






"""simple docstring"""







import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowerCAmelCase            =	[]
for i in range(6):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append(
				    (F'transformer.encoder.layers.{i}.self_attn.out_proj.weight', F'encoder.layers.{i}.self_attn.out_proj.weight')
				)
				rename_keys.append(
				    (F'transformer.encoder.layers.{i}.self_attn.out_proj.bias', F'encoder.layers.{i}.self_attn.out_proj.bias')
				)
				rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'encoder.layers.{i}.fc1.weight'))
				rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'encoder.layers.{i}.fc1.bias'))
				rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'encoder.layers.{i}.fc2.weight'))
				rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'encoder.layers.{i}.fc2.bias'))
				rename_keys.append(
				    (F'transformer.encoder.layers.{i}.norm1.weight', F'encoder.layers.{i}.self_attn_layer_norm.weight')
				)
				rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'encoder.layers.{i}.self_attn_layer_norm.bias'))
				rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'encoder.layers.{i}.final_layer_norm.weight'))
				rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'encoder.layers.{i}.final_layer_norm.bias'))
				# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'decoder.layers.{i}.self_attn.out_proj.weight')
				)
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'decoder.layers.{i}.self_attn.out_proj.bias')
				)
				rename_keys.append(
				    (
				        F'transformer.decoder.layers.{i}.cross_attn.out_proj.weight',
				        F'decoder.layers.{i}.encoder_attn.out_proj.weight',
				    )
				)
				rename_keys.append(
				    (
				        F'transformer.decoder.layers.{i}.cross_attn.out_proj.bias',
				        F'decoder.layers.{i}.encoder_attn.out_proj.bias',
				    )
				)
				rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'decoder.layers.{i}.fc1.weight'))
				rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'decoder.layers.{i}.fc1.bias'))
				rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'decoder.layers.{i}.fc2.weight'))
				rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'decoder.layers.{i}.fc2.bias'))
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.norm1.weight', F'decoder.layers.{i}.self_attn_layer_norm.weight')
				)
				rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'decoder.layers.{i}.self_attn_layer_norm.bias'))
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.norm2.weight', F'decoder.layers.{i}.encoder_attn_layer_norm.weight')
				)
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.norm2.bias', F'decoder.layers.{i}.encoder_attn_layer_norm.bias')
				)
				rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'decoder.layers.{i}.final_layer_norm.weight'))
				rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'decoder.layers.{i}.final_layer_norm.bias'))

				# q, k, v projections in self/cross-attention in decoder for conditional DETR
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.sa_qcontent_proj.weight', F'decoder.layers.{i}.sa_qcontent_proj.weight')
				)
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.sa_kcontent_proj.weight', F'decoder.layers.{i}.sa_kcontent_proj.weight')
				)
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.sa_qpos_proj.weight', F'decoder.layers.{i}.sa_qpos_proj.weight')
				)
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.sa_kpos_proj.weight', F'decoder.layers.{i}.sa_kpos_proj.weight')
				)
				rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.weight', F'decoder.layers.{i}.sa_v_proj.weight'))
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.ca_qcontent_proj.weight', F'decoder.layers.{i}.ca_qcontent_proj.weight')
				)
				# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.ca_kcontent_proj.weight', F'decoder.layers.{i}.ca_kcontent_proj.weight')
				)
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.ca_kpos_proj.weight', F'decoder.layers.{i}.ca_kpos_proj.weight')
				)
				rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.weight', F'decoder.layers.{i}.ca_v_proj.weight'))
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight', F'decoder.layers.{i}.ca_qpos_sine_proj.weight')
				)

