
import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


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






def 							_lowercase							(     UpperCamelCase_		)		->		Union[str, Any]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= SwinConfig.from_pretrained(
					    'microsoft/swin-tiny-patch4-window7-224'	,	out_features=['stage1', 'stage2', 'stage3', 'stage4']		)
					SCREAMING_SNAKE_CASE__													= MaskFormerConfig(backbone_config=UpperCamelCase_		)

					SCREAMING_SNAKE_CASE__													= 'huggingface/label-files'
					if "ade20k-full" in model_name:
										# this should be ok
										SCREAMING_SNAKE_CASE__													= 847
										SCREAMING_SNAKE_CASE__													= 'maskformer-ade20k-full-id2label.json'
					elif "ade" in model_name:
										# this should be ok
										SCREAMING_SNAKE_CASE__													= 150
										SCREAMING_SNAKE_CASE__													= 'ade20k-id2label.json'
					elif "coco-stuff" in model_name:
										# this should be ok
										SCREAMING_SNAKE_CASE__													= 171
										SCREAMING_SNAKE_CASE__													= 'maskformer-coco-stuff-id2label.json'
					elif "coco" in model_name:
										# TODO
										SCREAMING_SNAKE_CASE__													= 133
										SCREAMING_SNAKE_CASE__													= 'coco-panoptic-id2label.json'
					elif "cityscapes" in model_name:
										# this should be ok
										SCREAMING_SNAKE_CASE__													= 19
										SCREAMING_SNAKE_CASE__													= 'cityscapes-id2label.json'
					elif "vistas" in model_name:
										# this should be ok
										SCREAMING_SNAKE_CASE__													= 65
										SCREAMING_SNAKE_CASE__													= 'mapillary-vistas-id2label.json'

					SCREAMING_SNAKE_CASE__													= json.load(open(hf_hub_download(UpperCamelCase_	,	UpperCamelCase_	,	repo_type='dataset'		)	,	'r'		)		)
					SCREAMING_SNAKE_CASE__													= {int(UpperCamelCase_		): v for k, v in idalabel.items()}

					return config






def 							_lowercase							(     UpperCamelCase_		)		->		int:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= []
					# stem
					# fmt: off
					rename_keys.append(('backbone.patch_embed.proj.weight', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight')		)
					rename_keys.append(('backbone.patch_embed.proj.bias', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias')		)
					rename_keys.append(('backbone.patch_embed.norm.weight', 'model.pixel_level_module.encoder.model.embeddings.norm.weight')		)
					rename_keys.append(('backbone.patch_embed.norm.bias', 'model.pixel_level_module.encoder.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.layers.{i}.blocks.{j}.norm1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')		)
															rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')		)
															rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')		)
															rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')		)
															rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')		)
															rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')		)
															rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')		)
															rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')		)
															rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')		)
															rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')		)
															rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')		)
															rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')		)

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

					# FPN
					rename_keys.append(('sem_seg_head.layer_4.weight', 'model.pixel_level_module.decoder.fpn.stem.0.weight')		)
					rename_keys.append(('sem_seg_head.layer_4.norm.weight', 'model.pixel_level_module.decoder.fpn.stem.1.weight')		)
					rename_keys.append(('sem_seg_head.layer_4.norm.bias', 'model.pixel_level_module.decoder.fpn.stem.1.bias')		)
					for source_index, target_index in zip(range(3	,	0	,	-1		)	,	range(0	,	3		)		):
										rename_keys.append((F'sem_seg_head.adapter_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight')		)
										rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight')		)
										rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias')		)
										rename_keys.append((F'sem_seg_head.layer_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight')		)
										rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight')		)
										rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias')		)
					rename_keys.append(('sem_seg_head.mask_features.weight', 'model.pixel_level_module.decoder.mask_projection.weight')		)
					rename_keys.append(('sem_seg_head.mask_features.bias', 'model.pixel_level_module.decoder.mask_projection.bias')		)

