



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()
__A   			=logging.get_logger(__name__)

def        a						(      _UpperCAmelCase							:					int						):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[Any]													=	SwinConfig(
		    embed_dim=1_92 ,   depths=(2, 2, 18, 2) ,   num_heads=(6, 12, 24, 48) ,   window_size=12 ,   out_features=['''stage2''', '''stage3''', '''stage4'''] ,   )

		__UpperCAmelCase    :     Optional[int]													=	DetaConfig(
		    backbone_config=_UpperCAmelCase ,   num_queries=9_00 ,   encoder_ffn_dim=20_48 ,   decoder_ffn_dim=20_48 ,   num_feature_levels=5 ,   assign_first_stage=_UpperCAmelCase ,   with_box_refine=_UpperCAmelCase ,   two_stage=_UpperCAmelCase ,   )

		# set labels
		__UpperCAmelCase    :     Optional[int]													=	'''huggingface/label-files'''
		if "o365" in model_name:
				__UpperCAmelCase    :     Tuple													=	3_66
				__UpperCAmelCase    :     List[str]													=	'''object365-id2label.json'''
		else:
				__UpperCAmelCase    :     Any													=	91
				__UpperCAmelCase    :     int													=	'''coco-detection-id2label.json'''

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

		return config

def        a						(      _UpperCAmelCase							:					Dict						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[str]													=	[]

		# 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        a						(      _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					Union[str, Any]						):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[int]													=	dct.pop(_UpperCAmelCase						)
		__UpperCAmelCase    :     List[Any]													=	val

def        a						(      _UpperCAmelCase							:					Dict ,   _UpperCAmelCase							:					List[Any]						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[str]													=	[int(backbone_config.embed_dim * 2**i						) for i in range(len(backbone_config.depths						)						)]
		for i in range(len(backbone_config.depths						)						):
				__UpperCAmelCase    :     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)
						__UpperCAmelCase    :     List[str]													=	state_dict.pop(f'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'						)
						__UpperCAmelCase    :     List[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
						__UpperCAmelCase    :     Dict													=	in_proj_weight[:dim, :]
						__UpperCAmelCase    :     List[str]													=	in_proj_bias[: dim]
						__UpperCAmelCase    :     str													=	in_proj_weight[
						    dim : dim * 2, :
						]
						__UpperCAmelCase    :     Any													=	in_proj_bias[
						    dim : dim * 2
						]
						__UpperCAmelCase    :     Tuple													=	in_proj_weight[
						    -dim :, :
						]
						__UpperCAmelCase    :     int													=	in_proj_bias[-dim :]
						# fmt: on

def        a						(      _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					Any						):







		'''simple docstring'''





		__UpperCAmelCase    :     int													=	config.d_model
		for i in range(config.decoder_layers						):
				# read in weights + bias of input projection layer of self-attention
				__UpperCAmelCase    :     List[str]													=	state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_weight'						)
				__UpperCAmelCase    :     Tuple													=	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
				__UpperCAmelCase    :     Union[str, Any]													=	in_proj_weight[:hidden_size, :]
				__UpperCAmelCase    :     List[Any]													=	in_proj_bias[:hidden_size]
				__UpperCAmelCase    :     int													=	in_proj_weight[
				    hidden_size : hidden_size * 2, :
				]
				__UpperCAmelCase    :     str													=	in_proj_bias[hidden_size : hidden_size * 2]
				__UpperCAmelCase    :     Tuple													=	in_proj_weight[-hidden_size:, :]
				__UpperCAmelCase    :     Optional[Any]													=	in_proj_bias[-hidden_size:]

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		__UpperCAmelCase    :     int													=	Image.open(requests.get(_UpperCAmelCase ,   stream=_UpperCAmelCase						).raw						)

		return im

@torch.no_grad()
def        a						(      _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					List[Any] ,   _UpperCAmelCase							:					List[str]						):







		'''simple docstring'''





		__UpperCAmelCase    :     Tuple													=	get_deta_config(_UpperCAmelCase						)

		# load original state dict
		if model_name == "deta-swin-large":
				__UpperCAmelCase    :     Dict													=	hf_hub_download(repo_id='''nielsr/deta-checkpoints''' ,   filename='''adet_swin_ft.pth'''						)
		elif model_name == "deta-swin-large-o365":
				__UpperCAmelCase    :     Any													=	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'						)