				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.sa_qcontent_proj.bias', F'decoder.layers.{i}.sa_qcontent_proj.bias')
				)
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.sa_kcontent_proj.bias', F'decoder.layers.{i}.sa_kcontent_proj.bias')
				)
				rename_keys.append((F'transformer.decoder.layers.{i}.sa_qpos_proj.bias', F'decoder.layers.{i}.sa_qpos_proj.bias'))
				rename_keys.append((F'transformer.decoder.layers.{i}.sa_kpos_proj.bias', F'decoder.layers.{i}.sa_kpos_proj.bias'))
				rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.bias', F'decoder.layers.{i}.sa_v_proj.bias'))
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.ca_qcontent_proj.bias', F'decoder.layers.{i}.ca_qcontent_proj.bias')
				)
				# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.ca_kcontent_proj.bias', F'decoder.layers.{i}.ca_kcontent_proj.bias')
				)
				rename_keys.append((F'transformer.decoder.layers.{i}.ca_kpos_proj.bias', F'decoder.layers.{i}.ca_kpos_proj.bias'))
				rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.bias', F'decoder.layers.{i}.ca_v_proj.bias'))
				rename_keys.append(
				    (F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias', F'decoder.layers.{i}.ca_qpos_sine_proj.bias')
				)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('input_proj.weight', 'input_projection.weight'),
        ('input_proj.bias', 'input_projection.bias'),
        ('query_embed.weight', 'query_position_embeddings.weight'),
        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
        ('class_embed.weight', 'class_labels_classifier.weight'),
        ('class_embed.bias', 'class_labels_classifier.bias'),
        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
        ('transformer.decoder.ref_point_head.layers.0.weight', 'decoder.ref_point_head.layers.0.weight'),
        ('transformer.decoder.ref_point_head.layers.0.bias', 'decoder.ref_point_head.layers.0.bias'),
        ('transformer.decoder.ref_point_head.layers.1.weight', 'decoder.ref_point_head.layers.1.weight'),
        ('transformer.decoder.ref_point_head.layers.1.bias', 'decoder.ref_point_head.layers.1.bias'),
        ('transformer.decoder.query_scale.layers.0.weight', 'decoder.query_scale.layers.0.weight'),
        ('transformer.decoder.query_scale.layers.0.bias', 'decoder.query_scale.layers.0.bias'),
        ('transformer.decoder.query_scale.layers.1.weight', 'decoder.query_scale.layers.1.weight'),
        ('transformer.decoder.query_scale.layers.1.bias', 'decoder.query_scale.layers.1.bias'),
        ('transformer.decoder.layers.0.ca_qpos_proj.weight', 'decoder.layers.0.ca_qpos_proj.weight'),
        ('transformer.decoder.layers.0.ca_qpos_proj.bias', 'decoder.layers.0.ca_qpos_proj.bias'),
    ]
)


def      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       Optional[Any]:
				lowercase    :      List[Any]       = state_dict.pop(_A )
				lowercase    :      Optional[Any]       = val


def      UpperCamelCase       ( _A )     ->       Optional[int]:
				lowercase    :      Optional[Any]       = OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												lowercase    :      Tuple       = key.replace("""backbone.0.body"""  ,				"""backbone.conv_encoder.model""" )
												lowercase    :      Tuple       = value
								else:
												lowercase    :      Any       = value

				return new_state_dict


def      UpperCamelCase       ( _A  ,				_A=False )     ->       List[str]:
				lowercase    :      Optional[int]       = """"""
				if is_panoptic:
								lowercase    :      Any       = """conditional_detr."""

				# first: transformer encoder
				for i in range(6 ):
								# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
								lowercase    :      str       = state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight""" )
								lowercase    :      List[str]       = state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias""" )
								# next, add query, keys and values (in that order) to the state dict
								lowercase    :      str       = in_proj_weight[:256, :]
								lowercase    :      str       = in_proj_bias[:256]
								lowercase    :      str       = in_proj_weight[256:512, :]
								lowercase    :      List[Any]       = in_proj_bias[256:512]
								lowercase    :      Optional[Any]       = in_proj_weight[-256:, :]
								lowercase    :      Optional[Any]       = in_proj_bias[-256:]


def      UpperCamelCase       ( )     ->       Optional[Any]:
				lowercase    :      Dict       = """http://images.cocodataset.org/val2017/000000039769.jpg"""
				lowercase    :      Any       = Image.open(requests.get(_A  ,				stream=_A ).raw )