					# Transformer decoder
					for idx in range(config.decoder_config.decoder_layers		):
										# self-attention out projection
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight')		)
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias')		)
										# cross-attention out projection
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight')		)
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias')		)
										# MLP 1
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight', F'model.transformer_module.decoder.layers.{idx}.fc1.weight')		)
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias', F'model.transformer_module.decoder.layers.{idx}.fc1.bias')		)
										# MLP 2
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight', F'model.transformer_module.decoder.layers.{idx}.fc2.weight')		)
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias', F'model.transformer_module.decoder.layers.{idx}.fc2.bias')		)
										# layernorm 1 (self-attention layernorm)
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight')		)
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias')		)
										# layernorm 2 (cross-attention layernorm)
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight')		)
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias')		)
										# layernorm 3 (final layernorm)
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight')		)
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias')		)

					rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.weight', 'model.transformer_module.decoder.layernorm.weight')		)
					rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.bias', 'model.transformer_module.decoder.layernorm.bias')		)

					# heads on top
					rename_keys.append(('sem_seg_head.predictor.query_embed.weight', 'model.transformer_module.queries_embedder.weight')		)

					rename_keys.append(('sem_seg_head.predictor.input_proj.weight', 'model.transformer_module.input_projection.weight')		)
					rename_keys.append(('sem_seg_head.predictor.input_proj.bias', 'model.transformer_module.input_projection.bias')		)

					rename_keys.append(('sem_seg_head.predictor.class_embed.weight', 'class_predictor.weight')		)
					rename_keys.append(('sem_seg_head.predictor.class_embed.bias', 'class_predictor.bias')		)

					for i in range(3		):
										rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.weight', F'mask_embedder.{i}.0.weight')		)
										rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.bias', F'mask_embedder.{i}.0.bias')		)
					# fmt: on

					return rename_keys






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		int:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= dct.pop(UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= val






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		Optional[Any]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= [int(backbone_config.embed_dim * 2**i		) for i in range(len(backbone_config.depths		)		)]
					for i in range(len(backbone_config.depths		)		):
										SCREAMING_SNAKE_CASE__													= 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)
															SCREAMING_SNAKE_CASE__													= state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.weight'		)
															SCREAMING_SNAKE_CASE__													= state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.bias'		)
															# next, add query, keys and values (in that order) to the state dict
															SCREAMING_SNAKE_CASE__													= in_proj_weight[:dim, :]
															SCREAMING_SNAKE_CASE__													= in_proj_bias[: dim]
															SCREAMING_SNAKE_CASE__													= in_proj_weight[
															    dim : dim * 2, :
															]
															SCREAMING_SNAKE_CASE__													= in_proj_bias[
															    dim : dim * 2
															]
															SCREAMING_SNAKE_CASE__													= in_proj_weight[
															    -dim :, :
															]
															SCREAMING_SNAKE_CASE__													= in_proj_bias[-dim :]
															# fmt: on






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		Any:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= config.decoder_config.hidden_size
					for idx in range(config.decoder_config.decoder_layers		):
										# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
										SCREAMING_SNAKE_CASE__													= state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'		)
										SCREAMING_SNAKE_CASE__													= state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'		)
										# next, add query, keys and values (in that order) to the state dict
										SCREAMING_SNAKE_CASE__													= in_proj_weight[: hidden_size, :]
										SCREAMING_SNAKE_CASE__													= in_proj_bias[:config.hidden_size]
										SCREAMING_SNAKE_CASE__													= in_proj_weight[hidden_size : hidden_size * 2, :]
										SCREAMING_SNAKE_CASE__													= in_proj_bias[hidden_size : hidden_size * 2]
										SCREAMING_SNAKE_CASE__													= in_proj_weight[-hidden_size :, :]
										SCREAMING_SNAKE_CASE__													= in_proj_bias[-hidden_size :]
										# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
										SCREAMING_SNAKE_CASE__													= state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'		)
										SCREAMING_SNAKE_CASE__													= state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'		)
										# next, add query, keys and values (in that order) to the state dict
										SCREAMING_SNAKE_CASE__													= in_proj_weight[: hidden_size, :]
										SCREAMING_SNAKE_CASE__													= in_proj_bias[:config.hidden_size]
										SCREAMING_SNAKE_CASE__													= in_proj_weight[hidden_size : hidden_size * 2, :]
										SCREAMING_SNAKE_CASE__													= in_proj_bias[hidden_size : hidden_size * 2]
										SCREAMING_SNAKE_CASE__													= in_proj_weight[-hidden_size :, :]
										SCREAMING_SNAKE_CASE__													= in_proj_bias[-hidden_size :]
					# fmt: on






def 							_lowercase							(     )		->		torch.Tensor:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= 'http://images.cocodataset.org/val2017/000000039769.jpg'
					SCREAMING_SNAKE_CASE__													= Image.open(requests.get(UpperCamelCase_	,	stream=UpperCamelCase_		).raw		)
					return im