		__UpperCAmelCase    :     str													=	torch.load(_UpperCAmelCase ,   map_location='''cpu'''						)['''model''']

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

		# rename keys
		__UpperCAmelCase    :     int													=	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						)

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

    # finally, create HuggingFace model and load state dict
		__UpperCAmelCase    :     Union[str, Any]													=	DetaForObjectDetection(_UpperCAmelCase						)
		model.load_state_dict(_UpperCAmelCase						)
		model.eval()

		__UpperCAmelCase    :     Optional[int]													=	'''cuda''' if torch.cuda.is_available() else '''cpu'''
		model.to(_UpperCAmelCase						)

		# load image processor
		__UpperCAmelCase    :     str													=	DetaImageProcessor(format='''coco_detection'''						)

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

		# verify logits
		print('''Logits:''' ,   outputs.logits[0, :3, :3]						)
		print('''Boxes:''' ,   outputs.pred_boxes[0, :3, :3]						)
		if model_name == "deta-swin-large":
				__UpperCAmelCase    :     str													=	torch.tensor(
				    [[-7.63_08, -2.84_85, -5.37_37], [-7.20_37, -4.55_05, -4.80_27], [-7.29_43, -4.26_11, -4.66_17]]						)
				__UpperCAmelCase    :     Union[str, Any]													=	torch.tensor([[0.49_87, 0.49_69, 0.99_99], [0.25_49, 0.54_98, 0.48_05], [0.54_98, 0.27_57, 0.05_69]]						)
		elif model_name == "deta-swin-large-o365":
				__UpperCAmelCase    :     Optional[Any]													=	torch.tensor(
				    [[-8.01_22, -3.57_20, -4.97_17], [-8.15_47, -3.68_86, -4.63_89], [-7.66_10, -3.61_94, -5.01_34]]						)
				__UpperCAmelCase    :     str													=	torch.tensor([[0.25_23, 0.55_49, 0.48_81], [0.77_15, 0.41_49, 0.46_01], [0.55_03, 0.27_53, 0.05_75]]						)
		assert torch.allclose(outputs.logits[0, :3, :3] ,   expected_logits.to(_UpperCAmelCase						) ,   atol=1e-4						)
		assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,   expected_boxes.to(_UpperCAmelCase						) ,   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(_UpperCAmelCase						).mkdir(exist_ok=_UpperCAmelCase						)
				model.save_pretrained(_UpperCAmelCase						)
				processor.save_pretrained(_UpperCAmelCase						)

		# 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__":
				__A   			=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."
				)
				__A   			=parser.parse_args()
				convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, 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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


__A   			=logging.get_logger(__name__)

def        a						(      _UpperCAmelCase							:					Optional[Any] ,   _UpperCAmelCase							:					Dict						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[str]													=	b.T
		__UpperCAmelCase    :     Any													=	np.sum(np.square(_UpperCAmelCase						) ,   axis=1						)
		__UpperCAmelCase    :     int													=	np.sum(np.square(_UpperCAmelCase						) ,   axis=0						)
		__UpperCAmelCase    :     Optional[int]													=	np.matmul(_UpperCAmelCase ,   _UpperCAmelCase						)
		__UpperCAmelCase    :     List[str]													=	aa[:, None] - 2 * ab + ba[None, :]
		return d

def        a						(      _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					Optional[Any]						):







		'''simple docstring'''





		__UpperCAmelCase    :     str													=	x.reshape(-1 ,   3						)
		__UpperCAmelCase    :     Optional[int]													=	squared_euclidean_distance(_UpperCAmelCase ,   _UpperCAmelCase						)
		return np.argmin(_UpperCAmelCase ,   axis=1						)

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			UpperCamelCase									=       ["""pixel_values"""]




			def __init__(   self							: str       ,       a_							: Optional[Union[List[List[int]], np.ndarray]] = None       ,       a_							: bool = True       ,       a_							: Dict[str, int] = None       ,       a_							: PILImageResampling = PILImageResampling.BILINEAR       ,       a_							: bool = True       ,       a_							: bool = True       ,       **a_							: List[str]       ,       ):
					'''simple docstring'''