				return im


@torch.no_grad()
def      UpperCamelCase       ( _A  ,				_A )     ->       Optional[Any]:
				lowercase    :      Any       = ConditionalDetrConfig()
				# set backbone and dilation attributes
				if "resnet101" in model_name:
								lowercase    :      Union[str, Any]       = """resnet101"""
				if "dc5" in model_name:
								lowercase    :      int       = True
				lowercase    :      Union[str, Any]       = """panoptic""" in model_name
				if is_panoptic:
								lowercase    :      int       = 250
				else:
								lowercase    :      Union[str, Any]       = 91
								lowercase    :      List[Any]       = """huggingface/label-files"""
								lowercase    :      List[str]       = """coco-detection-id2label.json"""
								lowercase    :      int       = json.load(open(hf_hub_download(_A  ,				_A  ,				repo_type="""dataset""" )  ,				"""r""" ) )
								lowercase    :      Union[str, Any]       = {int(_A ): v for k, v in idalabel.items()}
								lowercase    :      Union[str, Any]       = idalabel
								lowercase    :      Union[str, Any]       = {v: k for k, v in idalabel.items()}

				# load image processor
				lowercase    :      Tuple       = """coco_panoptic""" if is_panoptic else """coco_detection"""
				lowercase    :      Optional[Any]       = ConditionalDetrImageProcessor(format=_A )

				# prepare image
				lowercase    :      str       = prepare_img()
				lowercase    :      Optional[Any]       = image_processor(images=_A  ,				return_tensors="""pt""" )
				lowercase    :      Optional[Any]       = encoding["""pixel_values"""]

				logger.info(F"""Converting model {model_name}...""" )

				# load original model from torch hub
				lowercase    :      Optional[int]       = torch.hub.load("""DeppMeng/ConditionalDETR"""  ,				_A  ,				pretrained=_A ).eval()
				lowercase    :      Any       = conditional_detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												lowercase    :      Dict       = """conditional_detr.""" + src
								rename_key(_A  ,				_A  ,				_A )
				lowercase    :      Union[str, Any]       = rename_backbone_keys(_A )
				# query, key and value matrices need special treatment
				read_in_q_k_v(_A  ,				is_panoptic=_A )
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				lowercase    :      Optional[Any]       = """conditional_detr.model.""" if is_panoptic else """model."""
				for key in state_dict.copy().keys():
								if is_panoptic:
												if (
												    key.startswith("""conditional_detr""" )
												    and not key.startswith("""class_labels_classifier""" )
												    and not key.startswith("""bbox_predictor""" )
												):
																lowercase    :      Union[str, Any]       = state_dict.pop(_A )
																lowercase    :      Optional[int]       = val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																lowercase    :      Union[str, Any]       = state_dict.pop(_A )
																lowercase    :      int       = val
												elif key.startswith("""bbox_attention""" ) or key.startswith("""mask_head""" ):
																continue
												else:
																lowercase    :      Tuple       = state_dict.pop(_A )
																lowercase    :      Optional[int]       = val
								else:
												if not key.startswith("""class_labels_classifier""" ) and not key.startswith("""bbox_predictor""" ):
																lowercase    :      int       = state_dict.pop(_A )
																lowercase    :      str       = val
    # finally, create HuggingFace model and load state dict
				lowercase    :      str       = ConditionalDetrForSegmentation(_A ) if is_panoptic else ConditionalDetrForObjectDetection(_A )
				model.load_state_dict(_A )
				model.eval()
				model.push_to_hub(repo_id=_A  ,				organization="""DepuMeng"""  ,				commit_message="""Add model""" )
				# verify our conversion
				lowercase    :      Dict       = conditional_detr(_A )
				lowercase    :      Optional[Any]       = model(_A )
				assert torch.allclose(outputs.logits  ,				original_outputs["""pred_logits"""]  ,				atol=1e-4 )
				assert torch.allclose(outputs.pred_boxes  ,				original_outputs["""pred_boxes"""]  ,				atol=1e-4 )
				if is_panoptic:
								assert torch.allclose(outputs.pred_masks  ,				original_outputs["""pred_masks"""]  ,				atol=1e-4 )

				# Save model and image processor
				logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
				Path(_A ).mkdir(exist_ok=_A )
				model.save_pretrained(_A )
				image_processor.save_pretrained(_A )


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

				parser.add_argument(
				    '--model_name',
				    default='conditional_detr_resnet50',
				    type=str,
				    help='Name of the CONDITIONAL_DETR 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.'
				)
				_lowerCAmelCase            =	parser.parse_args()
				convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
				import PIL