@torch.no_grad()
def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_ = False		)		->		Tuple:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= get_maskformer_config(UpperCamelCase_		)

					# load original state_dict
					with open(UpperCamelCase_	,	'rb'		) as f:
										SCREAMING_SNAKE_CASE__													= pickle.load(UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= data['model']

					# for name, param in state_dict.items():
					#     print(name, param.shape)

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

					# update to torch tensors
					for key, value in state_dict.items():
										SCREAMING_SNAKE_CASE__													= torch.from_numpy(UpperCamelCase_		)

					# load 🤗 model
					SCREAMING_SNAKE_CASE__													= MaskFormerForInstanceSegmentation(UpperCamelCase_		)
					model.eval()

					for name, param in model.named_parameters():
										print(UpperCamelCase_	,	param.shape		)

					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= model.load_state_dict(UpperCamelCase_	,	strict=UpperCamelCase_		)
					assert missing_keys == [
					    "model.pixel_level_module.encoder.model.layernorm.weight",
					    "model.pixel_level_module.encoder.model.layernorm.bias",
					]
					assert len(UpperCamelCase_		) == 0, F'Unexpected keys: {unexpected_keys}'

					# verify results
					SCREAMING_SNAKE_CASE__													= prepare_img()
					if "vistas" in model_name:
										SCREAMING_SNAKE_CASE__													= 65
					elif "cityscapes" in model_name:
										SCREAMING_SNAKE_CASE__													= 65535
					else:
										SCREAMING_SNAKE_CASE__													= 255
					SCREAMING_SNAKE_CASE__													= True if 'ade' in model_name else False
					SCREAMING_SNAKE_CASE__													= MaskFormerImageProcessor(ignore_index=UpperCamelCase_	,	reduce_labels=UpperCamelCase_		)

					SCREAMING_SNAKE_CASE__													= image_processor(UpperCamelCase_	,	return_tensors='pt'		)

					SCREAMING_SNAKE_CASE__													= model(**UpperCamelCase_		)

					print('Logits:'	,	outputs.class_queries_logits[0, :3, :3]		)

					if model_name == "maskformer-swin-tiny-ade":
										SCREAMING_SNAKE_CASE__													= torch.tensor(
										    [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]]		)
					assert torch.allclose(outputs.class_queries_logits[0, :3, :3]	,	UpperCamelCase_	,	atol=1e-4		)
					print('Looks ok!'		)

					if pytorch_dump_folder_path is not None:
										print(F'Saving model and image processor to {pytorch_dump_folder_path}'		)
										Path(UpperCamelCase_		).mkdir(exist_ok=UpperCamelCase_		)
										model.save_pretrained(UpperCamelCase_		)
										image_processor.save_pretrained(UpperCamelCase_		)

					if push_to_hub:
										print('Pushing model and image processor to the hub...'		)
										model.push_to_hub(F'nielsr/{model_name}'		)
										image_processor.push_to_hub(F'nielsr/{model_name}'		)


if __name__ == "__main__":
				__snake_case								=  argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--model_name""",
				    default="""maskformer-swin-tiny-ade""",
				    type=str,
				    help=("""Name of the MaskFormer model you'd like to convert""",),
				)
				parser.add_argument(
				    """--checkpoint_path""",
				    default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
				    type=str,
				    help="""Path to the original state dict (.pth file).""",
				)
				parser.add_argument(
				    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
				)
				parser.add_argument(
				    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
				)

				__snake_case								=  parser.parse_args()
				convert_maskformer_checkpoint(
				    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)







from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__snake_case								=  logging.get_logger(__name__)