					super().__init__(**a_ )
					__UpperCAmelCase    :     Optional[int]													=	size if size is not None else {'''height''': 2_56, '''width''': 2_56}
					__UpperCAmelCase    :     List[str]													=	get_size_dict(a_ )
					__UpperCAmelCase    :     str													=	np.array(a_ ) if clusters is not None else None
					__UpperCAmelCase    :     Dict													=	do_resize
					__UpperCAmelCase    :     Tuple													=	size
					__UpperCAmelCase    :     Union[str, Any]													=	resample
					__UpperCAmelCase    :     Tuple													=	do_normalize
					__UpperCAmelCase    :     Optional[int]													=	do_color_quantize




			def  snake_case__     (   self							: Optional[Any]       ,       a_							: np.ndarray       ,       a_							: Dict[str, int]       ,       a_							: PILImageResampling = PILImageResampling.BILINEAR       ,       a_							: Optional[Union[str, ChannelDimension]] = None       ,       **a_							: Dict       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     Tuple													=	get_size_dict(a_ )
					if "height" not in size or "width" not in size:
							raise ValueError(F'Size dictionary must contain both height and width keys. Got {size.keys()}' )
					return resize(
					    a_       ,       size=(size['''height'''], size['''width'''])       ,       resample=a_       ,       data_format=a_       ,       **a_ )




			def  snake_case__     (   self							: Tuple       ,       a_							: np.ndarray       ,       a_							: Optional[Union[str, ChannelDimension]] = None       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	rescale(image=a_       ,       scale=1 / 1_2_7.5       ,       data_format=a_ )
					__UpperCAmelCase    :     Union[str, Any]													=	image - 1
					return image




			def  snake_case__     (   self							: int       ,       a_							: ImageInput       ,       a_							: bool = None       ,       a_							: Dict[str, int] = None       ,       a_							: PILImageResampling = None       ,       a_							: bool = None       ,       a_							: Optional[bool] = None       ,       a_							: Optional[Union[List[List[int]], np.ndarray]] = None       ,       a_							: Optional[Union[str, TensorType]] = None       ,       a_							: Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST       ,       **a_							: Any       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     Any													=	do_resize if do_resize is not None else self.do_resize
					__UpperCAmelCase    :     List[str]													=	size if size is not None else self.size
					__UpperCAmelCase    :     Any													=	get_size_dict(a_ )
					__UpperCAmelCase    :     Optional[int]													=	resample if resample is not None else self.resample
					__UpperCAmelCase    :     List[str]													=	do_normalize if do_normalize is not None else self.do_normalize
					__UpperCAmelCase    :     int													=	do_color_quantize if do_color_quantize is not None else self.do_color_quantize
					__UpperCAmelCase    :     Optional[int]													=	clusters if clusters is not None else self.clusters
					__UpperCAmelCase    :     Any													=	np.array(a_ )

					__UpperCAmelCase    :     Optional[int]													=	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 or resample is None:
							raise ValueError('''Size and resample must be specified if do_resize is True.''' )

					if do_color_quantize and clusters is None:
							raise ValueError('''Clusters must be specified if do_color_quantize is True.''' )

					# All transformations expect numpy arrays.
					__UpperCAmelCase    :     List[Any]													=	[to_numpy_array(a_ ) for image in images]

					if do_resize:
							__UpperCAmelCase    :     List[str]													=	[self.resize(image=a_       ,       size=a_       ,       resample=a_ ) for image in images]

					if do_normalize:
							__UpperCAmelCase    :     Dict													=	[self.normalize(image=a_ ) for image in images]

					if do_color_quantize:
							__UpperCAmelCase    :     int													=	[to_channel_dimension_format(a_       ,       ChannelDimension.LAST ) for image in images]
							# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
							__UpperCAmelCase    :     List[str]													=	np.array(a_ )
							__UpperCAmelCase    :     Dict													=	color_quantize(a_       ,       a_ ).reshape(images.shape[:-1] )

							# flatten to (batch_size, height*width)
							__UpperCAmelCase    :     Any													=	images.shape[0]
							__UpperCAmelCase    :     Any													=	images.reshape(a_       ,       -1 )

							# We need to convert back to a list of images to keep consistent behaviour across processors.
							__UpperCAmelCase    :     List[Any]													=	list(a_ )
					else:
							__UpperCAmelCase    :     int													=	[to_channel_dimension_format(a_       ,       a_ ) for image in images]

					__UpperCAmelCase    :     int													=	{'''input_ids''': images}
					return BatchFeature(data=a_       ,       tensor_type=a_ )