_lowerCAmelCase            =	logging.get_logger(__name__)


def      UpperCamelCase       ( _A )     ->       List[List[ImageInput]]:
				if isinstance(_A  ,				(list, tuple) ) and isinstance(videos[0]  ,				(list, tuple) ) and is_valid_image(videos[0][0] ):
								return videos

				elif isinstance(_A  ,				(list, tuple) ) and is_valid_image(videos[0] ):
								return [videos]

				elif is_valid_image(_A ):
								return [[videos]]

				raise ValueError(F"""Could not make batched video from {videos}""" )


class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : str												=		["""pixel_values"""]






						def __init__(		self  :str			, __magic_name__  :bool = True			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :PILImageResampling = PILImageResampling.BILINEAR			, __magic_name__  :bool = True			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :bool = True			, __magic_name__  :Union[int, float] = 1 / 255			, __magic_name__  :bool = True			, __magic_name__  :bool = True			, __magic_name__  :Optional[Union[float, List[float]]] = None			, __magic_name__  :Optional[Union[float, List[float]]] = None			, **__magic_name__  :List[str]			, )      ->None:
										super().__init__(**__magic_name__				)
										lowercase    :      Union[str, Any]       = size if size is not None else {"""shortest_edge""": 256}
										lowercase    :      Tuple       = get_size_dict(__magic_name__			, default_to_square=__magic_name__				)
										lowercase    :      List[Any]       = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
										lowercase    :      List[str]       = get_size_dict(__magic_name__			, param_name="""crop_size"""				)

										lowercase    :      Tuple       = do_resize
										lowercase    :      Optional[Any]       = size
										lowercase    :      Union[str, Any]       = do_center_crop
										lowercase    :      List[Any]       = crop_size
										lowercase    :      Union[str, Any]       = resample
										lowercase    :      Optional[Any]       = do_rescale
										lowercase    :      int       = rescale_factor
										lowercase    :      Dict       = offset
										lowercase    :      Optional[int]       = do_normalize
										lowercase    :      Optional[Any]       = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										lowercase    :      Tuple       = image_std if image_std is not None else IMAGENET_STANDARD_STD






						def      __snake_case      (		self  :Any			, __magic_name__  :np.ndarray			, __magic_name__  :Dict[str, int]			, __magic_name__  :PILImageResampling = PILImageResampling.BILINEAR			, __magic_name__  :Optional[Union[str, ChannelDimension]] = None			, **__magic_name__  :Optional[Any]			, )      ->np.ndarray:
										lowercase    :      Optional[int]       = get_size_dict(__magic_name__			, default_to_square=__magic_name__				)
										if "shortest_edge" in size:
														lowercase    :      int       = get_resize_output_image_size(__magic_name__			, size["""shortest_edge"""]			, default_to_square=__magic_name__				)
										elif "height" in size and "width" in size:
														lowercase    :      Tuple       = (size["""height"""], size["""width"""])
										else:
														raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""				)
										return resize(__magic_name__			, size=__magic_name__			, resample=__magic_name__			, data_format=__magic_name__			, **__magic_name__				)






						def      __snake_case      (		self  :Optional[Any]			, __magic_name__  :np.ndarray			, __magic_name__  :Dict[str, int]			, __magic_name__  :Optional[Union[str, ChannelDimension]] = None			, **__magic_name__  :str			, )      ->np.ndarray:
										lowercase    :      Any       = get_size_dict(__magic_name__				)
										if "height" not in size or "width" not in size:
														raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""				)
										return center_crop(__magic_name__			, size=(size["""height"""], size["""width"""])			, data_format=__magic_name__			, **__magic_name__				)






						def      __snake_case      (		self  :Tuple			, __magic_name__  :np.ndarray			, __magic_name__  :Union[int, float]			, __magic_name__  :bool = True			, __magic_name__  :Optional[Union[str, ChannelDimension]] = None			, **__magic_name__  :Optional[Any]			, )      ->Any:
										lowercase    :      Optional[int]       = image.astype(np.floataa				)
										if offset:
														lowercase    :      Optional[int]       = image - (scale / 2)
										return rescale(__magic_name__			, scale=__magic_name__			, data_format=__magic_name__			, **__magic_name__				)