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   List[str]				       =["""pixel_values"""]
	def __init__(			self							:   Dict       ,   UpperCAmelCase_							:   bool = True       ,   UpperCAmelCase_							:   int = 32       ,   UpperCAmelCase_							:   int=PILImageResampling.BILINEAR       ,   UpperCAmelCase_							:   bool = True       ,   **UpperCAmelCase_							:   Any       ,   ):
						SCREAMING_SNAKE_CASE__													= do_resize
						SCREAMING_SNAKE_CASE__													= do_rescale
						SCREAMING_SNAKE_CASE__													= size_divisor
						SCREAMING_SNAKE_CASE__													= resample
						super().__init__(**UpperCAmelCase_							)
	def      A_				(			self							:   Optional[int]       ,   UpperCAmelCase_							:   np.ndarray       ,   UpperCAmelCase_							:   int       ,   UpperCAmelCase_							:   Dict       ,   UpperCAmelCase_							:   Optional[ChannelDimension] = None       ,   **UpperCAmelCase_							:   Dict							):
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= get_image_size(UpperCAmelCase_							)
						# Rounds the height and width down to the closest multiple of size_divisor
						SCREAMING_SNAKE_CASE__													= height // size_divisor * size_divisor
						SCREAMING_SNAKE_CASE__													= width // size_divisor * size_divisor
						SCREAMING_SNAKE_CASE__													= resize(UpperCAmelCase_       ,   (new_h, new_w)       ,   resample=UpperCAmelCase_       ,   data_format=UpperCAmelCase_       ,   **UpperCAmelCase_							)
						return image
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   np.ndarray       ,   UpperCAmelCase_							:   float       ,   UpperCAmelCase_							:   Optional[ChannelDimension] = None       ,   **UpperCAmelCase_							:   List[str]							):
						return rescale(image=UpperCAmelCase_       ,   scale=UpperCAmelCase_       ,   data_format=UpperCAmelCase_       ,   **UpperCAmelCase_							)





	def      A_				(			self							:   Dict       ,   UpperCAmelCase_							:   Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]       ,   UpperCAmelCase_							:   Optional[bool] = None       ,   UpperCAmelCase_							:   Optional[int] = None       ,   UpperCAmelCase_							:   List[str]=None       ,   UpperCAmelCase_							:   Optional[bool] = None       ,   UpperCAmelCase_							:   Optional[Union[TensorType, str]] = None       ,   UpperCAmelCase_							:   ChannelDimension = ChannelDimension.FIRST       ,   **UpperCAmelCase_							:   List[Any]       ,   ):
						SCREAMING_SNAKE_CASE__													= do_resize if do_resize is not None else self.do_resize
						SCREAMING_SNAKE_CASE__													= do_rescale if do_rescale is not None else self.do_rescale
						SCREAMING_SNAKE_CASE__													= size_divisor if size_divisor is not None else self.size_divisor
						SCREAMING_SNAKE_CASE__													= resample if resample is not None else self.resample

						if do_resize and size_divisor is None:
											raise ValueError('size_divisor is required for resizing'							)

						SCREAMING_SNAKE_CASE__													= make_list_of_images(UpperCAmelCase_							)

						if not valid_images(UpperCAmelCase_							):
											raise ValueError('Invalid image(s)'							)

						# All transformations expect numpy arrays.
						SCREAMING_SNAKE_CASE__													= [to_numpy_array(UpperCAmelCase_							) for img in images]

						if do_resize:
											SCREAMING_SNAKE_CASE__													= [self.resize(UpperCAmelCase_       ,   size_divisor=UpperCAmelCase_       ,   resample=UpperCAmelCase_							) for image in images]

						if do_rescale:
											SCREAMING_SNAKE_CASE__													= [self.rescale(UpperCAmelCase_       ,   scale=1 / 255							) for image in images]

						SCREAMING_SNAKE_CASE__													= [to_channel_dimension_format(UpperCAmelCase_       ,   UpperCAmelCase_							) for image in images]

						SCREAMING_SNAKE_CASE__													= {'pixel_values': images}
						return BatchFeature(data=UpperCAmelCase_       ,   tensor_type=UpperCAmelCase_							)





