =[self.rescale(image=__lowercase    ,     scale=__lowercase      ) for image in images]

											if do_normalize:
																		__lowerCamelCase   :   Union[str, Any]       =[self.normalize(image=__lowercase    ,     mean=__lowercase    ,     std=__lowercase      ) for image in images]

											__lowerCamelCase   :   Union[str, Any]       =[to_channel_dimension_format(__lowercase    ,     __lowercase      ) for image in images]

											__lowerCamelCase   :   Tuple       ={'''pixel_values''': images}
											return BatchFeature(data=__lowercase    ,     tensor_type=__lowercase      )