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






						def      __snake_case      (		self  :List[Any]			, __magic_name__  :ImageInput			, __magic_name__  :bool = None			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :PILImageResampling = None			, __magic_name__  :bool = None			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :bool = None			, __magic_name__  :float = None			, __magic_name__  :bool = None			, __magic_name__  :bool = None			, __magic_name__  :Optional[Union[float, List[float]]] = None			, __magic_name__  :Optional[Union[float, List[float]]] = None			, __magic_name__  :Optional[ChannelDimension] = ChannelDimension.FIRST			, )      ->np.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_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."""				)

										if offset and not do_rescale:
														raise ValueError("""For offset, do_rescale must also be set to True."""				)

										# All transformations expect numpy arrays.
										lowercase    :      Optional[Any]       = to_numpy_array(__magic_name__				)

										if do_resize:
														lowercase    :      Any       = self.resize(image=__magic_name__			, size=__magic_name__			, resample=__magic_name__				)

										if do_center_crop:
														lowercase    :      Any       = self.center_crop(__magic_name__			, size=__magic_name__				)

										if do_rescale:
														lowercase    :      Optional[int]       = self.rescale(image=__magic_name__			, scale=__magic_name__			, offset=__magic_name__				)

										if do_normalize:
														lowercase    :      Union[str, Any]       = self.normalize(image=__magic_name__			, mean=__magic_name__			, std=__magic_name__				)

										lowercase    :      Optional[Any]       = to_channel_dimension_format(__magic_name__			, __magic_name__				)
										return image






						def      __snake_case      (		self  :Union[str, Any]			, __magic_name__  :ImageInput			, __magic_name__  :bool = None			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :PILImageResampling = None			, __magic_name__  :bool = None			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :bool = None			, __magic_name__  :float = None			, __magic_name__  :bool = None			, __magic_name__  :bool = None			, __magic_name__  :Optional[Union[float, List[float]]] = None			, __magic_name__  :Optional[Union[float, List[float]]] = None			, __magic_name__  :Optional[Union[str, TensorType]] = None			, __magic_name__  :ChannelDimension = ChannelDimension.FIRST			, **__magic_name__  :List[str]			, )      ->PIL.Image.Image:
										lowercase    :      List[Any]       = do_resize if do_resize is not None else self.do_resize
										lowercase    :      Dict       = resample if resample is not None else self.resample
										lowercase    :      Optional[int]       = do_center_crop if do_center_crop is not None else self.do_center_crop
										lowercase    :      List[Any]       = do_rescale if do_rescale is not None else self.do_rescale
										lowercase    :      Tuple       = rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase    :      Optional[Any]       = offset if offset is not None else self.offset
										lowercase    :      str       = do_normalize if do_normalize is not None else self.do_normalize
										lowercase    :      List[str]       = image_mean if image_mean is not None else self.image_mean
										lowercase    :      Union[str, Any]       = image_std if image_std is not None else self.image_std

										lowercase    :      Optional[int]       = size if size is not None else self.size
										lowercase    :      Optional[Any]       = get_size_dict(__magic_name__			, default_to_square=__magic_name__				)
										lowercase    :      Tuple       = crop_size if crop_size is not None else self.crop_size
										lowercase    :      List[str]       = get_size_dict(__magic_name__			, param_name="""crop_size"""				)

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

										lowercase    :      Tuple       = make_batched(__magic_name__				)

										lowercase    :      List[str]       = [
										    [
										        self._preprocess_image(
										            image=__magic_name__			, do_resize=__magic_name__			, size=__magic_name__			, resample=__magic_name__			, do_center_crop=__magic_name__			, crop_size=__magic_name__			, do_rescale=__magic_name__			, rescale_factor=__magic_name__			, offset=__magic_name__			, do_normalize=__magic_name__			, image_mean=__magic_name__			, image_std=__magic_name__			, data_format=__magic_name__			, )
										        for img in video
										    ]
										    for video in videos
										]

										lowercase    :      Dict       = {"""pixel_values""": videos}
										return BatchFeature(data=__magic_name__			, tensor_type=__magic_name__				)
